The SED Machine: A Robotic Spectrograph for Fast Transient Classification

NASA Astrophysics Data System (ADS)

Blagorodnova, Nadejda; Neill, James D.; Walters, Richard; Kulkarni, Shrinivas R.; Fremling, Christoffer; Ben-Ami, Sagi; Dekany, Richard G.; Fucik, Jason R.; Konidaris, Nick; Nash, Reston; Ngeow, Chow-Choong; Ofek, Eran O.; O’ Sullivan, Donal; Quimby, Robert; Ritter, Andreas; Vyhmeister, Karl E.

2018-03-01

Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come online. Presently, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing “follow-up drought”. Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution (R ∼ 100) integral field unit (IFU) spectrograph with “Rainbow Camera” (RC), a multi-band field acquisition camera which also serves as multi-band (ugri) photometer. The SEDM was commissioned during the operation of the intermediate Palomar Transient Factory (iPTF) and has already lived up to its promise. The success of the SEDM demonstrates the value of spectrographs optimized for spectral classification.

The impact of maternal body mass index on external cephalic version success.

PubMed

Chaudhary, Shahrukh; Contag, Stephen; Yao, Ruofan

2018-01-21

The purpose of this study is to determine the association between body mass index (BMI) and success of ECV. This is a cross-sectional analysis of singleton live births in the USA from 2010 to 2014 using birth certificate data. Patients were assigned a BMI category according to standard WHO classification. Comparisons of success of ECV between the BMI categories were made using chi-square analysis with normal BMI as the reference group. Cochran-Armitage test was performed to look for a trend of decreasing success of ECV as BMI increased. The odds for successful ECV were estimated using multivariate logistic regression analysis, adjusting for possible confounders. A total of 51,002 patients with documented ECV were available for analysis. There was a decreased success rate for ECV as BMI increased (p < .01). Women with a BMI of 40 kg/m 2 or greater had a 58.5% success rate of ECV; women with a normal BMI had 65.0% success rate of ECV. Multivariate analyses demonstrated significant decrease in success of ECV in women with BMI of 40 kg/m 2 or greater (OR 0.621, CI 0.542-0.712). Among women with BMI of 40 kg/m 2 or greater with successful ECV, 59.5% delivered vaginally. In contrast, 81.0% of women with normal BMI and successful ECV delivered vaginally. Morbidly obese women have decreased success rate of ECV as BMI increases and decreased vaginal delivery rates after successful ECV.

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

PubMed Central

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods. PMID:26880879

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.

PubMed

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.

The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

PubMed

Aktaruzzaman, M; Migliorini, M; Tenhunen, M; Himanen, S L; Bianchi, A M; Sassi, R

2015-05-01

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

Overhead Bryant's Traction Does Not Improve the Success of Closed Reduction or Limit AVN in Developmental Dysplasia of the Hip.

PubMed

Sucato, Daniel J; De La Rocha, Adriana; Lau, Karlee; Ramo, Brandon A

2017-03-01

Preoperative Bryant's overhead traction before closed reduction (CR) in developmental dysplasia of the hip (DDH) remains controversial and its success in increasing CR rates and reducing avascular necrosis (AVN) rates has not been specifically reported in a large cohort. IRB-approved retrospective study of patients (below 3 y)who were treated with attempted CR for idiopathic DDH from 1980 to 2009. Successful CR was defined as a hip that remained reduced and did not require repeat CR or open reduction. Patients were grouped by age, hip instability [Ortolani positive (reducible) vs. fixed dislocation], and Tonnis classification and rates of successful CR were compared between groups with P<0.05. A total of 342 hips were included with a mean age of 0.9 years (0.2 to 2.8 y) and a mean follow-up of 10.4 years (2.0 to 27.7 y). There were 269 hips with fixed dislocations and 73 Ortolani-positive hips. Traction was used in 276 hips. There was no difference in traction utilization in the 3 age groups (below 1, below 1.5, and below 2 y) for either Ortolani-positive hips (P=0.947) or fixed dislocations (P=0.943). There was no difference in achieving a successful CR comparing traction (60.9%) and no-traction groups (60.6%) (P=1.00). For Ortolani-positive hips, traction did not improve the incidence of a successful CR for any age group: below 1 year: P=0.19; below 1.5 years: P=0.23; and below 2 years: P=0.25. Similarly, fixed dislocation patients had no benefit from traction: below 1 year: P=0.76; below 1.5 years: P=0.82; and below 2 years: P=0.85. Tonnis classification did predict success of CR but had no influence on traction success. There was no difference in the rate of AVN between the traction (18%) and no-traction (8%) groups for all patients (P=0.15). In this retrospective series, preoperative Bryant's traction does not improve the rate of a successful CR for patients with DDH and has no protective effect on the development of AVN of the femoral head. These results suggest that Bryant's overhead traction may not be warranted for patients below 3 years of age with DDH. Level III.

Classification of EMG signals using artificial neural networks for virtual hand prosthesis control.

PubMed

Mattioli, Fernando E R; Lamounier, Edgard A; Cardoso, Alexandre; Soares, Alcimar B; Andrade, Adriano O

2011-01-01

Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.

The Impact of NCAA Reclassification on Academic Success Rates

ERIC Educational Resources Information Center

Chandler, Jason I.

2014-01-01

Higher education institutions choose to invest in upward reclassification for their athletics program with anticipation of generating increased revenue and exposure for their school; however, this could have a detrimental effect on their student-athletes' academic progress. This study examined the relationship between four classifications of…

Comparing ungulate dietary proxies using discriminant function analysis.

PubMed

Fraser, Danielle; Theodor, Jessica M

2011-12-01

A variety of tooth-wear and morphological dietary proxies have been proposed for ungulates. In turn, they have been applied to fossil specimens with the purpose of reconstructing the diets of extinct taxa. Although these dietary proxies have been used in isolation and in combination, a consistent set of statistical analyses has never been applied to all of the available datasets. The purpose of this study is to determine how well the most commonly used dietary proxies classify ungulates as browsers, grazers, and mixed feeders individually and in combination. Discriminant function analysis is applied to individual dietary proxies (hypsodonty, mesowear, microwear, and several cranial dietary proxies) and to combinations thereof to compare rates of successful dietary classification. In general, the tooth-wear dietary proxies (mesowear and microwear) perform better than morphological dietary proxies, though none are strong proxies in isolation. The success rates of the cranial dietary proxies are not increased substantially when ruminants and bovids are analyzed separately, and significance among the three dietary guilds is reduced when controlling for phylogenetic relatedness. The combination of hypsodonty, mesowear, and microwear is found to have a high rate of successful dietary classification, but a combination of all commonly used proxies increases the success rate to 100%. In most cases, mixed feeders bear the greatest resemblance to browsers suggesting that a morphology intermediate to browsers and grazers may represent a fitness valley resulting from the inability to exploit both browse and graze efficiently. These results are important for future paleoecological studies and should be used as a guide for determining which dietary proxies are appropriate to the research question. Copyright © 2011 Wiley-Liss, Inc.

A Spiking Neural Network in sEMG Feature Extraction.

PubMed

Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor

2015-11-03

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension

ERIC Educational Resources Information Center

Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S.

2017-01-01

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

Wing Shape as an Indicator of Larval Rearing Conditions for Aedes albopictus and Ae. aegypti (Diptera: Culicidae)

PubMed Central

Stephens, C. R.; Juliano, S. A.

2012-01-01

Estimating a mosquito’s vector competence, or likelihood of transmitting disease, if it takes an infectious blood meal, is an important aspect of predicting when and where outbreaks of infectious diseases will occur. Vector competence can be affected by rearing temperature and inter- and intraspecific competition experienced by the individual mosquito during its larval development. This research investigates whether a new morphological indicator of larval rearing conditions, wing shape, can be used to distinguish reliably temperature and competitive conditions experienced during larval stages. Aedes albopictus and Aedes aegypti larvae were reared in low intra-specific, high intra-specific, or high inter-specific competition treatments at either 22°C or 32°C. The right wing of each dried female was removed and photographed. Nineteen landmarks and twenty semilandmarks were digitized on each wing. Shape variables were calculated using geometric morphometric software. Canonical variate analysis, randomization multivariate analysis of variance, and visualization of landmark movement using deformation grids provided evidence that although semilandmark position was significantly affected by larval competition and temperature for both species, the differences in position did not translate into differences in wing shape, as shown in deformation grids. Two classification procedures yielded success rates of 26–49%. Accounting for wing size produced no increase in classification success. There appeared to be a significant relationship between shape and size. These results, particularly the low success rate of classification based on wing shape, show that shape is unlikely to be a reliable indicator of larval rearing competition and temperature conditions for Aedes albopictus and Aedes aegypti. PMID:22897054

Better treatment strategies for patients with acute cholecystitis and American Society of Anesthesiologists classification 3 or greater.

PubMed

Yun, Sung Su; Hwang, Dae Wook; Kim, Se Won; Park, Sang Hwan; Park, Sang Jin; Lee, Dong Shick; Kim, Hong Jin

2010-07-01

Laparoscopic cholecystectomy is the best treatment choice for acute cholecystitis. However, it still carries high conversion and mortality rates. The purpose of this study was to find out better treatment strategies for high surgical risk patients with acute cholecystitis. Between January 2002 and June 2008, we performed percutaneous cholecystostomy instead of emergency cholecystectomy in 44 patients with acute cholecystitis and American Society of Anesthesiologists (ASA) classification 3 or greater. This was performed in 31 patients as a bridge procedure before elective cholecystectomy (bridge group) and as a palliative procedure in 11 patients (palliation group). The mean age of patients was 71.6 years (range 52-86 years). The mean ASA classifications before and after percutaneous cholecystostomy were 3.3 +/- 0.5 and 2.5 +/- 0.6, respectively, in the bridge group, and 3.6 +/- 0.7 and 3.1 +/- 1.0, in the palliation group, respectively. Percutaneous cholecystostomy was technically successful in all patients. There were two deaths after percutaneous cholecystostomy in the palliation group due to underlying ischemic heart disease and multiple organ failure. Resumption of oral intake was possible 2.9 +/- 1.8 days in the bridge group and 3.9 +/- 3.5 days in the palliation group after percutaneous cholecystostomy. We attempted 17 laparoscopic cholecystectomies and experienced one failure due to bile duct injury (success rate: 94.1%). The postoperative course of all cholecystectomy patients was uneventful. Percutaneous cholecystostomy is an effective bridge procedure before cholecystectomy in patients with acute cholecystitis and ASA classification 3 or greater.

Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling.

PubMed

Butail, Sachit; Salerno, Philip; Bollt, Erik M; Porfiri, Maurizio

2015-12-01

Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advancements in this area by affording the classification of these observables directly from images. While such methods have been successfully implemented in the classification of individual behavior, their potential in the study collective behavior is largely untested. In this paper, we compare three methods for the analysis of collective behavior: simple tracking (ST) without resolving occlusions, machine learning with real data (MLR), and machine learning with synthetic data (MLS). These methods are evaluated on videos recorded from an experiment studying the effect of ambient light on the shoaling tendency of Giant danios. In particular, we compute average nearest-neighbor distance (ANND) and polarization using the three methods and compare the values with manually-verified ground-truth data. To further assess possible dependence on sampling rate for computing ANND, the comparison is also performed at a low frame rate. Results show that while ST is the most accurate at higher frame rate for both ANND and polarization, at low frame rate for ANND there is no significant difference in accuracy between the three methods. In terms of computational speed, MLR and MLS take significantly less time to process an image, with MLS better addressing constraints related to generation of training data. Finally, all methods are able to successfully detect a significant difference in ANND as the ambient light intensity is varied irrespective of the direction of intensity change.

The Success Rate of Neurology Residents in EEG Interpretation After Formal Training.

PubMed

Dericioglu, Nese; Ozdemir, Pınar

2018-03-01

EEG is an important tool for neurologists in both diagnosis and classification of seizures. It is not uncommon in clinical practice to see patients who were erroneously diagnosed as epileptic. Most of the time incorrect interpretation of EEG contributes significantly to this problem. In this study, we aimed to investigate the success rate of neurology residents in EEG interpretation after formal training. Eleven neurology residents were included in the study. Duration of EEG training (3 vs 4 months) and time since completion of EEG education were determined. Residents were randomly presented 30 different slides of representative EEG screenshots. They received 1 point for each correct response. The effect of training duration and time since training were investigated statistically. Besides, we looked at the success rate of each question to see whether certain patterns were more readily recognized than others. EEG training duration ( P = .93) and time since completion of training ( P = .16) did not influence the results. The success rate of residents for correct responses was between 17% and 50%. On the other hand, the success rate for each question varied between 0% and 91%. Overall, benign variants and focal ictal onset patterns were the most difficult to recognize. On 13 occasions (6.5%) nonepileptiform patterns were thought to represent epileptiform abnormalities. After formal training, neurology residents could identify ≤50% of the EEG patterns correctly. The wide variation in success rate among residents and also between questions implies that both personal characteristics and inherent EEG features influence successful EEG interpretation.

Annual crop type classification of the U.S. Great Plains for 2000 to 2011

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.

2014-01-01

The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophysical environmental characteristics. This study accounted for the most prevalent crop types in the region, including, corn, soybeans, winter wheat, spring wheat, cotton, sorghum, and alfalfa. Annual CTM crop maps of the US Great Plains were created for 2000 to 2011 at a spatial resolution of 250 meters. The CTM achieved an 87 percent classification success rate on 1.8 million observation points that were withheld from model training. Product validation was performed on greater than 15,000 county records with a coefficient of determination of R2 = 0.76.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Significance of perceptually relevant image decolorization for scene classification

NASA Astrophysics Data System (ADS)

Viswanathan, Sowmya; Divakaran, Govind; Soman, Kutti Padanyl

2017-11-01

Color images contain luminance and chrominance components representing the intensity and color information, respectively. The objective of this paper is to show the significance of incorporating chrominance information to the task of scene classification. An improved color-to-grayscale image conversion algorithm that effectively incorporates chrominance information is proposed using the color-to-gray structure similarity index and singular value decomposition to improve the perceptual quality of the converted grayscale images. The experimental results based on an image quality assessment for image decolorization and its success rate (using the Cadik and COLOR250 datasets) show that the proposed image decolorization technique performs better than eight existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component for scene classification tasks is demonstrated using a deep belief network-based image classification system developed using dense scale-invariant feature transforms. The amount of chrominance information incorporated into the proposed image decolorization technique is confirmed with the improvement to the overall scene classification accuracy. Moreover, the overall scene classification performance improved by combining the models obtained using the proposed method and conventional decolorization methods.

Test of spectral/spatial classifier

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Kast, J. L.; Davis, B. J.

1977-01-01

The author has identified the following significant results. The supervised ECHO processor (which utilizes class statistics for object identification) successfully exploits the redundancy of states characteristic of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The nonsupervised ECHO processor (which identifies objects without the benefit of class statistics) successfully reduces the number of classifications required and the variability of the classification results.

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Rational dissection of a high institutional cesarean section rate: an analysis using the Robson Ten Group Classification System.

PubMed

Tan, Jarrod K H; Tan, Eng Loy; Kanagalingan, Devendra; Tan, Lay Kok

2015-04-01

Cesarean section (CS) rates have risen far in excess of the optimal 15% recommended by the World Health Organization. The Robson Ten Group Classification System (TGCS) allows meaningful analysis of a CS rate. The aim of this study is to identify the leading patient categories contributing to our institution's CS rate. Prospective study of all women who delivered at the Singapore General Hospital from January 2008 to December 2011. The following data was recorded: parity, singleton/multiple pregnancy, previous CS, mode of labor onset and gestational age at delivery. CS rates were computed for each group, as well as their relative contribution to the overall CS rate. There were 6074 deliveries, in which 2011 (33.1%) women had CS delivery. Group 5 was the largest contributor to the overall CS rate (25.9%). Of the patients in this group, 18.8% had a successful vaginal birth after cesarean (VBAC). Group 2 was the second largest contributor to the overall CS rate at 18.0%. Group 10 had a high contribution of 16.1%. The TGCS allows easy identification of the leading contributing patient groups. The surprisingly high contribution of group 10 suggests that our institution, a tertiary multidisciplinary teaching hospital, manages a sizeable group of high-risk patients in its obstetric case mix accounting for the high CS rate. Almost one in five term pregnancies with one previous CS had a successful vaginal delivery, suggesting that the institutional attempted VBAC rate is higher than 20%. © 2014 The Authors. Journal of Obstetrics and Gynaecology Research © 2014 Japan Society of Obstetrics and Gynecology.

Effect of a standardized treatment regime for infection after osteosynthesis.

PubMed

Hellebrekers, Pien; Leenen, Luke P H; Hoekstra, Meriam; Hietbrink, Falco

2017-03-09

Infection after osteosynthesis is an important complication with significant morbidity and even mortality. These infections are often caused by biofilm-producing bacteria. Treatment algorithms dictate an aggressive approach with surgical debridement and antibiotic treatment. The aim of this study is to analyze the effect of such an aggressive standardized treatment regime with implant retention for acute, existing <3 weeks, infection after osteosynthesis. We conducted a retrospective 2-year cohort in a single, level 1 trauma center on infection occurring within 12 months following any osteosynthesis surgery. The standardized treatment regime consisted of implant retention, thorough surgical debridement, and immediate antibiotic combination therapy with rifampicin. The primary outcome was success. Success was defined as consolidation of the fracture and resolved symptoms of infection. Culture and susceptibility testing were performed to identify bacteria and resistance patterns. Univariate analysis was conducted on patient-related factors in association with primary success and antibiotic resistance. Forty-nine patients were included for analysis. The primary success rate was 63% and overall success rate 88%. Factors negatively associated with primary success were the following: Gustilo classification (P = 0.023), higher number of debridements needed (P = 0.015), inability of primary closure (P = 0.017), and subsequent application of vacuum therapy (P = 0.030). Adherence to the treatment regime was positively related to primary success (P = 0.034). The described treatment protocol results in high success rates, comparable with success rates achieved in staged exchange in prosthetic joint infection treatment.

Activity recognition in planetary navigation field tests using classification algorithms applied to accelerometer data.

PubMed

Song, Wen; Ade, Carl; Broxterman, Ryan; Barstow, Thomas; Nelson, Thomas; Warren, Steve

2012-01-01

Accelerometer data provide useful information about subject activity in many different application scenarios. For this study, single-accelerometer data were acquired from subjects participating in field tests that mimic tasks that astronauts might encounter in reduced gravity environments. The primary goal of this effort was to apply classification algorithms that could identify these tasks based on features present in their corresponding accelerometer data, where the end goal is to establish methods to unobtrusively gauge subject well-being based on sensors that reside in their local environment. In this initial analysis, six different activities that involve leg movement are classified. The k-Nearest Neighbors (kNN) algorithm was found to be the most effective, with an overall classification success rate of 90.8%.

Sequenced subjective accents for brain-computer interfaces

NASA Astrophysics Data System (ADS)

Vlek, R. J.; Schaefer, R. S.; Gielen, C. C. A. M.; Farquhar, J. D. R.; Desain, P.

2011-06-01

Subjective accenting is a cognitive process in which identical auditory pulses at an isochronous rate turn into the percept of an accenting pattern. This process can be voluntarily controlled, making it a candidate for communication from human user to machine in a brain-computer interface (BCI) system. In this study we investigated whether subjective accenting is a feasible paradigm for BCI and how its time-structured nature can be exploited for optimal decoding from non-invasive EEG data. Ten subjects perceived and imagined different metric patterns (two-, three- and four-beat) superimposed on a steady metronome. With an offline classification paradigm, we classified imagined accented from non-accented beats on a single trial (0.5 s) level with an average accuracy of 60.4% over all subjects. We show that decoding of imagined accents is also possible with a classifier trained on perception data. Cyclic patterns of accents and non-accents were successfully decoded with a sequence classification algorithm. Classification performances were compared by means of bit rate. Performance in the best scenario translates into an average bit rate of 4.4 bits min-1 over subjects, which makes subjective accenting a promising paradigm for an online auditory BCI.

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.

PubMed

Morales, Dinora Araceli; Bengoetxea, Endika; Larrañaga, Pedro; García, Miguel; Franco, Yosu; Fresnada, Mónica; Merino, Marisa

2008-05-01

In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman's uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.

The T-plasty: a modified YV-plasty for highly recurrent bladder neck contracture after transurethral surgery for benign hyperplasia of the prostate: clinical outcome and patient satisfaction.

PubMed

Reiss, C P; Rosenbaum, C M; Becker, A; Schriefer, P; Ludwig, T A; Engel, O; Riechardt, S; Fisch, M; Dahlem, R

2016-10-01

To describe a modified surgical technique for treatment of highly recurrent bladder neck contracture (BNC) after transurethral surgery for benign hyperplasia and to evaluate success rate and patient satisfaction of this novel technique. Ten patients with highly recurrent BNC and multiple prior attempts of endoscopic treatment underwent the T-plasty. Perioperative complications were recorded and classified according to the Clavien classification. Patient reported functional outcomes were retrospectively analysed using a standardized questionnaire assessing recurrence of stenosis, incontinence, satisfaction and changes in quality of life (QoL). The questionnaires included validated IPSS and SF-8-health survey items. Mean age at the time of surgery was 69.2 years (range 61-79), and the mean follow-up was 26 months (range 3-46). No complications grade 3 or higher according to the Clavien classification occurred. Success rate was 100 %. No de novo stress incontinence occurred. Urinary stream was described as very strong to moderate by 80 % of the patients, mean post-operative IPSS-score was 11.3 (range 4-29), and mean post-operative IPSS-QoL was 2.4 (range 1-5). Patients satisfaction was very high or high in 90 %, and QoL improved in 90 %. The SF-8-health survey showed values comparable to the reference population. The T-plasty represents a safe and valuable option in treating highly recurrent BNC after surgery for benign hyperplasia. It offers multiple advantages compared to other techniques such as a single-staged approach and the opportunity for reconstruction of a reliable wide bladder neck by usage of two well-vascularized flaps. Success rate, low rate of complications and preservation of continence are highly encouraging.

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging

PubMed Central

Ibrahim, Mohd Firdaus; Ahmad Sa’ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-01-01

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. PMID:27801799

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging.

PubMed

Ibrahim, Mohd Firdaus; Ahmad Sa'ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-10-27

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t -test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.

Shift-invariant discrete wavelet transform analysis for retinal image classification.

PubMed

Khademi, April; Krishnan, Sridhar

2007-12-01

This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

PubMed

Chen, Huan-Yuan; Chen, Chih-Chang; Hwang, Wen-Jyi

2017-09-28

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

PubMed Central

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

Classifier fusion for VoIP attacks classification

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Rezac, Filip

2017-05-01

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Utility of the ACC/AHA lesion classification as a predictor of procedural, 30-day and 12-month outcomes in the contemporary percutaneous coronary intervention era.

PubMed

Theuerle, James; Yudi, Matias B; Farouque, Omar; Andrianopoulos, Nick; Scott, Peter; Ajani, Andrew E; Brennan, Angela; Duffy, Stephen J; Reid, Christopher M; Clark, David J

2017-11-15

Correlations between the ACC/AHA coronary lesion classification and clinical outcomes in the contemporary percutaneous coronary intervention (PCI) era are not well established. We analyzed clinical characteristics and outcomes according to ACC/AHA lesion classification (A, B1, B2, C) in 13,701 consecutive patients from the Melbourne Interventional Group (MIG) registry. Patients presenting with STEMI, cardiogenic shock and out-of-hospital cardiac arrest were excluded. The primary endpoints were 30-day and 12-month mortality. Secondary endpoints were procedural success as well as 30-day and 12-month major adverse cardiac events. Of the 13,701 patients treated, 1,246 (9.1%) had type A lesions, 5,519 (40.3%) had type B1 lesions, 4,449 (32.5%) had Type B2 lesions and 2,487 (18.2%) had Type C lesions. Patients with type C lesions were more likely to be older and have impaired renal function, diabetes, previous myocardial infarction, peripheral vascular disease and prior bypass graft surgery (all P < 0.01). They were also more likely to require rotational atherectomy, drug-eluting stents and longer stent lengths (all P < 0.01). Increasing lesion complexity was associated with lower procedural success (99.6% vs. 99.1% vs. 96.6% vs. 82.7%, P < 0.001) and worse 30-day (0.2% vs. 0.3% vs. 0.7% vs. 0.6%, P < 0.001) and 12-month mortality (2.2% vs. 2.0% vs. 3.2% vs. 2.9%, P <0.01). Kaplan Meier analysis showed complex lesions (type B2 and C) had lower survival at 12-months (P = 0.003). PCI to more complex lesions continues to be associated with lower procedural success rates as well as inferior medium-term clinical outcomes. Thus the ACC/AHA lesion classification should still be calculated preprocedure to predict acute PCI success and clinical outcomes. © 2017 Wiley Periodicals, Inc.

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

PubMed Central

Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

2016-01-01

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

Classification of wheat: Badhwar profile similarity technique

NASA Technical Reports Server (NTRS)

Austin, W. W.

1980-01-01

The Badwar profile similarity classification technique used successfully for classification of corn was applied to spring wheat classifications. The software programs and the procedures used to generate full-scene classifications are presented, and numerical results of the acreage estimations are given.

A comparison of blood vessel features and local binary patterns for colorectal polyp classification

NASA Astrophysics Data System (ADS)

Gross, Sebastian; Stehle, Thomas; Behrens, Alexander; Auer, Roland; Aach, Til; Winograd, Ron; Trautwein, Christian; Tischendorf, Jens

2009-02-01

Colorectal cancer is the third leading cause of cancer deaths in the United States of America for both women and men. By means of early detection, the five year survival rate can be up to 90%. Polyps can to be grouped into three different classes: hyperplastic, adenomatous, and carcinomatous polyps. Hyperplastic polyps are benign and are not likely to develop into cancer. Adenomas, on the other hand, are known to grow into cancer (adenoma-carcinoma sequence). Carcinomas are fully developed cancers and can be easily distinguished from adenomas and hyperplastic polyps. A recent narrow band imaging (NBI) study by Tischendorf et al. has shown that hyperplastic polyps and adenomas can be discriminated by their blood vessel structure. We designed a computer-aided system for the differentiation between hyperplastic and adenomatous polyps. Our development aim is to provide the medical practitioner with an additional objective interpretation of the available image data as well as a confidence measure for the classification. We propose classification features calculated on the basis of the extracted blood vessel structure. We use the combined length of the detected blood vessels, the average perimeter of the vessels and their average gray level value. We achieve a successful classification rate of more than 90% on 102 polyps from our polyp data base. The classification results based on these features are compared to the results of Local Binary Patterns (LBP). The results indicate that the implemented features are superior to LBP.

Interobserver and intraobserver variability in the identification of the Lenke classification lumbar modifier in adolescent idiopathic scoliosis.

PubMed

Duong, Luc; Cheriet, Farida; Labelle, Hubert; Cheung, Kenneth M C; Abel, Mark F; Newton, Peter O; McCall, Richard E; Lenke, Lawrence G; Stokes, Ian A F

2009-08-01

Interobserver and intraobserver reliability study for the identification of the Lenke classification lumbar modifier by a panel of experts compared with a computer algorithm. To measure the variability of the Lenke classification lumbar modifier and determine if computer assistance using 3-dimensional spine models can improve the reliability of classification. The lumbar modifier has been proposed to subclassify Lenke scoliotic curve types into A, B, and C on the basis of the relationship between the central sacral vertical line (CSVL) and the apical lumbar vertebra. Landmarks for identification of the CSVL have not been clearly defined, and the reliability of the actual CSVL position and lumbar modifier selection have never been tested independently. Therefore, the value of the lumbar modifier for curve classification remains unknown. The preoperative radiographs of 68 patients with adolescent idiopathic scoliosis presenting a Lenke type 1 curve were measured manually twice by 6 members of the Scoliosis Research Society 3-dimensional classification committee at 6 months interval. Intraobserver and interobserver reliability was quantified using the percentage of agreement and kappa statistics. In addition, the lumbar curve of all subjects was reconstructed in 3-dimension using a stereoradiographic technique and was submitted to a computer algorithm to infer the lumbar modifier according to measurements from the pedicles. Interobserver rates for the first trial showed a mean kappa value of 0.56. Second trial rates were higher with a mean kappa value of 0.64. Intraobserver rates were evaluated at a mean kappa value of 0.69. The computer algorithm was successful in identifying the lumbar curve type and was in agreement with the observers by a proportion up to 93%. Agreement between and within observers for the Lenke lumbar modifier is only moderate to substantial with manual methods. Computer assistance with 3-dimensional models of the spine has the potential to decrease this variability.

CNN for breaking text-based CAPTCHA with noise

NASA Astrophysics Data System (ADS)

Liu, Kaixuan; Zhang, Rong; Qing, Ke

2017-07-01

A CAPTCHA ("Completely Automated Public Turing test to tell Computers and Human Apart") system is a program that most humans can pass but current computer programs could hardly pass. As the most common type of CAPTCHAs , text-based CAPTCHA has been widely used in different websites to defense network bots. In order to breaking textbased CAPTCHA, in this paper, two trained CNN models are connected for the segmentation and classification of CAPTCHA images. Then base on these two models, we apply sliding window segmentation and voting classification methods realize an end-to-end CAPTCHA breaking system with high success rate. The experiment results show that our method is robust and effective in breaking text-based CAPTCHA with noise.

Automatic identification of individual killer whales.

PubMed

Brown, Judith C; Smaragdis, Paris; Nousek-McGregor, Anna

2010-09-01

Following the successful use of HMM and GMM models for classification of a set of 75 calls of northern resident killer whales into call types [Brown, J. C., and Smaragdis, P., J. Acoust. Soc. Am. 125, 221-224 (2009)], the use of these same methods has been explored for the identification of vocalizations from the same call type N2 of four individual killer whales. With an average of 20 vocalizations from each of the individuals the pairwise comparisons have an extremely high success rate of 80 to 100% and the identifications within the entire group yield around 78%.

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.

PubMed

Guvensan, M Amac; Dusun, Burak; Can, Baris; Turkmen, H Irem

2017-12-30

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

NASA Technical Reports Server (NTRS)

Li, Winston; Leung, Henry; Kwan, Chiman; Linnell, Bruce R.

2005-01-01

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Endovascular treatment of visceral artery aneurysms and pseudoaneurysms with stent-graft: Analysis of immediate and long-term results.

PubMed

Cappucci, Matteo; Zarco, Federico; Orgera, Gianluigi; López-Rueda, Antonio; Moreno, Javier; Laurino, Florindo; Barnes, Daniel; Tipaldi, Marcello Andrea; Gomez, Fernando; Macho Fernandez, Juan; Rossi, Michele

2017-05-01

The aim of this study is to analyze the safety and efficacy of stent-graft endovascular treatment for visceral artery aneurysms and pseudoaneurysms. Multicentric retrospective series of patients with visceral aneurysms and pseudoaneurysms treated by means of stent graft. The following variables were analyzed: Age, sex, type of lesion (aneurysms/pseudoaneurysms), localization, rate of success, intraprocedural and long term complication rate (SIR classification). Follow-up was performed under clinical and radiological assessment. Twenty-five patients (16 men), with a mean age of 59 (range 27-79), were treated. The indication was aneurysm in 19 patients and pseudoaneurysms in 6. The localizations were: splenic artery (12), hepatic artery (5), renal artery (4), celiac trunk (3) and gastroduodenal artery (1). Successful treatment rate was 96% (24/25 patients). Intraprocedural complication rate was 12% (4% major; 8% minor). Complete occlusion was demonstrated during follow up (mean 33 months, range 6-72) in the 24 patients with technical success. Two stent migrations (2/24; 8%) and 4stent thrombosis (4/24; 16%) were detected. Mortality rate was 0%. In our study, stent-graft endovascular treatment of visceral aneurysmns and pseudoaneurysms has demonstrated to be safe and is effective in the long-term in both elective and emergent cases, with a high rate of successful treatment and a low complication rate. Copyright © 2017 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

Did our current initial treatment practice change after EAU/ESPU vesicoureteral reflux risk grouping?

PubMed

Tokat, Eda; Gurocak, Serhat; Ure, Iyimser; Acar, Cenk; Sınık, Zafer; Tan, Mustafa Ozgur

2018-06-02

The "European Association of Urology (EAU) Guidelines on Vesicoureteral Reflux (VUR) in Children (September 2012)" established risk classification by analyzing and defining risk factors for each patient. In this study we aimed to investigate how our initial treatment procedures were affected by EAU/ESPU guideline vesicoureteral reflux risk grouping and to compare the early clinical results of treatments performed before and after the risk classification in our patients with VUR. 334 renal units with regular clinical follow-up who were treated owing to VUR (vesicoureteral reflux) between years 2009 and 2017 were retrospectively reviewed. Preoperative clinical parameters such as grade and laterality of reflux, presence of renal scar, initial and follow-up treatments, findings of medical treatment and surgical procedures were analyzed. The initial medical and surgical methods were compared by categorizing patients according to risk groups before and after 2013. Mean age and follow-up duration were 71.4(6-216) months and 47(4-141) months, respectively. Among the preoperative parameters, only high EAU risk group (p = 0.01) and treating lower urinary tract symptoms (p < 0.001) were determining the postoperative success rates significantly, while age, sex, and presence of renal scar at DMSA were not affecting the success of treatment significantly. While no significant difference in medical and surgical treatment rates is observed after risk grouping system in low risk group, the percentages of patients who are treated with surgical methods initially were significantly decreased in moderate and high risk groups (p = 0.002 and p = 0.012, respectively). We determined that VUR risk grouping did not change clinical success significantly in all risk groups. Despite the fact that EAU/ESPU VUR risk classification changed our current practice in terms of initial treatment method, this different approach did not seem to affect early clinical success positively. There is still an absolute need for studies with larger sample size and long-term follow-up to reach more reliable results. Therapeutic. Level 4. Copyright © 2018 Elsevier Inc. All rights reserved.

On-line classification of pollutants in water using wireless portable electronic noses.

PubMed

Herrero, José Luis; Lozano, Jesús; Santos, José Pedro; Suárez, José Ignacio

2016-06-01

A portable electronic nose with database connection for on-line classification of pollutants in water is presented in this paper. It is a hand-held, lightweight and powered instrument with wireless communications capable of standalone operation. A network of similar devices can be configured for distributed measurements. It uses four resistive microsensors and headspace as sampling method for extracting the volatile compounds from glass vials. The measurement and control program has been developed in LabVIEW using the database connection toolkit to send the sensors data to a server for training and classification with Artificial Neural Networks (ANNs). The use of a server instead of the microprocessor of the e-nose increases the capacity of memory and the computing power of the classifier and allows external users to perform data classification. To address this challenge, this paper also proposes a web-based framework (based on RESTFul web services, Asynchronous JavaScript and XML and JavaScript Object Notation) that allows remote users to train ANNs and request classification values regardless user's location and the type of device used. Results show that the proposed prototype can discriminate the samples measured (Blank water, acetone, toluene, ammonia, formaldehyde, hydrogen peroxide, ethanol, benzene, dichloromethane, acetic acid, xylene and dimethylacetamide) with a 94% classification success rate. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Multi-q pattern classification of polarization curves

NASA Astrophysics Data System (ADS)

Fabbri, Ricardo; Bastos, Ivan N.; Neto, Francisco D. Moura; Lopes, Francisco J. P.; Gonçalves, Wesley N.; Bruno, Odemir M.

2014-02-01

Several experimental measurements are expressed in the form of one-dimensional profiles, for which there is a scarcity of methodologies able to classify the pertinence of a given result to a specific group. The polarization curves that evaluate the corrosion kinetics of electrodes in corrosive media are applications where the behavior is chiefly analyzed from profiles. Polarization curves are indeed a classic method to determine the global kinetics of metallic electrodes, but the strong nonlinearity from different metals and alloys can overlap and the discrimination becomes a challenging problem. Moreover, even finding a typical curve from replicated tests requires subjective judgment. In this paper, we used the so-called multi-q approach based on the Tsallis statistics in a classification engine to separate the multiple polarization curve profiles of two stainless steels. We collected 48 experimental polarization curves in an aqueous chloride medium of two stainless steel types, with different resistance against localized corrosion. Multi-q pattern analysis was then carried out on a wide potential range, from cathodic up to anodic regions. An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data. These results show the potential of the proposed approach towards efficient, robust, systematic and automatic classification of highly nonlinear profile curves.

Identifying the optimal segmentors for mass classification in mammograms

NASA Astrophysics Data System (ADS)

Zhang, Yu; Tomuro, Noriko; Furst, Jacob; Raicu, Daniela S.

2015-03-01

In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image enhancement techniques to each suspicious mass (region of interest (ROI)) to obtain several enhanced images, then applied segmentation to each image to obtain several contours of a given mass. Each segmentation in this ensemble is essentially a "weak segmentor" because no single segmentation can produce the optimal result for all images. Then after shape features are computed from the segmented contours, the final classification model was built using logistic regression. The work in this paper focuses on identifying the optimal segmentor(s) from an ensemble mix of weak segmentors. For our purpose, optimal segmentors are those in the ensemble mix which contribute the most to the overall classification rather than the ones that produced high precision segmentation. To measure the segmentors' contribution, we examined weights on the features in the derived logistic regression model and computed the average feature weight for each segmentor. The result showed that, while in general the segmentors with higher segmentation success rates had higher feature weights, some segmentors with lower segmentation rates had high classification feature weights as well.

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

PubMed

Chai, Rifai; Naik, Ganesh R; Ling, Sai Ho; Nguyen, Hung T

2017-01-07

One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks

PubMed Central

Barkallah, Eya; Freulard, Johan; Otis, Martin J. -D.; Ngomo, Suzy; Ayena, Johannes C.; Desrosiers, Christian

2017-01-01

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture. PMID:28862665

Enterocutaneous Fistula: Proven Strategies and Updates

PubMed Central

Gribovskaja-Rupp, Irena; Melton, Genevieve B.

2016-01-01

Management of enterocutaneous fistula represents one of the most protracted and difficult problems in colorectal surgery with substantial morbidity and mortality rates. This article summarizes the current classification systems and successful management protocols, provides an in-depth review of fluid resuscitation, sepsis control, nutrition management, medication management of output quantity, wound care, nonoperative intervention measures, operative timeline, and considerations, and discusses special considerations such as inflammatory bowel disease and enteroatmospheric fistula. PMID:27247538

The Long-Term Effectiveness of Reading Recovery and the Cost-Efficiency of Reading Recovery Relative to the Learning Disabled Classification Rate

ERIC Educational Resources Information Center

Galluzzo, Charles A.

2010-01-01

There is a great deal of research supporting Reading Recovery as a successful reading intervention program that assists below level first graders readers in closing the gap in reading at the same level of their average peers. There is a lack of research that analyses the cost-effectiveness of the Reading Recovery program compared to the cost in…

Improving the Selection, CLassification and Utilization of Army Enlisted Personnel

DTIC Science & Technology

1992-11-01

installations. These jobs were grouped into one combat (11B, 13B, and 19E NOS) and three non -combat clusters [Cleri- cal (71L MOS), Operations (31C...factor contains items from the Non -delinquency, Traditional Values, Conscientiousness, Cooperative- ness, and Internal Control scales. The Adjustment...validities and success rates for various groups of cases: all males, females, graduate males, and non -graduate males. Table 3 Descriptive statistics

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer.

PubMed

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, p<0.001). Kyoto gastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all p<0.001). No significant differences were noted in the rates of gastric fold hypertrophy or diffuse redness between the two groups. In a multivariate analysis, the risks for H. pylori-positive gastric cancer increased with intestinal metaplasia (odds ratio: 4.453, 95% confidence interval: 3.332-5.950, <0.001) and male sex (1.737, 1.102-2.739, p=0.017). Conclusion Making an appropriate diagnosis and detecting patients at high risk is crucial for achieving total eradication of gastric cancer. The scores of intestinal metaplasia and atrophy of the scoring system in the Kyoto gastritis classification may thus be useful for detecting these patients.

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer

PubMed Central

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, p<0.001). Kyoto gastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all p<0.001). No significant differences were noted in the rates of gastric fold hypertrophy or diffuse redness between the two groups. In a multivariate analysis, the risks for H. pylori-positive gastric cancer increased with intestinal metaplasia (odds ratio: 4.453, 95% confidence interval: 3.332-5.950, <0.001) and male sex (1.737, 1.102-2.739, p=0.017). Conclusion Making an appropriate diagnosis and detecting patients at high risk is crucial for achieving total eradication of gastric cancer. The scores of intestinal metaplasia and atrophy of the scoring system in the Kyoto gastritis classification may thus be useful for detecting these patients. PMID:28321054

Bioactive titan cage Implaspin in treatment of degenerative disease of the cervcal spine--the results from 2007 till 2008.

PubMed

Filip, Michal; Linzer, Petr; Sámal, Filip; Jurek, Patrik; Strnad, Zdenek; Strnad, Jakub

2010-01-01

The authors present results of surgical treatment of cervical spine degenerative disease via Implaspin biotitanium replacement. Surgery was indicated for a group of 24 patients with symptoms of cervical spondylogenic myelopathy or the irritation decay root syndrome non-reacting to conservative treatment. Pre-surgery X-ray and MRI examinations showed spinal canal stenosis caused by the intervertebral disk osteochondrosis combined with prolapse or dorsal osteophytes. Clinical problems of the group of patients were evaluated through the JOA classification before surgery and during the 2nd, the 6th and month 12th after surgery. The surgery rate of success was evaluated in percentages during post-surgery examinations that took place in the 12th month. Based on the JOA classification, that rate of success falls into the good surgery results zone. The post-surgery X-ray examinations showed two sank replacements by 1/3 of its height into the surrounding vertebral bodies. In these cases we performed the control MRI. No signs of the new spinal compression were found and the spinal canal was free in the operated site. Based on our short-term experiences, the Implaspin bioactive replacement seems to be a suitable alternative to the other types of replacements designed for intervertebral fusion in the lower cervical spine area.

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

Comprehension and reproducibility of the Judet and Letournel classification

PubMed Central

Polesello, Giancarlo Cavalli; Nunes, Marcus Aurelius Araujo; Azuaga, Thiago Leonardi; de Queiroz, Marcelo Cavalheiro; Honda, Emerson Kyoshi; Ono, Nelson Keiske

2012-01-01

Objective To evaluate the effectiveness of the method of radiographic interpretation of acetabular fractures, according to the classification of Judet and Letournel, used by a group of residents of Orthopedics at a university hospital. Methods We selected ten orthopedic residents, who were divided into two groups; one group received training in a methodology for the classification of acetabular fractures, which involves transposing the radiographic images to a graphic two-dimensional representation. We classified fifty cases of acetabular fracture on two separate occasions, and determined the intraobserver and interobserver agreement. Result The success rate was 16.2% (10-26%) for the trained group and 22.8% (10-36%) for the untrained group. The mean kappa coefficients for interobserver and intraobserver agreement in the trained group were 0.08 and 0.12, respectively, and for the untrained group, 0.14 and 0.29. Conclusion Training in the method of radiographic interpretation of acetabular fractures was not effective for assisting in the classification of acetabular fractures. Level of evidence I, Testing of previously developed diagnostic criteria on consecutive patients (with universally applied reference "gold" standard). PMID:24453583

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

DOE PAGES

Tibi, Rigobert; Koper, Keith D.; Pankow, Kristine L.; ...

2018-03-20

Most of the commonly used seismic discrimination approaches are designed for regional data. Relatively little attention has focused on discriminants for local distances (< 200 km), the range at which the smallest events are recorded. We take advantage of the variety of seismic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events, and tectonic earthquakes.more » We achieved a limited success. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category, pairwise classification, seven out of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and mining-induced events. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4–14% in misclassification rates compared to Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74%, compared to the rate of about 86% for two-category, pairwise classification.« less

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tibi, Rigobert; Koper, Keith D.; Pankow, Kristine L.

Most of the commonly used seismic discrimination approaches are designed for regional data. Relatively little attention has focused on discriminants for local distances (< 200 km), the range at which the smallest events are recorded. We take advantage of the variety of seismic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events, and tectonic earthquakes.more » We achieved a limited success. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category, pairwise classification, seven out of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and mining-induced events. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4–14% in misclassification rates compared to Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74%, compared to the rate of about 86% for two-category, pairwise classification.« less

Proximal Femoral Varus Derotation Osteotomy in Children with Cerebral Palsy: The Effect of Age, Gross Motor Function Classification System Level, and Surgeon Volume on Surgical Success.

PubMed

Shore, Benjamin J; Zurakowski, David; Dufreny, Chantal; Powell, Dustin; Matheney, Travis H; Snyder, Brian D

2015-12-16

The purpose of this study was to evaluate mid-term results of proximal femoral varus derotation osteotomy (VDRO) in children with cerebral palsy and determine what effect age, Gross Motor Function Classification System (GMFCS) level, and surgeon volume had on surgical success. We analyzed a cohort of children with cerebral palsy who underwent VDRO for hip displacement at a tertiary-level pediatric hospital between 1994 and 2007. Age, sex, GMFCS level, preoperative radiographic parameters, previous botulinum toxin administration or soft-tissue release, adjunctive pelvic osteotomy, the performance of bilateral surgery at the index VDRO, and surgeon volume (the number of procedures performed) were recorded. Results were analyzed via univariate and multivariate analyses for association with the need for revision hip surgery. Kaplan-Meier survivorship curves were generated, determining the time from index surgery to failure (defined as the need for subsequent surgical procedures on the hip and/or pelvis, or a hip migration percentage of >50% at the time of final follow-up), and were further stratified according to osseous versus soft-tissue revision. A total of 567 VDROs were performed in 320 children (mean age [and standard deviation], 8.2 ± 3.8 years). The mean follow-up was 8.3 years (range, three to eighteen years). Of the initial 320 patients, 117 (37%) were considered to have had failure. Multivariate Cox regression analysis confirmed that younger age at surgery (p < 0.001), increased GMFCS level (p = 0.01), and lower annual surgical hip volume (p = 0.02) were significant independent predictors of any type of surgical revision. Furthermore, soft-tissue release at VDRO was protective against revision (p = 0.02). Five-year survivorship analysis revealed a 92% success rate for children classified as GMFCS levels I and II compared with a 76% success rate for those of GMFCS level V (p < 0.01). This study demonstrated a 37% failure rate after VDRO in children with cerebral palsy. Older age, lower GMFCS level, and increased surgeon volume were strong predictors of surgical success. Copyright © 2015 by The Journal of Bone and Joint Surgery, Incorporated.

Success and complications of implant-retained prostheses provided by the Post-Doctoral Prosthodontics Program, University of Puerto Rico: a cross-sectional study.

PubMed

Loza-Herrero, María A; Rivas-Tumanyan, Sona; Morou-Bermudez, Evangelia

2015-11-01

The success rate of implant-retained prostheses in a postdoctoral prosthodontics program was unknown and could not be related to any set of potential clinical issues or patient characteristics. The purpose of this study was to determine the success rate of implant-retained prostheses placed by prosthodontic residents between 1997 and 2012 and to evaluate the associations between patient classifications and specific restoration characteristics as related to prosthesis success or failure. A total of 272 prostheses in 119 patients were clinically evaluated. Success was defined as the absence of prosthetic complications or any implant-related complication that affected prosthesis survival. Logistic regression was used to evaluate associations between prosthesis success/failure and a wide array of study variables, adjusting for patient age, sex, and prosthesis longevity. The overall success rate was 71%, with a mean prosthesis age of 4.5 years (range: 4 months to 16.8 years). Implant single crowns were the most successful prosthesis type (81% success). The most common complications observed were porcelain fractures in fixed dental prostheses (15%) and lack of stability (31%) and retention (29%) in removable dental prostheses. Having a removable prosthesis (versus natural dentition) in the opposing occlusion significantly decreased the odds of success (OR=0.26, 95% CI: 0.11-0.64). Definitively cemented fixed prostheses were more successful than those cemented with an interim cement (OR=4.56, 95% CI: 1.37-15.22). The overall success rate of the implant-retained prostheses placed in the program was low compared with previously published studies. This study revealed the need for an efficient, comprehensive recall system for patients receiving implant-retained prostheses, either fixed or removable. Copyright © 2015 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Endoscopic stenting for hilar cholangiocarcinoma: efficacy of unilateral and bilateral placement of plastic and metal stents in a retrospective review of 480 patients

PubMed Central

2012-01-01

Background Endoscopic biliary drainage of hilar cholangiocarcinoma is controversial with respect to the optimal types of stents and the extent of drainage. This study evaluated endoscopic palliation in patients with hilar cholangiocarcinoma using self-expandable metallic stents (SEMS) and plastic stents (PS).We also compared unilateral and bilateral stent placement according to the Bismuth classification. Methods Data on 480 patients receiving endoscopic biliary drainage for hilar cholangiocarcinoma between September 1995 and December 2010 were retrospectively reviewed to evaluate the following outcome parameters: technical success (TS), functional success (FS), early and late complications, stent patency and survival. Patients were followed from stent insertion until death or stent occlusion. Patients were divided into 3 groups according to the Bismuth classification (Group 1, type I; Group 2, type II; Group 3, type > III). Results The initial stent insertion was successful in 450 (93.8%) patients. TS was achieved in 204 (88.3%) patients treated with PS and in 246 (98.8%) patients palliated with SEMS (p < 0.001). In the intention-to-treat (ITT) analysis, the FS in patients treated with SEMS (97.9%) was significantly higher than in patients treated with PS (84.8%) (p < 0.001). Late complications occurred in 115 (56.4%) patients treated with PS and 60 (24.4%) patients treated with SEMS (p < 0.001). The median duration of stent patency in weeks (w) were as follows: 20 w in patients palliated with PS and 27 w in patients treated with SEMS (p < 0.0001). In Group 2, the median duration of PS patency was 17 w and 18 w for unilateral and bilateral placement, respectively (p = 0.0004); the median duration of SEMS patency was 24 w and 29 w for unilateral and bilateral placement, respectively (p < 0.0001). Multivariate analysis using the Poisson regression showed that SEMS placement (B = 0.48; P < 0.01) and bilateral deployment (B = 0.24; P < 0.01) were the only independent prognostic factors associated with stent patency. Conclusions SEMS insertion for the palliation of hilar cholangiocarcinoma offers higher technical and clinical success rates in the ITT analysis as well as lower complication rates and a superior cumulative stent patency when compared with PS placement in all Bismuth classifications. The cumulative patency of bilateral SEMS or PS stents was significantly higher than that of unilateral SEMS or PS stents, with lower occlusion rates in Bismuth II patients. PMID:22873816

U.S. Army Classification Research Panel: Conclusions and Recommendations on Classification Research Strategies

DTIC Science & Technology

2007-05-01

criteria, specifically occupational and organizational retention criteria; and (c) indices of career success (cf. Barrick & Mount, 1991; Hogan & Holland... career success (cf. Barrick & Mount, 1991; Hogan & Holland, 2003; Hough & Furnham, 2003; Hurtz & Donovan, 2000; Judge et al., 1999; Ozer, & Benet...traits, general mental ability, and career success across the life span. Personnel Psychology, 52, 621-652. Knapp, D. J., & Campbell, R. C. (Eds.) (2006

Predictors of success of external cephalic version and cephalic presentation at birth among 1253 women with non-cephalic presentation using logistic regression and classification tree analyses.

PubMed

Hutton, Eileen K; Simioni, Julia C; Thabane, Lehana

2017-08-01

Among women with a fetus with a non-cephalic presentation, external cephalic version (ECV) has been shown to reduce the rate of breech presentation at birth and cesarean birth. Compared with ECV at term, beginning ECV prior to 37 weeks' gestation decreases the number of infants in a non-cephalic presentation at birth. The purpose of this secondary analysis was to investigate factors associated with a successful ECV procedure and to present this in a clinically useful format. Data were collected as part of the Early ECV Pilot and Early ECV2 Trials, which randomized 1776 women with a fetus in breech presentation to either early ECV (34-36 weeks' gestation) or delayed ECV (at or after 37 weeks). The outcome of interest was successful ECV, defined as the fetus being in a cephalic presentation immediately following the procedure, as well as at the time of birth. The importance of several factors in predicting successful ECV was investigated using two statistical methods: logistic regression and classification and regression tree (CART) analyses. Among nulliparas, non-engagement of the presenting part and an easily palpable fetal head were independently associated with success. Among multiparas, non-engagement of the presenting part, gestation less than 37 weeks and an easily palpable fetal head were found to be independent predictors of success. These findings were consistent with results of the CART analyses. Regardless of parity, descent of the presenting part was the most discriminating factor in predicting successful ECV and cephalic presentation at birth. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Use of the Wound, Ischemia, foot Infection classification system in hemodialysis patients after endovascular treatment for critical limb ischemia.

PubMed

Tokuda, Takahiro; Hirano, Keisuke; Sakamoto, Yasunari; Mori, Shisuke; Kobayashi, Norihiro; Araki, Motoharu; Yamawaki, Masahiro; Ito, Yoshiaki

2017-12-07

The Wound, Ischemia, foot Infection (WIfI) classification system is used to predict the amputation risk in patients with critical limb ischemia (CLI). The validity of the WIfI classification system for hemodialysis (HD) patients with CLI is still unknown. This single-center study evaluated the prognostic value of WIfI stages in HD patients with CLI who had been treated with endovascular therapy (EVT). A retrospective analysis was performed of collected data on CLI patients treated with EVT between April 2007 and December 2015. All patients were classified according to their wound status, ischemia index, and extent of foot infection into the following four groups: very low risk, low risk, moderate risk, and high risk. Comorbidities and vascular lesions in each group were analyzed. The prognostic value of the WIfI classification was analyzed on the basis of the wound healing rate and amputation-free survival at 1 year. This study included 163 consecutive CLI patients who underwent HD and successful endovascular intervention. The rate of the high-risk group (36%) was the highest among the four groups, and the proportions of very-low-risk, low-risk, and moderate-risk patients were 10%, 18%, and 34%, respectively. The mean follow-up duration was 784 ± 650 days. The wound healing rates at 1 year were 92%, 70%, 75%, and 42% in the very-low-risk, low-risk, moderate-risk, and high-risk groups, respectively (P <.01). A similar trend was observed for the 1-year amputation-free survival among the groups (76%, 58%, 61%, and 46%, respectively; P = .02). The WIfI classification system predicted the wound healing and amputation risks in a highly selected group of HD patients with CLI treated with EVT, with a statistically significant difference between high-risk patients and other patients. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Local Kernel for Brains Classification in Schizophrenia

NASA Astrophysics Data System (ADS)

Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Classification of probability densities on the basis of Pearson?s curves with application to coronal heating simulations

NASA Astrophysics Data System (ADS)

Podladchikova, O.; Lefebvre, B.; Krasnoselskikh, V.; Podladchikov, V.

An important task for the problem of coronal heating is to produce reliable evaluation of the statistical properties of energy release and eruptive events such as micro-and nanoflares in the solar corona. Different types of distributions for the peak flux, peak count rate measurements, pixel intensities, total energy flux or emission measures increases or waiting times have appeared in the literature. This raises the question of a precise evaluation and classification of such distributions. For this purpose, we use the method proposed by K. Pearson at the beginning of the last century, based on the relationship between the first 4 moments of the distribution. Pearson's technique encompasses and classifies a broad range of distributions, including some of those which have appeared in the literature about coronal heating. This technique is successfully applied to simulated data from the model of Krasnoselskikh et al. (2002). It allows to provide successful fits to the empirical distributions of the dissipated energy, and to classify them as a function of model parameters such as dissipation mechanisms and threshold.

Emergency endotracheal intubation under fluoroscopy guidance for patients with acute dyspnea or asphyxia.

PubMed

Jiao, Dechao; Xie, Na; Han, Xinwei; Wu, Gang

2016-11-01

To evaluate the feasibility and effectiveness of emergency endotracheal intubation (EEI) under fluoroscopy guidance for patients with acute dyspnea or asphyxia. From October 2011 to October 2014, of 1521 patients with acute dyspnea or asphyxia who required EEI in 6 departments, 43 patients who experienced intubation difficulty or failure were entered into this study. Data on technical success, procedure time, complications, and clinical outcome were collected. The pulse oxygen saturation and Hugh-Jones classification changes were analyzed. Fluoroscopy-guided EEI was technically successful in all patients. Acute dyspnea had resolved in all patients with clinical success rate 100% after the procedure. There were no serious complications during or after the procedure. The pulse oxygen saturation and Hugh-Jones classification showed significant increase after EEI (P < .05). Further treatments, including tracheal stents (n = 21), surgical resection (n = 16), palliative tracheotomy (n = 4), and bronchoscopic treatment (n = 2), were performed 1 to 72 hours after EEI. During a mean follow-up period of 13.2 months, 13 patients had died and 30 patients remained alive without dyspnea. Fluoroscopy-guided EEI is a safe and feasible procedure, and may serve as an alternative treatment option for patients when traditional EEI is unsuccessful. Copyright © 2016 Elsevier Inc. All rights reserved.

Leaf epidermis images for robust identification of plants

PubMed Central

da Silva, Núbia Rosa; Oliveira, Marcos William da Silva; Filho, Humberto Antunes de Almeida; Pinheiro, Luiz Felipe Souza; Rossatto, Davi Rodrigo; Kolb, Rosana Marta; Bruno, Odemir Martinez

2016-01-01

This paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis. All the experiments were carried out using 32 plant species with 309 epidermal samples captured by an optical microscope coupled to a digital camera. The results of the computational methods using texture features were compared to the conventional approach, where quantitative measurements of stomatal traits (density, length and width) were manually obtained. Epidermis image classification using texture has achieved a success rate of over 96%, while success rate was around 60% for quantitative measurements taken manually. Furthermore, we verified the robustness of our method accounting for natural phenotypic plasticity of stomata, analysing samples from the same species grown in different environments. Texture methods were robust even when considering phenotypic plasticity of stomatal traits with a decrease of 20% in the success rate, as quantitative measurements proved to be fully sensitive with a decrease of 77%. Results from the comparison between the computational approach and the conventional quantitative measurements lead us to discover how computational systems are advantageous and promising in terms of solving problems related to Botany, such as species identification. PMID:27217018

Auditory “bubbles”: Efficient classification of the spectrotemporal modulations essential for speech intelligibility

PubMed Central

Venezia, Jonathan H.; Hickok, Gregory; Richards, Virginia M.

2016-01-01

Speech intelligibility depends on the integrity of spectrotemporal patterns in the signal. The current study is concerned with the speech modulation power spectrum (MPS), which is a two-dimensional representation of energy at different combinations of temporal and spectral (i.e., spectrotemporal) modulation rates. A psychophysical procedure was developed to identify the regions of the MPS that contribute to successful reception of auditory sentences. The procedure, based on the two-dimensional image classification technique known as “bubbles” (Gosselin and Schyns (2001). Vision Res. 41, 2261–2271), involves filtering (i.e., degrading) the speech signal by removing parts of the MPS at random, and relating filter patterns to observer performance (keywords identified) over a number of trials. The result is a classification image (CImg) or “perceptual map” that emphasizes regions of the MPS essential for speech intelligibility. This procedure was tested using normal-rate and 2×-time-compressed sentences. The results indicated: (a) CImgs could be reliably estimated in individual listeners in relatively few trials, (b) CImgs tracked changes in spectrotemporal modulation energy induced by time compression, though not completely, indicating that “perceptual maps” deviated from physical stimulus energy, and (c) the bubbles method captured variance in intelligibility not reflected in a common modulation-based intelligibility metric (spectrotemporal modulation index or STMI). PMID:27586738

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

Banerjee, Portia; Safdarnejad, Seyed; Udpa, Lalita; Udpa, Satish

2016-02-01

Ensemble of classifiers in general, aims to improve classification accuracy by combining results from multiple weak hypotheses into a single strong classifier through weighted majority voting. Improved versions of ensemble of classifiers generate self-rated confidence scores which estimate the reliability of each of its prediction and boost the classifier using these confidence-rated predictions. However, such a confidence metric is based only on the rate of correct classification. In existing works, although ensemble of classifiers has been widely used in computational intelligence, the effect of all factors of unreliability on the confidence of classification is highly overlooked. With relevance to NDE, classification results are affected by inherent ambiguity of classifica-tion, non-discriminative features, inadequate training samples and noise due to measurement. In this paper, we extend the existing ensemble classification by maximizing confidence of every classification decision in addition to minimizing the classification error. Initial results of the approach on data from eddy current inspection show improvement in classification performance of defect and non-defect indications.

Military personnel recognition system using texture, colour, and SURF features

NASA Astrophysics Data System (ADS)

Irhebhude, Martins E.; Edirisinghe, Eran A.

2014-06-01

This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

Measurement of the center edge angle and determination of the Severin classification using digital radiography, computer-assisted measurement tools, and a Severin algorithm: intraobserver and interobserver reliability revisited.

PubMed

Carroll, Kristen L; Murray, Kathleen A; MacLeod, Lynne M; Hennessey, Theresa A; Woiczik, Marcella R; Roach, James W

2011-06-01

Numerous studies underscore the poor intraobserver and interobserver reliability of both the center edge angle (CEA) and the Severin classification using plain film measurements. In this study, experienced observers applied a computer-assisted measurement program to determine the CEA in digital pelvic radiographs of adults who had been previously treated for dysplasia of the hip (DDH). Using a teaching aid/algorithm of the Severin classification, the observers then assigned a Severin rating to these hips. Intraobserver and interobserver errors were then calculated on both the CEA measurements and the Severin classifications. Four pediatric orthopaedic surgeons and 1 pediatric radiologist calculated the CEAs using the OrthoView TM planning system and then determined the Severin classification on 41 blinded digital pelvic radiographs. The radiographs were evaluated by each examiner twice, with evaluations separated by 2 months. All examiners reviewed a Severin classification algorithm before making their Severin assignments. The intraobserver and interobserver reliability for both the CEA and the Severin classification were calculated using the interclass correlation coefficients and Cohen and Fleiss κ scores, respectively. The intraobserver and interobserver reliability for CEA measurement was moderate to almost perfect. When we separated the Severin classification into 3 clinically relevant groups of good (Severin I and II), dysplastic (Severin III), and poor (Severin IV and above), our interobserver reliability neared almost perfect. The Severin classification is an extremely useful and oft-used radiographic measure for the success of DDH treatment. Our research found digital radiography, computer-aided measurement tools, the use of a Severin algorithm, and separating the Severin classification into 3 clinically relevant groups significantly increased the intraobserver and interobserver reliability of both the CEA and Severin classification. This finding will assist future studies using the CEA and Severin classification in the radiographic assessment of DDH treatment outcomes.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Classification of Alzheimer's Patients through Ubiquitous Computing.

PubMed

Nieto-Reyes, Alicia; Duque, Rafael; Montaña, José Luis; Lage, Carmen

2017-07-21

Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c's patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83 % indicates the potential of the proposed methodology.

Patient-initiated switching between private and public inpatient hospitalisation in Western Australia 1980 – 2001: An analysis using linked data

PubMed Central

Moorin, Rachael E; Holman, C D'Arcy J

2005-01-01

Background The aim of the study was to identify any distinct behavioural patterns in switching between public and privately insured payment classifications between successive episodes of inpatient care within Western Australia between 1980 and 2001 using a novel 'couplet' method of analysing longitudinal data. Methods The WA Data Linkage System was used to extract all hospital morbidity records from 1980 to 2001. For each individual, episodes of hospitalisation were paired into couplets, which were classified according to the sequential combination of public and privately insured episodes. Behavioural patterns were analysed using the mean intra-couplet interval and proportion of discordant couplets in each year. Results Discordant couplets were consistently associated with the longest intra-couplet intervals (ratio to the average annual mean interval being 1.35), while the shortest intra-couplet intervals were associated with public concordant couplets (0.5). Overall, privately insured patients were more likely to switch payment classification at their next admission compared with public patients (the average rate of loss across all age groups being 0.55% and 2.16% respectively). The rate of loss from the privately insured payment classification was inversely associated with time between episodes (2.49% for intervals of 0 to 13 years and 0.83% for intervals of 14 to 21 years). In all age groups, the average rate of loss from the privately insured payment classification was greater between 1981 and 1990 compared with that between 1991 and 2001 (3.45% and 3.10% per year respectively). Conclusion A small but statistically significant reduction in rate of switching away from PHI over the latter period of observation indicated that health care policies encouraging uptake of PHI implemented in the 1990s by the federal government had some of their intended impact on behaviour. PMID:15978139

A real-time method for autonomous passive acoustic detection-classification of humpback whales.

PubMed

Abbot, Ted A; Premus, Vincent E; Abbot, Philip A

2010-05-01

This paper describes a method for real-time, autonomous, joint detection-classification of humpback whale vocalizations. The approach adapts the spectrogram correlation method used by Mellinger and Clark [J. Acoust. Soc. Am. 107, 3518-3529 (2000)] for bowhead whale endnote detection to the humpback whale problem. The objective is the implementation of a system to determine the presence or absence of humpback whales with passive acoustic methods and to perform this classification with low false alarm rate in real time. Multiple correlation kernels are used due to the diversity of humpback song. The approach also takes advantage of the fact that humpbacks tend to vocalize repeatedly for extended periods of time, and identification is declared only when multiple song units are detected within a fixed time interval. Humpback whale vocalizations from Alaska, Hawaii, and Stellwagen Bank were used to train the algorithm. It was then tested on independent data obtained off Kaena Point, Hawaii in February and March of 2009. Results show that the algorithm successfully classified humpback whales autonomously in real time, with a measured probability of correct classification in excess of 74% and a measured probability of false alarm below 1%.

Real-time distributed fiber optic sensor for security systems: Performance, event classification and nuisance mitigation

NASA Astrophysics Data System (ADS)

Mahmoud, Seedahmed S.; Visagathilagar, Yuvaraja; Katsifolis, Jim

2012-09-01

The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the nuisance alarm rate (NAR), and the false alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings (LCs) based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder (MZ) interferometer.

Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon

PubMed Central

Li, Guiying; Moran, Emilio; Hetrick, Scott

2013-01-01

This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates. PMID:24127130

Classifications for Cesarean Section: A Systematic Review

PubMed Central

Torloni, Maria Regina; Betran, Ana Pilar; Souza, Joao Paulo; Widmer, Mariana; Allen, Tomas; Gulmezoglu, Metin; Merialdi, Mario

2011-01-01

Background Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system. Methods and Findings Three electronic databases were searched for classifications published 1968–2008. Two reviewers independently assessed classifications using a form created based on items rated as important by international experts. Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications identified. Indications classifications present important limitations and their overall score ranged from 2–9 (maximum grade = 14). Degree of urgency classifications also had several drawbacks (overall scores 6–9). Woman-based classifications performed best (scores 5–14). Other types of classifications require data not routinely collected and may not be relevant in all settings (scores 3–8). Conclusions This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and Robson's classification, in particular, would be in the best position to fulfill current international and local needs and that efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this classification. The use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary. PMID:21283801

A manual and an automatic TERS based virus discrimination

NASA Astrophysics Data System (ADS)

Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen

2015-02-01

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j

34 CFR 222.68 - What tax rates does the Secretary use if two or more different classifications of real property...

Code of Federal Regulations, 2011 CFR

2011-07-01

... different classifications of real property are taxed at different rates? 222.68 Section 222.68 Education... different classifications of real property are taxed at different rates? If the real property of an LEA and its generally comparable LEAs consists of two or more classifications of real property taxed at...

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

The National Shipbuilding Research Program 1985 Ship Production Symposium Volume 2 Paper No. 12: North European Craft Training: A Trip Report

DTIC Science & Technology

1985-09-01

laws concerning worker classification and are administered by the company and the local chamber of commerce . There are roughly 450 occupations or...the local chamber of commerce to determine if the apprentice has reached a level of craftsmanship suitable to be awarded the title of facharbeiter...for the apprentice and a review of the meisters’ credentials by the local chamber of commerce . But, this is a rare event. The success rate for

Audit and feedback using the Robson classification to reduce caesarean section rates: a systematic review.

PubMed

Boatin, A A; Cullinane, F; Torloni, M R; Betrán, A P

2018-01-01

In most regions worldwide, caesarean section (CS) rates are increasing. In these settings, new strategies are needed to reduce CS rates. To identify, critically appraise and synthesise studies using the Robson classification as a system to categorise and analyse data in clinical audit cycles to reduce CS rates. Medline, Embase, CINAHL and LILACS were searched from 2001 to 2016. Studies reporting use of the Robson classification to categorise and analyse data in clinical audit cycles to reduce CS rates. Data on study design, interventions used, CS rates, and perinatal outcomes were extracted. Of 385 citations, 30 were assessed for full text review and six studies, conducted in Brazil, Chile, Italy and Sweden, were included. All studies measured initial CS rates, provided feedback and monitored performance using the Robson classification. In two studies, the audit cycle consisted exclusively of feedback using the Robson classification; the other four used audit and feedback as part of a multifaceted intervention. Baseline CS rates ranged from 20 to 36.8%; after the intervention, CS rates ranged from 3.1 to 21.2%. No studies were randomised or controlled and all had a high risk of bias. We identified six studies using the Robson classification within clinical audit cycles to reduce CS rates. All six report reductions in CS rates; however, results should be interpreted with caution because of limited methodological quality. Future trials are needed to evaluate the role of the Robson classification within audit cycles aimed at reducing CS rates. Use of the Robson classification in clinical audit cycles to reduce caesarean rates. © 2017 The Authors. BJOG An International Journal of Obstetrics and Gynaecology published by John Wiley & Sons Ltd on behalf of Royal College of Obstetricians and Gynaecologists.

Methodological Issues in the Classification of Attention-Related Disorders.

ERIC Educational Resources Information Center

Fletcher, Jack M.; And Others

1991-01-01

For successful classification of children with attention deficit-hyperactivity disorder, major issues include (1) the need for explicit studies of identification criteria; (2) the need for systematic sampling strategies; (3) development of hypothetical classifications; and (4) systematic assessment of reliability and validity of hypothetical…

Early effectiveness of endoscopic posterior urethra primary alignment.

PubMed

Kim, Fernando J; Pompeo, Alexandre; Sehrt, David; Molina, Wilson R; Mariano da Costa, Renato M; Juliano, Cesar; Moore, Ernest E; Stahel, Philip F

2013-08-01

Posterior urethra primary realignment (PUPR) after complete transection may decrease the gap between the ends of the transected urethra, tamponade the retropubic bleeding, and optimize urinary drainage without the need of suprapubic catheter facilitating concurrent pelvic orthopedic and trauma procedures. Historically, the distorted anatomy after pelvic trauma has been a major surgical challenge. The purpose of the study was to assess the relationship of the severity of the pelvic fracture to the success of endoscopic and immediate PUPR following complete posterior urethral disruption using the Young-Burgess classification system. A review of our Level I trauma center database for patients diagnosed with pelvic fracture and complete posterior urethral disruption from January 2005 to April 2012 was performed. Pelvic fracture severity was categorized according to the Young-Burgees classification system. Management consisted of suprapubic catheter insertion at diagnosis followed by early urethral realignment when the patient was clinically stable. Failure of realignment was defined as inability to achieve urethral continuity with Foley catheterization. Clinical follow-up consisted of radiologic, pressure studies and cystoscopic evaluation. A total of 481 patients with pelvic trauma from our trauma registry were screened initially, and 18 (3.7%) were diagnosed with a complete posterior urethral disruption. A total of 15 primary realignments (83.3%) were performed all within 5 days of trauma. The success rate of early realignment was 100%. There was no correlation between the type of pelvic ring fracture and the success of PUPR. Postoperatively, 8 patients (53.3%) developed urethral strictures, 3 patients (20.0%) developed incontinence, and 7 patients (46.7%) reported erectile dysfunction after the trauma. The mean follow-up of these patients was 31.8 months. Endoscopic PUPR may be an effective option for the treatment of complete posterior urethral disruption and enables urinary drainage to best suit the multispecialty surgical team. The success rate of achieving primary realignment did not appear to be related to the complexity and type of pelvic ring fracture.

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2010 CFR

2010-10-01

... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on the rating applicable to the freight classification description published in the National Motor...

Classification of Alzheimer’s Patients through Ubiquitous Computing †

PubMed Central

Nieto-Reyes, Alicia; Duque, Rafael; Montaña, José Luis; Lage, Carmen

2017-01-01

Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c’s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83% indicates the potential of the proposed methodology. PMID:28753975

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Metacarpophalangeal pattern profile analysis: useful diagnostic tool for differentiating between dyschondrosteosis, Turner syndrome, and hypochondroplasia.

PubMed

Laurencikas, E; Sävendahl, L; Jorulf, H

2006-06-01

To assess the value of the metacarpophalangeal pattern profile (MCPP) analysis as a diagnostic tool for differentiating between patients with dyschondrosteosis, Turner syndrome, and hypochondroplasia. Radiographic and clinical data from 135 patients between 1 and 51 years of age were collected and analyzed. The study included 25 patients with hypochondroplasia (HCP), 39 with dyschondrosteosis (LWD), and 71 with Turner syndrome (TS). Hand pattern profiles were calculated and compared with those of 110 normal individuals. Pearson correlation coefficient (r) and multivariate discriminant analysis were used for pattern profile analysis. Pattern variability index, a measure of dysmorphogenesis, was calculated for LWD, TS, HCP, and normal controls. Our results demonstrate that patients with LWD, TS, or HCP have distinct pattern profiles that are significantly different from each other and from those of normal controls. Discriminant analysis yielded correct classification of normal versus abnormal individuals in 84% of cases. Classification of the patients into LWD, TS, and HCP groups was successful in 75%. The correct classification rate was higher (85%) when differentiating two pathological groups at a time. Pattern variability index was not helpful for differential diagnosis of LWD, TS, and HCP. Patients with LWD, TS, or HCP have distinct MCPPs and can be successfully differentiated from each other using advanced MCPP analysis. Discriminant analysis is to be preferred over Pearson correlation coefficient because it is a more sensitive and specific technique. MCPP analysis is a helpful tool for differentiating between syndromes with similar clinical and radiological abnormalities.

Degree Classification and Recent Graduates' Ability: Is There Any Signalling Effect?

ERIC Educational Resources Information Center

Di Pietro, Giorgio

2017-01-01

Research across several countries has shown that degree classification (i.e. the final grade awarded to students successfully completing university) is an important determinant of graduates' first destination outcome. Graduates leaving university with higher degree classifications have better employment opportunities and a higher likelihood of…

Applications of remote sensing, volume 1

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1977-01-01

The author has identified the following significant results. ECHO successfully exploits the redundancy of states characteristics of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The information required to produce ECHO classifications are cell size, cell homogeneity, cell-to-field annexation parameters, input data, and a class conditional marginal density statistics deck.

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Automated Detection of a Crossing Contact Based on Its Doppler Shift

DTIC Science & Technology

2009-03-01

contacts in passive sonar systems. A common approach is the application of high- gain processing followed by successive classification criteria. Most...contacts in passive sonar systems. A common approach is the application of high-gain processing followed by successive classification criteria...RESEARCH MOTIVATION The trade-off between the false alarm and detection probability is fundamental in radar and sonar . (Chevalier, 2002) A common

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.

Scoliosis curve type classification using kernel machine from 3D trunk image

NASA Astrophysics Data System (ADS)

Adankon, Mathias M.; Dansereau, Jean; Parent, Stefan; Labelle, Hubert; Cheriet, Farida

2012-03-01

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Development of bacterial colony phenotyping instrument using reflected scatter light

NASA Astrophysics Data System (ADS)

Doh, Iyll-Joon

Bacterial rapid detection using optical scattering technology (BARDOT) involves in differentiating elastic scattering pattern of bacterial colony. This elastic light scatter technology has shown promising label-free classification rate. However, there is limited success in certain circumstances where either a growth media or a colony has higher opacity. This situation is due to the physical principles of the current BARDOT which mainly relies on optical patterns generated by transmitted signals. Incoming light is obstructed and cannot be transmitted through the dense bacterial colonies, such as Lactobacillus, Yeast, mold and soil bacteria. Moreover, a blood agar, widely used in clinical field, is an example of an opaque media that does not allow light to be transmitted through. Therefore, in this research, a newly designed reflection type scatterometer is presented. The reflection type scatterometer measures the elastic scattering pattern generated by reflected signal. A theoretical model to study the optical pattern characteristic with respect to bacterial colony morphology is presented. Both theoretical and experiment results show good agreement that the size of backward scattering pattern has positive correlation to colony aspect ratio, a colony elevation to diameter ratio. Four pathogenic bacteria on blood agar, Escherichia coli K12, Listeria innocua, Salmonella Typhimurium, and Staphylococcus aureus, are tested and measured with proposed instrument. The measured patterns are analyzed with a classification software, and high classification rate can be achieved.

Texture classification using non-Euclidean Minkowski dilation

NASA Astrophysics Data System (ADS)

Florindo, Joao B.; Bruno, Odemir M.

2018-03-01

This study presents a new method to extract meaningful descriptors of gray-scale texture images using Minkowski morphological dilation based on the Lp metric. The proposed approach is motivated by the success previously achieved by Bouligand-Minkowski fractal descriptors on texture classification. In essence, such descriptors are directly derived from the morphological dilation of a three-dimensional representation of the gray-level pixels using the classical Euclidean metric. In this way, we generalize the dilation for different values of p in the Lp metric (Euclidean is a particular case when p = 2) and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image. The proposed method is compared to other state-of-the-art approaches (such as local binary patterns and textons for example) in the classification of two benchmark data sets (UIUC and Outex). The proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified. The interesting results suggest the potential of these descriptors in this type of task, with a wide range of possible applications to real-world problems.

Recognition of hand movements in a trans-radial amputated subject by sEMG.

PubMed

Atzori, Manfredo; Muller, Henning; Baechler, Micheal

2013-06-01

Trans-radially amputated persons who own a myoelectric prosthesis have currently some control via surface electromyography (sEMG). However, the control systems are still limited (as they include very few movements) and not always natural (as the subject has to learn to associate movements of the muscles with the movements of the prosthesis). The Ninapro project tries helping the scientific community to overcome these limits through the creation of electromyography data sources to test machine learning algorithms. In this paper the results gained from first tests made on an amputated subject with the Ninapro acquisition protocol are detailed. In agreement with neurological studies on cortical plasticity and on the anatomy of the forearm, the amputee produced stable signals for each movement in the test. Using a k-NN classification algorithm, we obtain an average classification rate of 61.5% on all 53 movements. Successively, we simplify the task reducing the number of movements to 13, resulting in no misclassified movements. This shows that for fewer movements a very high classification accuracy is possible without the subject having to learn the movements specifically.

An incremental approach to genetic-algorithms-based classification.

PubMed

Guan, Sheng-Uei; Zhu, Fangming

2005-04-01

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.

Automated Analysis of Planktic Foraminifers Part III: Neural Network Classification

NASA Astrophysics Data System (ADS)

Schiebel, R.; Bollmann, J.; Quinn, P.; Vela, M.; Schmidt, D. N.; Thierstein, H. R.

2003-04-01

The abundance and assemblage composition of microplankton, together with the chemical and stable isotopic composition of their shells, are among the most successful methods in paleoceanography and paleoclimatology. However, the manual collection of statistically significant numbers of unbiased, reproducible data is time consuming. Consequently, automated microfossil analysis and species recognition has been a long-standing goal in micropaleontology. We have developed a Windows based software package COGNIS for the segmentation, preprocessing, and classification of automatically acquired microfossil images (see Part II, Bollmann et al., this volume), using operator designed neural network structures. With a five-layered convolutional neural network we obtain an average recognition rate of 75 % (max. 88 %) for 6 taxa (N. dutertrei, N. pachyderma dextral, N. pachyderma sinistral, G. inflata, G. menardii/tumida, O. universa), represented by 50 images each for 20 classes (separation of spiral and umbilical views, and of sinistral and dextral forms). Our investigation indicates that neural networks hold great potential for the automated classification of planktic foraminifers and offer new perspectives in micropaleontology, paleoceanography, and paleoclimatology (see Part I, Schmidt et al., this volume).

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

PubMed

Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

2012-01-01

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

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Detection of stress factors in crop and weed species using hyperspectral remote sensing reflectance

NASA Astrophysics Data System (ADS)

Henry, William Brien

The primary objective of this work was to determine if stress factors such as moisture stress or herbicide injury stress limit the ability to distinguish between weeds and crops using remotely sensed data. Additional objectives included using hyperspectral reflectance data to measure moisture content within a species, and to measure crop injury in response to drift rates of non-selective herbicides. Moisture stress did not reduce the ability to discriminate between species. Regardless of analysis technique, the trend was that as moisture stress increased, so too did the ability to distinguish between species. Signature amplitudes (SA) of the top 5 bands, discrete wavelet transforms (DWT), and multiple indices were promising analysis techniques. Discriminant models created from one year's data set and validated on additional data sets provided, on average, approximately 80% accurate classification among weeds and crop. This suggests that these models are relatively robust and could potentially be used across environmental conditions in field scenarios. Distinguishing between leaves grown at high-moisture stress and no-stress was met with limited success, primarily because there was substantial variation among samples within the treatments. Leaf water potential (LWP) was measured, and these were classified into three categories using indices. Classification accuracies were as high as 68%. The 10 bands most highly correlated to LWP were selected; however, there were no obvious trends or patterns in these top 10 bands with respect to time, species or moisture level, suggesting that LWP is an elusive parameter to quantify spectrally. In order to address herbicide injury stress and its impact on species discrimination, discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an overall average classification accuracy of 65% for all species. This suggests that these models are relatively robust and could potentially be used across a wide range of herbicide applications in field scenarios. From the pooled data set, a single discriminant model was created with multiple indices that discriminated soybean from weeds 88%, on average, regardless of herbicide, rate or species. Several analysis techniques including multiple indices, signature amplitude with spectral bands as features, and wavelet analysis were employed to distinguish between herbicide-treated and nontreated plants. Classification accuracy using signature amplitude (SA) analysis of paraquat injury on soybean was better than 75% for both 1/2 and 1/8X rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2X rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. (Abstract shortened by UMI.)

Dietary Inference from Upper and Lower Molar Morphology in Platyrrhine Primates

PubMed Central

Allen, Kari L.; Cooke, Siobhán B.; Gonzales, Lauren A.; Kay, Richard F.

2015-01-01

The correlation between diet and dental topography is of importance to paleontologists seeking to diagnose ecological adaptations in extinct taxa. Although the subject is well represented in the literature, few studies directly compare methods or evaluate dietary signals conveyed by both upper and lower molars. Here, we address this gap in our knowledge by comparing the efficacy of three measures of functional morphology for classifying an ecologically diverse sample of thirteen medium- to large-bodied platyrrhines by diet category (e.g., folivore, frugivore, hard object feeder). We used Shearing Quotient (SQ), an index derived from linear measurements of molar cutting edges and two indices of crown surface topography, Occlusal Relief (OR) and Relief Index (RFI). Using SQ, OR, and RFI, individuals were then classified by dietary category using Discriminate Function Analysis. Both upper and lower molar variables produce high classification rates in assigning individuals to diet categories, but lower molars are consistently more successful. SQs yield the highest classification rates. RFI and OR generally perform above chance. Upper molar RFI has a success rate below the level of chance. Adding molar length enhances the discriminatory power for all variables. We conclude that upper molar SQs are useful for dietary reconstruction, especially when combined with body size information. Additionally, we find that among our sample of platyrrhines, SQ remains the strongest predictor of diet, while RFI is less useful at signaling dietary differences in absence of body size information. The study demonstrates new ways for inferring the diets of extinct platyrrhine primates when both upper and lower molars are available, or, for taxa known only from upper molars. The techniques are useful in reconstructing diet in stem representatives of anthropoid clade, who share key aspects of molar morphology with extant platyrrhines. PMID:25738266

An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Bin

2004-03-01

The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

A neurally inspired musical instrument classification system based upon the sound onset.

PubMed

Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

Classification of Respiratory Sounds by Using An Artificial Neural Network

DTIC Science & Technology

2001-10-28

CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK M.C. Sezgin, Z. Dokur, T. Ölmez, M. Korürek Department of Electronics and...successfully classified by the GAL network. Keywords-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural Network . I. INTRODUCTION...process, feature extraction, and classification by the artificial neural network . At first, the RS signal obtained from a real-time measurement equipment is

Automatic Classification of Medical Text: The Influence of Publication Form1

PubMed Central

Cole, William G.; Michael, Patricia A.; Stewart, James G.; Blois, Marsden S.

1988-01-01

Previous research has shown that within the domain of medical journal abstracts the statistical distribution of words is neither random nor uniform, but is highly characteristic. Many words are used mainly or solely by one medical specialty or when writing about one particular level of description. Due to this regularity of usage, automatic classification within journal abstracts has proved quite successful. The present research asks two further questions. It investigates whether this statistical regularity and automatic classification success can also be achieved in medical textbook chapters. It then goes on to see whether the statistical distribution found in textbooks is sufficiently similar to that found in abstracts to permit accurate classification of abstracts based solely on previous knowledge of textbooks. 14 textbook chapters and 45 MEDLINE abstracts were submitted to an automatic classification program that had been trained only on chapters drawn from a standard textbook series. Statistical analysis of the properties of abstracts vs. chapters revealed important differences in word use. Automatic classification performance was good for chapters, but poor for abstracts.

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen

PubMed Central

Jia, Shengyao; Li, Hongyang; Wang, Yanjie; Tong, Renyuan; Li, Qing

2017-01-01

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types. PMID:28974005

Using a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States

USGS Publications Warehouse

Gutierrez, Benjamin T.; Plant, Nathaniel G.; Pendleton, Elizabeth A.; Thieler, E. Robert

2014-01-01

Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey’s Coastal Vulnerability Index (CVI) for the U.S. Atlantic coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change. Highest accuracy was determined for the coast of Hawaii (98 percent success rate) and lowest accuracy was determined for the Gulf of Mexico (34 percent success rate). The minimum success rate rose to nearly 80 percent (Atlantic and Gulf coasts) when success included shoreline-change outcomes that were adjacent to the most likely outcome. Additionally, the probabilistic approach determines the confidence in calculated outcomes as the probability of the most likely outcome. The confidence was highest along the Pacific coast and it was lowest along the Alaskan coast.

Moving research tools into practice: the successes and challenges in promoting uptake of classification tools.

PubMed

Cunningham, Barbara Jane; Hidecker, Mary Jo Cooley; Thomas-Stonell, Nancy; Rosenbaum, Peter

2018-05-01

In this paper, we present our experiences - both successes and challenges - in implementing evidence-based classification tools into clinical practice. We also make recommendations for others wanting to promote the uptake and application of new research-based assessment tools. We first describe classification systems and the benefits of using them in both research and practice. We then present a theoretical framework from Implementation Science to report strategies we have used to implement two research-based classification tools into practice. We also illustrate some of the challenges we have encountered by reporting results from an online survey investigating 58 Speech-language Pathologists' knowledge and use of the Communication Function Classification System (CFCS), a new tool to classify children's functional communication skills. We offer recommendations for researchers wanting to promote the uptake of new tools in clinical practice. Specifically, we identify structural, organizational, innovation, practitioner, and patient-related factors that we recommend researchers address in the design of implementation interventions. Roles and responsibilities of both researchers and clinicians in making implementations science a success are presented. Implications for rehabilitation Promoting uptake of new and evidence-based tools into clinical practice is challenging. Implementation science can help researchers to close the knowledge-to-practice gap. Using concrete examples, we discuss our experiences in implementing evidence-based classification tools into practice within a theoretical framework. Recommendations are provided for researchers wanting to implement new tools in clinical practice. Implications for researchers and clinicians are presented.

The use of wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices.

PubMed

Crovato, César David Paredes; Schuck, Adalberto

2007-10-01

This paper presents a dysphonic voice classification system using the wavelet packet transform and the best basis algorithm (BBA) as dimensionality reductor and 06 artificial neural networks (ANN) acting as specialist systems. Each ANN was a 03-layer multilayer perceptron with 64 input nodes, 01 output node and in the intermediary layer the number of neurons depends on the related training pathology group. The dysphonic voice database was separated in five pathology groups and one healthy control group. Each ANN was trained and associated with one of the 06 groups, and fed by the best base tree (BBT) nodes' entropy values, using the multiple cross validation (MCV) method and the leave-one-out (LOO) variation technique and success rates obtained were 87.5%, 95.31%, 87.5%, 100%, 96.87% and 89.06% for the groups 01 to 06, respectively.

Computer vision-based classification of hand grip variations in neurorehabilitation.

PubMed

Zariffa, José; Steeves, John D

2011-01-01

The complexity of hand function is such that most existing upper limb rehabilitation robotic devices use only simplified hand interfaces. This is in contrast to the importance of the hand in regaining function after neurological injury. Computer vision technology has been used to identify hand posture in the field of Human Computer Interaction, but this approach has not been translated to the rehabilitation context. We describe a computer vision-based classifier that can be used to discriminate rehabilitation-relevant hand postures, and could be integrated into a virtual reality-based upper limb rehabilitation system. The proposed system was tested on a set of video recordings from able-bodied individuals performing cylindrical grasps, lateral key grips, and tip-to-tip pinches. The overall classification success rate was 91.2%, and was above 98% for 6 out of the 10 subjects. © 2011 IEEE

Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

NASA Astrophysics Data System (ADS)

Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

2013-04-01

Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and characterization of non-stationarities such that ECoG could be a viable signal source for grasp control for amputees or individuals with paralysis.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Documenting Community Engagement Practices and Outcomes: Insights from Recipients of the 2010 Carnegie Community Engagement Classification

ERIC Educational Resources Information Center

Noel, Jana; Earwicker, David P.

2015-01-01

This study was performed to document the strategies and methods used by successful applicants for the 2010 Carnegie Community Engagement Classification and to document the cultural shifts connected with the application process and receipt of the Classification. Four major findings emerged: (1) Applicants benefited from a team approach; (2)…

Some Dimensions of Auditory Sonar Signal Perception and Their Relationships to Target Classification

DTIC Science & Technology

1981-02-13

a priori how the sample of experimental stimuli related to the classification stereotypes of experienced sonar personnel, Question 6 was addressed by...projections on some of the experimentally identified dimensions are associ- ated with a high degree of classification success, but signals that lack ,strong...11 Hypotheses ......................... 11 Procedure ....... .. .. ......................... 11 Experimental Stimuli

Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

NASA Astrophysics Data System (ADS)

Boutros, Tony; Liang, Ming

2011-08-01

Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.

C-Arm Computed Tomography-Assisted Adrenal Venous Sampling Improved Right Adrenal Vein Cannulation and Sampling Quality in Primary Aldosteronism.

PubMed

Park, Chung Hyun; Hong, Namki; Han, Kichang; Kang, Sang Wook; Lee, Cho Rok; Park, Sungha; Rhee, Yumie

2018-05-04

Adrenal venous sampling (AVS) is a gold standard for subtype classification of primary aldosteronism (PA). However, this procedure has a high failure rate because of the anatomical difficulties in accessing the right adrenal vein. We investigated whether C-arm computed tomography-assisted AVS (C-AVS) could improve the success rate of adrenal sampling. A total of 156 patients, diagnosed with PA who underwent AVS from May 2004 through April 2017, were included. Based on the medical records, we retrospectively compared the overall, left, and right catheterization success rates of adrenal veins during the periods without C-AVS (2004 to 2010, n=32) and with C-AVS (2011 to 2016, n=134). The primary outcome was adequate bilateral sampling defined as a selectivity index (SI) >5. With C-AVS, the rates of adequate bilateral AVS increased from 40.6% to 88.7% (P<0.001), with substantial decreases in failure rates (43.7% to 0.8%, P<0.001). There were significant increases in adequate sampling rates from right (43.7% to 91.9%, P<0.001) and left adrenal veins (53.1% to 95.9%, P<0.001) as well as decreases in catheterization failure from right adrenal vein (9.3% to 0.0%, P<0.001). Net improvement of SI on right side remained significant after adjustment for left side (adjusted SI, 1.1 to 9.0; P=0.038). C-AVS was an independent predictor of adequate bilateral sampling in the multivariate model (odds ratio, 9.01; P<0.001). C-AVS improved the overall success rate of AVS, possibly as a result of better catheterization of right adrenal vein. Copyright © 2018 Korean Endocrine Society.

Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials.

PubMed

Finke, Mareike; Billinger, Martin; Büchner, Andreas

Cochlear implants (CIs) restore hearing to the profoundly deaf by direct electrical stimulation of the auditory nerve. To provide an optimal electrical stimulation pattern the CI must be individually fitted to each CI user. To date, CI fitting is primarily based on subjective feedback from the user. However, not all CI users are able to provide such feedback, for example, small children. This study explores the possibility of using the electroencephalogram (EEG) to objectively determine if CI users are able to hear differences in tones presented to them, which has potential applications in CI fitting or closed loop systems. Deviant and standard stimuli were presented to 12 CI users in an active auditory oddball paradigm. The EEG was recorded in two sessions and classification of the EEG data was performed with shrinkage linear discriminant analysis. Also, the impact of CI artifact removal on classification performance and the possibility to reuse a trained classifier in future sessions were evaluated. Overall, classification performance was above chance level for all participants although performance varied considerably between participants. Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance. Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI users. Despite the slightly poorer classification performance over sessions, classifier and CI artifact correction appear stable over successive sessions. Thus, classifier and artifact correction weights can be reused without repeating the set-up procedure in every session, which makes the technique easier applicable. With our present data, we can show successful classification of event-related cortical potential patterns in CI users. In the future, this has the potential to objectify and automate parts of CI fitting procedures.

Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie Renee

The large flood of data flowing from observatories presents significant challenges to astronomy and cosmology--challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for 5 x 107 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of 80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the 1 million SDSS galaxies an average of 40 times. Galaxy Zoo thus solved both the visual classification problem of time efficiency and improved accuracy by producing a distribution of independent classifications for each galaxy. While crowd-sourced galaxy classifications have proven their worth, challenges remain before establishing this method as a critical and standard component of the data processing pipelines for the next generation of surveys. In particular, though innovative, crowd-sourcing techniques do not have the capacity to handle the data volume and rates expected in the next generation of surveys. These algorithms will be delegated to handle the majority of the classification tasks, freeing citizen scientists to contribute their efforts on subtler and more complex assignments. This thesis presents a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides a factor of 11.4 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Multivariate classification of the infrared spectra of cell and tissue samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

NASA Astrophysics Data System (ADS)

Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

2018-03-01

This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

An experiment in multispectral, multitemporal crop classification using relaxation techniques

NASA Technical Reports Server (NTRS)

Davis, L. S.; Wang, C.-Y.; Xie, H.-C

1983-01-01

The paper describes the result of an experimental study concerning the use of probabilistic relaxation for improving pixel classification rates. Two LACIE sites were used in the study and in both cases, relaxation resulted in a marked improvement in classification rates.

Morphological classification of odontogenic keratocysts using Bouligand-Minkowski fractal descriptors.

PubMed

Florindo, Joao B; Bruno, Odemir M; Landini, Gabriel

2017-02-01

The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws (for instance, radicular cyst, which is the most common jaw cyst type). For this reason OKCs are considered by some to be benign neoplasms. There exist two sub-types of OKCs (sporadic and syndromic) and the ability to discriminate between these sub-types, as well as other jaw cysts, is an important task in terms of disease diagnosis and prognosis. With the development of digital pathology, computational algorithms have become central to addressing this type of problem. Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach (the Bouligand-Minkowski descriptors) to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts. This does not require the level of abstraction necessary to extract histologically-relevant features and therefore has the potential of being more robust than previous approaches. The descriptors were obtained by mapping pixel intensities into a three dimensional cloud of points in discrete space and applying morphological dilations with spheres of increasing radii. The descriptors were computed from the volume of the dilated set and submitted to a machine learning algorithm to classify the samples into diagnostic groups. This approach was capable of discriminating between OKCs and radicular cysts in 98% of images (100% of cases) and between the two sub-types of OKCs in 68% of images (71% of cases). These results improve over previously reported classification rates reported elsewhere and suggest that Bouligand-Minkowski descriptors are useful features to be used in histopathological images of these cysts. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Cell dynamic morphology classification using deep convolutional neural networks.

PubMed

Li, Heng; Pang, Fengqian; Shi, Yonggang; Liu, Zhiwen

2018-05-15

Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs. Considering the installation of deep learning, the classification problem was simplified from video data to image data, and was then solved by CNNs in a self-taught manner with the generated image data. CNNs were separately performed in three installation scenarios and compared with existing methods. Experimental results demonstrated the potential of CNNs in cell dynamic morphology classification, and validated the effectiveness of the proposed strategy. CNNs were successfully applied to the classification problem, and outperformed the existing methods in the classification accuracy. For the installation of CNNs, transfer learning was proved to be a promising scheme. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

7 CFR 400.304 - Nonstandard Classification determinations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... changes are necessary in assigned yields or premium rates under the conditions set forth in § 400.304(f... Classification determinations. (a) Nonstandard Classification determinations can affect a change in assigned yields, premium rates, or both from those otherwise prescribed by the insurance actuarial tables. (b...

Developmental dysplasia of the hip in the newborn: A systematic review

PubMed Central

Gulati, Vivek; Eseonu, Kelechi; Sayani, Junaid; Ismail, Nizar; Uzoigwe, Chika; Choudhury, Muhammed Zaki; Gulati, Pooja; Aqil, Adeel; Tibrewal, Saket

2013-01-01

Developmental dysplasia of the hip (DDH) denotes a wide spectrum of conditions ranging from subtle acetabular dysplasia to irreducible hip dislocations. Clinical diagnostic tests complement ultrasound imaging in allowing diagnosis, classification and monitoring of this condition. Classification systems relate to the alpha and beta angles in addition to the dynamic coverage index (DCI). Screening programmes for DDH show considerable geographic variation; certain risk factors have been identified which necessitate ultrasound assessment of the newborn. The treatment of DDH has undergone significant evolution, but the current gold standard is still the Pavlik harness. Duration of Pavlik harness treatment has been reported to range from 3 to 9.3 mo. The beta angle, DCI and the superior/lateral femoral head displacement can be assessed via ultrasound to estimate the likelihood of success. Success rates of between 7% and 99% have been reported when using the harness to treat DDH. Avascular necrosis remains the most devastating complication of harness usage with a reported rate of between 0% and 28%. Alternative non-surgical treatment methods used for DDH include devices proposed by LeDamany, Frejka, Lorenz and Ortolani. The Rosen splint and Wagner stocking have also been used for DDH treatment. Surgical treatment for DDH comprises open reduction alongside a combination of femoral or pelvic osteotomies. Femoral osteotomies are carried out in cases of excessive anteversion or valgus deformity of the femoral neck. The two principal pelvic osteotomies most commonly performed are the Salter osteotomy and Pemberton acetabuloplasty. Serious surgical complications include epiphyseal damage, sciatic nerve damage and femoral neck fracture. PMID:23610749

Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): Introduction of the Weak Rock Mass Rating System (W-RMR)

NASA Astrophysics Data System (ADS)

Warren, Sean N.; Kallu, Raj R.; Barnard, Chase K.

2016-11-01

Underground gold mines in Nevada are exploiting increasingly deeper ore bodies comprised of weak to very weak rock masses. The Rock Mass Rating (RMR) classification system is widely used at underground gold mines in Nevada and is applicable in fair to good-quality rock masses, but is difficult to apply and loses reliability in very weak rock mass to soil-like material. Because very weak rock masses are transition materials that border engineering rock mass and soil classification systems, soil classification may sometimes be easier and more appropriate to provide insight into material behavior and properties. The Unified Soil Classification System (USCS) is the most likely choice for the classification of very weak rock mass to soil-like material because of its accepted use in tunnel engineering projects and its ability to predict soil-like material behavior underground. A correlation between the RMR and USCS systems was developed by comparing underground geotechnical RMR mapping to laboratory testing of bulk samples from the same locations, thereby assigning a numeric RMR value to the USCS classification that can be used in spreadsheet calculations and geostatistical analyses. The geotechnical classification system presented in this paper including a USCS-RMR correlation, RMR rating equations, and the Geo-Pick Strike Index is collectively introduced as the Weak Rock Mass Rating System (W-RMR). It is the authors' hope that this system will aid in the classification of weak rock masses and more usable design tools based on the RMR system. More broadly, the RMR-USCS correlation and the W-RMR system help define the transition between engineering soil and rock mass classification systems and may provide insight for geotechnical design in very weak rock masses.

Unsupervised classification of operator workload from brain signals.

PubMed

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

Unsupervised classification of operator workload from brain signals

NASA Astrophysics Data System (ADS)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

77 FR 39747 - Changes in Postal Rates

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-05

... with the Commission of a proposal characterized as a minor classification change under 39 CFR parts 3090 and 3091, along with a conforming revision to the Mail Classification Schedule (MCS).\\1\\ The... Flat Rate Envelope options. \\1\\ Notice of United States Postal Service of Classification Changes, June...

The effects of high-frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks

NASA Astrophysics Data System (ADS)

Chiu, Alan W. L.; Jahromi, Shokrollah S.; Khosravani, Houman; Carlen, Peter L.; Bardakjian, Berj L.

2006-03-01

The existence of hippocampal high-frequency electrical activities (greater than 100 Hz) during the progression of seizure episodes in both human and animal experimental models of epilepsy has been well documented (Bragin A, Engel J, Wilson C L, Fried I and Buzsáki G 1999 Hippocampus 9 137-42 Khosravani H, Pinnegar C R, Mitchell J R, Bardakjian B L, Federico P and Carlen P L 2005 Epilepsia 46 1-10). However, this information has not been studied between successive seizure episodes or utilized in the application of seizure classification. In this study, we examine the dynamical changes of an in vitro low Mg2+ rat hippocampal slice model of epilepsy at different frequency bands using wavelet transforms and artificial neural networks. By dividing the time-frequency spectrum of each seizure-like event (SLE) into frequency bins, we can analyze their burst-to-burst variations within individual SLEs as well as between successive SLE episodes. Wavelet energy and wavelet entropy are estimated for intracellular and extracellular electrical recordings using sufficiently high sampling rates (10 kHz). We demonstrate that the activities of high-frequency oscillations in the 100-400 Hz range increase as the slice approaches SLE onsets and in later episodes of SLEs. Utilizing the time-dependent relationship between different frequency bands, we can achieve frequency-dependent state classification. We demonstrate that activities in the frequency range 100-400 Hz are critical for the accurate classification of the different states of electrographic seizure-like episodes (containing interictal, preictal and ictal states) in brain slices undergoing recurrent spontaneous SLEs. While preictal activities can be classified with an average accuracy of 77.4 ± 6.7% utilizing the frequency spectrum in the range 0-400 Hz, we can also achieve a similar level of accuracy by using a nonlinear relationship between 100-400 Hz and <4 Hz frequency bands only.

Suppressed Alpha Oscillations Predict Intelligibility of Speech and its Acoustic Details

PubMed Central

Weisz, Nathan

2012-01-01

Modulations of human alpha oscillations (8–13 Hz) accompany many cognitive processes, but their functional role in auditory perception has proven elusive: Do oscillatory dynamics of alpha reflect acoustic details of the speech signal and are they indicative of comprehension success? Acoustically presented words were degraded in acoustic envelope and spectrum in an orthogonal design, and electroencephalogram responses in the frequency domain were analyzed in 24 participants, who rated word comprehensibility after each trial. First, the alpha power suppression during and after a degraded word depended monotonically on spectral and, to a lesser extent, envelope detail. The magnitude of this alpha suppression exhibited an additional and independent influence on later comprehension ratings. Second, source localization of alpha suppression yielded superior parietal, prefrontal, as well as anterior temporal brain areas. Third, multivariate classification of the time–frequency pattern across participants showed that patterns of late posterior alpha power allowed best for above-chance classification of word intelligibility. Results suggest that both magnitude and topography of late alpha suppression in response to single words can indicate a listener's sensitivity to acoustic features and the ability to comprehend speech under adverse listening conditions. PMID:22100354

Unsupervised hierarchical partitioning of hyperspectral images: application to marine algae identification

NASA Astrophysics Data System (ADS)

Chen, B.; Chehdi, K.; De Oliveria, E.; Cariou, C.; Charbonnier, B.

2015-10-01

In this paper a new unsupervised top-down hierarchical classification method to partition airborne hyperspectral images is proposed. The unsupervised approach is preferred because the difficulty of area access and the human and financial resources required to obtain ground truth data, constitute serious handicaps especially over large areas which can be covered by airborne or satellite images. The developed classification approach allows i) a successive partitioning of data into several levels or partitions in which the main classes are first identified, ii) an estimation of the number of classes automatically at each level without any end user help, iii) a nonsystematic subdivision of all classes of a partition Pj to form a partition Pj+1, iv) a stable partitioning result of the same data set from one run of the method to another. The proposed approach was validated on synthetic and real hyperspectral images related to the identification of several marine algae species. In addition to highly accurate and consistent results (correct classification rate over 99%), this approach is completely unsupervised. It estimates at each level, the optimal number of classes and the final partition without any end user intervention.

Automated aural classification used for inter-species discrimination of cetaceans.

PubMed

Binder, Carolyn M; Hines, Paul C

2014-04-01

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

An embedded Simplified Fuzzy ARTMAP implemented on a microcontroller for food classification.

PubMed

Garcia-Breijo, Eduardo; Garrigues, Jose; Sanchez, Luis Gil; Laguarda-Miro, Nicolas

2013-08-13

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.

An Embedded Simplified Fuzzy ARTMAP Implemented on a Microcontroller for Food Classification

PubMed Central

Garcia-Breijo, Eduardo; Garrigues, Jose; Sanchez, Luis Gil; Laguarda-Miro, Nicolas

2013-01-01

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food. PMID:23945736

Identification of Terrestrial Reflectance From Remote Sensing

NASA Technical Reports Server (NTRS)

Alter-Gartenberg, Rachel; Nolf, Scott R.; Stacy, Kathryn (Technical Monitor)

2000-01-01

Correcting for atmospheric effects is an essential part of surface-reflectance recovery from radiance measurements. Model-based atmospheric correction techniques enable an accurate identification and classification of terrestrial reflectances from multi-spectral imagery. Successful and efficient removal of atmospheric effects from remote-sensing data is a key factor in the success of Earth observation missions. This report assesses the performance, robustness and sensitivity of two atmospheric-correction and reflectance-recovery techniques as part of an end-to-end simulation of hyper-spectral acquisition, identification and classification.

Body Mass Index is a Poor Predictor of Bedside Appendix Ultrasound Success or Accuracy

PubMed Central

Lam, Samuel H.F.; Kerwin, Christopher; Konicki, P. John; Goodwine, Diana; Lambert, Michael J.

2016-01-01

Introduction The objective of this study was to determine whether there is a relationship between body mass index (BMI) and success or accuracy rate of beside ultrasound (BUS) for the diagnosis of appendicitis. Methods Patients four years of age and older presenting to the emergency department with suspected appendicitis were eligible. Enrollment was by convenience sampling. After informed consent, BUS was performed by trained emergency physicians who had undergone a minimum of one-hour didactic training on the use of BUS to diagnose appendicitis. We ascertained subject outcomes by a combination of medical record review and telephone follow up. Calculated BMI for adults and children were divided into four categories (underweight, normal, overweight, obese) according to Centers for Disease Control and Prevention classifications. Results A total of 125 subjects consented for the study, and 116 of them had adequate image data for final analysis. Seventy (60%) of the subjects were children. Prevalence of appendicitis was 39%. Fifty-two (45%) of the BUS studies were diagnostic (successful). Overall accuracy rate was 75%. Analysis by chi-square test or Mann-Whitney U test did not find any significant correlation between BMI category and BUS success. Similarly, there was no significant correlation between BMI category and BUS accuracy. The same conclusion was reached when children and adults were analyzed separately, or when subjects were dichotomized into underweight/normal and overweight/obese categories. Conclusion BMI category alone is a poor predictor of appendix BUS success or accuracy. PMID:27429696

Comparison of outcomes of patients with left ventricular ejection fractions ≤30% versus ≥30% having transcatheter aortic valve implantation (from the German Transcatheter Aortic Valve Interventions Registry).

PubMed

Schaefer, Ulrich; Zahn, Ralf; Abdel-Wahab, Mohamed; Gerckens, Ulrich; Linke, Axel; Schneider, Steffen; Eggebrecht, Holger; Sievert, Horst; Figulla, Hans Reiner; Senges, Jochen; Kuck, Karl Heinz

2015-03-01

Transcatheter aortic valve implantation (TAVI) is rapidly evolving in Germany. Especially severe reduced left ventricular ejection fraction (LVEF) is known as a prominent risk factor for adverse outcome in open heart surgery. Thus, the data of the prospective multicenter German Transcatheter Aortic Valve Interventions Registry were analyzed for outcomes in patients with severe depressed LVEF. Data of 1,432 patients were consecutively collected after transcatheter aortic valve implantation. Patients were divided into 2 groups (A: LVEF ≤30%, n = 169, age 79.9 ± 6.7 years, logES 34.2 ± 17.8%; B: LVEF >30%, n = 1,263, age 82.0 ± 6.1 years, logES 18.9 ± 12.0%), and procedural success rates, New York Heart Association classification, and quality of life were compared at 30 days and 1 year, respectively. Technical success was achieved in 95.9% (A) and 97.6% (B). Survival and the New York Heart Association classification at 30 days demonstrated an excellent outcome in both groups. There was a significant improvement according to the self-assessment in health condition (0 to 100 scale) with a much larger gain in group A (28 vs 19 patients, p <0.0001). Nevertheless, low cardiac output syndrome (12.3% vs 5.9%, p <0.01) and resuscitation (10.4% vs 5.6%, p <0.05) were more frequently seen in group A, contributing to a higher mortality at 30 days (14.3% vs 7.2%) and 1 year (33.7% vs 18.1%, p <0.001). In conclusion, this real-world registry demonstrated a comparably high success rate for patients with severe reduced LVEF and an early improvement in functional status as demonstrated by substantial benefit, despite a doubled postprocedural mortality. Copyright © 2015 Elsevier Inc. All rights reserved.

Dreamers, Poets, Citizens, and Scientists: Motivations for Engaging in GalaxyZoo Citizen Science

NASA Astrophysics Data System (ADS)

Slater, S. J.; Mankowski, T.; Slater, T. F.; CenterAstronomy; Physics Education Research Caper Team

2010-12-01

A particularly successful effort to engage the public in science has been to move the nearly countless galaxies imaged by the Sloan Digital Sky Survey to citizen scientists in a project known widely as Galaxy Zoo (URL; http://www.galaxyzoo.org). To everyone’s surprise, the unexpectedly large participation in the website has caused the data set, numbering over a million images, to be classified multiple times, quicker than the project leader anticipated, and continues to boast a high hit count on the website (15 classifications per second). Within 24 hours of launch, the site was receiving 70,000 classifications an hour, and more than 50 million classifications were received by the project during its first year, from almost 150,000 people. In a parallel effort, the Galaxy Zoo forum was created to handle the flood of emails that occurred alongside the flood of classifications, the team hoping that it would encourage the participants to handle each others' questions. By examining the motivations, methods and appeal of Galaxy Zoo to the participating public, other models of citizen science might be purposefully formulated to take advantage of the success exhibited in Galaxy Zoo. In addition, we want to understand the reasons people engage in science in informal settings in order to better enhance teaching methods in formal settings. Although in the past citizen science has primarily been used as a data collection method, there are many new opportunities contained in citizen science motivations and methods that we can use in future applications. This new and innovative method of online citizen science creates data for researchers of galaxies, but there is a parallel set of underlying data that has not yet been deeply analyzed: the motivations and underlying themes within the population of citizen scientists that could lead us to improve future citizen science projects. To address this, we pursued an investigation of the underlying reasons for the success of Galaxy Zoo revealed by inductively analyzing contributor’s posts and discussions through the accompanying Galaxy Zoo online bulletin board forum - When investigating the data interpretively collected from the Galaxy Zoo forum, what sort of trends emerge as motivations which contribute to the overall success of this citizen science model? Using a grounded theory approach, we learned that many of these motivations originate in the aesthetic power of astronomical images, which Galaxy Zoo successfully harnesses while not compromising the scientific value of the project. From within the data emerged several trends of motivation, the primary being: the sense of community created within the project that promotes professional-amateur collaboration; fulfilling a dream of being an astronomer, physicist, or astronaut; tapping into a potential well of interest created during the space race era; the spiritual aspect generated when the imagination interacts with Galaxy Zoo; and uniting them all, the aesthetic appeal of the galaxy images. In addition, a very powerful tool also emerged as a method of retention unique to Galaxy Zoo. This tool, known as variable ratio reinforcement in behavioral psychology, uses the most appealing images as positive reinforcement to maintain classification rates over time.

Compensatory neurofuzzy model for discrete data classification in biomedical

NASA Astrophysics Data System (ADS)

Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Autonomous target recognition using remotely sensed surface vibration measurements

NASA Astrophysics Data System (ADS)

Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

1993-09-01

The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

NASA Astrophysics Data System (ADS)

Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

2018-04-01

Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

An automatic device for detection and classification of malaria parasite species in thick blood film.

PubMed

Kaewkamnerd, Saowaluck; Uthaipibull, Chairat; Intarapanich, Apichart; Pannarut, Montri; Chaotheing, Sastra; Tongsima, Sissades

2012-01-01

Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation. The prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick blood films (40 blood films containing infected red blood cells and 20 control blood films of normal red blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative blood films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick blood films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%. This work presents an automatic device for both detection and classification of malaria parasite species on thick blood film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

Recursive heuristic classification

NASA Technical Reports Server (NTRS)

Wilkins, David C.

1994-01-01

The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

From Classification to Epilepsy Ontology and Informatics

PubMed Central

Zhang, Guo-Qiang; Sahoo, Satya S; Lhatoo, Samden D

2012-01-01

Summary The 2010 International League Against Epilepsy (ILAE) classification and terminology commission report proposed a much needed departure from previous classifications to incorporate advances in molecular biology, neuroimaging, and genetics. It proposed an interim classification and defined two key requirements that need to be satisfied. The first is the ability to classify epilepsy in dimensions according to a variety of purposes including clinical research, patient care, and drug discovery. The second is the ability of the classification system to evolve with new discoveries. Multi-dimensionality and flexibility are crucial to the success of any future classification. In addition, a successful classification system must play a central role in the rapidly growing field of epilepsy informatics. An epilepsy ontology, based on classification, will allow information systems to facilitate data-intensive studies and provide a proven route to meeting the two foregoing key requirements. Epilepsy ontology will be a structured terminology system that accommodates proposed and evolving ILAE classifications, the NIH/NINDS Common Data Elements, the ICD systems and explicitly specifies all known relationships between epilepsy concepts in a proper framework. This will aid evidence based epilepsy diagnosis, investigation, treatment and research for a diverse community of clinicians and researchers. Benefits range from systematization of electronic patient records to multi-modal data repositories for research and training manuals for those involved in epilepsy care. Given the complexity, heterogeneity and pace of research advances in the epilepsy domain, such an ontology must be collaboratively developed by key stakeholders in the epilepsy community and experts in knowledge engineering and computer science. PMID:22765502

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

PubMed Central

Griffiths, Jason I.; Fronhofer, Emanuel A.; Garnier, Aurélie; Seymour, Mathew; Altermatt, Florian; Petchey, Owen L.

2017-01-01

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology. PMID:28472193

Disregarding population specificity: its influence on the sex assessment methods from the tibia.

PubMed

Kotěrová, Anežka; Velemínská, Jana; Dupej, Ján; Brzobohatá, Hana; Pilný, Aleš; Brůžek, Jaroslav

2017-01-01

Forensic anthropology has developed classification techniques for sex estimation of unknown skeletal remains, for example population-specific discriminant function analyses. These methods were designed for populations that lived mostly in the late nineteenth and twentieth centuries. Their level of reliability or misclassification is important for practical use in today's forensic practice; it is, however, unknown. We addressed the question of what the likelihood of errors would be if population specificity of discriminant functions of the tibia were disregarded. Moreover, five classification functions in a Czech sample were proposed (accuracies 82.1-87.5 %, sex bias ranged from -1.3 to -5.4 %). We measured ten variables traditionally used for sex assessment of the tibia on a sample of 30 male and 26 female models from recent Czech population. To estimate the classification accuracy and error (misclassification) rates ignoring population specificity, we selected published classification functions of tibia for the Portuguese, south European, and the North American populations. These functions were applied on the dimensions of the Czech population. Comparing the classification success of the reference and the tested Czech sample showed that females from Czech population were significantly overestimated and mostly misclassified as males. Overall accuracy of sex assessment significantly decreased (53.6-69.7 %), sex bias -29.4-100 %, which is most probably caused by secular trend and the generally high variability of body size. Results indicate that the discriminant functions, developed for skeletal series representing geographically and chronologically diverse populations, are not applicable in current forensic investigations. Finally, implications and recommendations for future research are discussed.

3P: Personalized Pregnancy Prediction in IVF Treatment Process

NASA Astrophysics Data System (ADS)

Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

Probability-based classifications for spatially characterizing the water temperatures and discharge rates of hot springs in the Tatun Volcanic Region, Taiwan.

PubMed

Jang, Cheng-Shin

2015-05-01

Accurately classifying the spatial features of the water temperatures and discharge rates of hot springs is crucial for environmental resources use and management. This study spatially characterized classifications of the water temperatures and discharge rates of hot springs in the Tatun Volcanic Region of Northern Taiwan by using indicator kriging (IK). The water temperatures and discharge rates of the springs were first assigned to high, moderate, and low categories according to the two thresholds of the proposed spring classification criteria. IK was then used to model the occurrence probabilities of the water temperatures and discharge rates of the springs and probabilistically determine their categories. Finally, nine combinations were acquired from the probability-based classifications for the spatial features of the water temperatures and discharge rates of the springs. Moreover, various combinations of spring water features were examined according to seven subzones of spring use in the study region. The research results reveal that probability-based classifications using IK provide practicable insights related to propagating the uncertainty of classifications according to the spatial features of the water temperatures and discharge rates of the springs. The springs in the Beitou (BT), Xingyi Road (XYR), Zhongshanlou (ZSL), and Lengshuikeng (LSK) subzones are suitable for supplying tourism hotels with a sufficient quantity of spring water because they have high or moderate discharge rates. Furthermore, natural hot springs in riverbeds and valleys should be developed in the Dingbeitou (DBT), ZSL, Xiayoukeng (XYK), and Macao (MC) subzones because of low discharge rates and low or moderate water temperatures.

Subintimal recanalization of femoropopliteal occlusive lesions in patients with critical ischemia: 66 cases.

PubMed

Mousnier, Aurélien; Jean-Baptiste, Elixène; Sadaghianloo, Nirvana; Declemy, Serge; Brizzi, Sophie; Hassen-Khodja, Réda

2013-05-01

Subintimal recanalization allows for the treatment of percutaneously extended occlusive lesions. The aim of this study was to evaluate the feasibility and short- and mid-term results of subintimal recanalization in the treatment of femoropopliteal occlusive lesions in patients with critical ischemia. Between January 2009 and December 2010, consecutive patients with critical ischemia presenting with femoropopliteal arterial occlusion were included in this study. These patients underwent subintimal recanalization, and all patients had clinical and ultrasound follow-up. Sixty-six procedures were performed on 66 patients (mean age, 79±10 yrs). All patients were American Society of Anesthesiologists classification 3 or 4. Thirty-two patients had diabetes and 27 had chronic renal insufficiency. More than two-thirds (76%) of the lesions had a Trans-Atlantic Inter-Society Classification of C or D. The mean occlusion length was 13.5±7 cm. The rate of technical success was 85%. The peroperative complication rate was 4.5%. There were no cases of operative mortality. Occlusion length was the only predictive factor of restenosis (P=0.049). At 1 year, primary and secondary patency rates were 56% and 70%, respectively, for a 92% rate of limb salvage. The subintimal recanalization technique is feasible and minimally invasive. At 1 year postprocedure, the primary patency is poor, but this technique is associated with a high rate of limb salvage. It is suitable for the treatment of critical ischemia, taking into account the often precarious clinical backgrounds of these patients. Copyright © 2013 Elsevier Inc. All rights reserved.

Impact of oesophagitis classification in evaluating healing of erosive oesophagitis after therapy with proton pump inhibitors: a pooled analysis.

PubMed

Yaghoobi, Mohammad; Padol, Sara; Yuan, Yuhong; Hunt, Richard H

2010-05-01

The results of clinical trials with proton pump inhibitors (PPIs) are usually based on the Hetzel-Dent (HD), Savary-Miller (SM), or Los Angeles (LA) classifications to describe the severity and assess the healing of erosive oesophagitis. However, it is not known whether these classifications are comparable. The aim of this study was to review systematically the literature to compare the healing rates of erosive oesophagitis with PPIs in clinical trials assessed by the HD, SM, or LA classifications. A recursive, English language literature search in PubMed and Cochrane databases to December 2006 was performed. Double-blind randomized control trials comparing a PPI with another PPI, an H2-RA or placebo using endoscopic assessment of the healing of oesophagitis by the HD, SM or LA, or their modified classifications at 4 or 8 weeks, were included in the study. The healing rates on treatment with the same PPI(s), and same endoscopic grade(s) were pooled and compared between different classifications using Fisher's exact test or chi2 test where appropriate. Forty-seven studies from 965 potential citations met inclusion criteria. Seventy-eight PPI arms were identified, with 27 using HD, 29 using SM, and 22 using LA for five marketed PPIs. There was insufficient data for rabeprazole and esomeprazole (week 4 only) to compare because they were evaluated by only one classification. When data from all PPIs were pooled, regardless of baseline oesophagitis grades, the LA healing rate was significantly higher than SM and HD at both 4 and 8 weeks (74, 71, and 68% at 4 weeks and 89, 84, and 83% at 8 weeks, respectively). The distribution of different grades in study population was available only for pantoprazole where it was not significantly different between LA and SM subgroups. When analyzing data for PPI and dose, the LA classification showed a higher healing rate for omeprazole 20 mg/day and pantoprazole 40 mg/day (significant at 8 weeks), whereas healing by SM classification was significantly higher for omeprazole 40 mg/day (no data for LA) and lansoprazole 30 mg/day at 4 and 8 weeks. The healing rate by individual oesophagitis grade was not always available or robust enough for meaningful analysis. However, a difference between classifications remained. There is a significant, but not always consistent, difference in oesophagitis healing rates with the same PPI(s) reported by the LA, SM, or HD classifications. The possible difference between grading classifications should be considered when interpreting or comparing healing rates for oesophagitis from different studies.

Congenital portosystemic shunts in children: a new anatomical classification correlated with surgical strategy.

PubMed

Blanc, Thomas; Guerin, Florent; Franchi-Abella, Stéphanie; Jacquemin, Emmanuel; Pariente, Danièle; Soubrane, Olivier; Branchereau, Sophie; Gauthier, Frédéric

2014-07-01

To propose an anatomical classification of congenital portosystemic shunts (CPSs) correlating with conservative surgery. CPSs entail a risk of life-threatening complications because of poor portal inflow, which may be prevented or cured by their closure. Current classifications based on portal origin of the shunt are not helpful for planning conservative surgery. Twenty-three patients who underwent at least 1 surgical procedure to close the CPSs were included in this retrospective study (1997-2012). We designed a classification according to the ending of the shunt in the caval system. We analyzed the results and outcomes of surgery according to this classification. Two patients had an extrahepatic portosystemic shunt, 17 had a portacaval shunt [subdivided in 5 end-to-side-like portal-caval, 7 side-to-side-like portal-caval, and 5 H-shaped (H-type portal-caval)], 2 had portal-to-hepatic vein shunts (portohepatic), and 2 had a persistent ductus venosus. All extrahepatic portosystemic shunts, H-type portal-caval, portohepatic, and patent ductus venosus patients had a successful 1-stage ligation. All 5 end-to-side-like portal-caval patients had a threadlike intrahepatic portal venous system; a 2-stage complete closure was successfully achieved for 4 and a partial closure for 1. The first 2 side-to-side-like portal-caval patients had a successful 2-stage closure whereas the 5 others had a 1-stage longitudinal caval partition. All patients are alive and none needed a liver transplantation. Our classification correlates the anatomy of CPSs and the surgical strategy: outcomes are good provided end-to-side-like portal-caval shunts patients have a 2-stage closure, side-to-side portal-caval shunts patients have a 1-stage caval partition, and the others have a 1-stage ligation.

Natural Language Processing Based Instrument for Classification of Free Text Medical Records

PubMed Central

2016-01-01

According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful. PMID:27668260

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

NASA Astrophysics Data System (ADS)

Prochazka, D.; Mazura, M.; Samek, O.; Rebrošová, K.; Pořízka, P.; Klus, J.; Prochazková, P.; Novotný, J.; Novotný, K.; Kaiser, J.

2018-01-01

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods.

Global dengue death before and after the new World Health Organization 2009 case classification: A systematic review and meta-regression analysis.

PubMed

Low, Gary Kim-Kuan; Ogston, Simon A; Yong, Mun-Hin; Gan, Seng-Chiew; Chee, Hui-Yee

2018-06-01

Since the introduction of 2009 WHO dengue case classification, no literature was found regarding its effect on dengue death. This study was to evaluate the effect of 2009 WHO dengue case classification towards dengue case fatality rate. Various databases were used to search relevant articles since 1995. Studies included were cohort and cross-sectional studies, all patients with dengue infection and must report the number of death or case fatality rate. The Joanna Briggs Institute appraisal checklist was used to evaluate the risk of bias of the full-texts. The studies were grouped according to the classification adopted: WHO 1997 and WHO 2009. Meta-regression was employed using a logistic transformation (log-odds) of the case fatality rate. The result of the meta-regression was the adjusted case fatality rate and odds ratio on the explanatory variables. A total of 77 studies were included in the meta-regression analysis. The case fatality rate for all studies combined was 1.14% with 95% confidence interval (CI) of 0.82-1.58%. The combined (unadjusted) case fatality rate for 69 studies which adopted WHO 1997 dengue case classification was 1.09% with 95% CI of 0.77-1.55%; and for eight studies with WHO 2009 was 1.62% with 95% CI of 0.64-4.02%. The unadjusted and adjusted odds ratio of case fatality using WHO 2009 dengue case classification was 1.49 (95% CI: 0.52, 4.24) and 0.83 (95% CI: 0.26, 2.63) respectively, compared to WHO 1997 dengue case classification. There was an apparent increase in trend of case fatality rate from the year 1992-2016. Neither was statistically significant. The WHO 2009 dengue case classification might have no effect towards the case fatality rate although the adjusted results indicated a lower case fatality rate. Future studies are required for an update in the meta-regression analysis to confirm the findings. Copyright © 2018 Elsevier B.V. All rights reserved.

Non-linear molecular pattern classification using molecular beacons with multiple targets.

PubMed

Lee, In-Hee; Lee, Seung Hwan; Park, Tai Hyun; Zhang, Byoung-Tak

2013-12-01

In vitro pattern classification has been highlighted as an important future application of DNA computing. Previous work has demonstrated the feasibility of linear classifiers using DNA-based molecular computing. However, complex tasks require non-linear classification capability. Here we design a molecular beacon that can interact with multiple targets and experimentally shows that its fluorescent signals form a complex radial-basis function, enabling it to be used as a building block for non-linear molecular classification in vitro. The proposed method was successfully applied to solving artificial and real-world classification problems: XOR and microRNA expression patterns. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Binning in Gaussian Kernel Regularization

DTIC Science & Technology

2005-04-01

OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, but reduces the...71.40%) on 966 randomly sampled data. Using the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the...the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, and reduces

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network

PubMed Central

Adak, M. Fatih; Yumusak, Nejat

2016-01-01

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data. PMID:26927124

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network.

PubMed

Adak, M Fatih; Yumusak, Nejat

2016-02-27

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.

Classification of ion mobility spectra by functional groups using neural networks

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Eiceman, G. A.

1999-01-01

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

[Comparison of airway scope and videolaryngoscope portable VLP-100 in the presence of a neck collar--a manikin study].

PubMed

Saito, Tomoyuki; Asai, Takashi; Arai, Takerou; Tsuchida, Misa; Ogawa, Katsumi; Okuda, Yasuhisa

2010-12-01

Tracheal intubation should be performed with great care in the multiple injury patient, particularly when damage to the cervical spine is suspected. The patient with unstable cervical spine requires a neck collar in prehospital area, and medical personnel may be required to perform tracheal intubation. We compared Macintosh laryngoscope with the Airway Scope (AWS), and Coopdeck videolaryngoscope portable VLP-100 (VLP-100) in a manikin model with the presence of a neck collar. We conducted a prospective study in 20 medical residents with little prior airway management experience. They inserted the AWS, VLP-100 and Macintosh laryngoscope, in turn, and the view of the glottis at laryngoscopy was graded, using a classification reported by Cormack and Lehane. Tracheal intubation time and the success rate of tracheal intubation (within 120 sec) were also recorded. The AWS provided a best view of the glottis, a shorter tracheal intubation time and a higher success rate of tracheal intubation, compared with VLP- 100 and Macintosh laryngoscope. The AWS may possess advantages over conventional direct laryngoscopes in patients with restricted neck movement.

Stability and bias of classification rates in biological applications of discriminant analysis

USGS Publications Warehouse

Williams, B.K.; Titus, K.; Hines, J.E.

1990-01-01

We assessed the sampling stability of classification rates in discriminant analysis by using a factorial design with factors for multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. Simulation results indicated strong bias in correct classification rates when group sample sizes were small and when overlap among groups was high. We also found that stability of the correct classification rates was influenced by these factors, indicating that the number of samples required for a given level of precision increases with the amount of overlap among groups. In a review of 60 published studies, we found that 57% of the articles presented results on classification rates, though few of them mentioned potential biases in their results. Wildlife researchers should choose the total number of samples per group to be at least 2 times the number of variables to be measured when overlap among groups is low. Substantially more samples are required as the overlap among groups increases

Characterization of Escherichia coli isolates from different fecal sources by means of classification tree analysis of fatty acid methyl ester (FAME) profiles.

PubMed

Seurinck, Sylvie; Deschepper, Ellen; Deboch, Bishaw; Verstraete, Willy; Siciliano, Steven

2006-03-01

Microbial source tracking (MST) methods need to be rapid, inexpensive and accurate. Unfortunately, many MST methods provide a wealth of information that is difficult to interpret by the regulators who use this information to make decisions. This paper describes the use of classification tree analysis to interpret the results of a MST method based on fatty acid methyl ester (FAME) profiles of Escherichia coli isolates, and to present results in a format readily interpretable by water quality managers. Raw sewage E. coli isolates and animal E. coli isolates from cow, dog, gull, and horse were isolated and their FAME profiles collected. Correct classification rates determined with leaveone-out cross-validation resulted in an overall low correct classification rate of 61%. A higher overall correct classification rate of 85% was obtained when the animal isolates were pooled together and compared to the raw sewage isolates. Bootstrap aggregation or adaptive resampling and combining of the FAME profile data increased correct classification rates substantially. Other MST methods may be better suited to differentiate between different fecal sources but classification tree analysis has enabled us to distinguish raw sewage from animal E. coli isolates, which previously had not been possible with other multivariate methods such as principal component analysis and cluster analysis.

The ESO Nearby Abell Cluster Survey. VI. Spatial distribution and kinematics of early- and late-type galaxies

NASA Astrophysics Data System (ADS)

de Theije, P. A. M.; Katgert, P.

1999-01-01

Analysis of the data obtained in the ESO Nearby Abell Cluster Survey (ENACS) has shown that the space distribution and kinematics of galaxies with detectable emission lines in their spectra differ significantly from those of galaxies without emission lines. This result, and details of the kinematics, were considered as support for the idea that at least the spirals with emission lines are on orbits that are not isotropic. This might indicate that this subset of late-type galaxies either has `first approach'-orbits towards the dense core of their respective clusters, or has orbits that `avoid' the core. The galaxies with emission lines are essentially all late-type galaxies. On the other hand, the emission-line galaxies represent only about a third of the late-type galaxies, the majority of which do not show detectable emission lines. The galaxies without emission lines are therefore a mix of early- and late-type galaxies. In this paper we attempt to separate early- and late-type galaxies, and we study possible differences in distribution and kinematics of the two galaxy classes. For only about 10% of the galaxies in the ENACS, the morphology is known from imaging. Here, we describe our classification on the basis of the ENACS spectrum. The significant information in each spectrum is compressed into 15 Principal Components, which are used as input for an Artificial Neural Network. The latter is `trained' with 150 of the 270 galaxies for which a morphological type is available from Dressler, and subsequently used to classify each galaxy. This yields a classification for two-thirds of the ENACS galaxies. The Artificial Neural Network has two output classes: early-type (E+S0) and late-type (S+I) galaxies. We do not distinguish E and S0 galaxies, because these cannot be separated very robustly on the basis of the spectrum. The success rate of the classification is estimated from the sample of 120 galaxies with Dressler morphologies which were not used to train the ANN. The success rate is higher for early-type than for late-type galaxies (78+/-6% vs. {63+/-6%}). The weighted average success rate, irrespective of type, is {73+/-4%}. The success rate is somewhat larger for the training set, and highest for the galaxies with emission lines. Of the 3798 galaxies that were classified from their spectrum {57+/-7%} are of early type, and {43+/-7%} of late type. Using a subset of these 3798 galaxies, we constructed a composite cluster of 2594 galaxies, 399 of which have emission lines and are therefore almost exclusively spirals and irregulars. The kinematics and spatial distribution of the late-type galaxies without emission lines resemble much more those of the early-type galaxies than those of the late-type galaxies with emission lines. Yet, the late-type galaxies without emission lines may have a somewhat larger velocity dispersion and a slightly less centrally concentrated distribution than the early-type galaxies. Only the late-type galaxies with emission lines appear to have a considerably larger global velocity dispersion and a much less concentrated projected density profile than the other galaxies. Thus, the suggestion of fairly radial, and possibly `first approach' orbits applies only to spirals with emission lines. The early-type galaxies with emission lines (among which the AGN), may also have a large velocity dispersion and be concentrated towards the cluster centre. Based on observations collected at the European Southern Observatory (La Silla, Chile)

A nearest neighbor approach for automated transporter prediction and categorization from protein sequences.

PubMed

Li, Haiquan; Dai, Xinbin; Zhao, Xuechun

2008-05-01

Membrane transport proteins play a crucial role in the import and export of ions, small molecules or macromolecules across biological membranes. Currently, there are a limited number of published computational tools which enable the systematic discovery and categorization of transporters prior to costly experimental validation. To approach this problem, we utilized a nearest neighbor method which seamlessly integrates homologous search and topological analysis into a machine-learning framework. Our approach satisfactorily distinguished 484 transporter families in the Transporter Classification Database, a curated and representative database for transporters. A five-fold cross-validation on the database achieved a positive classification rate of 72.3% on average. Furthermore, this method successfully detected transporters in seven model and four non-model organisms, ranging from archaean to mammalian species. A preliminary literature-based validation has cross-validated 65.8% of our predictions on the 11 organisms, including 55.9% of our predictions overlapping with 83.6% of the predicted transporters in TransportDB.

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

NASA Astrophysics Data System (ADS)

Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

2013-11-01

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

Seismic waveform classification using deep learning

NASA Astrophysics Data System (ADS)

Kong, Q.; Allen, R. M.

2017-12-01

MyShake is a global smartphone seismic network that harnesses the power of crowdsourcing. It has an Artificial Neural Network (ANN) algorithm running on the phone to distinguish earthquake motion from human activities recorded by the accelerometer on board. Once the ANN detects earthquake-like motion, it sends a 5-min chunk of acceleration data back to the server for further analysis. The time-series data collected contains both earthquake data and human activity data that the ANN confused. In this presentation, we will show the Convolutional Neural Network (CNN) we built under the umbrella of supervised learning to find out the earthquake waveform. The waveforms of the recorded motion could treat easily as images, and by taking the advantage of the power of CNN processing the images, we achieved very high successful rate to select the earthquake waveforms out. Since there are many non-earthquake waveforms than the earthquake waveforms, we also built an anomaly detection algorithm using the CNN. Both these two methods can be easily extended to other waveform classification problems.

Infinite hidden conditional random fields for human behavior analysis.

PubMed

Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja

2013-01-01

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs--chosen via cross-validation--for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Successful short-segment instrumentation and fusion for thoracolumbar spine fractures: a consecutive 41/2-year series.

PubMed

Parker, J W; Lane, J R; Karaikovic, E E; Gaines, R W

2000-05-01

A retrospective review of all the surgically managed spinal fractures at the University of Missouri Medical Center during the 41/2-year period from January 1989 to July 1993 was performed. Of the 51 surgically managed patients, 46 were instrumented by short-segment technique (attachment of one level above the fracture to one level below the fracture). The other 5 patients in this consecutive series had multiple trauma. These patients were included in the review because this was a consecutive series. However, they were grouped separately because they were instrumented by long-segment technique because of their multiple organ system injuries. The choice of the anterior or posterior approach for short-segment instrumentation was based on the Load-Sharing Classification published in a 1994 issue of Spine. The purpose of this review was to demonstrate that grading comminution by use of the Load-Sharing Classification for approach selection and the choice of patients with isolated fractures who are cooperative with spinal bracing for 4 months provide the keys to successful short-segment treatment of isolated spinal fractures. The current literature implies that the use of pedicle screws for short-segment instrumentation of spinal fracture is dangerous and inappropriate because of the high screw fracture rate. Charts, operative notes, preoperative and postoperative radiographs, computed tomography scans, and follow-up records of all patients were reviewed carefully from the time of surgery until final follow-up assessment. The Load-Sharing Classification had been used prospectively for all patients before their surgery to determine the approach for short-segment instrumentation. Denis' Pain Scale and Work Scales were obtained during follow-up evaluation for all patients. All patients were observed over 40 months except for 1 patient who died of unrelated causes after 35 months. The mean follow-up period was 66 months (51/2 years). No patient was lost to follow-up evaluation. Prospective application of the Load-Sharing Classification to the patients' injury and restriction of the short-segment approach to cooperative patients with isolated spinal fractures (excluding multisystem trauma patients) allowed 45 of 46 patients instrumented by the short-segment technique to proceed to successful healing in virtual anatomic alignment. The Load-Sharing Classification is a straightforward way to describe the amount of bony comminution in a spinal fracture. When applied to patients with isolated spine fractures who are cooperative with 3 to 4 months of spinal bracing, it can help the surgeon select short-segment pedicle-screw-based fixation using the posterior approach for less comminuted injuries and the anterior approach for those more comminuted. The choice of which fracture-dislocations should be strut grafted anteriorly and which need only posterior short-segment pedicle-screw-based instrumentation also can be made using the Load-Sharing Classification.

Temporal stability and rates of post-depositional change in geochemical signatures of brown trout Salmo trutta scales.

PubMed

Ryan, D; Shephard, S; Kelly, F L

2016-09-01

This study investigates temporal stability in the scale microchemistry of brown trout Salmo trutta in feeder streams of a large heterogeneous lake catchment and rates of change after migration into the lake. Laser-ablation inductively coupled plasma mass spectrometry was used to quantify the elemental concentrations of Na, Mg, Mn, Cu, Zn, Ba and Sr in archived (1997-2002) scales of juvenile S. trutta collected from six major feeder streams of Lough Mask, County Mayo, Ireland. Water-element Ca ratios within these streams were determined for the fish sampling period and for a later period (2013-2015). Salmo trutta scale Sr and Ba concentrations were significantly (P < 0·05) correlated with stream water sample Sr:Ca and Ba:Ca ratios respectively from both periods, indicating multi-annual stability in scale and water-elemental signatures. Discriminant analysis of scale chemistries correctly classified 91% of sampled juvenile S. trutta to their stream of origin using a cross-validated classification model. This model was used to test whether assumed post-depositional change in scale element concentrations reduced correct natal stream classification of S. trutta in successive years after migration into Lough Mask. Fish residing in the lake for 1-3 years could be reliably classified to their most likely natal stream, but the probability of correct classification diminished strongly with longer lake residence. Use of scale chemistry to identify natal streams of lake S. trutta should focus on recent migrants, but may not require contemporary water chemistry data. © 2016 The Fisheries Society of the British Isles.

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

The Operational Commander’s Role in Planning and Executing a Successful Campaign

DTIC Science & Technology

1992-04-20

44 IS. PRICE CODE RIDGWAY IN KOREAN WAR AS CDR 8th ARMY ________ It. SECURITY CLASSIFICATION 10. SECURITY CLASSIFICATION 13. SECURITY CLASSIFICATION...Field-Marshal Slim as the 14th Army commander in Burma; General MacArthur in the World War II Cartwheel Operation and General Ridgway as the 8th Army...64 :. Introduction In his book, Command in War , Martin Van Creveld referred to the period of strategic

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

Antegrade Sclerotherapy to Treat All Types of Varicoceles in the Pediatric Population: Experience of a Single Center.

PubMed

Paradiso, Filomena Valentina; Mason, Elena Jane; Nanni, Lorenzo

2016-12-01

To analyze our experience with antegrade sclerotherapy for the treatment of Coolsaet types I, II, and III varicoceles in a pediatric and adolescent population. Between 2005 and 2015, 73 patients who underwent antegrade sclerotherapy were retrospectively evaluated. Patient age, side, clinical and Doppler ultrasound grade, and anatomical variations were collected. Varicoceles were grouped following Coolsaet's classification: all types were sclerosed. Follow-up consisted in clinical examination 3 and 6 months following surgery and testicular Doppler ultrasound 6 and 12 months following surgery. Patients were then telephonically interviewed. Success was defined as varicocele resolution or reduction to a grade not requiring surgery. Mean patient age was 14.7 years and mean operating time was 50.8 minutes. Based on phlebographic imaging and following Coolsaet's classification, we identified 57 (78.1%) type I, 3 (4.1%) type II, and 13 (17.8%) type III varicoceles. No intraoperative complications were observed. Three patients (4.1%) presented a short-term complication in the form of pampiniform plexus thrombosis; 1 patient also developed wound dehiscence: all complications occurred in Coolsaet type I varicoceles and during surgeon learning curve. No hydrocele occurred. Out of 59 patients with a satisfactory follow-up (range: 14 months-10 years), 2 recurrences occurred, the success rate thus being 96.6%. Tauber's antegrade sclerotherapy is a simple and feasible technique, effective in treating all kinds of varicocele with low complication, recurrence, and persistence rates. Phlebography reveals frequent venous anatomical variations, allows grouping of varicoceles into Coolsaet types, and enables performing of sclerosis safely in all 3 kinds. Copyright © 2016 Elsevier Inc. All rights reserved.

Insights from Integrative Systematics Reveal Cryptic Diversity in Pristimantis Frogs (Anura: Craugastoridae) from the Upper Amazon Basin

PubMed Central

Ortega-Andrade, H. Mauricio; Rojas-Soto, Octavio R.; Valencia, Jorge H.; Espinosa de los Monteros, Alejandro; Morrone, Juan J.; Ron, Santiago R.; Cannatella, David C.

2015-01-01

Pluralistic approaches to taxonomy facilitate a more complete appraisal of biodiversity, especially the diversification of cryptic species. Although species delimitation has traditionally been based primarily on morphological differences, the integration of new methods allows diverse lines of evidence to solve the problem. Robber frogs (Pristimantis) are exemplary, as many of the species show high morphological variation within populations, but few traits that are diagnostic of species. We used a combination of DNA sequences from three mitochondrial genes, morphometric data, and comparisons of ecological niche models (ENMs) to infer a phylogenetic hypothesis for the Pristimantis acuminatus complex. Molecular phylogenetic analyses revealed a close relationship between three new species—Pristimantis enigmaticus sp. nov., P. limoncochensis sp. nov. and P. omeviridis sp. nov.—originally confused with Pristimantis acuminatus. In combination with morphometric data and geographic distributions, several morphological characters such as degree of tympanum exposure, skin texture, ulnar/tarsal tubercles and sexual secondary characters (vocal slits and nuptial pads in males) were found to be useful for diagnosing species in the complex. Multivariate discriminant analyses provided a successful classification rate for 83–100% of specimens. Discriminant analysis of localities in environmental niche space showed a successful classification rate of 75–98%. Identity tests of ENMs rejected hypotheses of niche equivalency, although not strongly because the high values on niche overlap. Pristimantis acuminatus and P. enigmaticus sp. nov. are distributed along the lowlands of central–southern Ecuador and northern Peru, in contrast with P. limoncochensis sp. nov. and P. omeviridis sp. nov., which are found in northern Ecuador and southern Colombia, up to 1200 m in the upper Amazon Basin. The methods used herein provide an integrated framework for inventorying the greatly underestimated biodiversity in Amazonia. PMID:26600198

The impact of a simplified documentation method for the Edmonton classification system for cancer pain (ECS-CP) on clinician utilization.

PubMed

Tanco, Kimberson; Arthur, Joseph; Haider, Ali; Stephen, Saneese; Yennu, Sriram; Liu, Diane; Bruera, Eduardo

2017-02-01

The use of standardized pain classification systems such as the ECS-CP can assist in the assessment and management of cancer pain. However, its completion has been limited due to its perceived complexity of decoding each feature. The objectives of this study were to determine the rate of clinician documentation and completion of the ECS-CP features after revision and simplification of the response for each feature. Electronic records of consecutive patient visits at the outpatient supportive care center seen by 12 palliative medicine specialists were collected at 6 months before (pre-interventional period), 6 and 24 months after (post-interventional period) the implementation of the simplified ECS-CP tool. Rate of ECS-CP documentation, completion, and analysis of patient and physician predictors were completed. One thousand and twelve patients' documentation was analyzed: 343 patients, before; 341 patients, 6 months after, and 328 patients, 24 months after the intervention. ≥2/5 items were completed before the intervention, 6 months after the intervention and 24 months after intervention in 0/343 (0 %), 136/341 (40 %), and 238/328 (73 %), respectively (p < 0.001). 5/5 items were completed before the intervention, 6 months after the intervention and 24 months after intervention in 0/343 (0 %), 131/341 (38 %), and 222/328 (68 %), respectively, (p < 0.001). There were no patient or physician predictors found significant for successful documentation of ECS-CP. Our findings suggest that significant simplification and intensive education is necessary for successful adoption of a scoring system. More research is needed in order to identify how to adopt tools for daily clinical practice in palliative care.

A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling

PubMed Central

Blackman, Arne V.; Grabuschnig, Stefan; Legenstein, Robert; Sjöström, P. Jesper

2014-01-01

Accurate 3D reconstruction of neurons is vital for applications linking anatomy and physiology. Reconstructions are typically created using Neurolucida after biocytin histology (BH). An alternative inexpensive and fast method is to use freeware such as Neuromantic to reconstruct from fluorescence imaging (FI) stacks acquired using 2-photon laser-scanning microscopy during physiological recording. We compare these two methods with respect to morphometry, cell classification, and multicompartmental modeling in the NEURON simulation environment. Quantitative morphological analysis of the same cells reconstructed using both methods reveals that whilst biocytin reconstructions facilitate tracing of more distal collaterals, both methods are comparable in representing the overall morphology: automated clustering of reconstructions from both methods successfully separates neocortical basket cells from pyramidal cells but not BH from FI reconstructions. BH reconstructions suffer more from tissue shrinkage and compression artifacts than FI reconstructions do. FI reconstructions, on the other hand, consistently have larger process diameters. Consequently, significant differences in NEURON modeling of excitatory post-synaptic potential (EPSP) forward propagation are seen between the two methods, with FI reconstructions exhibiting smaller depolarizations. Simulated action potential backpropagation (bAP), however, is indistinguishable between reconstructions obtained with the two methods. In our hands, BH reconstructions are necessary for NEURON modeling and detailed morphological tracing, and thus remain state of the art, although they are more labor intensive, more expensive, and suffer from a higher failure rate due to the occasional poor outcome of histological processing. However, for a subset of anatomical applications such as cell type identification, FI reconstructions are superior, because of indistinguishable classification performance with greater ease of use, essentially 100% success rate, and lower cost. PMID:25071470

Insights from Integrative Systematics Reveal Cryptic Diversity in Pristimantis Frogs (Anura: Craugastoridae) from the Upper Amazon Basin.

PubMed

Ortega-Andrade, H Mauricio; Rojas-Soto, Octavio R; Valencia, Jorge H; Espinosa de Los Monteros, Alejandro; Morrone, Juan J; Ron, Santiago R; Cannatella, David C

2015-01-01

Pluralistic approaches to taxonomy facilitate a more complete appraisal of biodiversity, especially the diversification of cryptic species. Although species delimitation has traditionally been based primarily on morphological differences, the integration of new methods allows diverse lines of evidence to solve the problem. Robber frogs (Pristimantis) are exemplary, as many of the species show high morphological variation within populations, but few traits that are diagnostic of species. We used a combination of DNA sequences from three mitochondrial genes, morphometric data, and comparisons of ecological niche models (ENMs) to infer a phylogenetic hypothesis for the Pristimantis acuminatus complex. Molecular phylogenetic analyses revealed a close relationship between three new species-Pristimantis enigmaticus sp. nov., P. limoncochensis sp. nov. and P. omeviridis sp. nov.-originally confused with Pristimantis acuminatus. In combination with morphometric data and geographic distributions, several morphological characters such as degree of tympanum exposure, skin texture, ulnar/tarsal tubercles and sexual secondary characters (vocal slits and nuptial pads in males) were found to be useful for diagnosing species in the complex. Multivariate discriminant analyses provided a successful classification rate for 83-100% of specimens. Discriminant analysis of localities in environmental niche space showed a successful classification rate of 75-98%. Identity tests of ENMs rejected hypotheses of niche equivalency, although not strongly because the high values on niche overlap. Pristimantis acuminatus and P. enigmaticus sp. nov. are distributed along the lowlands of central-southern Ecuador and northern Peru, in contrast with P. limoncochensis sp. nov. and P. omeviridis sp. nov., which are found in northern Ecuador and southern Colombia, up to 1200 m in the upper Amazon Basin. The methods used herein provide an integrated framework for inventorying the greatly underestimated biodiversity in Amazonia.

Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

PubMed

Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John

2018-03-01

Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon previous methods for shallow lakes because it allows classification and regression to occur simultaneously and inform one another, directly estimates TP thresholds and the uncertainty associated with thresholds and state classifications, and enables meaningful constraints to be built into models. The BLR framework is broadly applicable to other ecosystems known to exhibit alternative stable states in which regression can be used to establish relationships between driving variables and state variables. © 2017 by the Ecological Society of America.

Staging of chronic myeloid leukemia in the imatinib era: an evaluation of the World Health Organization proposal.

PubMed

Cortes, Jorge E; Talpaz, Moshe; O'Brien, Susan; Faderl, Stefan; Garcia-Manero, Guillermo; Ferrajoli, Alessandra; Verstovsek, Srdan; Rios, Mary B; Shan, Jenny; Kantarjian, Hagop M

2006-03-15

Several staging classification systems, all of which were designed in the preimatinib era, are used for chronic myeloid leukemia (CML). The World Health Organization (WHO) recently proposed a new classification system that has not been validated clinically. The authors investigated the significance of the WHO classification system and compared it with the classification systems used to date in imatinib trials ("standard definition") to determine its impact in establishing the outcome of patients after therapy with imatinib. In total, 809 patients who received imatinib for CML were classified into chronic phase (CP), accelerated phase (AP), and blast phase (BP) based on standard definitions and then were reclassified according to the new WHO classification system. Their outcomes with imatinib therapy were compared, and the value of individual components of these classification systems was determined. With the WHO classification, 78 patients (10%) were reclassified: 45 patients (6%) were reclassified from CP to AP, 14 patients (2%) were reclassified from AP to CP, and 19 patients (2%) were reclassified from AP to BP. The rates of complete cytogenetic response for patients in CP, AP, and BP according to the standard definition were 72%, 45%, and 8%, respectively. After these patients were reclassified according to WHO criteria, the response rates were 77% (P = 0.07), 39% (P = 0.28), and 11% (P = 0.61), respectively. The 3-year survival rates were 91%, 65%, and 10%, respectively, according to the standard classification and 95% (P = 0.05), 63% (P = 0.76), and 16% (P = 0.18), respectively, according to the WHO classification. Patients who had a blast percentage of 20-29%, which is considered CML-BP according to the WHO classification, had a significantly better response rate (21% vs. 8%; P = 0.11) and 3-year survival rate (42% vs. 10%; P = 0.0001) compared with patients who had blasts > or = 30%. Different classification systems had an impact on the outcome of patients, and some prognostic features had different prognostic implications in the imatinib era. The authors believe that a new, uniform staging system for CML is warranted, and they propose such a system. (c) 2006 American Cancer Society.

Who Must We Target Now to Minimize Future Cardiovascular Events and Total Mortality?: Lessons From the Surveillance, Prevention and Management of Diabetes Mellitus (SUPREME-DM) Cohort Study.

PubMed

Desai, Jay R; Vazquez-Benitez, Gabriela; Xu, Zhiyuan; Schroeder, Emily B; Karter, Andrew J; Steiner, John F; Nichols, Gregory A; Reynolds, Kristi; Xu, Stanley; Newton, Katherine; Pathak, Ram D; Waitzfelder, Beth; Lafata, Jennifer Elston; Butler, Melissa G; Kirchner, H Lester; Thomas, Abraham; O'Connor, Patrick J

2015-09-01

Examining trends in cardiovascular events and mortality in US health systems can guide the design of targeted clinical and public health strategies to reduce cardiovascular events and mortality rates. We conducted an observational cohort study from 2005 to 2011 among 1.25 million diabetic subjects and 1.25 million nondiabetic subjects from 11 health systems that participate in the Surveillance, Prevention and Management of Diabetes Mellitus (SUPREME-DM) DataLink. Annual rates (per 1000 person-years) of myocardial infarction/acute coronary syndrome (International Classification of Diseases-Ninth Revision, 410.0–410.91, 411.1–411.8), stroke (International Classification of Diseases-Ninth Revision, 430–432.9, 433–434.9), heart failure (International Classification of Diseases-Ninth Revision, 428–428.9), and all-cause mortality were monitored by diabetes mellitus (DM) status, age, sex, race/ethnicity, and a prior cardiovascular history. We observed significant declines in cardiovascular events and mortality rates in subjects with and without DM. However, there was substantial variation by age, sex, race/ethnicity, and prior cardiovascular history. Mortality declined from 44.7 to 27.1 (P<0.0001) for those with DM and cardiovascular disease (CVD), from 11.2 to 10.9 (P=0.03) for those with DM only, and from 18.9 to 13.0 (P<0.0001) for those with CVD only. Yet, in the [almost equal to]85% of subjects with neither DM nor CVD, overall mortality (7.0 to 6.8; P=0.10) and stroke rates (1.6–1.6; P=0.77) did not decline and heart failure rates increased (0.9–1.15; P=0.0005). To sustain improvements in myocardial infarction, stroke, heart failure, and mortality, health systems that have successfully focused on care improvement in high-risk adults with DM or CVD must broaden their improvement strategies to target lower risk adults who have not yet developed DM or CVD.

Effects of spatial disturbance on common loon nest site selection and territory success

USGS Publications Warehouse

McCarthy, K.P.; DeStefano, S.

2011-01-01

The common loon (Gavia immer) breeds during the summer on northern lakes and water bodies that are also often desirable areas for aquatic recreation and human habitation. In northern New England, we assessed how the spatial nature of disturbance affects common loon nest site selection and territory success. We found through classification and regression analysis that distance to and density of disturbance factors can be used to classify observed nest site locations versus random points, suggesting that these factors affect loon nest site selection (model 1: Correct classification = 75%, null = 50%, K = 0.507, P < 0.001; model 2: Correct classification = 78%, null = 50%, K = 0.551, P < 0.001). However, in an exploratory analysis, we were unable to show a relation between spatial disturbance variables and breeding success (P = 0.595, R 2 = 0.436), possibly because breeding success was so low during the breeding seasons of 2007-2008. We suggest that by selecting nest site locations that avoid disturbance factors, loons thereby limit the effect that disturbance will have on their breeding success. Still, disturbance may force loons to use sub-optimal nesting habitat, limiting the available number of territories, and overall productivity. We advise that management efforts focus on limiting disturbance factors to allow breeding pairs access to the best nesting territories, relieving disturbance pressures that may force sub-optimal nest placement. ?? 2011 The Wildlife Society.

Ling classification describes endoscopic progressive process of achalasia and successful peroral endoscopy myotomy prevents endoscopic progression of achalasia.

PubMed

Zhang, Wen-Gang; Linghu, En-Qiang; Chai, Ning-Li; Li, Hui-Kai

2017-05-14

To verify the hypothesis that the Ling classification describes the endoscopic progressive process of achalasia and determine the ability of successful peroral endoscopic myotomy (POEM) to prevent endoscopic progression of achalasia. We retrospectively reviewed the endoscopic findings, symptom duration, and manometric data in patients with achalasia. A total of 359 patients (197 women, 162 men) with a mean age of 42.1 years (range, 12-75 years) were evaluated. Symptom duration ranged from 2 to 360 mo, with a median of 36 mo. Patients were classified with Ling type I ( n = 119), IIa ( n = 106), IIb ( n = 60), IIc ( n = 60), or III ( n = 14), according to the Ling classification. Of the 359 patients, 349 underwent POEM, among whom 21 had an endoscopic follow-up for more than 2 years. Pre-treatment and post-treatment Ling classifications of these 21 patients were compared. Symptom duration increased significantly with increasing Ling classification (from I to III) ( P < 0.05), whereas lower esophageal sphincter pressure decreased with increasing Ling type (from I to III) ( P < 0.05). There was no difference in sex ratio or onset age among the Ling types, although the age at time of diagnosis was higher in Ling types IIc and III than in Ling types I, IIa, and IIb. Of the 21 patients, 19 underwent high-resolution manometry both before and after treatment. The mean preoperative and postoperative lower esophageal sphincter pressure were 34.6 mmHg (range, 15.3-59.4 mmHg) and 15.0 mmHg (range, 2.1-21.6 mmHg), respectively, indicating a statistically significant decrease after POEM. All of the 21 patients were treated successfully by POEM (postoperative Eckardt score ≤ 3) and still had the same Ling type during a mean follow-up period of 37.8 mo (range, 24-51 mo). The Ling classification represents the endoscopic progressive process of achalasia and may be able to serve as an endoscopic assessment criterion for achalasia. Successful POEM (Eckardt score ≤ 3) seems to have the ability to prevent endoscopic evolvement of achalasia. However, studies with larger populations are warranted to confirm our findings.

Low complexity feature extraction for classification of harmonic signals

NASA Astrophysics Data System (ADS)

William, Peter E.

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain. The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a standalone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics' amplitudes of the harmonic structure employing a simplified least squares method without the need to estimate the true harmonic parameters of the source signal. The third algorithm, resulting from a collaborative effort with Daniel White at the DSP Lab, University of Nebraska-Lincoln, presents an analog front end approach that utilizes a multichannel analog projection and integration to extract the sparse spectral features from the analog time domain signal. Classification is performed using a multilayer feedforward neural network. Evaluation of the proposed feature extraction algorithms for classification through the processing of several acoustic and vibration data sets (including military vehicles and rotating electric machines) with comparison to spectral features shows that, for harmonic signals, time domain features are simpler to extract and provide equivalent or improved reliability over the spectral features in both the detection probabilities and false alarm rate.

Childhood executive function inventory (CHEXI): a promising measure for identifying young children with ADHD?

PubMed

Thorell, Lisa B; Eninger, Lilianne; Brocki, Karin C; Bohlin, Gunilla

2010-01-01

The present study investigated whether the Childhood Executive Function Inventory (CHEXI) can discriminate between young children fulfilling the diagnostic criteria for attention-deficit/hyperactivity disorder (ADHD) and normally developing children. Unlike other executive function rating instruments, the CHEXI focuses specifically on inhibitory control and working memory, without including items that overlap with the diagnostic criteria of ADHD. The CHEXI was found to discriminate very well between children fulfilling the criteria for ADHD and normally developing children, also when controlling for the effect of IQ and socioeconomic status (SES). Both sensitivity and specificity of the two CHEXI subscales were shown to be high using either parent or teacher ratings. The highest overall classification rate was found for parent ratings on the inhibition subscale, with sensitivity and specificity reaching 93.3. To summarize, the CHEXI should be considered a promising measure for identifying young children with ADHD, although it is for future research to determine whether the CHEXI can be successfully used to also discriminate between different psychopathological groups.

Predicting occupancy for pygmy rabbits in Wyoming: an independent evaluation of two species distribution models

USGS Publications Warehouse

Germaine, Stephen S.; Ignizio, Drew; Keinath, Doug; Copeland, Holly

2014-01-01

Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an area or wanting to direct gas-drilling efforts away from pygmy rabbit habitat may want to consider both models in an ensemble manner, where more confidence is placed in mapped areas (i.e., pixels) for which both models agree than for areas where there is model disagreement.

Forest succession on four habitat types in western Montana

Treesearch

Stephen F. Arno; Dennis G. Simmerman; Robert E. Keane

1985-01-01

Presents classifications of successional community types on four major forest habitat types in western Montana. Classifications show the sequences of seral community types developing after stand-replacing wildfire and clearcutting with broadcast burning, mechanical scarification, or no followup treatment. Information is provided for associating vegetational response to...

A Classfication of Management Teachers

ERIC Educational Resources Information Center

Walker, Bob

1974-01-01

There are many classifications of management teachers today. Each has his style, successes, and faults. Some of the more prominent are: the company man, the mamagement technician, the man of principle, the evangelist, and the entrepreneur. A mixture of these classifications would be ideal since each by itself has its faults. (DS)

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

NASA Technical Reports Server (NTRS)

Mausel, P. W.; Todd, W. J.; Baumgardner, M. F.

1976-01-01

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Inter-examiner classification reliability of Mechanical Diagnosis and Therapy for extremity problems - Systematic review.

PubMed

Takasaki, Hiroshi; Okuyama, Kousuke; Rosedale, Richard

2017-02-01

Mechanical Diagnosis and Therapy (MDT) is used in the treatment of extremity problems. Classifying clinical problems is one method of providing effective treatment to a target population. Classification reliability is a key factor to determine the precise clinical problem and to direct an appropriate intervention. To explore inter-examiner reliability of the MDT classification for extremity problems in three reliability designs: 1) vignette reliability using surveys with patient vignettes, 2) concurrent reliability, where multiple assessors decide a classification by observing someone's assessment, 3) successive reliability, where multiple assessors independently assess the same patient at different times. Systematic review with data synthesis in a quantitative format. Agreement of MDT subgroups was examined using the Kappa value, with the operational definition of acceptable reliability set at ≥ 0.6. The level of evidence was determined considering the methodological quality of the studies. Six studies were included and all studies met the criteria for high quality. Kappa values for the vignette reliability design (five studies) were ≥ 0.7. There was data from two cohorts in one study for the concurrent reliability design and the Kappa values ranged from 0.45 to 1.0. Kappa values for the successive reliability design (data from three cohorts in one study) were < 0.6. The current review found strong evidence of acceptable inter-examiner reliability of MDT classification for extremity problems in the vignette reliability design, limited evidence of acceptable reliability in the concurrent reliability design and unacceptable reliability in the successive reliability design. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

PubMed Central

Fu, Longsheng; Sun, Shipeng; Li, Rui; Wang, Shaojin

2016-01-01

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera. PMID:27376292

Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee

PubMed Central

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-01-01

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure. PMID:25254303

Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

PubMed

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-09-24

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.

PubMed

Micera, Silvestro; Rossini, Paolo M; Rigosa, Jacopo; Citi, Luca; Carpaneto, Jacopo; Raspopovic, Stanisa; Tombini, Mario; Cipriani, Christian; Assenza, Giovanni; Carrozza, Maria C; Hoffmann, Klaus-Peter; Yoshida, Ken; Navarro, Xavier; Dario, Paolo

2011-09-05

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications.

PubMed

Malta, Tathiane M; de Souza, Camila F; Sabedot, Thais S; Silva, Tiago C; Mosella, Maritza S; Kalkanis, Steven N; Snyder, James; Castro, Ana Valeria B; Noushmehr, Houtan

2018-04-09

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.

Drill wear monitoring in cortical bone drilling.

PubMed

Staroveski, Tomislav; Brezak, Danko; Udiljak, Toma

2015-06-01

Medical drills are subject to intensive wear due to mechanical factors which occur during the bone drilling process, and potential thermal and chemical factors related to the sterilisation process. Intensive wear increases friction between the drill and the surrounding bone tissue, resulting in higher drilling temperatures and cutting forces. Therefore, the goal of this experimental research was to develop a drill wear classification model based on multi-sensor approach and artificial neural network algorithm. A required set of tool wear features were extracted from the following three types of signals: cutting forces, servomotor drive currents and acoustic emission. Their capacity to classify precisely one of three predefined drill wear levels has been established using a pattern recognition type of the Radial Basis Function Neural Network algorithm. Experiments were performed on a custom-made test bed system using fresh bovine bones and standard medical drills. Results have shown high classification success rate, together with the model robustness and insensitivity to variations of bone mechanical properties. Features extracted from acoustic emission and servomotor drive signals achieved the highest precision in drill wear level classification (92.8%), thus indicating their potential in the design of a new type of medical drilling machine with process monitoring capabilities. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Provisional in-silico biopharmaceutics classification (BCS) to guide oral drug product development

PubMed Central

Wolk, Omri; Agbaria, Riad; Dahan, Arik

2014-01-01

The main objective of this work was to investigate in-silico predictions of physicochemical properties, in order to guide oral drug development by provisional biopharmaceutics classification system (BCS). Four in-silico methods were used to estimate LogP: group contribution (CLogP) using two different software programs, atom contribution (ALogP), and element contribution (KLogP). The correlations (r2) of CLogP, ALogP and KLogP versus measured LogP data were 0.97, 0.82, and 0.71, respectively. The classification of drugs with reported intestinal permeability in humans was correct for 64.3%–72.4% of the 29 drugs on the dataset, and for 81.82%–90.91% of the 22 drugs that are passively absorbed using the different in-silico algorithms. Similar permeability classification was obtained with the various in-silico methods. The in-silico calculations, along with experimental melting points, were then incorporated into a thermodynamic equation for solubility estimations that largely matched the reference solubility values. It was revealed that the effect of melting point on the solubility is minor compared to the partition coefficient, and an average melting point (162.7°C) could replace the experimental values, with similar results. The in-silico methods classified 20.76% (±3.07%) as Class 1, 41.51% (±3.32%) as Class 2, 30.49% (±4.47%) as Class 3, and 6.27% (±4.39%) as Class 4. In conclusion, in-silico methods can be used for BCS classification of drugs in early development, from merely their molecular formula and without foreknowledge of their chemical structure, which will allow for the improved selection, engineering, and developability of candidates. These in-silico methods could enhance success rates, reduce costs, and accelerate oral drug products development. PMID:25284986

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

Single-Rooted Extraction Sockets: Classification and Treatment Protocol.

PubMed

El Chaar, Edgar; Oshman, Sarah; Fallah Abed, Pooria

2016-09-01

Clinicians have many treatment techniques from which to choose when extracting a failing tooth and replacing it with an implant-supported restoration and when successful management of an extraction socket during the course of tooth replacement is necessary to achieve predictable and esthetic outcomes. This article presents a straightforward, yet thorough, classification for extraction sockets of single-rooted teeth and provides guidance to clinicians in the selection of appropriate and predictable treatment. The presented classification of extraction sockets for single-rooted teeth focuses on the topography of the extraction socket, while the protocol for treatment of each socket type factors in the shape of the remaining bone, the biotype, and the location of the socket whether it be in the mandible or maxilla. This system is based on the biologic foundations of wound healing and can help guide clinicians to successful treatment outcomes.

Pattern recognition and image processing for environmental monitoring

NASA Astrophysics Data System (ADS)

Siddiqui, Khalid J.; Eastwood, DeLyle

1999-12-01

Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

Classification and management of gynecomastia: defining the role of ultrasound-assisted liposuction.

PubMed

Rohrich, Rod J; Ha, Richard Y; Kenkel, Jeffrey M; Adams, William P

2003-02-01

Gynecomastia, or excessive male breast development, has an incidence of 32 to 65 percent in the male population. This condition has important physical and psychological impacts. Advances in elucidating the pathophysiology of gynecomastia have been made, though understanding remains limited. Recommendations for evaluation and workup have varied and are often arbitrary. A diagnostic algorithm is suggested, with emphasis on a comprehensive history, physical examination, and minimizing unnecessary diagnostic testing. Medical management has had limited success; surgical therapy, primarily through excisional techniques, has been the accepted standard. Although effective, excisional techniques subject patients to large, visible scars. Ultrasound-assisted liposuction has recently emerged as a safe and effective method for the treatment of gynecomastia. It is particularly efficient in the removal of the dense, fibrous male breast tissue while offering advantages in minimal external scarring. A new system of classification and graduated treatment is proposed, based on glandular versus fibrous hypertrophy and degree of breast ptosis (skin excess). The authors' series of 61 patients with gynecomastia from 1987 to 2000 at the University of Texas Southwestern Department of Plastic Surgery demonstrated an overall success rate of 86.9 percent using suction-assisted lipectomy (1987 to 1997) and ultrasound-assisted liposuction (1997 to 2000). The authors have found ultrasound-assisted liposuction to be effective in treating most grades of gynecomastia. Excisional techniques are reserved for severe gynecomastia with significant skin excess after attempted ultrasound-assisted liposuction.

High temporal resolution of extreme rainfall rate variability and the acoustic classification of rainfall

NASA Astrophysics Data System (ADS)

Nystuen, Jeffrey A.; Amitai, Eyal

2003-04-01

The underwater sound generated by raindrop splashes on a water surface is loud and unique allowing detection, classification and quantification of rainfall. One of the advantages of the acoustic measurement is that the listening area, an effective catchment area, is proportional to the depth of the hydrophone and can be orders of magnitude greater than other in situ rain gauges. This feature allows high temporal resolution of the rainfall measurement. A series of rain events with extremely high rainfall rates, over 100 mm/hr, is examined acoustically. Rapid onset and cessation of rainfall intensity are detected within the convective cells of these storms with maximum 5-s resolution values exceeding 1000 mm/hr. The probability distribution functions (pdf) for rainfall rate occurrence and water volume using the longer temporal resolutions typical of other instruments do not include these extreme values. The variance of sound intensity within different acoustic frequency bands can be used as an aid to classify rainfall type. Objective acoustic classification algorithms are proposed. Within each rainfall classification the relationship between sound intensity and rainfall rate is nearly linear. The reflectivity factor, Z, also has a linear relationship with rainfall rate, R, for each rainfall classification.

Molecular classifications of breast carcinoma with similar terminology and different definitions: are they the same?

PubMed

Tang, Ping; Wang, Jianmin; Bourne, Patria

2008-04-01

There are 4 major molecular classifications in the literature that divide breast carcinoma into basal and nonbasal subtypes, with basal subtypes associated with poor prognosis. Basal subtype is defined as positive for cytokeratin (CK) 5/6, CK14, and/or CK17 in CK classification; negative for ER, PR, and HER2 in triple negative (TN) classification; negative for ER and negative or positive for HER2 in ER/HER2 classification; and positive for CK5/6, CK14, CK17, and/or EGFR; and negative for ER, PR, and HER2 in CK/TN classification. These classifications use similar terminology but different definitions; it is critical to understand the precise relationship between them. We compared these 4 classifications in 195 breast carcinomas and found that (1) the rates of basal subtypes varied from 5% to 36% for ductal carcinoma in situ and 14% to 40% for invasive ductal carcinoma. (2) The rates of basal subtypes varied from 19% to 76% for HG carcinoma and 1% to 7% for NHG carcinoma. (3) The rates of basal subtypes were strongly associated with tumor grades (P < .001) in all classifications and associated with tumor types (in situ versus invasive ductal carcinomas) in TN (P < .001) and CK/TN classifications (P = .035). (4) These classifications were related but not interchangeable (kappa ranges from 0.140 to 0.658 for HG carcinoma and from 0.098 to 0.654 for NHG carcinoma). In conclusion, although these classifications all divide breast carcinoma into basal and nonbasal subtypes, they are not interchangeable. More studies are needed to evaluate to their values in predicting prognosis and guiding individualized therapy.

Solo Sonographically Guided PCNL under Spinal Anesthesia: Defining Predictors of Success.

PubMed

Nouralizadeh, Akbar; Pakmanesh, Hamid; Basiri, Abbas; Aayanifard, Mohammad; Soltani, Mohammad Hossein; Tabibi, Ali; Sharifiaghdas, Farzaneh; Ziaee, Seyed Amir Mohsen; Shakhssalim, Naser; Valipour, Reza; Narouie, Behzad; Radfar, Mohammad Hadi

2016-01-01

Aim. Sonography has been brought in percutaneous nephrolithotripsy (PCNL) as an adjunct to or substitute for X-ray to restrict radiation exposure. This study was designed to investigate the possible predictors for the success of the solo sonographically guided PCNL. Methods. 148 consecutive cases were prospectively enrolled. All steps of PCNL were performed solely with sonography guidance under spinal anesthesia. Residual stones were evaluated the day after surgery using sonography and plain radiography. Results. The mean age was 46 ± 15 years; 40% of kidneys had hydronephrosis. The mean stone burden was 504 ± 350 mm(2). The mean duration of surgery was 43 ± 21 minutes. The early stone-free rate was 92% in inferior or middle calyceal stones, 89.5% in single pelvic stones, 81.5% in partial staghorn stones, and 61.9% in staghorn stones. The mean residual stone size was 13 ± 8 mm. Logistic regression showed that a lower age and a larger stone burden significantly predicted positive residual stones. Fifteen percent of patients presented with grade I or II and six percent showed grade III complication based on Clavien classification. There was no cases of organ injury or death. Conclusion. Solo ultrasonographically guided PCNL under spinal anesthesia is feasible with an acceptable stone-free rate and complication rate.

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2014 CFR

2014-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2013 CFR

2013-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2012 CFR

2012-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2011 CFR

2011-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

77 FR 59989 - Labor Surplus Area Classification Under Executive Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-01

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification Under... Bureau of Labor Statistics are used in making these classifications. The average unemployment rate for all states includes data for the Commonwealth of Puerto Rico. The basic LSA classification criteria...

Multilingual vocal emotion recognition and classification using back propagation neural network

NASA Astrophysics Data System (ADS)

Kayal, Apoorva J.; Nirmal, Jagannath

2016-03-01

This work implements classification of different emotions in different languages using Artificial Neural Networks (ANN). Mel Frequency Cepstral Coefficients (MFCC) and Short Term Energy (STE) have been considered for creation of feature set. An emotional speech corpus consisting of 30 acted utterances per emotion has been developed. The emotions portrayed in this work are Anger, Joy and Neutral in each of English, Marathi and Hindi languages. Different configurations of Artificial Neural Networks have been employed for classification purposes. The performance of the classifiers has been evaluated by False Negative Rate (FNR), False Positive Rate (FPR), True Positive Rate (TPR) and True Negative Rate (TNR).

Computer Assisted Navigation in Knee Arthroplasty

PubMed Central

Bae, Dae Kyung

2011-01-01

Computer assisted surgery (CAS) was used to improve the positioning of implants during total knee arthroplasty (TKA). Most studies have reported that computer assisted navigation reduced the outliers of alignment and component malpositioning. However, additional sophisticated studies are necessary to determine if the improvement of alignment will improve long-term clinical results and increase the survival rate of the implant. Knowledge of CAS-TKA technology and understanding the advantages and limitations of navigation are crucial to the successful application of the CAS technique in TKA. In this article, we review the components of navigation, classification of the system, surgical method, potential error, clinical results, advantages, and disadvantages. PMID:22162787

Automatic emotional expression analysis from eye area

NASA Astrophysics Data System (ADS)

Akkoç, Betül; Arslan, Ahmet

2015-02-01

Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

Material quality assessment of silk nanofibers based on swarm intelligence

NASA Astrophysics Data System (ADS)

Brandoli Machado, Bruno; Nunes Gonçalves, Wesley; Martinez Bruno, Odemir

2013-02-01

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the live agents curve at each time. Experimental results revealed that combining the minimum and maximum signatures into one increase the classification rate. In addition, we pioneer the use of autonomous agents for characterizing silk fibroin scaffolds. The results strongly suggest that our approach can be successfully employed for texture analysis.

The impact of ICD-9 revascularization procedure codes on estimates of racial disparities in ischemic stroke.

PubMed

Boan, Andrea D; Voeks, Jenifer H; Feng, Wuwei Wayne; Bachman, David L; Jauch, Edward C; Adams, Robert J; Ovbiagele, Bruce; Lackland, Daniel T

2014-01-01

The use of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) diagnostic codes can identify racial disparities in ischemic stroke hospitalizations; however, inclusion of revascularization procedure codes as acute stroke events may affect the magnitude of the risk difference. This study assesses the impact of excluding revascularization procedure codes in the ICD-9 definition of ischemic stroke, compared with the traditional inclusive definition, on racial disparity estimates for stroke incidence and recurrence. Patients discharged with a diagnosis of ischemic stroke (ICD-9 codes 433.00-434.91 and 436) were identified from a statewide inpatient discharge database from 2010 to 2012. Race-age specific disparity estimates of stroke incidence and recurrence and 1-year cumulative recurrent stroke rates were compared between the routinely used traditional classification and a modified classification of stroke that excluded primary ICD-9 cerebral revascularization procedures codes (38.12, 00.61, and 00.63). The traditional classification identified 7878 stroke hospitalizations, whereas the modified classification resulted in 18% fewer hospitalizations (n = 6444). The age-specific black to white rate ratios were significantly higher in the modified than in the traditional classification for stroke incidence (rate ratio, 1.50; 95% confidence interval [CI], 1.43-1.58 vs. rate ratio, 1.24; 95% CI, 1.18-1.30, respectively). In whites, the 1-year cumulative recurrence rate was significantly reduced by 46% (45-64 years) and 49% (≥ 65 years) in the modified classification, largely explained by a higher rate of cerebral revascularization procedures among whites. There were nonsignificant reductions of 14% (45-64 years) and 19% (≥ 65 years) among blacks. Including cerebral revascularization procedure codes overestimates hospitalization rates for ischemic stroke and significantly underestimates the racial disparity estimates in stroke incidence and recurrence. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

Semantic Classification of Diseases in Discharge Summaries Using a Context-aware Rule-based Classifier

PubMed Central

Solt, Illés; Tikk, Domonkos; Gál, Viktor; Kardkovács, Zsolt T.

2009-01-01

Objective Automated and disease-specific classification of textual clinical discharge summaries is of great importance in human life science, as it helps physicians to make medical studies by providing statistically relevant data for analysis. This can be further facilitated if, at the labeling of discharge summaries, semantic labels are also extracted from text, such as whether a given disease is present, absent, questionable in a patient, or is unmentioned in the document. The authors present a classification technique that successfully solves the semantic classification task. Design The authors introduce a context-aware rule-based semantic classification technique for use on clinical discharge summaries. The classification is performed in subsequent steps. First, some misleading parts are removed from the text; then the text is partitioned into positive, negative, and uncertain context segments, then a sequence of binary classifiers is applied to assign the appropriate semantic labels. Measurement For evaluation the authors used the documents of the i2b2 Obesity Challenge and adopted its evaluation measures: F1-macro and F1-micro for measurements. Results On the two subtasks of the Obesity Challenge (textual and intuitive classification) the system performed very well, and achieved a F1-macro = 0.80 for the textual and F1-macro = 0.67 for the intuitive tasks, and obtained second place at the textual and first place at the intuitive subtasks of the challenge. Conclusions The authors show in the paper that a simple rule-based classifier can tackle the semantic classification task more successfully than machine learning techniques, if the training data are limited and some semantic labels are very sparse. PMID:19390101

A multivariate analytical method to characterize sediment attributes from high-frequency acoustic backscatter and ground-truthing data (Jade Bay, German North Sea coast)

NASA Astrophysics Data System (ADS)

Biondo, Manuela; Bartholomä, Alexander

2017-04-01

One of the burning issues on the topic of acoustic seabed classification is the lack of solid, repeatable, statistical procedures that can support the verification of acoustic variability in relation to seabed properties. Acoustic sediment classification schemes often lead to biased and subjective interpretation, as they ultimately aim at an oversimplified categorization of the seabed based on conventionally defined sediment types. However, grain size variability alone cannot be accounted for acoustic diversity, which will be ultimately affected by multiple physical processes, scale of heterogeneity, instrument settings, data quality, image processing and segmentation performances. Understanding and assessing the weight of all of these factors on backscatter is a difficult task, due to the spatially limited and fragmentary knowledge of the seabed from of direct observations (e.g. grab samples, cores, videos). In particular, large-scale mapping requires an enormous availability of ground-truthing data that is often obtained from heterogeneous and multidisciplinary sources, resulting into a further chance of misclassification. Independently from all of these limitations, acoustic segments still contain signals for seabed changes that, if appropriate procedures are established, can be translated into meaningful knowledge. In this study we design a simple, repeatable method, based on multivariate procedures, with the scope to classify a 100 km2, high-frequency (450 kHz) sidescan sonar mosaic acquired in the year 2012 in the shallow upper-mesotidal inlet of the Jade Bay (German North Sea coast). The tool used for the automated classification of the backscatter mosaic is the QTC SWATHVIEWTMsoftware. The ground-truthing database included grab sample data from multiple sources (2009-2011). The method was designed to extrapolate quantitative descriptors for acoustic backscatter and model their spatial changes in relation to grain size distribution and morphology. The modelled relationships were used to: 1) asses the automated segmentation performance, 2) obtain a ranking of most discriminant seabed attributes responsible for acoustic diversity, 3) select the best-fit ground-truthing information to characterize each acoustic class. Using a supervised Linear Discriminant Analysis (LDA), relationships between seabed parameters and acoustic classes discrimination were modelled, and acoustic classes for each data point were predicted. The model predicted a success rate of 63.5%. An unsupervised LDA was used to model relationships between acoustic variables and clustered seabed categories with the scope of identifying misrepresentative ground-truthing data points. The model prediction scored a success rate of 50.8%. Misclassified data points were disregarded for final classification. Analyses led to clearer, more accurate appreciation of relationship patterns and improved understanding of site-specific processes affecting the acoustic signal. Value to the qualitative classification output was added by comparing the latter with a more recent set of acoustic and ground-truthing information (2014). Classification resulted in the first acoustic sediment map ever produced in the area and offered valuable knowledge for detailed sediment variability. The method proved to be a simple, repeatable strategy that may be applied to similar work and environments.

78 FR 63248 - Labor Surplus Area Classification under Executive Orders 12073 and 10582

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification under... Statistics unemployment estimates to make these classifications. The average unemployment rate for all states includes data for the Commonwealth of Puerto Rico. The basic LSA classification criteria include a ``floor...

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and weighting factors. (a) Diagnosis-related groups. CMS establishs a classification of inpatient hospital...

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and weighting factors. (a) Diagnosis-related groups. CMS establishs a classification of inpatient hospital...

Application of the elusieve process to the classification of meat and bone meal particles

USDA-ARS?s Scientific Manuscript database

Meat and bone meal (MBM), a product of the rendering industry, comprises a mixture of two particle types. The utility and value of MBM would increase if the two particle types could be separated economically. Past efforts at classification of MBM particles have achieved limited success. In the pr...

Research on Automatic Classification, Indexing and Extracting. Annual Progress Report.

ERIC Educational Resources Information Center

Baker, F.T.; And Others

In order to contribute to the success of several studies for automatic classification, indexing and extracting currently in progress, as well as to further the theoretical and practical understanding of textual item distributions, the development of a frequency program capable of supplying these types of information was undertaken. The program…

Seral stage classification and monitoring model for big sagebrush/western wheatgrass/blue grama habitat

Treesearch

Lakhdar Benkobi; Daniel W. Uresk

1996-01-01

An ecological classification model for seral stages was developed for big sagebrush (Artemisia tridentata) shrub steppe habitat in Thunder Basin National Grassland, Wyoming. Four seral stages (early to late succession) were defined in this habitat type. Ecological seral stages were quantitatively identified with an estimated 92% level of accuracy...

Making the Ineffable Explicit: Estimating the Information Employed for Face Classifications

ERIC Educational Resources Information Center

Mangini, Michael C.; Biederman, Irving

2004-01-01

When we look at a face, we readily perceive that person's gender, expression, identity, age, and attractiveness. Perceivers as well as scientists have hitherto had little success in articulating just what information we are employing to achieve these subjectively immediate and effortless classifications. We describe here a method that estimates…

On-line analysis of algae in water by discrete three-dimensional fluorescence spectroscopy.

PubMed

Zhao, Nanjing; Zhang, Xiaoling; Yin, Gaofang; Yang, Ruifang; Hu, Li; Chen, Shuang; Liu, Jianguo; Liu, Wenqing

2018-03-19

In view of the problem of the on-line measurement of algae classification, a method of algae classification and concentration determination based on the discrete three-dimensional fluorescence spectra was studied in this work. The discrete three-dimensional fluorescence spectra of twelve common species of algae belonging to five categories were analyzed, the discrete three-dimensional standard spectra of five categories were built, and the recognition, classification and concentration prediction of algae categories were realized by the discrete three-dimensional fluorescence spectra coupled with non-negative weighted least squares linear regression analysis. The results show that similarities between discrete three-dimensional standard spectra of different categories were reduced and the accuracies of recognition, classification and concentration prediction of the algae categories were significantly improved. By comparing with that of the chlorophyll a fluorescence excitation spectra method, the recognition accuracy rate in pure samples by discrete three-dimensional fluorescence spectra is improved 1.38%, and the recovery rate and classification accuracy in pure diatom samples 34.1% and 46.8%, respectively; the recognition accuracy rate of mixed samples by discrete-three dimensional fluorescence spectra is enhanced by 26.1%, the recovery rate of mixed samples with Chlorophyta 37.8%, and the classification accuracy of mixed samples with diatoms 54.6%.

Classification scheme for phenomenological universalities in growth problems in physics and other sciences.

PubMed

Castorina, P; Delsanto, P P; Guiot, C

2006-05-12

A classification in universality classes of broad categories of phenomenologies, belonging to physics and other disciplines, may be very useful for a cross fertilization among them and for the purpose of pattern recognition and interpretation of experimental data. We present here a simple scheme for the classification of nonlinear growth problems. The success of the scheme in predicting and characterizing the well known Gompertz, West, and logistic models, suggests to us the study of a hitherto unexplored class of nonlinear growth problems.

Levels of Evidence in Cosmetic Surgery: Analysis and Recommendations Using a New CLEAR Classification

PubMed Central

2013-01-01

Background: The Level of Evidence rating was introduced in 2011 to grade the quality of publications. This system evaluates study design but does not assess several other quality indicators. This study introduces a new “Cosmetic Level of Evidence And Recommendation” (CLEAR) classification that includes additional methodological criteria and compares this new classification with the existing system. Methods: All rated publications in the Cosmetic Section of Plastic and Reconstructive Surgery, July 2011 through June 2013, were evaluated. The published Level of Evidence rating (1–5) and criteria relevant to study design and methodology for each study were tabulated. A new CLEAR rating was assigned to each article, including a recommendation grade (A–D). The published Level of Evidence rating (1–5) was compared with the recommendation grade determined using the CLEAR classification. Results: Among the 87 cosmetic articles, 48 studies (55%) were designated as level 4. Three articles were assigned a level 1, but they contained deficiencies sufficient to undermine the conclusions. The correlation between the published Level of Evidence classification (1–5) and CLEAR Grade (A–D) was weak (ρ = 0.11, not significant). Only 41 studies (48%) evaluated consecutive patients or consecutive patients meeting inclusion criteria. Conclusions: The CLEAR classification considers methodological factors in evaluating study reliability. A prospective study among consecutive patients meeting eligibility criteria, with a reported inclusion rate, the use of contemporaneous controls when indicated, and consideration of confounders is a realistic goal. Such measures are likely to improve study quality. PMID:25289261

Pyelocaliceal Distribution of Kidney Stones Used as an Outcome Predictor in Percutaneous Nephrolithotomy After Being Evaluated with Preoperative and Postoperative CT Scan.

PubMed

Tirapegui, Federico Ignacio; González, Mariano Sebastian; González, Ignacio Pablo Tobía; Daels, Francisco P

2015-06-01

To identify kidney stone characteristics that will determine either success or failure of a percutaneous nephrolithotomy (PCNL) and design a classification system to predict results according to these characteristics. One hundred thirty-eight patients were assessed with multislice abdominal and pelvic CT before and after PCNL. With regard to pyelocaliceal stone distribution, we classified our patients in two groups that we called "no extra stone in middle calix" (NESMC) and "extra stone in middle calix" (ESMC), according to the difficulty in reaching the stones. We did a univariate and a multivariate analysis, as well as a receiving operating curve (ROC) of the proposed classification, based on the foreseen probabilities, to determine the diagnostic yield. Global residual lithiasis (RL) was 26.08%. The proportion of patients with RL according to classification was NESMC 11.5% and ESMC 59.5%. In the univariate logistic regression analysis of the distribution, number, total volumetry, side, type, radio-opacity of stones, and the presence or not of preoperatory urinary tract infection, the variables related to RL were the distribution (11.3; 95% confidence interval [95% CI] 4.7, 27.4), volumetry (odds ratio [OR] 1.01; 95% CI 1.004, 1.014), and the presence of staghorn stones (OR 6.64; 95% CI 2.463, 17.905). In the multivariate analysis, distribution was statistically significant (OR 8.687; 95% CI 2.69, 28.06), whereas total volumetry and the presence of staghorn stones were not (OR 1; 95% CI 1.000, 1.000 and OR 2.7; 95% CI 0.35, 20.57, respectively). The ROC showed an area under the curve of 0.77. In our experience, the distribution of kidney stones is the most important predictor of RL after PCNL. The results also suggest that the presence of stones in the middle calix has a direct impact on the stone-free rate. We put forward a simple and reproducible classification, easy to apply, and useful to estimate the chances of success of the procedure using preoperatory CT scans.

Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

PubMed

Galpert, Deborah; Fernández, Alberto; Herrera, Francisco; Antunes, Agostinho; Molina-Ruiz, Reinaldo; Agüero-Chapin, Guillermin

2018-05-03

The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were alignment-free features related to amino acid composition. The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures. However, the similarity of their classification performance to that of traditional ortholog detection methods encourages the evaluation of other alignment-free protein pair descriptors in future research.

Gender classification in low-resolution surveillance video: in-depth comparison of random forests and SVMs

NASA Astrophysics Data System (ADS)

Geelen, Christopher D.; Wijnhoven, Rob G. J.; Dubbelman, Gijs; de With, Peter H. N.

2015-03-01

This research considers gender classification in surveillance environments, typically involving low-resolution images and a large amount of viewpoint variations and occlusions. Gender classification is inherently difficult due to the large intra-class variation and interclass correlation. We have developed a gender classification system, which is successfully evaluated on two novel datasets, which realistically consider the above conditions, typical for surveillance. The system reaches a mean accuracy of up to 90% and approaches our human baseline of 92.6%, proving a high-quality gender classification system. We also present an in-depth discussion of the fundamental differences between SVM and RF classifiers. We conclude that balancing the degree of randomization in any classifier is required for the highest classification accuracy. For our problem, an RF-SVM hybrid classifier exploiting the combination of HSV and LBP features results in the highest classification accuracy of 89.9 0.2%, while classification computation time is negligible compared to the detection time of pedestrians.

Propofol for procedural sedation and analgesia reduced dedicated emergency nursing time while maintaining safety in a community emergency department.

PubMed

Reynolds, Joshua C; Abraham, Michael K; Barrueto, Fermin F; Lemkin, Daniel L; Hirshon, Jon M

2013-09-01

Procedural sedation and analgesia is a core competency in emergency medicine. Propofol is replacing midazolam in many emergency departments. Barriers to performing procedural sedation include resource utilization. We hypothesized that emergency nursing time is shorter with propofol than midazolam, without increasing complications. Retrospective analysis of a procedural sedation registry for two community emergency departments with combined census of 100,000 patients/year. Demographics, procedure, and ASA physical classification status of adult patients receiving procedural sedation between 2007-2010 with midazolam or propofol were analyzed. Primary outcome was dedicated emergency nursing time. Secondary outcomes were procedural success, ED length of stay, and complication rate. Comparative statistics were performed with Mann-Whitney, Kruskal-Wallis, chi-square, or Fisher's exact test. Linear regression was performed with log-transformed procedural sedation time to define predictors. Of 328 procedural sedation and analgesia, 316 met inclusion criteria, of which 60 received midazolam and 256 propofol. Sex distribution varied between groups (midazolam 3% male; propofol 55% male; P = 0.04). Age, procedure, and ASA status were not significantly different. Propofol had shorter procedural sedation time (propofol 32.5 ± 24.2 minutes; midazolam 78.7 ± 51.5 minutes; P < 0.001) and higher rates of procedural success (propofol 98%; midazolam 92%; P = 0.02). There were no significant differences between complication rates (propofol 14%; midazolam 13%; P = 0.88) or emergency department length of stay (propofol 262.5 ± 132.8 minutes; midazolam 288.6 ± 130.6 minutes; P = 0.09). Use of propofol resulted in shorter emergency nursing time and higher procedural success rate than midazolam with a comparable safety profile. Copyright © 2013 Emergency Nurses Association. Published by Mosby, Inc. All rights reserved.

The history of transdisciplinary race classification: methods, politics and institutions, 1840s-1940s.

PubMed

McMahon, Richard

2018-03-01

A recently blossoming historiographical literature recognizes that physical anthropologists allied with scholars of diverse aspects of society and history to racially classify European peoples over a period of about a hundred years. They created three successive race classification coalitions - ethnology, from around 1840; anthropology, from the 1850s; and interwar raciology - each of which successively disintegrated. The present genealogical study argues that representing these coalitions as 'transdisciplinary' can enrich our understanding of challenges to disciplinary specialization. This is especially the case for the less well-studied nineteenth century, when disciplines and challenges to disciplinary specialization were both gradually emerging. Like Marxism or structuralism, race classification was a holistic interpretive framework, which, at its most ambitious, aimed to structure the human sciences as a whole. It resisted the organization of academia and knowledge into disciplines with separate organizational institutions and research practices. However, the 'transdisciplinarity' of this nationalistic project also bridged emerging borderlines between science and politics. I ascribe race classification's simultaneous longevity and instability to its complex and intricately entwined processes of political and interdisciplinary coalition building. Race classification's politically useful conclusions helped secure public support for institutionalizing the coalition's component disciplines. Institutionalization in turn stimulated disciplines to professionalize. They emphasized disciplinary boundaries and insisted on apolitical science, thus ultimately undermining the 'transdisciplinary' project.

Spatial Characteristics of Geothermal Spring Temperatures and Discharge Rates in the Tatun Volcanic Area, Taiwan

NASA Astrophysics Data System (ADS)

Jang, C. S.; Liu, C. W.

2014-12-01

The Tatun volcanic area is the only potential volcanic geothermal region in the Taiwan island, and abundant in hot spring resources owing to stream water mixing with fumarolic gases. According to the Meinzer's classification, spring temperatures and discharge rates are the most important properties for characterizing spring classifications. This study attempted to spatially characterize spring temperatures and discharge rates in the Tatun volcanic area, Taiwanusing indicator kriging (IK). First, data on spring temperatures and discharge rates, which were collected from surveyed data of the Taipei City Government, were divided into high, moderate and low categories according to spring classification criteria, and the various categories were regarded as estimation thresholds. Then, IK was adopted to model occurrence probabilities of specified temperatures and discharge rates in springs, and to determine their classifications based on estimated probabilities. Finally, nine combinations were obtained from the classifications of temperatures and discharge rates in springs. Moreover, the combinations and features of spring water were spatially quantified according to seven sub-zones of spring utilization. A suitable and sustainable development strategy of the spring area was proposed in each sub-zone based on probability-based combinations and features of spring water.The research results reveal that the probability-based classifications using IK provide an excellent insight in exploring the uncertainty of spatial features in springs, and can provide Taiwanese government administrators with detailed information on sustainable spring utilization and conservation in the overexploited spring tourism areas. The sub-zones BT (Beitou), RXY (Rd. Xingyi), ZSL (Zhongshanlou) and LSK (Lengshuikeng) with high or moderate discharge rates are suitable to supply spring water for tourism hotels.Local natural hot springs should be planned in the sub-zones DBT (Dingbeitou), ZSL, XYK (Xiayoukeng), and MC (Macao) with low discharge rates, and low or moderate temperatures, particularly in riverbeds or valleys.Keywords: Spring; Temperature; Discharge rate; Indicator kriging; Uncertainty

Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis

NASA Astrophysics Data System (ADS)

Szuflitowska, B.; Orlowski, P.

2017-08-01

Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: epilepsy, healthy and Alzheimer's Disease. The classification error below 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer's Disease.

Spectral band selection for classification of soil organic matter content

NASA Technical Reports Server (NTRS)

Henderson, Tracey L.; Szilagyi, Andrea; Baumgardner, Marion F.; Chen, Chih-Chien Thomas; Landgrebe, David A.

1989-01-01

This paper describes the spectral-band-selection (SBS) algorithm of Chen and Landgrebe (1987, 1988, and 1989) and uses the algorithm to classify the organic matter content in the earth's surface soil. The effectiveness of the algorithm was evaluated comparing the results of classification of the soil organic matter using SBS bands with those obtained using Landsat MSS bands and TM bands, showing that the algorithm was successful in finding important spectral bands for classification of organic matter content. Using the calculated bands, the probabilities of correct classification for climate-stratified data were found to range from 0.910 to 0.980.

A novel application of deep learning for single-lead ECG classification.

PubMed

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

Preoperative classification assessment reliability and influence on the length of intertrochanteric fracture operations.

PubMed

Shen, Jing; Hu, FangKe; Zhang, LiHai; Tang, PeiFu; Bi, ZhengGang

2013-04-01

The accuracy of intertrochanteric fracture classification is important; indeed, the patient outcomes are dependent on their classification. The aim of this study was to use the AO classification system to evaluate the variation in classification between X-ray and computed tomography (CT)/3D CT images. Then, differences in the length of surgery were evaluated based on two examinations. Intertrochanteric fractures were reviewed and surgeons were interviewed. The rates of correct discrimination and misclassification (overestimates and underestimates) probabilities were determined. The impact of misclassification on length of surgery was also evaluated. In total, 370 patents and four surgeons were included in the study. All patients had X-ray images and 210 patients had CT/3D CT images. Of them, 214 and 156 patients were treated by intramedullary and extramedullary fixation systems, respectively. The mean length of surgery was 62.1 ± 17.7 min. The overall rate of correct discrimination was 83.8 % and in the classification of A1, A2 and A3 were 80.0, 85.7 and 82.4 %, respectively. The rate of misclassification showed no significant difference between stable and unstable fractures (21.3 vs 13.1 %, P = 0.173). The overall rates of overestimates and underestimates were significantly different (5 vs 11.25 %, P = 0.041). Subtracting the rate of overestimates from underestimates had a positive correlation with prolonged surgery and showed a significant difference with intramedullary fixation (P < 0.001). Classification based on the AO system was good in terms of consistency. CT/3D CT examination was more reliable and more helpful for preoperative assessment, especially for performance of an intramedullary fixation.

Rate of caesarean sections according to the Robson classification: Analysis in a French perinatal network - Interest and limitations of the French medico-administrative data (PMSI).

PubMed

Lafitte, A-S; Dolley, P; Le Coutour, X; Benoist, G; Prime, L; Thibon, P; Dreyfus, M

2018-02-01

The objective of our study was to determine, in accordance with WHO recommendations, the rates of Caesarean sections in a French perinatal network according to the Robson classification and determine the benefit of the medico-administrative data (PMSI) to collect this indicator. This study aimed to identify the main groups contributing to local variations in the rates of Caesarean sections. A descriptive multicentric study was conducted in 13 maternity units of a French perinatal network. The rates of Caesarean sections and the contribution of each group of the Robson classification were calculated for all Caesarean sections performed in 2014. The agreement of the classification of Caesarean sections according to Robson using medico-administrative data and data collected in the patient records was measured by the Kappa index. We also analysed a 6 groups simplified Robson classification only using data from PMSI, which do not inform about parity and onset of labour. The rate of Caesarean sections was 19% (14.5-33.2) in 2014 (2924 out of 15413 deliveries). The most important contributors to the total rates were groups 1, 2 and 5, representing respectively 14.3%, 16.7% and 32.1% of the Caesarean sections. The rates were significantly different in level 1, 2b and 3 maternity units in groups 1 to 4, level 2a maternity units in group 5, and level 3 maternity units in groups 6 and 7. The agreement between the simplified Robson classification produced using the medical records and the medico-administrative data was excellent, with a Kappa index of 0.985 (0.980-0.990). To reduce the rates of Caesarean sections, audits should be conducted on groups 1, 2 and 5 and local protocols developed. Simply by collecting the parity data, the excellent metrological quality of the medico-administrative data would allow systematisation of the Robson classification for each hospital. Copyright © 2017. Published by Elsevier Masson SAS.

Micromachined cascade virtual impactor with a flow rate distributor for wide range airborne particle classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Yong-Ho; Maeng, Jwa-Young; Park, Dongho

2007-07-23

This letter reports a module for airborne particle classification, which consists of a micromachined three-stage virtual impactor for classifying airborne particles according to their size and a flow rate distributor for supplying the required flow rate to the virtual impactor. Dioctyl sebacate particles, 100-600 nm in diameter, and carbon particles, 0.6-10 {mu}m in diameter, were used for particle classification. The collection efficiency and cutoff diameter were examined. The measured cutoff diameters of the first, second, and third stages were 135 nm, 1.9 {mu}m, and 4.8 {mu}m, respectively.

Typecasting catchments: Classification, directionality, and the pursuit of universality

NASA Astrophysics Data System (ADS)

Smith, Tyler; Marshall, Lucy; McGlynn, Brian

2018-02-01

Catchment classification poses a significant challenge to hydrology and hydrologic modeling, restricting widespread transfer of knowledge from well-studied sites. The identification of important physical, climatological, or hydrologic attributes (to varying degrees depending on application/data availability) has traditionally been the focus for catchment classification. Classification approaches are regularly assessed with regard to their ability to provide suitable hydrologic predictions - commonly by transferring fitted hydrologic parameters at a data-rich catchment to a data-poor catchment deemed similar by the classification. While such approaches to hydrology's grand challenges are intuitive, they often ignore the most uncertain aspect of the process - the model itself. We explore catchment classification and parameter transferability and the concept of universal donor/acceptor catchments. We identify the implications of the assumption that the transfer of parameters between "similar" catchments is reciprocal (i.e., non-directional). These concepts are considered through three case studies situated across multiple gradients that include model complexity, process description, and site characteristics. Case study results highlight that some catchments are more successfully used as donor catchments and others are better suited as acceptor catchments. These results were observed for both black-box and process consistent hydrologic models, as well as for differing levels of catchment similarity. Therefore, we suggest that similarity does not adequately satisfy the underlying assumptions being made in parameter regionalization approaches regardless of model appropriateness. Furthermore, we suggest that the directionality of parameter transfer is an important factor in determining the success of parameter regionalization approaches.

An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests

ERIC Educational Resources Information Center

Strobl, Carolin; Malley, James; Tutz, Gerhard

2009-01-01

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and…

Classification Scheme for Items in CAAT.

ERIC Educational Resources Information Center

Epstein, Marion G.

In planning the development of the system for computer assisted assembly of tests, it was agreed at the outset that one of the basic requirements for the successful initiation of any such system would be the development of a detailed item content classification system. The design of the system for classifying item content is a key element in…

Activity classification using the GENEA: optimum sampling frequency and number of axes.

PubMed

Zhang, Shaoyan; Murray, Peter; Zillmer, Ruediger; Eston, Roger G; Catt, Michael; Rowlands, Alex V

2012-11-01

The GENEA shows high accuracy for classification of sedentary, household, walking, and running activities when sampling at 80 Hz on three axes. It is not known whether it is possible to decrease this sampling frequency and/or the number of axes without detriment to classification accuracy. The purpose of this study was to compare the classification rate of activities on the basis of data from a single axis, two axes, and three axes, with sampling rates ranging from 5 to 80 Hz. Sixty participants (age, 49.4 yr (6.5 yr); BMI, 24.6 kg·m (3.4 kg·m)) completed 10-12 semistructured activities in the laboratory and outdoor environment while wearing a GENEA accelerometer on the right wrist. We analyzed data from single axis, dual axes, and three axes at sampling rates of 5, 10, 20, 40, and 80 Hz. Mathematical models based on features extracted from mean, SD, fast Fourier transform, and wavelet decomposition were built, which combined one of the numbers of axes with one of the sampling rates to classify activities into sedentary, household, walking, and running. Classification accuracy was high irrespective of the number of axes for data collected at 80 Hz (96.93% ± 0.97%), 40 Hz (97.4% ± 0.73%), 20 Hz (96.86% ± 1.12%), and 10 Hz (97.01% ± 1.01%) but dropped for data collected at 5 Hz (94.98% ± 1.36%). Sampling frequencies >10 Hz and/or more than one axis of measurement were not associated with greater classification accuracy. Lower sampling rates and measurement of a single axis would result in a lower data load, longer battery life, and higher efficiency of data processing. Further research should investigate whether a lower sampling rate and a single axis affects classification accuracy when considering a wider range of activities.

Assessing nonchoosers' eyewitness identification accuracy from photographic showups by using confidence and response times.

PubMed

Sauerland, Melanie; Sagana, Anna; Sporer, Siegfried L

2012-10-01

While recent research has shown that the accuracy of positive identification decisions can be assessed via confidence and decision times, gauging lineup rejections has been less successful. The current study focused on 2 different aspects which are inherent in lineup rejections. First, we hypothesized that decision times and confidence ratings should be postdictive of identification rejections if they refer to a single lineup member only. Second, we hypothesized that dividing nonchoosers according to the reasons they provided for their decisions can serve as a useful postdictor for nonchoosers' accuracy. To test these assumptions, we used (1) 1-person lineups (showups) in order to obtain confidence and response time measures referring to a single lineup member, and (2) asked nonchoosers about their reasons for making a rejection. Three hundred and eighty-four participants were asked to identify 2 different persons after watching 1 of 2 stimulus films. The results supported our hypotheses. Nonchoosers' postdecision confidence ratings were well-calibrated. Likewise, we successfully established optimum time and confidence boundaries for nonchoosers. Finally, combinations of postdictors increased the number of accurate classifications compared with individual postdictors. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Classifying seismic waveforms from scratch: a case study in the alpine environment

NASA Astrophysics Data System (ADS)

Hammer, C.; Ohrnberger, M.; Fäh, D.

2013-01-01

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

PubMed Central

Zhao, Jiangsan; Bodner, Gernot; Rewald, Boris

2016-01-01

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding – especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) – Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0–5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars. PMID:27999587

Organisational Role Climates: Success-Failure Configurations in Educational Leadership (or Are Educational Administrators Doomed to Succeed?)

ERIC Educational Resources Information Center

Inbar, Dan E.

1980-01-01

Presents an analytical framework based on a threefold classification--unequivocal failure, "satisficing," and unequivocal success--and four basic role climates--apathetic, frustrating, tense, and tranquil--that is applied to the elementary school principalship. (Author/WD)

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Improvement of an algorithm for recognition of liveness using perspiration in fingerprint devices

NASA Astrophysics Data System (ADS)

Parthasaradhi, Sujan T.; Derakhshani, Reza; Hornak, Lawrence A.; Schuckers, Stephanie C.

2004-08-01

Previous work in our laboratory and others have demonstrated that spoof fingers made of a variety of materials including silicon, Play-Doh, clay, and gelatin (gummy finger) can be scanned and verified when compared to a live enrolled finger. Liveness, i.e. to determine whether the introduced biometric is coming from a live source, has been suggested as a means to circumvent attacks using spoof fingers. We developed a new liveness method based on perspiration changes in the fingerprint image. Recent results showed approximately 90% classification rate using different classification methods for various technologies including optical, electro-optical, and capacitive DC, a shorter time window and a diverse dataset. This paper focuses on improvement of the live classification rate by using a weight decay method during the training phase in order to improve the generalization and reduce the variance of the neural network based classifier. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental impression material and Play-Doh, and fourteen cadaver fingers. 100% live classification was achieved with 81.8 to 100% spoof classification, depending on the device technology. The weight-decay method improves upon past reports by increasing the live and spoof classification rate.

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

PubMed

Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

2016-08-31

Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

PubMed

Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

2018-01-01

Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

Comparisons of neural networks to standard techniques for image classification and correlation

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1994-01-01

Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

Applying the Multiple Signal Classification Method to Silent Object Detection Using Ambient Noise

NASA Astrophysics Data System (ADS)

Mori, Kazuyoshi; Yokoyama, Tomoki; Hasegawa, Akio; Matsuda, Minoru

2004-05-01

The revolutionary concept of using ocean ambient noise positively to detect objects, called acoustic daylight imaging, has attracted much attention. The authors attempted the detection of a silent target object using ambient noise and a wide-band beam former consisting of an array of receivers. In experimental results obtained in air, using the wide-band beam former, we successfully applied the delay-sum array (DSA) method to detect a silent target object in an acoustic noise field generated by a large number of transducers. This paper reports some experimental results obtained by applying the multiple signal classification (MUSIC) method to a wide-band beam former to detect silent targets. The ocean ambient noise was simulated by transducers decentralized to many points in air. Both MUSIC and DSA detected a spherical target object in the noise field. The relative power levels near the target obtained with MUSIC were compared with those obtained by DSA. Then the effectiveness of the MUSIC method was evaluated according to the rate of increase in the maximum and minimum relative power levels.

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

Infrared thermography based on artificial intelligence for carpal tunnel syndrome diagnosis.

PubMed

Jesensek Papez, B; Palfy, M; Turk, Z

2008-01-01

Thermography for the measurement of surface temperatures is well known in industry, although is not established in medicine despite its safety, lack of pain and invasiveness, easy reproducibility, and low running costs. Promising results have been achieved in nerve entrapment syndromes, although thermography has never represented a real alternative to electromyography. Here an attempt is described to improve the diagnosis of carpal tunnel syndrome with thermography using a computer-based system employing artificial neural networks to analyse the images. Method reliability was tested on 112 images (depicting the dorsal and palmar sides of 26 healthy and 30 pathological hands), with the hand divided into 12 segments and compared relative to a reference. Palmar segments appeared to have no beneficial influence on classification outcome, whereas dorsal segments gave improved outcome with classification success rates near to or over 80%, and finger segments influenced by the median nerve appeared to be of greatest importance. These are preliminary results from a limited number of images and further research will be undertaken as our image database grows.

Log-ratio transformed major element based multidimensional classification for altered High-Mg igneous rocks

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Rivera-Gómez, M. Abdelaly; Díaz-González, Lorena; Quiroz-Ruiz, Alfredo

2016-12-01

A new multidimensional classification scheme consistent with the chemical classification of the International Union of Geological Sciences (IUGS) is proposed for the nomenclature of High-Mg altered rocks. Our procedure is based on an extensive database of major element (SiO2, TiO2, Al2O3, Fe2O3t, MnO, MgO, CaO, Na2O, K2O, and P2O5) compositions of a total of 33,868 (920 High-Mg and 32,948 "Common") relatively fresh igneous rock samples. The database consisting of these multinormally distributed samples in terms of their isometric log-ratios was used to propose a set of 11 discriminant functions and 6 diagrams to facilitate High-Mg rock classification. The multinormality required by linear discriminant and canonical analysis was ascertained by a new computer program DOMuDaF. One multidimensional function can distinguish the High-Mg and Common igneous rocks with high percent success values of about 86.4% and 98.9%, respectively. Similarly, from 10 discriminant functions the High-Mg rocks can also be classified as one of the four rock types (komatiite, meimechite, picrite, and boninite), with high success values of about 88%-100%. Satisfactory functioning of this new classification scheme was confirmed by seven independent tests. Five further case studies involving application to highly altered rocks illustrate the usefulness of our proposal. A computer program HMgClaMSys was written to efficiently apply the proposed classification scheme, which will be available for online processing of igneous rock compositional data. Monte Carlo simulation modeling and mass-balance computations confirmed the robustness of our classification with respect to analytical errors and postemplacement compositional changes.

Automated Classification of Pathology Reports.

PubMed

Oleynik, Michel; Finger, Marcelo; Patrão, Diogo F C

2015-01-01

This work develops an automated classifier of pathology reports which infers the topography and the morphology classes of a tumor using codes from the International Classification of Diseases for Oncology (ICD-O). Data from 94,980 patients of the A.C. Camargo Cancer Center was used for training and validation of Naive Bayes classifiers, evaluated by the F1-score. Measures greater than 74% in the topographic group and 61% in the morphologic group are reported. Our work provides a successful baseline for future research for the classification of medical documents written in Portuguese and in other domains.

Role of adrenal vein sampling in primary aldosteronism: the Monash Health experience.

PubMed

Teng, J; Hutchinson, M E; Doery, J C G; Choy, K W; Chong, W; Fuller, P J; Yang, J

2015-11-01

Adrenal vein sampling (AVS) is useful for distinguishing unilateral versus bilateral hypersecretion in primary aldosteronism (PA), but is technically challenging. Furthermore, the use of adrenocorticotropic hormone (ACTH)-stimulation in AVS is controversial. We implemented a Monash Health-specific AVS protocol in 2010. The audit aimed to: (i) examine the impact of a dedicated protocol on success rates of AVS at a tertiary referral centre; (ii) evaluate the impact of AVS on sub-typing of PA; and (iii) assess the utility of ACTH stimulation in AVS. AVS was performed on patients with PA confirmed by positive saline suppression testing (aldosterone level >140 pmol/L post-saline infusion), with sequential sampling of adrenal and peripheral veins, pre- and post-ACTH infusion. Patients with unilateral aldosterone-producing adenoma diagnosed on successful AVS were referred for adrenalectomy. Between 2010 and 2014 inclusive, a total of 28 AVS procedures was performed, with complete pre- and post-ACTH data for 19 procedures. Bilateral successful cannulation rates improved post-implementation of our protocol (61% vs 41%). Of the patients, 32% had discordant imaging and AVS results: four patients with unilateral adenomas did not lateralise on AVS and were managed medically; four patients with bilateral or no adenomas on imaging, lateralised on AVS and had surgery. Overall, use of ACTH did not increase successful cannulation and tended to mask lateralisation. AVS is crucial in subtype classification of PA and should be performed by a dedicated radiologist with a standardised protocol. AVS outcomes were not improved with the use of ACTH stimulation. © 2015 Royal Australasian College of Physicians.

19 CFR 141.90 - Notation of tariff classification and value on invoice.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Notation of tariff classification and value on... classification and value on invoice. (a) [Reserved] (b) Classification and rate of duty. The importer or customs... invoice value which have been made to arrive at the aggregate entered value. In addition, the entered unit...

Using near infrared spectroscopy to classify soybean oil according to expiration date.

PubMed

da Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Gomes, Adriano A; de Almeida, Valber Elias; Veras, Germano

2016-04-01

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines

NASA Astrophysics Data System (ADS)

Lama, Norsang; Umbaugh, Scott E.; Mishra, Deependra; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.

A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification.

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). A Bayesian classifier was used to determine the probability that 2s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

Natural control capabilities of robotic hands by hand amputated subjects.

PubMed

Atzori, Manfredo; Gijsberts, Arjan; Caputo, Barbara; Muller, Henning

2014-01-01

People with transradial hand amputations who own a myoelectric prosthesis currently have some control capabilities via sEMG. However, the control systems are still limited and not natural. The Ninapro project is aiming at helping the scientific community to overcome these limits through the creation of publicly available electromyography data sources to develop and test machine learning algorithms. In this paper we describe the movement classification results gained from three subjects with an homogeneous level of amputation, and we compare them with the results of 40 intact subjects. The number of considered subjects can seem small at first sight, but it is not considering the literature of the field (which has to face the difficulty of recruiting trans-radial hand amputated subjects). The classification is performed with four different classifiers and the obtained balanced classification rates are up to 58.6% on 50 movements, which is an excellent result compared to the current literature. Successively, for each subject we find a subset of up to 9 highly independent movements, (defined as movements that can be distinguished with more than 90% accuracy), which is a deeply innovative step in literature. The natural control of a robotic hand in so many movements could lead to an immediate progress in robotic hand prosthetics and it could deeply change the quality of life of amputated subjects.

STACCATO: a novel solution to supernova photometric classification with biased training sets

NASA Astrophysics Data System (ADS)

Revsbech, E. A.; Trotta, R.; van Dyk, D. A.

2018-01-01

We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. - a diffusion map combined with a random forest classifier - to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the 'gold standard' of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.

Mössbauer investigations to characterize Fe lattice sites in sheet silicates and Peru Basin deep-sea sediments

NASA Astrophysics Data System (ADS)

Lougear, André; König, Iris; Trautwein, Alfred X.; Suess, Erwin

A procedure to classify different Fe lattice sites, i.e., OH-group geometries, in the clay mineral content of deep-sea sediments was developed using Mössbauer spectroscopy at low temperature (77 K). This speciation is of interest with regard to the redox behavior, reactivity and color of marine sediments, since substantial iron redox transitions (associated with sediment color change) have been documented for the structural sheet silicate iron. Lattice site classification was achieved for the Fe(II) fraction, all of which is structural clay Fe(II) in the sediments under investigation. Whereas the major part of the Fe(III) is structural clay iron as well, there is a small Fe(III) fraction in oxide minerals. Therefore, further elaboration of the procedure would be required to also achieve lattice site classification for the Fe(III) fraction. Analysis of the Mössbauer spectra is based on computer fits, the input parameters of which were derived from a separate study of Fe(II)-rich pure chlorites. The procedure of classification is qualified to investigate, e.g., in laboratory experiments, the site-specific reaction rates and the effects on sediment color of iron redox transitions in the sheet silicate content of sediments. The new skills were successfully applied in environmental impact studies on the mining of polymetallic nodules from the Peru Basin deep-sea floor.

Image Ambiguity and Fluency

PubMed Central

Jakesch, Martina; Leder, Helmut; Forster, Michael

2013-01-01

Ambiguity is often associated with negative affective responses, and enjoying ambiguity seems restricted to only a few situations, such as experiencing art. Nevertheless, theories of judgment formation, especially the “processing fluency account”, suggest that easy-to-process (non-ambiguous) stimuli are processed faster and are therefore preferred to (ambiguous) stimuli, which are hard to process. In a series of six experiments, we investigated these contrasting approaches by manipulating fluency (presentation duration: 10ms, 50ms, 100ms, 500ms, 1000ms) and testing effects of ambiguity (ambiguous versus non-ambiguous pictures of paintings) on classification performance (Part A; speed and accuracy) and aesthetic appreciation (Part B; liking and interest). As indicated by signal detection analyses, classification accuracy increased with presentation duration (Exp. 1a), but we found no effects of ambiguity on classification speed (Exp. 1b). Fifty percent of the participants were able to successfully classify ambiguous content at a presentation duration of 100 ms, and at 500ms even 75% performed above chance level. Ambiguous artworks were found more interesting (in conditions 50ms to 1000ms) and were preferred over non-ambiguous stimuli at 500ms and 1000ms (Exp. 2a - 2c, 3). Importantly, ambiguous images were nonetheless rated significantly harder to process as non-ambiguous images. These results suggest that ambiguity is an essential ingredient in art appreciation even though or maybe because it is harder to process. PMID:24040172

Application of otolith shape analysis for stock discrimination and species identification of five goby species (Perciformes: Gobiidae) in the northern Chinese coastal waters

NASA Astrophysics Data System (ADS)

Yu, Xin; Cao, Liang; Liu, Jinhu; Zhao, Bo; Shan, Xiujuan; Dou, Shuozeng

2014-09-01

We tested the use of otolith shape analysis to discriminate between species and stocks of five goby species ( Ctenotrypauchen chinensis, Odontamblyopus lacepedii, Amblychaeturichthys hexanema, Chaeturichthys stigmatias, and Acanthogobius hasta) found in northern Chinese coastal waters. The five species were well differentiated with high overall classification success using shape indices (83.7%), elliptic Fourier coefficients (98.6%), or the combination of both methods (94.9%). However, shape analysis alone was only moderately successful at discriminating among the four stocks (Liaodong Bay, LD; Bohai Bay, BH; Huanghe (Yellow) River estuary HRE, and Jiaozhou Bay, JZ stocks) of A. hasta (50%-54%) and C. stigmatias (65.7%-75.8%). For these two species, shape analysis was moderately successful at discriminating the HRE or JZ stocks from other stocks, but failed to effectively identify the LD and BH stocks. A large number of otoliths were misclassified between the HRE and JZ stocks, which are geographically well separated. The classification success for stock discrimination was higher using elliptic Fourier coefficients alone (70.2%) or in combination with shape indices (75.8%) than using only shape indices (65.7%) in C. stigmatias whereas there was little difference among the three methods for A. hasta. Our results supported the common belief that otolith shape analysis is generally more effective for interspecific identification than intraspecific discrimination. Moreover, compared with shape indices analysis, Fourier analysis improves classification success during inter- and intra-species discrimination by otolith shape analysis, although this did not necessarily always occur in all fish species.

Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

PubMed

Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

2014-12-24

This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

Classification System for Individualized Treatment of Adult Buried Penis Syndrome.

PubMed

Tausch, Timothy J; Tachibana, Isamu; Siegel, Jordan A; Hoxworth, Ronald; Scott, Jeremy M; Morey, Allen F

2016-09-01

The authors present their experience with reconstructive strategies for men with various manifestations of adult buried penis syndrome, and propose a comprehensive anatomical classification system and treatment algorithm based on pathologic changes in the penile skin and involvement of neighboring abdominal and/or scrotal components. The authors reviewed all patients who underwent reconstruction of adult buried penis syndrome at their referral center between 2007 and 2015. Patients were stratified by location and severity of involved anatomical components. Procedures performed, demographics, comorbidities, and clinical outcomes were reviewed. Fifty-six patients underwent reconstruction of buried penis at the authors' center from 2007 to 2015. All procedures began with a ventral penile release. If the uncovered penile skin was determined to be viable, a phalloplasty was performed by anchoring penoscrotal skin to the proximal shaft, and the ventral shaft skin defect was closed with scrotal flaps. In more complex patients with circumferential nonviable penile skin, the penile skin was completely excised and replaced with a split-thickness skin graft. Complex patients with severe abdominal lipodystrophy required adjacent tissue transfer. For cases of genital lymphedema, the procedure involved complete excision of the lymphedematous tissue, and primary closure with or without a split-thickness skin graft, also often involving the scrotum. The authors' overall success rate was 88 percent (49 of 56), defined as resolution of symptoms without the need for additional procedures. Successful correction of adult buried penis often necessitates an interdisciplinary, multimodal approach. Therapeutic, IV.

Finger-specific loss of independent control of movements in musicians with focal dystonia.

PubMed

Furuya, S; Altenmüller, E

2013-09-05

The loss of independent control of finger movements impairs the dexterous use of the hand. Focal hand dystonia is characterised by abnormal structural and functional changes at the cortical and subcortical regions responsible for individuated finger movements and by the loss of surround inhibition in the finger muscles. However, little is known about the pathophysiological impact of focal dystonia on the independent control of finger movements. Here we addressed this issue by asking pianists with and without focal dystonia to repetitively strike a piano key with one of the four fingers as fast as possible while the remaining digits kept the adjacent keys depressed. Using principal component analysis and cluster analysis to the derived keystroke data, we successfully classified pianists according to the presence or absence of dystonic symptoms with classification rates and cross-validation scores of approximately 90%. This confirmed the effects of focal dystonia on the individuated finger movements. Interestingly, the movement features that contributed to successful classification differed across fingers. Compared to healthy pianists, pianists with an affected index finger were characterised predominantly by stronger keystrokes, whereas pianists with affected middle or ring fingers exhibited abnormal temporal control of the keystrokes, such as slowness and rhythmic inconsistency. The selective alternation of the movement features indicates a finger-specific loss of the independent control of finger movements in focal dystonia of musicians. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Classification of Dual-Wavelength Airborne Laser Scanning Point Cloud Based on the Radiometric Properties of the Objects

NASA Astrophysics Data System (ADS)

Pilarska, M.

2018-05-01

Airborne laser scanning (ALS) is a well-known and willingly used technology. One of the advantages of this technology is primarily its fast and accurate data registration. In recent years ALS is continuously developed. One of the latest achievements is multispectral ALS, which consists in obtaining simultaneously the data in more than one laser wavelength. In this article the results of the dual-wavelength ALS data classification are presented. The data were acquired with RIEGL VQ-1560i sensor, which is equipped with two laser scanners operating in different wavelengths: 532 nm and 1064 nm. Two classification approaches are presented in the article: classification, which is based on geometric relationships between points and classification, which mostly relies on the radiometric properties of registered objects. The overall accuracy of the geometric classification was 86 %, whereas for the radiometric classification it was 81 %. As a result, it can be assumed that the radiometric features which are provided by the multispectral ALS have potential to be successfully used in ALS point cloud classification.

A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance

ERIC Educational Resources Information Center

Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui

2015-01-01

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…

Defining Characteristics of Diagnostic Classification Models and the Problem of Retrofitting in Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Gierl, Mark J.; Cui, Ying

2008-01-01

One promising application of diagnostic classification models (DCM) is in the area of cognitive diagnostic assessment in education. However, the successful application of DCM in educational testing will likely come with a price--and this price may be in the form of new test development procedures and practices required to yield data that satisfy…

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

PubMed Central

Morison, Gordon; Boreham, Philip

2018-01-01

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. PMID:29385030

A novel deep learning approach for classification of EEG motor imagery signals.

PubMed

Tabar, Yousef Rezaei; Halici, Ugur

2017-02-01

Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon's entropy models

NASA Astrophysics Data System (ADS)

Al-Abadi, Alaa M.

2017-05-01

In recent years, delineation of groundwater productivity zones plays an increasingly important role in sustainable management of groundwater resource throughout the world. In this study, groundwater productivity index of northeastern Wasit Governorate was delineated using probabilistic frequency ratio (FR) and Shannon's entropy models in framework of GIS. Eight factors believed to influence the groundwater occurrence in the study area were selected and used as the input data. These factors were elevation (m), slope angle (degree), geology, soil, aquifer transmissivity (m2/d), storativity (dimensionless), distance to river (m), and distance to faults (m). In the first step, borehole location inventory map consisting of 68 boreholes with relatively high yield (>8 l/sec) was prepared. 47 boreholes (70 %) were used as training data and the remaining 21 (30 %) were used for validation. The predictive capability of each model was determined using relative operating characteristic technique. The results of the analysis indicate that the FR model with a success rate of 87.4 % and prediction rate 86.9 % performed slightly better than Shannon's entropy model with success rate of 84.4 % and prediction rate of 82.4 %. The resultant groundwater productivity index was classified into five classes using natural break classification scheme: very low, low, moderate, high, and very high. The high-very high classes for FR and Shannon's entropy models occurred within 30 % (217 km2) and 31 % (220 km2), respectively indicating low productivity conditions of the aquifer system. From final results, both of the models were capable to prospect GWPI with very good results, but FR was better in terms of success and prediction rates. Results of this study could be helpful for better management of groundwater resources in the study area and give planners and decision makers an opportunity to prepare appropriate groundwater investment plans.

34 CFR 222.68 - What tax rates does the Secretary use if two or more different classifications of real property...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 34 Education 1 2010-07-01 2010-07-01 false What tax rates does the Secretary use if two or more different classifications of real property are taxed at different rates? 222.68 Section 222.68 Education Regulations of the Offices of the Department of Education OFFICE OF ELEMENTARY AND SECONDARY EDUCATION...

Caesarean Section in Peru: Analysis of Trends Using the Robson Classification System

PubMed Central

2016-01-01

Introduction Cesarean section rates continue to increase worldwide while the reasons appear to be multiple, complex and, in many cases, country specific. Over the last decades, several classification systems for caesarean section have been created and proposed to monitor and compare caesarean section rates in a standardized, reliable, consistent and action-oriented manner with the aim to understand the drivers and contributors of this trend. The aims of the present study were to conduct an analysis in the three Peruvian geographical regions to assess levels and trends of delivery by caesarean section using the Robson classification for caesarean section, identify the groups of women with highest caesarean section rates and assess variation of maternal and perinatal outcomes according to caesarean section levels in each group over time. Material and Methods Data from 549,681 pregnant women included in the Peruvian Perinatal Information System database from 43 maternal facilities in three Peruvian geographical regions from 2000 and 2010 were studied. The data were analyzed using the Robson classification and women were studied in the ten groups in the classification. Cochran-Armitage test was used to evaluate time trends in the rates of caesarean section rates and; logistic regression was used to evaluate risk for each classification. Results The caesarean section rate was 27% and a yearly increase in the overall caesarean section rates from 2000 to 2010 from 23.5% to 30% (time trend p<0.001) was observed. Robson groups 1, 3 (nulliparous and multiparas, respectively, with a single cephalic term pregnancy in spontaneous labour), 5 (multiparas with a previous uterine scar with a single, cephalic, term pregnancy) and 7 (multiparas with a single breech pregnancy with or without previous scars) showed an increase in the caesarean section rates over time. Robson groups 1 and 3 were significantly associated with stillbirths (OR 1.43, CI95% 1.17–1.72; OR 3.53, CI95% 2.95–4.2) and maternal mortality (OR 3.39, CI95% 1.59–7.22; OR 8.05, CI95% 3.34–19.41). Discussion The caesarean section rates increased in the last years as result of increased CS in groups with spontaneous labor and in-group of multiparas with a scarred uterus. Women included in groups 1 y 3 were associated to maternal perinatal complications. Women with previous cesarean section constitute the most important determinant of overall cesarean section rates. The use of Robson classification becomes an useful tool for monitoring cesarean section in low human development index countries. PMID:26840693

A method of assigning socio-economic status classification to British Armed Forces personnel.

PubMed

Yoong, S Y; Miles, D; McKinney, P A; Smith, I J; Spencer, N J

1999-10-01

The objective of this paper was to develop and evaluate a socio-economic status classification method for British Armed Forces personnel. Two study groups comprising of civilian and Armed Forces families were identified from livebirths delivered between 1 January-30 June 1996 within the Northallerton Health district which includes Catterick Garrison and RAF Leeming. The participants were the parents of babies delivered at a District General Hospital, comprising of 436 civilian and 162 Armed Forces families. A new classification method was successfully used to assign Registrar General's social classification to Armed Forces personnel. Comparison of the two study groups showed a significant difference in social class distribution (p = 0.0001). This study has devised a new method for classifying occupations within the Armed Forces to categories of social class thus permitting comparison with Registrar General's classification.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

PubMed

Boudissa, M; Orfeuvre, B; Chabanas, M; Tonetti, J

2017-09-01

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi 2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. III: prospective case-control study of a diagnostic procedure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

PubMed

Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

2018-01-22

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. © 2018 Society for Conservation Biology.

The new Epstein gleason score classification significantly reduces upgrading in prostate cancer patients.

PubMed

De Nunzio, Cosimo; Pastore, Antonio Luigi; Lombardo, Riccardo; Simone, Giuseppe; Leonardo, Costantino; Mastroianni, Riccardo; Collura, Devis; Muto, Giovanni; Gallucci, Michele; Carbone, Antonio; Fuschi, Andrea; Dutto, Lorenzo; Witt, Joern Heinrich; De Dominicis, Carlo; Tubaro, Andrea

2018-06-01

To evaluate the differences between the old and the new Gleason score classification systems in upgrading and downgrading rates. Between 2012 and 2015, we identified 9703 patients treated with retropubic radical prostatectomy (RP) in four tertiary centers. Biopsy specimens as well as radical prostatectomy specimens were graded according to both 2005 Gleason and 2014 ISUP five-tier Gleason grading system (five-tier GG system). Upgrading and downgrading rates on radical prostatectomy were first recorded for both classifications and then compared. The accuracy of the biopsy for each histological classification was determined by using the kappa coefficient of agreement and by assessing sensitivity, specificity, positive and negative predictive value. The five-tier GG system presented a lower clinically significant upgrading rate (1895/9703: 19,5% vs 2332/9703:24.0%; p = .001) and a similar clinically significant downgrading rate (756/9703: 7,7% vs 779/9703: 8%; p = .267) when compared to the 2005 ISUP classification. When evaluating their accuracy, the new five-tier GG system presented a better specificity (91% vs 83%) and a better negative predictive value (78% vs 60%). The kappa-statistics measures of agreement between needle biopsy and radical prostatectomy specimens were poor and good respectively for the five-tier GG system and for the 2005 Gleason score (k = 0.360 ± 0.007 vs k = 0.426 ± 0.007). The new Epstein classification significantly reduces upgrading events. The implementation of this new classification could better define prostate cancer aggressiveness with important clinical implications, particularly in prostate cancer management. Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Monitoring tropical vegetation succession with LANDSAT data

NASA Technical Reports Server (NTRS)

Robinson, V. B. (Principal Investigator)

1983-01-01

The shadowing problem, which is endemic to the use of LANDSAT in tropical areas, and the ability to model changes over space and through time are problems to be addressed when monitoring tropical vegetation succession. Application of a trend surface analysis model to major land cover classes in a mountainous region of the Phillipines shows that the spatial modeling of radiance values can provide a useful approach to tropical rain forest succession monitoring. Results indicate shadowing effects may be due primarily to local variations in the spectral responses. These variations can be compensated for through the decomposition of the spatial variation in both elevation and MSS data. Using the model to estimate both elevation and spectral terrain surface as a posteriori inputs in the classification process leads to improved classification accuracy for vegetation of cover of this type. Spatial patterns depicted by the MSS data reflect the measurement of responses to spatial processes acting at several scales.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

How Well Do Molecular and Pedigree Relatedness Correspond, in Populations with Diverse Mating Systems, and Various Types and Quantities of Molecular and Demographic Data?

PubMed

Kopps, Anna M; Kang, Jungkoo; Sherwin, William B; Palsbøll, Per J

2015-06-30

Kinship analyses are important pillars of ecological and conservation genetic studies with potentially far-reaching implications. There is a need for power analyses that address a range of possible relationships. Nevertheless, such analyses are rarely applied, and studies that use genetic-data-based-kinship inference often ignore the influence of intrinsic population characteristics. We investigated 11 questions regarding the correct classification rate of dyads to relatedness categories (relatedness category assignments; RCA) using an individual-based model with realistic life history parameters. We investigated the effects of the number of genetic markers; marker type (microsatellite, single nucleotide polymorphism SNP, or both); minor allele frequency; typing error; mating system; and the number of overlapping generations under different demographic conditions. We found that (i) an increasing number of genetic markers increased the correct classification rate of the RCA so that up to >80% first cousins can be correctly assigned; (ii) the minimum number of genetic markers required for assignments with 80 and 95% correct classifications differed between relatedness categories, mating systems, and the number of overlapping generations; (iii) the correct classification rate was improved by adding additional relatedness categories and age and mitochondrial DNA data; and (iv) a combination of microsatellite and single-nucleotide polymorphism data increased the correct classification rate if <800 SNP loci were available. This study shows how intrinsic population characteristics, such as mating system and the number of overlapping generations, life history traits, and genetic marker characteristics, can influence the correct classification rate of an RCA study. Therefore, species-specific power analyses are essential for empirical studies. Copyright © 2015 Kopps et al.

An extension of the receiver operating characteristic curve and AUC-optimal classification.

PubMed

Takenouchi, Takashi; Komori, Osamu; Eguchi, Shinto

2012-10-01

While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

An Experimental Study of the Placement of Classification Skills in the Science-A Process Approach Curriculum Employing Piaget's Theory of Cognitive Development.

ERIC Educational Resources Information Center

Johnson, Theodore M.; Alfke, Dorothy

The purpose of this study was to investigate whether success in the Science - A Process Approach (SAPA) process of classification designed for primary grade children is contingent upon the children's developmental level as defined by Piaget's theory. The investigators sought to determine whether children who had reached the concrete operational…

Classification of complementary and alternative medical practices: Family physicians' ratings of effectiveness.

PubMed

Fries, Christopher J

2008-11-01

ABSTRACTOBJECTIVETo develop a classification of complementary and alternative medicine (CAM) practices widely available in Canada based on physicians' effectiveness ratings of the therapies.DESIGNA self-administered postal questionnaire asking family physicians to rate their "belief in the degree of therapeutic effectiveness" of 15 CAM therapies.SETTINGProvince of Alberta.PARTICIPANTSA total of 875 family physicians.MAIN OUTCOME MEASURESDescriptive statistics of physicians' awareness of and effectiveness ratings for each of the therapies; factor analysis was applied to the ratings of the 15 therapies in order to explore whether or not the data support the proposed classification of CAM practices into categories of accepted and rejected.RESULTSPhysicians believed that acupuncture, massage therapy, chiropractic care, relaxation therapy, biofeedback, and spiritual or religious healing were effective when used in conjunction with biomedicine to treat chronic or psychosomatic indications. Physicians attributed little effectiveness to homeopathy or naturopathy, Feldenkrais or Alexander technique, Rolfing, herbal medicine, traditional Chinese medicine, and reflexology. The factor analysis revealed an underlying dimensionality to physicians' effectiveness ratings of the CAM therapies that supports the classification of these practices as either accepted or rejected.CONCLUSIONThis study provides Canadian family physicians with information concerning which CAM therapies are generally accepted by their peers as effective and which are not.

[Electromagnetic navigation interlocking intramedullary nail technology for treatment of femoral shaft fractures].

PubMed

Zuo, Kangkang; Qin, Wei; Guo, Qing; Palati-Ababaikeli; Qiao, Peiliu; Shen, Mingquan; Yin, Lele; Pan, Qilin; Xu, Xiaoxiong

2014-10-01

To explore the value of electromagnetic navigation interlocking intramedullary nail in the treatment of femoral shaft fracture. Between July 2012 and October 2013, 53 cases of femoral shaft fracture were treated. There were 40 males and 13 females, aged 16-52 years (mean, 38.3 years). The causes of injury were traffic accident in 28 cases, falling from height in 11 cases, falling in 7 cases, crush injury in 4 cases, and other in 3 cases. Of 53 cases, there were 3 cases of open fracture (Gustilo I degree) and 50 cases of closed fracture. Fracture was located in the proximal femur in 17 cases, middle femur in 29 cases, and distal femur in 7 cases. According to Winquist classification, 7 cases were rated as type I, 8 cases as type II, 22 cases as type III, and 16 cases as type IV; according to AO classification, 18 cases were rated as type 32-A, 28 cases as type 32-B, and 7 cases as type 32-C. The time from injury to operation was 3-11 days (mean, 5 days). Distal interlocking intramedullary nail was implanted using electromagnetic navigation. The distal locking nail operation with interlocking intramedullary nail was successfully completed under electromagnetic navigation; the one-time success rate of distal locking nail operation reached 100%; and the locking nail time was 5.0-9.5 minutes (mean, 7.0 minutes). Healing of incision by first intention was obtained after operation, and no complication of skin necrosis, infection, and sinus tract occurred. Fifty-three cases were all followed up 5-12 months (mean, 9 months). One case had hip pain and weaken middle gluteal muscle strength, and the symptoms disappeared after removing the nail. During the follow-up period, no broken nails, nail exit, infection, or re-fracture occurred. All fractures achieved clinical healing, and the healing time was 8-22 weeks (mean, 14.5 weeks). In 49 patients followed up 8 months, the Lysholm score was excellent in 44 cases, good in 4 cases, and acceptable in 1 case, with an excellent and good rate of 98%. Electromagnetic navigation system is safe and reliable, with the advantages of high positioning accuracy, short operation time, and no radiation, the clinical application of the system for distal locking nail operation can obtain excellent short-term effectiveness.

The Rural Inpatient Mortality Study: Does Urban-Rural County Classification Predict Hospital Mortality in California?

PubMed

Linnen, Daniel T; Kornak, John; Stephens, Caroline

2018-03-28

Evidence suggests an association between rurality and decreased life expectancy. To determine whether rural hospitals have higher hospital mortality, given that very sick patients may be transferred to regional hospitals. In this ecologic study, we combined Medicare hospital mortality ratings (N = 1267) with US census data, critical access hospital classification, and National Center for Health Statistics urban-rural county classifications. Ratings included mortality for coronary artery bypass grafting, stroke, chronic obstructive pulmonary disease, heart attack, heart failure, and pneumonia across 277 California hospitals between July 2011 and June 2014. We used generalized estimating equations to evaluate the association of urban-rural county classifications on mortality ratings. Unfavorable Medicare hospital mortality rating "worse than the national rate" compared with "better" or "same." Compared with large central "metro" (metropolitan) counties, hospitals in medium-sized metro counties had 6.4 times the odds of rating "worse than the national rate" for hospital mortality (95% confidence interval = 2.8-14.8, p < 0.001). For hospitals in small metro counties, the odds of having such a rating were 3.7 times greater (95% confidence interval = 0.7-23.4, p = 0.12), although not statistically significant. Few ratings were provided for rural counties, and analysis of rural counties was underpowered. Hospitals in medium-sized metro counties are associated with unfavorable Medicare mortality ratings, but current methods to assign mortality ratings may hinder fair comparisons. Patient transfers from rural locations to regional medical centers may contribute to these results, a potential factor that future research should examine.

Gas Classification Using Deep Convolutional Neural Networks.

PubMed

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Gas Classification Using Deep Convolutional Neural Networks

PubMed Central

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Statistical sensor fusion of ECG data using automotive-grade sensors

NASA Astrophysics Data System (ADS)

Koenig, A.; Rehg, T.; Rasshofer, R.

2015-11-01

Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

29 CFR 697.2 - Industry wage rates and effective dates.

Code of Federal Regulations, 2011 CFR

2011-07-01

... of goods for commerce, as these terms are defined in section 3 of the Fair Labor Standards Act of... classifications in which such employee is engaged. Industry Minimum wage EffectiveOctober 3, 2005 EffectiveOctober...) Classification A 4.09 4.09 4.09 (2) Classification B 3.92 3.92 3.92 (3) Classification C 3.88 3.88 3.88 (e...

Development and application of operational techniques for the inventory and monitoring of resources and uses for the Texas coastal zone

NASA Technical Reports Server (NTRS)

Harwood, P. (Principal Investigator); Finley, R.; Mcculloch, S.; Marphy, D.; Hupp, B.

1976-01-01

The author has identified the following significant results. Image interpretation mapping techniques were successfully applied to test site 5, an area with a semi-arid climate. The land cover/land use classification required further modification. A new program, HGROUP, added to the ADP classification schedule provides a convenient method for examining the spectral similarity between classes. This capability greatly simplifies the task of combining 25-30 unsupervised subclasses into about 15 major classes that approximately correspond to the land use/land cover classification scheme.

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

PubMed Central

Aguilera, Teodoro; Lozano, Jesús; Paredes, José A.; Álvarez, Fernando J.; Suárez, José I.

2012-01-01

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. PMID:22969387

Classification Accuracy and Acceptability of the Integrated Screening and Intervention System Teacher Rating Form

ERIC Educational Resources Information Center

Daniels, Brian; Volpe, Robert J.; Fabiano, Gregory A.; Briesch, Amy M.

2017-01-01

This study examines the classification accuracy and teacher acceptability of a problem-focused screener for academic and disruptive behavior problems, which is directly linked to evidence-based intervention. Participants included 39 classroom teachers from 2 public school districts in the Northeastern United States. Teacher ratings were obtained…

An Examination of the Changing Rates of Autism in Special Education

ERIC Educational Resources Information Center

Brock, Stephen E.

2006-01-01

Using U.S. Department of Education data, the current study examined changes in the rates of special education eligibility classifications. This was done to determine if classification substitution might be an explanation for increases in the number of students being found eligible for special education using the Autism criteria. Results reveal…

Thermographic image analysis as a pre-screening tool for the detection of canine bone cancer

NASA Astrophysics Data System (ADS)

Subedi, Samrat; Umbaugh, Scott E.; Fu, Jiyuan; Marino, Dominic J.; Loughin, Catherine A.; Sackman, Joseph

2014-09-01

Canine bone cancer is a common type of cancer that grows fast and may be fatal. It usually appears in the limbs which is called "appendicular bone cancer." Diagnostic imaging methods such as X-rays, computed tomography (CT scan), and magnetic resonance imaging (MRI) are more common methods in bone cancer detection than invasive physical examination such as biopsy. These imaging methods have some disadvantages; including high expense, high dose of radiation, and keeping the patient (canine) motionless during the imaging procedures. This project study identifies the possibility of using thermographic images as a pre-screening tool for diagnosis of bone cancer in dogs. Experiments were performed with thermographic images from 40 dogs exhibiting the disease bone cancer. Experiments were performed with color normalization using temperature data provided by the Long Island Veterinary Specialists. The images were first divided into four groups according to body parts (Elbow/Knee, Full Limb, Shoulder/Hip and Wrist). Each of the groups was then further divided into three sub-groups according to views (Anterior, Lateral and Posterior). Thermographic pattern of normal and abnormal dogs were analyzed using feature extraction and pattern classification tools. Texture features, spectral feature and histogram features were extracted from the thermograms and were used for pattern classification. The best classification success rate in canine bone cancer detection is 90% with sensitivity of 100% and specificity of 80% produced by anterior view of full-limb region with nearest neighbor classification method and normRGB-lum color normalization method. Our results show that it is possible to use thermographic imaging as a pre-screening tool for detection of canine bone cancer.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

NASA Technical Reports Server (NTRS)

Instrella, Ron; Chirayath, Ved

2016-01-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

NASA Astrophysics Data System (ADS)

Instrella, R.; Chirayath, V.

2015-12-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Probability of pregnancy after sterilization: a comparison of hysteroscopic versus laparoscopic sterilization.

PubMed

Gariepy, Aileen M; Creinin, Mitchell D; Smith, Kenneth J; Xu, Xiao

2014-08-01

To compare the expected probability of pregnancy after hysteroscopic versus laparoscopic sterilization based on available data using decision analysis. We developed an evidence-based Markov model to estimate the probability of pregnancy over 10 years after three different female sterilization procedures: hysteroscopic, laparoscopic silicone rubber band application and laparoscopic bipolar coagulation. Parameter estimates for procedure success, probability of completing follow-up testing and risk of pregnancy after different sterilization procedures were obtained from published sources. In the base case analysis at all points in time after the sterilization procedure, the initial and cumulative risk of pregnancy after sterilization is higher in women opting for hysteroscopic than either laparoscopic band or bipolar sterilization. The expected pregnancy rates per 1000 women at 1 year are 57, 7 and 3 for hysteroscopic sterilization, laparoscopic silicone rubber band application and laparoscopic bipolar coagulation, respectively. At 10 years, the cumulative pregnancy rates per 1000 women are 96, 24 and 30, respectively. Sensitivity analyses suggest that the three procedures would have an equivalent pregnancy risk of approximately 80 per 1000 women at 10 years if the probability of successful laparoscopic (band or bipolar) sterilization drops below 90% and successful coil placement on first hysteroscopic attempt increases to 98% or if the probability of undergoing a hysterosalpingogram increases to 100%. Based on available data, the expected population risk of pregnancy is higher after hysteroscopic than laparoscopic sterilization. Consistent with existing contraceptive classification, future characterization of hysteroscopic sterilization should distinguish "perfect" and "typical" use failure rates. Pregnancy probability at 1 year and over 10 years is expected to be higher in women having hysteroscopic as compared to laparoscopic sterilization. Copyright © 2014 Elsevier Inc. All rights reserved.

Variability of undetermined manner of death classification in the US.

PubMed

Breiding, M J; Wiersema, B

2006-12-01

To better understand variations in classification of deaths of undetermined intent among states in the National Violent Death Reporting System (NVDRS). Data from the NVDRS and the National Vital Statistics System were used to compare differences among states. Percentages of deaths assigned undetermined intent, rates of deaths of undetermined intent, rates of fatal poisonings broken down by cause of death, composition of poison types within the undetermined-intent classification. Three states within NVDRS (Maryland, Massachusetts, and Rhode Island) evidenced increased numbers of deaths of undetermined intent. These same states exhibited high rates of undetermined death and, more specifically, high rates of undetermined poisoning deaths. Further, these three states evidenced correspondingly lower rates of unintentional poisonings. The types of undetermined poisonings present in these states, but not present in other states, are typically the result of a combination of recreational drugs, alcohol, or prescription drugs. The differing classification among states of many poisoning deaths has implications for the analysis of undetermined deaths within the NVDRS and for the examination of possible/probable suicides contained within the undetermined- or accidental-intent classifications. The NVDRS does not collect information on unintentional poisonings, so in most states data are not collected on these possible/probable suicides. The authors believe this is an opportunity missed to understand the full range of self-harm deaths in the greater detail provided by the NVDRS system. They advocate a broader interpretation of suicide to include the full continuum of deaths resulting from self-harm.

Characteristics of successful family-care parents.

PubMed

Sanderson, H W; Crawley, M

1982-03-01

Characteristics of successful and unsuccessful family-care parents who served mentally retarded/developmentally disabled children and adults residing in a county in upstate New York were examined. Location (urban vs. rural) did not significantly discriminate successful and unsuccessful homes. Successful family caretakers were, however, generally older and more frequently were active in Protestant churches. When examined from the perspective of Holland's vocational orientation, successful woman caretakers most often came from the Conventional category, whereas men most often fit Holland's Realistic classification. Implications of these findings for family-care provider recruitment were discussed.

40 CFR 86.085-20 - Incomplete vehicles, classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., classification. (a) An incomplete truck less than 8,500 pounds gross vehicle weight rating shall be classified by... 40 Protection of Environment 18 2010-07-01 2010-07-01 false Incomplete vehicles, classification... PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES General...

Conifer health classification for Colorado, 2008

USGS Publications Warehouse

Cole, Christopher J.; Noble, Suzanne M.; Blauer, Steven L.; Friesen, Beverly A.; Curry, Stacy E.; Bauer, Mark A.

2010-01-01

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Classification of Indonesian quote on Twitter using Naïve Bayes

NASA Astrophysics Data System (ADS)

Rachmadany, A.; Pranoto, Y. M.; Gunawan; Multazam, M. T.; Nandiyanto, A. B. D.; Abdullah, A. G.; Widiaty, I.

2018-01-01

Quote is sentences made in the hope that someone can become strong personalities, individuals who always improve themselves to move forward and achieve success. Social media is a place for people to express his heart to the world that sometimes the expression of the heart is quotes. Here, the purpose of this study was to classify Indonesian quote on Twitter using Naïve Bayes. This experiment uses text classification from Twitter data written by Twitter users which are quote then classification again grouped into 6 categories (Love, Life, Motivation, Education, Religion, Others). The language used is Indonesian. The method used is Naive Bayes. The results of this experiment are a web application collection of Indonesian quote that have been classified. This classification gives the user ease in finding quote based on class or keyword. For example, when a user wants to find a 'motivation' quote, this classification would be very useful.

[The establishment, development and application of classification approach of freshwater phytoplankton based on the functional group: a review].

PubMed

Yang, Wen; Zhu, Jin-Yong; Lu, Kai-Hong; Wan, Li; Mao, Xiao-Hua

2014-06-01

Appropriate schemes for classification of freshwater phytoplankton are prerequisites and important tools for revealing phytoplanktonic succession and studying freshwater ecosystems. An alternative approach, functional group of freshwater phytoplankton, has been proposed and developed due to the deficiencies of Linnaean and molecular identification in ecological applications. The functional group of phytoplankton is a classification scheme based on autoecology. In this study, the theoretical basis and classification criterion of functional group (FG), morpho-functional group (MFG) and morphology-based functional group (MBFG) were summarized, as well as their merits and demerits. FG was considered as the optimal classification approach for the aquatic ecology research and aquatic environment evaluation. The application status of FG was introduced, with the evaluation standards and problems of two approaches to assess water quality on the basis of FG, index methods of Q and QR, being briefly discussed.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

On Classification in the Study of Failure, and a Challenge to Classifiers

NASA Technical Reports Server (NTRS)

Wasson, Kimberly S.

2003-01-01

Classification schemes are abundant in the literature of failure. They serve a number of purposes, some more successfully than others. We examine several classification schemes constructed for various purposes relating to failure and its investigation, and discuss their values and limits. The analysis results in a continuum of uses for classification schemes, that suggests that the value of certain properties of these schemes is dependent on the goals a classification is designed to forward. The contrast in the value of different properties for different uses highlights a particular shortcoming: we argue that while humans are good at developing one kind of scheme: dynamic, flexible classifications used for exploratory purposes, we are not so good at developing another: static, rigid classifications used to trap and organize data for specific analytic goals. Our lack of strong foundation in developing valid instantiations of the latter impedes progress toward a number of investigative goals. This shortcoming and its consequences pose a challenge to researchers in the study of failure: to develop new methods for constructing and validating static classification schemes of demonstrable value in promoting the goals of investigations. We note current productive activity in this area, and outline foundations for more.

Improving the Selection, Classification, and Utilization of Army Enlisted Personnel. Project A

DTIC Science & Technology

1987-06-01

performance measures, to determine whether the new predictors have incremental validity over and above the present system. These two components must be...critical aspect of this task is the demonstration of the incremental validity added by new predictors. Task 3. Measurement of School/Training Success...chances of incremental validity and classification efficiency. 3. Retain measures with adequate reliability. Using all accumulated information, the final

A new classification system for congenital laryngeal cysts.

PubMed

Forte, Vito; Fuoco, Gabriel; James, Adrian

2004-06-01

A new classification system for congenital laryngeal cysts based on the extent of the cyst and on the embryologic tissue of origin is proposed. Retrospective chart review. The charts of 20 patients with either congenital or acquired laryngeal cysts that were treated surgically between 1987 and 2002 at the Hospital for Sick Children, Toronto were retrospectively reviewed. Clinical presentation, radiologic findings, surgical management, histopathology, and outcome were recorded. A new classification system is proposed to better appreciate the origin of these cysts and to guide in their successful surgical management. Fourteen of the supraglottic and subglottic simple mucous retention cysts posed no diagnostic or therapeutic challenge and were treated successfully by a single endoscopic excision or marsupialization. The remaining six patients with congenital cysts in the study were deemed more complex, and all required open surgical procedures for cure. On the basis of the analysis of the data of these patients, a new classification of congenital laryngeal cysts is proposed. Type I cysts are confined to the larynx, the cyst wall composed of endodermal elements only, and can be managed endoscopically. Type II cysts extend beyond the confines of the larynx and require an external approach. The Type II cysts are further subclassified histologically on the basis of the embryologic tissue of origin: IIa, composed of endoderm only and IIb, containing endodermal and mesodermal elements (epithelium and cartilage) in the wall of the cyst. A new classification system for congenital laryngeal cysts is proposed on the basis of the extent of the cyst and the embryologic tissue of origin. This classification can help guide the surgeon with initial management and help us better understand the origin of these cysts.

Development of a database of health insurance claims: standardization of disease classifications and anonymous record linkage.

PubMed

Kimura, Shinya; Sato, Toshihiko; Ikeda, Shunya; Noda, Mitsuhiko; Nakayama, Takeo

2010-01-01

Health insurance claims (ie, receipts) record patient health care treatments and expenses and, although created for the health care payment system, are potentially useful for research. Combining different types of receipts generated for the same patient would dramatically increase the utility of these receipts. However, technical problems, including standardization of disease names and classifications, and anonymous linkage of individual receipts, must be addressed. In collaboration with health insurance societies, all information from receipts (inpatient, outpatient, and pharmacy) was collected. To standardize disease names and classifications, we developed a computer-aided post-entry standardization method using a disease name dictionary based on International Classification of Diseases (ICD)-10 classifications. We also developed an anonymous linkage system by using an encryption code generated from a combination of hash values and stream ciphers. Using different sets of the original data (data set 1: insurance certificate number, name, and sex; data set 2: insurance certificate number, date of birth, and relationship status), we compared the percentage of successful record matches obtained by using data set 1 to generate key codes with the percentage obtained when both data sets were used. The dictionary's automatic conversion of disease names successfully standardized 98.1% of approximately 2 million new receipts entered into the database. The percentage of anonymous matches was higher for the combined data sets (98.0%) than for data set 1 (88.5%). The use of standardized disease classifications and anonymous record linkage substantially contributed to the construction of a large, chronologically organized database of receipts. This database is expected to aid in epidemiologic and health services research using receipt information.

U.S. Fish and Wildlife Service 1979 wetland classification: a review

USGS Publications Warehouse

Cowardin, L.M.; Golet, F.C.

1995-01-01

In 1979 the US Fish and Wildlife Service published and adopted a classification of wetlands and deepwater habitats of the United States. The system was designed for use in a national inventory of wetlands. It was intended to be ecologically based, to furnish the mapping units needed for the inventory, and to provide national consistency in terminology and definition. We review the performance of the classification after 13 years of use. The definition of wetland is based on national lists of hydric soils and plants that occur in wetlands. Our experience suggests that wetland classifications must facilitate mapping and inventory because these data gathering functions are essential to management and preservation of the wetland resource, but the definitions and taxa must have ecological basis. The most serious problem faced in construction of the classification was lack of data for many of the diverse wetland types. Review of the performance of the classification suggests that, for the most part, it was successful in accomplishing its objectives, but that problem areas should be corrected and modification could strengthen its utility. The classification, at least in concept, could be applied outside the United States. Experience gained in use of the classification can furnish guidance as to pitfalls to be avoided in the wetland classification process.

Correlation-based pattern recognition for implantable defibrillators.

PubMed Central

Wilkins, J.

1996-01-01

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

Classification of protein quaternary structure by functional domain composition

PubMed Central

Yu, Xiaojing; Wang, Chuan; Li, Yixue

2006-01-01

Background The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences. Results To explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11% Conclusion Compared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics. PMID:16584572

Identification of fungal phytopathogens using Fourier transform infrared-attenuated total reflection spectroscopy and advanced statistical methods

NASA Astrophysics Data System (ADS)

Salman, Ahmad; Lapidot, Itshak; Pomerantz, Ami; Tsror, Leah; Shufan, Elad; Moreh, Raymond; Mordechai, Shaul; Huleihel, Mahmoud

2012-01-01

The early diagnosis of phytopathogens is of a great importance; it could save large economical losses due to crops damaged by fungal diseases, and prevent unnecessary soil fumigation or the use of fungicides and bactericides and thus prevent considerable environmental pollution. In this study, 18 isolates of three different fungi genera were investigated; six isolates of Colletotrichum coccodes, six isolates of Verticillium dahliae and six isolates of Fusarium oxysporum. Our main goal was to differentiate these fungi samples on the level of isolates, based on their infrared absorption spectra obtained using the Fourier transform infrared-attenuated total reflection (FTIR-ATR) sampling technique. Advanced statistical and mathematical methods: principal component analysis (PCA), linear discriminant analysis (LDA), and k-means were applied to the spectra after manipulation. Our results showed significant spectral differences between the various fungi genera examined. The use of k-means enabled classification between the genera with a 94.5% accuracy, whereas the use of PCA [3 principal components (PCs)] and LDA has achieved a 99.7% success rate. However, on the level of isolates, the best differentiation results were obtained using PCA (9 PCs) and LDA for the lower wavenumber region (800-1775 cm-1), with identification success rates of 87%, 85.5%, and 94.5% for Colletotrichum, Fusarium, and Verticillium strains, respectively.

Risk factors and classification of stillbirth in a Middle Eastern population: a retrospective study.

PubMed

Kunjachen Maducolil, Mariam; Abid, Hafsa; Lobo, Rachael Marian; Chughtai, Ambreen Qayyum; Afzal, Arjumand Muhammad; Saleh, Huda Abdullah Hussain; Lindow, Stephen W

2017-12-21

To estimate the incidence of stillbirth, explore the associated maternal and fetal factors and to evaluate the most appropriate classification of stillbirth for a multiethnic population. This is a retrospective population-based study of stillbirth in a large tertiary unit. Data of each stillbirth with a gestational age >/=24 weeks in the year 2015 were collected from electronic medical records and analyzed. The stillbirth rate for our multiethnic population is 7.81 per 1000 births. Maternal medical factors comprised 52.4% in which the rates of hypertensive disorders, diabetes and other medical disorders were 22.5%, 20.8% and 8.3%, respectively. The most common fetal factor was intrauterine growth restriction (IUGR) (22.5%) followed by congenital anomalies (21.6%). All cases were categorized using the Wigglesworth, Aberdeen, Tulip, ReCoDe and International Classification of Diseases-perinatal mortality (ICD-PM) classifications and the rates of unclassified stillbirths were 59.2%, 46.6%, 16.6%, 11.6% and 7.5%, respectively. An autopsy was performed in 9.1% of cases reflecting local religious and cultural sensitivities. This study highlighted the modifiable risk factors among the Middle Eastern population. The most appropriate classification was the ICD-PM. The low rates of autopsy prevented a detailed evaluation of stillbirths, therefore it is suggested that a minimally invasive autopsy [postmortem magnetic resonance imaging (MRI)] may improve the quality of care.

Treatment of acute periprosthetic infections with prosthesis retention: Review of current concepts.

PubMed

Kuiper, Jesse Wp; Willink, Robin Tjeenk; Moojen, Dirk Jan F; van den Bekerom, Michel Pj; Colen, Sascha

2014-11-18

Periprosthetic joint infection (PJI) is a devastating complication after total joint arthroplasty, occurring in approximately 1%-2% of all cases. With growing populations and increasing age, PJI will have a growing effect on health care costs. Many risk factors have been identified that increase the risk of developing PJI, including obesity, immune system deficiencies, malignancy, previous surgery of the same joint and longer operating time. Acute PJI occurs either postoperatively (4 wk to 3 mo after initial arthroplasty, depending on the classification system), or via hematogenous spreading after a period in which the prosthesis had functioned properly. Diagnosis and the choice of treatment are the cornerstones to success. Although different definitions for PJI have been used in the past, most are more or less similar and include the presence of a sinus tract, blood infection values, synovial white blood cell count, signs of infection on histopathological analysis and one or more positive culture results. Debridement, antibiotics and implant retention (DAIR) is the primary treatment for acute PJI, and should be performed as soon as possible after the development of symptoms. Success rates differ, but most studies report success rates of around 60%-80%. Whether single or multiple debridement procedures are more successful remains unclear. The use of local antibiotics in addition to the administration of systemic antibiotic agents is also subject to debate, and its pro's and con's should be carefully considered. Systemic treatment, based on culture results, is of importance for all PJI treatments. Additionally, rifampin should be given in Staphylococcal PJIs, unless all foreign material is removed. The most important factors contributing to treatment failure are longer duration of symptoms, a longer time after initial arthroplasty, the need for more debridement procedures, the retention of exchangeable components, and PJI caused by Staphylococcus (aureus or coagulase negative). If DAIR treatment is unsuccessful, the following treatment option should be based on the patient health status and his or her expectations. For the best functional outcome, one- or two-stage revision should be performed after DAIR failure. In conclusion, DAIR is the obvious choice for treatment of acute PJI, with good success rates in selected patients.

Site classification for National Strong Motion Observation Network System (NSMONS) stations in China using an empirical H/V spectral ratio method

NASA Astrophysics Data System (ADS)

Ji, Kun; Ren, Yefei; Wen, Ruizhi

2017-10-01

Reliable site classification of the stations of the China National Strong Motion Observation Network System (NSMONS) has not yet been assigned because of lacking borehole data. This study used an empirical horizontal-to-vertical (H/V) spectral ratio (hereafter, HVSR) site classification method to overcome this problem. First, according to their borehole data, stations selected from KiK-net in Japan were individually assigned a site class (CL-I, CL-II, or CL-III), which is defined in the Chinese seismic code. Then, the mean HVSR curve for each site class was computed using strong motion recordings captured during the period 1996-2012. These curves were compared with those proposed by Zhao et al. (2006a) for four types of site classes (SC-I, SC-II, SC-III, and SC-IV) defined in the Japanese seismic code (JRA, 1980). It was found that an approximate range of the predominant period Tg could be identified by the predominant peak of the HVSR curve for the CL-I and SC-I sites, CL-II and SC-II sites, and CL-III and SC-III + SC-IV sites. Second, an empirical site classification method was proposed based on comprehensive consideration of peak period, amplitude, and shape of the HVSR curve. The selected stations from KiK-net were classified using the proposed method. The results showed that the success rates of the proposed method in identifying CL-I, CL-II, and CL-III sites were 63%, 64%, and 58% respectively. Finally, the HVSRs of 178 NSMONS stations were computed based on recordings from 2007 to 2015 and the sites classified using the proposed method. The mean HVSR curves were re-calculated for three site classes and compared with those from KiK-net data. It was found that both the peak period and the amplitude were similar for the mean HVSR curves derived from NSMONS classification results and KiK-net borehole data, implying the effectiveness of the proposed method in identifying different site classes. The classification results have good agreement with site classes based on borehole data of 81 stations in China, which indicates that our site classification results are acceptable and that the proposed method is practicable.

Does body mass index effect the success of percutaneous nephrolithotomy?

PubMed Central

Şimşek, Abdülmuttalip; Özgör, Faruk; Akbulut, Mehmet Fatih; Küçüktopçu, Onur; Berberoğlu, Ahmet Yalçın; Sarılar, Ömer; Binbay, Murat; Müslümanoğlu, Ahmet Yaser

2014-01-01

Objective: In obese patients, the management of renal calculi presents a number of challenges for urologists. In this study, we aimed to evaluate the efficacy and safety of percutaneous nephrolithotomy (PNL) procedure in obese and morbidly obese patients. Material and methods: We retrospectively reviewed the medical files of 2360 patients treated with PNL between March 2002 and April 2013. The patients were stratified into four groups according to the World Health Organization (WHO) classification of body mass index (BMI): <25 kg/m2 (average), 25–29.9 kg/m2 (overweight), 30–39.9 kg/m2 (obese), and >40 kg/m2 (morbidly obese). Patients under 18 years of age and those with a body mass index under 18 kg/m2 were excluded from the study. Intra-, and postoperative outcomes of PNL were compared between groups. Results: A total of 2102 patients with a mean age of 43±13.62 years were enrolled in the study. The mean stone size, mean number of stones, staghorn stone rate and history of previous shock wave lithotripsy were similar in all groups. The overall stone-free rate was 82 percent. The mean operation time was longer in the morbidly obese group but it was not significantly different from that in the other groups. No differences were observed in hospital stay, complication or stone-free rate among four study groups. Conclusion: Percutaneous nephrolithotomy is a safe and effective treatment for renal stone disease. Body mass index does not affect the success or complication rate in PNL. PMID:26328160

Mexican Hat Wavelet Kernel ELM for Multiclass Classification.

PubMed

Wang, Jie; Song, Yi-Fan; Ma, Tian-Lei

2017-01-01

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

Comparative Analysis of RF Emission Based Fingerprinting Techniques for ZigBee Device Classification

DTIC Science & Technology

quantify the differences invarious RF fingerprinting techniques via comparative analysis of MDA/ML classification results. The findings herein demonstrate...correct classification rates followed by COR-DNA and then RF-DNA in most test cases and especially in low Eb/N0 ranges, where ZigBee is designed to operate.

78 FR 15377 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Requests To...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-11

... for OMB Review; Comment Request; Requests To Approve Conformed Wage Classifications and Unconventional... Classifications and Unconventional Fringe Benefit Plans Under the Davis-Bacon and Related Acts and Contract Work... collection consist of: (A) Reports of conformed classifications and wage rates and (B) requests for approval...

48 CFR 52.222-6 - Davis-Bacon Act.

Code of Federal Regulations, 2011 CFR

2011-10-01

... defined in paragraph (a)(1)(i), or the “secondary site of the work” as defined in paragraph (a)(1)(ii) of... the classification of work actually performed, without regard to skill, except as provided in the... classification may be compensated at the rate specified for each classification for the time actually worked...

48 CFR 52.222-6 - Davis-Bacon Act.

Code of Federal Regulations, 2010 CFR

2010-10-01

... defined in paragraph (a)(1)(i), or the “secondary site of the work” as defined in paragraph (a)(1)(ii) of... the classification of work actually performed, without regard to skill, except as provided in the... classification may be compensated at the rate specified for each classification for the time actually worked...

Drug-induced sedation endoscopy (DISE) classification systems: a systematic review and meta-analysis.

PubMed

Dijemeni, Esuabom; D'Amone, Gabriele; Gbati, Israel

2017-12-01

Drug-induced sedation endoscopy (DISE) classification systems have been used to assess anatomical findings on upper airway obstruction, and decide and plan surgical treatments and act as a predictor for surgical treatment outcome for obstructive sleep apnoea management. The first objective is to identify if there is a universally accepted DISE grading and classification system for analysing DISE findings. The second objective is to identify if there is one DISE grading and classification treatment planning framework for deciding appropriate surgical treatment for obstructive sleep apnoea (OSA). The third objective is to identify if there is one DISE grading and classification treatment outcome framework for determining the likelihood of success for a given OSA surgical intervention. A systematic review was performed to identify new and significantly modified DISE classification systems: concept, advantages and disadvantages. Fourteen studies proposing a new DISE classification system and three studies proposing a significantly modified DISE classification were identified. None of the studies were based on randomised control trials. DISE is an objective method for visualising upper airway obstruction. The classification and assessment of clinical findings based on DISE is highly subjective due to the increasing number of DISE classification systems. Hence, this creates a growing divergence in surgical treatment planning and treatment outcome. Further research on a universally accepted objective DISE assessment is critically needed.

Voice based gender classification using machine learning

NASA Astrophysics Data System (ADS)

Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

2017-11-01

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

Cesarean Section Rate Analysis in University Hospital Tuzla - According to Robson's Classification.

PubMed

Fatusic, Jasenko; Hudic, Igor; Fatusic, Zlatan; Zildzic-Moralic, Aida; Zivkovic, Milorad

2016-06-01

For last decades, there has public concern about increasing Cesarean Section (CS) rates, and it is an issue of international public health concern. According to World Health Organisation (WHO) there is no justification to have more than 10-15% CS births. WHO proposes the Robson ten-group classification, as a global standard for assessing, monitoring and comparing cesarean section rates. The aim of this study was to investigate Cesarean section rate at University Hospital Tuzla, Bosnia and Herzegovina. Cross sectional study was conducted for one-year period, 2015. Statistical analysis and graph-table presentation was performed using Excel 2010 and Microsoft Office programs. Out of 3,672 births, a total of 936 births were performed by CS. Percentage of the total number of CS to the total birth number was 25,47%. According to Robson classification, the largest was group 5 with relative contribution of 29,80%. On second and third place were group 1 and 2 with relative contribution of 26,06% and 15,78% respectively. Groups 1, 2, 5 made account of realtive contribution of 71,65%. All other groups had entirely relative contribution of 28,35%. Robson 10-group classification provides easy way in collecting information about CS rate. It is important that efforts to reduce the overall CS rate should focus on reducing the primary CS. Data from our study confirm this attitude.

Thermography based prescreening software tool for veterinary clinics

NASA Astrophysics Data System (ADS)

Dahal, Rohini; Umbaugh, Scott E.; Mishra, Deependra; Lama, Norsang; Alvandipour, Mehrdad; Umbaugh, David; Marino, Dominic J.; Sackman, Joseph

2017-05-01

Under development is a clinical software tool which can be used in the veterinary clinics as a prescreening tool for these pathologies: anterior cruciate ligament (ACL) disease, bone cancer and feline hyperthyroidism. Currently, veterinary clinical practice uses several imaging techniques including radiology, computed tomography (CT), and magnetic resonance imaging (MRI). But, harmful radiation involved during imaging, expensive equipment setup, excessive time consumption and the need for a cooperative patient during imaging, are major drawbacks of these techniques. In veterinary procedures, it is very difficult for animals to remain still for the time periods necessary for standard imaging without resorting to sedation - which creates another set of complexities. Therefore, clinical application software integrated with a thermal imaging system and the algorithms with high sensitivity and specificity for these pathologies, can address the major drawbacks of the existing imaging techniques. A graphical user interface (GUI) has been created to allow ease of use for the clinical technician. The technician inputs an image, enters patient information, and selects the camera view associated with the image and the pathology to be diagnosed. The software will classify the image using an optimized classification algorithm that has been developed through thousands of experiments. Optimal image features are extracted and the feature vector is then used in conjunction with the stored image database for classification. Classification success rates as high as 88% for bone cancer, 75% for ACL and 90% for feline hyperthyroidism have been achieved. The software is currently undergoing preliminary clinical testing.

Mechanisms of starch digestion by α-amylase-Structural basis for kinetic properties.

PubMed

Dhital, Sushil; Warren, Frederick J; Butterworth, Peter J; Ellis, Peter R; Gidley, Michael J

2017-03-24

Recent studies of the mechanisms determining the rate and extent of starch digestion by α-amylase are reviewed in the light of current widely-used classifications for (a) the proportions of rapidly-digestible (RDS), slowly-digestible (SDS), and resistant starch (RS) based on in vitro digestibility, and (b) the types of resistant starch (RS 1,2,3,4…) based on physical and/or chemical form. Based on methodological advances and new mechanistic insights, it is proposed that both classification systems should be modified. Kinetic analysis of digestion profiles provides a robust set of parameters that should replace the classification of starch as a combination of RDS, SDS, and RS from a single enzyme digestion experiment. This should involve determination of the minimum number of kinetic processes needed to describe the full digestion profile, together with the proportion of starch involved in each process, and the kinetic properties of each process. The current classification of resistant starch types as RS1,2,3,4 should be replaced by one which recognizes the essential kinetic nature of RS (enzyme digestion rate vs. small intestinal passage rate), and that there are two fundamental origins for resistance based on (i) rate-determining access/binding of enzyme to substrate and (ii) rate-determining conversion of substrate to product once bound.

Ecosystem classifications based on summer and winter conditions.

PubMed

Andrew, Margaret E; Nelson, Trisalyn A; Wulder, Michael A; Hobart, George W; Coops, Nicholas C; Farmer, Carson J Q

2013-04-01

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

Jang, Hojin; Plis, Sergey M.; Calhoun, Vince D.; Lee, Jong-Hwan

2016-01-01

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. PMID:27079534

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.

The joint use of the tangential electric field and surface Laplacian in EEG classification.

PubMed

Carvalhaes, C G; de Barros, J Acacio; Perreau-Guimaraes, M; Suppes, P

2014-01-01

We investigate the joint use of the tangential electric field (EF) and the surface Laplacian (SL) derivation as a method to improve the classification of EEG signals. We considered five classification tasks to test the validity of such approach. In all five tasks, the joint use of the components of the EF and the SL outperformed the scalar potential. The smallest effect occurred in the classification of a mental task, wherein the average classification rate was improved by 0.5 standard deviations. The largest effect was obtained in the classification of visual stimuli and corresponded to an improvement of 2.1 standard deviations.

[The efficacy of large spot indirect ophthalmoscopy laser alone or combined with systemic chemotherapy in retinoblastoma therapy].

PubMed

Liang, J H; Cheng, Y; Deng, X; Yu, Y Y; Li, X X

2016-10-11

Objective: To evaluate the efficacy of large spot indirect ophthalmoscopy laser alone or combined with systemic chemotherapy in the treatment of early and middle stage retinoblastoma. Methods: Retrospective series case study. Clinical data of 21 patients (22 eyes) who were diagnosed as retinoblastoma (RB) in Peking University People's Hospital from March 2009 to August 2014 were collected. Medical and family history, ocular ultrasound, orbital and cranial MRI or CT examination of RB Children were detailed recorded. Ocular examination and laser treatment were performed under general anesthesia, once every 3-4 weeks until the tumor was under control. The observation period was at least 3 months after the last treatment. The ocular examination included intraocular pressure measurement, anterior segment and fundus examination and the fundus photography with Retcam. Laser therapeutic instrument was large spot indirect ophthalmoscopy laser of 810nm wavelength. Results: Of the 21 children, 16 were male and 5 were female. The range of age was 3 to 82 months averaged 17.3 months. Among 22 eyes, four with small tumor, eight with medium tumor, and ten with large tumor. Two eyes underwent laser treatment only and 20 eyes underwent laser treatment combined with systemic chemotherapy. During the average observation period of 33.9 months, 15 tumors were treated successfully, but 7 failed. The total success rate was 68.2%. The number and success rate of small, medium and large tumor eyes were 4 (100%), 5 (62.5%) and 5 (50%), respectively. There was one case of tumor brain metastases, and the classification of contralateral eye of the child was E phase. Iris burns happened in one eye, obvious vitreous proliferation in one eye and mild vitreous hemorrhage occurred in two eyes, which did not affect the treatment of laser. However, obvious tumor hemorrhage happened in two eyes and affected laser therapy. There was no complicated cataract, iatrogenic retinal hole and tumor intravitreal implant caused by laser blasting effect. Conclusions: Indirect ophthalmoscope laser in 810-nm diode laser with large-spot alone or combined with systemic chemotherapy may be effective treatment for retinoblastoma in earlier than stage C of international classification. It is suitable for large and multiple retinoblastoma. (Chin J Ophthalmol, 2016, 52: 745-748) .

Real-time people and vehicle detection from UAV imagery

NASA Astrophysics Data System (ADS)

Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan

2011-01-01

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

Assessment of statistical methods used in library-based approaches to microbial source tracking.

PubMed

Ritter, Kerry J; Carruthers, Ethan; Carson, C Andrew; Ellender, R D; Harwood, Valerie J; Kingsley, Kyle; Nakatsu, Cindy; Sadowsky, Michael; Shear, Brian; West, Brian; Whitlock, John E; Wiggins, Bruce A; Wilbur, Jayson D

2003-12-01

Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

Clinical efficacy of transcatheter aortic valve replacement for severe aortic stenosis in high-risk patients: the PREVAIL JAPAN trial.

PubMed

Sawa, Yoshiki; Takayama, Morimasa; Mitsudo, Kazuaki; Nanto, Shinsuke; Takanashi, Shuichiro; Komiya, Tatsuhiko; Kuratani, Toru; Tobaru, Tetsuya; Goto, Tsuyoshi

2015-01-01

Transcatheter aortic valve replacement (TAVR) is suggested to be less invasive and/or equally effective in comparison to conventional aortic valve replacement for high-risk symptomatic aortic stenosis patients. We herein report the initial results of a pivotal clinical trial of TAVR in Japan (the PREVAIL JAPAN). Sixty-four aortic stenosis patients (mean age 84.3 ± 6.1 years) not suitable for surgery were enrolled at three centers in Japan, with a primary composite endpoint of the 6-month post-procedure improvements in the aortic valve area and New York Heart Association (NYHA) functional classification. A transfemoral approach was used in 37 patients and a transapical approach was used in 27. The device success rate was 91.9 %. After 30 days and 6 months, the rates of mortality from any cause were 8.1 and 11.3 %, respectively. At 6 months, symptomatic stroke was found in 3.1 % of the patients, and silent infarction in 7.8 %. The aortic valve area and mean pressure gradient were significantly improved over time with both approaches (p < 0.001). At 6 months, the NYHA functional classification based on a conventional physician's assessment was improved in 87.9 % of the patients. We found results that were equivalent to those in other major TAVR trials, such as an acceptable 30-day survival (91.9 %), suggesting that balloon-expandable TAVR is effective for small Japanese AS patients classified as high-risk or inoperable.

HMM based automated wheelchair navigation using EOG traces in EEG

NASA Astrophysics Data System (ADS)

Aziz, Fayeem; Arof, Hamzah; Mokhtar, Norrima; Mubin, Marizan

2014-10-01

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

HMM based automated wheelchair navigation using EOG traces in EEG.

PubMed

Aziz, Fayeem; Arof, Hamzah; Mokhtar, Norrima; Mubin, Marizan

2014-10-01

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

Development of a Tool Condition Monitoring System for Impregnated Diamond Bits in Rock Drilling Applications

NASA Astrophysics Data System (ADS)

Perez, Santiago; Karakus, Murat; Pellet, Frederic

2017-05-01

The great success and widespread use of impregnated diamond (ID) bits are due to their self-sharpening mechanism, which consists of a constant renewal of diamonds acting at the cutting face as the bit wears out. It is therefore important to keep this mechanism acting throughout the lifespan of the bit. Nonetheless, such a mechanism can be altered by the blunting of the bit that ultimately leads to a less than optimal drilling performance. For this reason, this paper aims at investigating the applicability of artificial intelligence-based techniques in order to monitor tool condition of ID bits, i.e. sharp or blunt, under laboratory conditions. Accordingly, topologically invariant tests are carried out with sharp and blunt bits conditions while recording acoustic emissions (AE) and measuring-while-drilling variables. The combined output of acoustic emission root-mean-square value (AErms), depth of cut ( d), torque (tob) and weight-on-bit (wob) is then utilized to create two approaches in order to predict the wear state condition of the bits. One approach is based on the combination of the aforementioned variables and another on the specific energy of drilling. The two different approaches are assessed for classification performance with various pattern recognition algorithms, such as simple trees, support vector machines, k-nearest neighbour, boosted trees and artificial neural networks. In general, Acceptable pattern recognition rates were obtained, although the subset composed by AErms and tob excels due to the high classification performances rates and fewer input variables.

A scattering analysis of echoes due to biosonar signals emitted by foraging beaked whales

NASA Astrophysics Data System (ADS)

Jones, Benjamin A.; Stanton, Timothy K.; Lavery, Andone C.; Johnson, Mark P.; Madsen, Peter T.; Tyack, Peter L.

2005-09-01

Blainville's beaked whales (Mesoplodon densirostris) hunt their prey by echolocation at depths of more than 500 meters. These whales use a FM upswept, ultrasonic click, of greater than an octave bandwidth to search for, localize, and close on individual prey which generally consist of mesopelagic fishes and squid. It is well known that acoustic scattering from organisms of varying morphology (e.g., swimbladder-bearing or fluidlike) is strongly frequency dependent. However, it is unknown if the broadband nature of the whales' outgoing signal, and the frequency dependence of the echoes, is a key component in the classification and selection of their prey. Non-invasive, acoustic ``Dtags,'' which sample stereo acoustic data at a rate which satisfies the high-frequency Nyquist criterion for the animal's transmit signal, were affixed to beaked whales. The Dtags successfully recorded transmitted signals and associated echoes. Structure was observed in the frequency content of echoes from isolated targets in the water column which may be used for classification by the whales. An analysis of the echoes identified as possibly due to prey has demonstrated that multiple classes of frequency responses are present. These results will be compared with the frequency responses of possible prey types.

A new complexity measure for time series analysis and classification

NASA Astrophysics Data System (ADS)

Nagaraj, Nithin; Balasubramanian, Karthi; Dey, Sutirth

2013-07-01

Complexity measures are used in a number of applications including extraction of information from data such as ecological time series, detection of non-random structure in biomedical signals, testing of random number generators, language recognition and authorship attribution etc. Different complexity measures proposed in the literature like Shannon entropy, Relative entropy, Lempel-Ziv, Kolmogrov and Algorithmic complexity are mostly ineffective in analyzing short sequences that are further corrupted with noise. To address this problem, we propose a new complexity measure ETC and define it as the "Effort To Compress" the input sequence by a lossless compression algorithm. Here, we employ the lossless compression algorithm known as Non-Sequential Recursive Pair Substitution (NSRPS) and define ETC as the number of iterations needed for NSRPS to transform the input sequence to a constant sequence. We demonstrate the utility of ETC in two applications. ETC is shown to have better correlation with Lyapunov exponent than Shannon entropy even with relatively short and noisy time series. The measure also has a greater rate of success in automatic identification and classification of short noisy sequences, compared to entropy and a popular measure based on Lempel-Ziv compression (implemented by Gzip).

LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.

PubMed

Ma, Chao; Wang, Lirong; Yang, Peng; Myint, Kyaw Z; Xie, Xiang-Qun

2013-01-28

The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.

Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain-Computer Interface.

PubMed

Horschig, Jörn M; Oosterheert, Wouter; Oostenveld, Robert; Jensen, Ole

2015-11-01

Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain-computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing.

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

NASA Astrophysics Data System (ADS)

Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

Rates, amounts, and determinants of ambulatory blood pressure monitoring claim reimbursements among Medicare beneficiaries.

PubMed

Kent, Shia T; Shimbo, Daichi; Huang, Lei; Diaz, Keith M; Viera, Anthony J; Kilgore, Meredith; Oparil, Suzanne; Muntner, Paul

2014-12-01

Ambulatory blood pressure monitoring (ABPM) can be used to identify white coat hypertension and guide hypertensive treatment. We determined the percentage of ABPM claims submitted between 2007 and 2010 that were reimbursed. Among 1970 Medicare beneficiaries with submitted claims, ABPM was reimbursed for 93.8% of claims that had an International Classification of Diseases, Ninth Revision, diagnosis code of 796.2 ("elevated blood pressure reading without diagnosis of hypertension") versus 28.5% of claims without this code. Among claims without an International Classification of Diseases, Ninth Revision, diagnosis code of 796.2 listed, those for the component (eg, recording, scanning analysis, physician review, reporting) versus full ABPM procedures and performed by institutional versus non-institutional providers were each more than two times as likely to be successfully reimbursed. Of the claims reimbursed, the median payment was $52.01 (25th-75th percentiles, $32.95-$64.98). In conclusion, educating providers on the ABPM claims reimbursement process and evaluation of Medicare reimbursement may increase the appropriate use of ABPM and improve patient care. Copyright © 2014 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Hermann, Thomas; Bukschat, Yannick; Kramer, Tilmann; Schad, Lothar R.; Zöllner, Frank Gerrit

2017-03-01

Electrocardiography (ECG) data are multidimensional temporal data with ubiquitous applications in the clinic. Conventionally, these data are presented visually. It is presently unclear to what degree data sonification (auditory display), can enable the detection of clinically relevant cardiac pathologies in ECG data. In this study, we introduce a method for polyphonic sonification of ECG data, whereby different ECG channels are simultaneously represented by sound of different pitch. We retrospectively applied this method to 12 samples from a publicly available ECG database. We and colleagues from our professional environment then analyzed these data in a blinded way. Based on these analyses, we found that the sonification technique can be intuitively understood after a short training session. On average, the correct classification rate for observers trained in cardiology was 78%, compared to 68% and 50% for observers not trained in cardiology or not trained in medicine at all, respectively. These values compare to an expected random guessing performance of 25%. Strikingly, 27% of all observers had a classification accuracy over 90%, indicating that sonification can be very successfully used by talented individuals. These findings can serve as a baseline for potential clinical applications of ECG sonification.

Two Approaches to Estimation of Classification Accuracy Rate under Item Response Theory

ERIC Educational Resources Information Center

Lathrop, Quinn N.; Cheng, Ying

2013-01-01

Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…

Flare rates and the McIntosh active-region classifications

NASA Technical Reports Server (NTRS)

Bornmann, P. L.; Shaw, D.

1994-01-01

Multiple linear regression analysis was used to derive the effective solar flare contributions of each of the McIntosh classification parameters. The best fits to the combined average number of M- and X-class X-ray flares per day were found when the flare contributions were assumed to be multiplicative rather than additive. This suggests that nonlinear processes may amplify the effects of the following different active-region properties encoded in the McIntosh classifications: the length of the sunspot group, the size and shape of the largest spot, and the distribution of spots within the group. Since many of these active-region properties are correlated with magnetic field strengths and fluxes, we suggest that the derived correlations reflect a more fundamental relationship between flare production and the magnetic properties of the region. The derived flare contributions for the individual McIntosh parameters can be used to derive a flare rate for each of the three-parameter McIntosh classes. These derived flare rates can be interpreted as smoothed values that may provide better estimates of an active region's expected flare rate when rare classes are reported or when the multiple observing sites report slightly different classifications.

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

PubMed Central

Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

2017-01-01

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

Concurrent validation of a neurocognitive assessment protocol for clients with mental illness in job matching as shop sales in supported employment.

PubMed

Ng, S S W; Lak, D C C; Lee, S C K; Ng, P P K

2015-03-01

Occupational therapists play a major role in the assessment and referral of clients with severe mental illness for supported employment. Nonetheless, there is scarce literature about the content and predictive validity of the process. In addition, the criteria of successful job matching have not been analysed and job supervisors have relied on experience rather than objective standards in recruitment. This study aimed to explore the profile of successful clients working in 'shop sales' in a supportive environment using a neurocognitive assessment protocol, and to validate the protocol against 'internal standards' of the job supervisors. This was a concurrent validation study of criterion-related scales for a single job type. The subjective ratings from the supervisors were concurrently validated against the results of neurocognitive assessment of intellectual function and work-related cognitive behaviour. A regression model was established for clients who succeeded and failed in employment using supervisor's ratings and a cutoff value of 10.5 for the Performance Fitness Rating Scale (R(2) = 0.918, F[41] = 3.794, p = 0.003). Classification And Regression Tree was also plotted to identify the profile of cases, with an overall accuracy of 0.861 (relative error, 0.26). Use of both inference statistics and data mining techniques enables the decision tree of neurocognitive assessments to be more readily applied by therapists in vocational rehabilitation, and thus directly improve the efficiency and efficacy of the process.

Unsupervised classification of surface defects in wire rod production obtained by eddy current sensors.

PubMed

Saludes-Rodil, Sergio; Baeyens, Enrique; Rodríguez-Juan, Carlos P

2015-04-29

An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors.

Sleep stage classification with low complexity and low bit rate.

PubMed

Virkkala, Jussi; Värri, Alpo; Hasan, Joel; Himanen, Sari-Leena; Müller, Kiti

2009-01-01

Standard sleep stage classification is based on visual analysis of central (usually also frontal and occipital) EEG, two-channel EOG, and submental EMG signals. The process is complex, using multiple electrodes, and is usually based on relatively high (200-500 Hz) sampling rates. Also at least 12 bit analog to digital conversion is recommended (with 16 bit storage) resulting in total bit rate of at least 12.8 kbit/s. This is not a problem for in-house laboratory sleep studies, but in the case of online wireless self-applicable ambulatory sleep studies, lower complexity and lower bit rates are preferred. In this study we further developed earlier single channel facial EMG/EOG/EEG-based automatic sleep stage classification. An algorithm with a simple decision tree separated 30 s epochs into wakefulness, SREM, S1/S2 and SWS using 18-45 Hz beta power and 0.5-6 Hz amplitude. Improvements included low complexity recursive digital filtering. We also evaluated the effects of a reduced sampling rate, reduced number of quantization steps and reduced dynamic range on the sleep data of 132 training and 131 testing subjects. With the studied algorithm, it was possible to reduce the sampling rate to 50 Hz (having a low pass filter at 90 Hz), and the dynamic range to 244 microV, with an 8 bit resolution resulting in a bit rate of 0.4 kbit/s. Facial electrodes and a low bit rate enables the use of smaller devices for sleep stage classification in home environments.

The dynamics of human-induced land cover change in miombo ecosystems of southern Africa

NASA Astrophysics Data System (ADS)

Jaiteh, Malanding Sambou

Understanding human-induced land cover change in the miombo require the consistent, geographically-referenced, data on temporal land cover characteristics as well as biophysical and socioeconomic drivers of land use, the major cause of land cover change. The overall goal of this research to examine the applications of high-resolution satellite remote sensing data in studying the dynamics of human-induced land cover change in the miombo. Specific objectives are to: (1) evaluate the applications of computer-assisted classification of Landsat Thematic Mapper (TM) data for land cover mapping in the miombo and (2) analyze spatial and temporal patterns of landscape change locations in the miombo. Stepwise Thematic Classification, STC (a hybrid supervised-unsupervised classification) procedure for classifying Landsat TM data was developed and tested using Landsat TM data. Classification accuracy results were compared to those from supervised and unsupervised classification. The STC provided the highest classification accuracy i.e., 83.9% correspondence between classified and referenced data compared to 44.2% and 34.5% for unsupervised and supervised classification respectively. Improvements in the classification process can be attributed to thematic stratification of the image data into spectrally homogenous (thematic) groups and step-by-step classification of the groups using supervised or unsupervised classification techniques. Supervised classification failed to classify 18% of the scene evidence that training data used did not adequately represent all of the variability in the data. Application of the procedure in drier miombo produced overall classification accuracy of 63%. This is much lower than that of wetter miombo. The results clearly demonstrate that digital classification of Landsat TM can be successfully implemented in the miombo without intensive fieldwork. Spatial characteristics of land cover change in agricultural and forested landscapes in central Malawi were analyzed for the period 1984 to 1995 spatial pattern analysis methods. Shifting cultivation areas, Agriculture in forested landscape, experienced highest rate of woodland cover fragmentation with mean patch size of closed woodland cover decreasing from 20ha to 7.5ha. Permanent bare (cropland and settlement) in intensive agricultural matrix landscapes increased 52% largely through the conversion of fallow areas. Protected National Park area remained fairly unchanged although closed woodland area increased by 4%, mainly from regeneration of open woodland. This study provided evidence that changes in spatial characteristics in the miombo differ with landscape. Land use change (i.e. conversion to cropland) is the primary driving force behind changes in landscape spatial patterns. Also, results revealed that exclusion of intense human use (i.e. cultivation and woodcutting) through regulations and/or fencing increased both closed woodland area (through regeneration of open woodland) and overall connectivity in the landscape. Spatial characteristics of land cover change were analyzed at locations in Malawi (wetter miombo) and Zimbabwe (drier miombo). Results indicate land cover dynamics differ both between and within case study sites. In communal areas in the Kasungu scene, land cover change is dominated by woodland fragmentation to open vegetation. Change in private commercial lands was dominantly expansion of bare (settlement and cropland) areas primarily at the expense of open vegetation (fallow land).

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.

PubMed

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Desmet, Dirk; Roosen, Philip; Keijsers, Noel; Nobels, Frank; Bruyninckx, Herman; Staes, Filip

2016-09-01

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system. Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes. Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets. The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making. Copyright © 2016 Elsevier B.V. All rights reserved.

The Effect of Using Relative and Absolute Criteria to Decide Students' Passing or Failing a Course

ERIC Educational Resources Information Center

Sayin, Ayfer

2016-01-01

In the formation education that is carried out within the scope of undergraduate and non-thesis graduate programs within the same university, different criteria are used to evaluate students' success. In this study, classification accuracy of letter grades that are generated to evaluate students' success using relative and absolute criteria and…

Counselor-Client Diagnostic Agreement and Perceived Outcomes of Counseling: A Progress Report.

ERIC Educational Resources Information Center

Hurst, James C.; And Others

This study was designed to investigate the effect of congruity of counselor and client diagnoses upon client-perceived success in counseling. The Missouri Diagnostic Classification Plan (MDCP) was used as the basic diagnostic method. Agreement in the 15 categories was related to client-perceived success of counseling. Subjects, all clients at the…

Estimating the Recurrence Rate of Gestational Diabetes Mellitus (GDM) in Massachusetts 1998-2007: Methods and Findings.

PubMed

England, Lucinda; Kotelchuck, Milton; Wilson, Hoyt G; Diop, Hafsatou; Oppedisano, Paul; Kim, Shin Y; Cui, Xiaohui; Shapiro-Mendoza, Carrie K

2015-10-01

Women with gestational diabetes mellitus (GDM) may be able to reduce their risk of recurrent GDM and progression to type 2 diabetes mellitus through lifestyle change; however, there is limited population-based information on GDM recurrence rates. We used data from a population of women delivering two sequential live singleton infants in Massachusetts (1998-2007) to estimate the prevalence of chronic diabetes mellitus (CDM) and GDM in parity one pregnancies and recurrence of GDM and progression from GDM to CDM in parity two pregnancies. We examined four diabetes classification approaches; birth certificate (BC) data alone, hospital discharge (HD) data alone, both sources hierarchically combined with a diagnosis of CDM from either source taking priority over a diagnosis of GDM, and both sources combined including only pregnancies with full agreement in diagnosis. Descriptive statistics were used to describe population characteristics, prevalence of CDM and GDM, and recurrence of diabetes in successive pregnancies. Diabetes classification agreement was assessed using the Kappa statistic. Associated maternal characteristics were examined through adjusted model-based t tests and Chi square tests. A total of 134,670 women with two sequential deliveries of parities one and two were identified. While there was only slight agreement on GDM classification across HD and BC records, estimates of GDM recurrence were fairly consistent; nearly half of women with GDM in their parity one pregnancy developed GDM in their subsequent pregnancy. While estimates of progression from GDM to CDM across sequential pregnancies were more variable, all approaches yielded estimates of ≤5 %. The development of either GDM or CDM following a parity one pregnancy with no diagnosis of diabetes was <3 % across approaches. Women with recurrent GDM were disproportionately older and foreign born. Recurrent GDM is a serious life course public health issue; the inter-pregnancy interval provides an important window for diabetes prevention.

A systematic review of the Robson classification for caesarean section: what works, doesn't work and how to improve it.

PubMed

Betrán, Ana Pilar; Vindevoghel, Nadia; Souza, Joao Paulo; Gülmezoglu, A Metin; Torloni, Maria Regina

2014-01-01

Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation.

A Systematic Review of the Robson Classification for Caesarean Section: What Works, Doesn't Work and How to Improve It

PubMed Central

Betrán, Ana Pilar; Vindevoghel, Nadia; Souza, Joao Paulo; Gülmezoglu, A. Metin; Torloni, Maria Regina

2014-01-01

Background Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Methods Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. Results 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. Conclusions The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation. PMID:24892928

Predicting β-Turns in Protein Using Kernel Logistic Regression

PubMed Central

Elbashir, Murtada Khalafallah; Sheng, Yu; Wang, Jianxin; Wu, FangXiang; Li, Min

2013-01-01

A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case. PMID:23509793

Predicting β-turns in protein using kernel logistic regression.

PubMed

Elbashir, Murtada Khalafallah; Sheng, Yu; Wang, Jianxin; Wu, Fangxiang; Li, Min

2013-01-01

A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Application of Sal classification to parotid gland fine-needle aspiration cytology: 10-year retrospective analysis of 312 patients.

PubMed

Kilavuz, Ahmet Erdem; Songu, Murat; İmre, Abdulkadir; Arslanoğlu, Secil; Özkul, Yilmaz; Pinar, Ercan; Ateş, Düzgün

2018-05-01

The accuracy of fine-needle aspiration biopsy (FNAB) is controversial in parotid tumors. We aimed to compare FNAB results with the final histopathological diagnosis and to apply the "Sal classification" to our data and discuss its results and its place in parotid gland cytology. The FNAB cytological findings and final histological diagnosis were assessed retrospectively in 2 different scenarios based on the distribution of nondefinitive cytology, and we applied the Sal classification and determined malignancy rate, sensitivity, and specificity for each category. In 2 different scenarios FNAB sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were found to be 81%, 87%, 54.7%, and 96.1%; and 65.3%, 100%, 100%, and 96.1%, respectively. The malignancy rates and sensitivity and specificity were also calculated and discussed for each Sal category. We believe that the Sal classification has a great potential to be a useful tool in classification of parotid gland cytology. © 2018 Wiley Periodicals, Inc.

Infant homicide and accidental death in the United States, 1940-2005: ethics and epidemiological classification.

PubMed

Riggs, Jack E; Hobbs, Gerald R

2011-07-01

Potential ethical issues can arise during the process of epidemiological classification. For example, unnatural infant deaths are classified as accidental deaths or homicides. Societal sensitivity to the physical abuse and neglect of children has increased over recent decades. This enhanced sensitivity could impact reported infant homicide rates. Infant homicide and accident mortality rates in boys and girls in the USA from 1940 to 2005 were analysed. In 1940, infant accident mortality rates were over 20 times greater than infant homicide rates in both boys and girls. After about 1980, when the ratio of infant accident mortality rates to infant homicide rates decreased to less than five, and the sum of infant accident and homicide rates became relatively constant, further decreases in infant accident mortality rates were associated with increases in reported infant homicide rates. These findings suggest that the dramatic decline of accidental infant mortality and recent increased societal sensitivity to child abuse may be related to the increased infant homicide rates observed in the USA since 1980 rather than an actual increase in societal violence directed against infants. Ethical consequences of epidemiological classification, involving the principles of beneficence, non-maleficence and justice, are suggested by observed patterns in infant accidental deaths and homicides in the USA from 1940 to 2005.

Deep learning for tumor classification in imaging mass spectrometry.

PubMed

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

Management of pediatric blunt splenic injury at a rural trauma center.

PubMed

Bird, Julio J; Patel, Nirav Y; Mathiason, Michelle A; Schroeppel, Thomas J; D'huyvetter, Cecile J; Cogbill, Thomas H

2012-10-01

Patterns for nonoperative management of pediatric blunt splenic injuries (BSIs) vary significantly within and between institutions. The indications for repeated imaging, duration of activity restrictions, as well as the impact of volume and type of trauma center (pediatric vs. adult) on outcomes remain unclear. A retrospective review of all patients younger than 16 years with BSI managed at a rural American College of Surgeons-verified adult Level II trauma center from January 1995 to December 2008 was completed. Patients were identified from the trauma registry by DRG International Classification of Diseases-9th Rev. (865.00-865.09) and management codes (41.5, 41.43, and 41.95). Variables reviewed included demographics, mechanism of injury, Injury Severity Score, grade of splenic injury, degree of hemoperitoneum, presence of arterial phase contrast blush on computed tomography at admission, admission and nadir hemoglobin level, blood transfused, length of stay, disposition, outpatient clinical and radiographic follow-up, interval of return to unrestricted activity, and clinical outcomes. During the 13-year study period, 38 children with BSI were identified. Thirty-seven (97%) were successfully managed nonoperatively. Median grade of splenic injury was 3 (range, 1-5); 73% had moderate-to-large hemoperitoneum. Median Injury Severity Score was 10 (range, 4-34). Three patients with isolated contrast blush on initial computed tomography were successfully managed nonoperatively with no angiographic intervention. One patient failed nonoperative management and underwent successful splenorrhaphy. All patients were discharged home. Thirty-day mortality was zero. Median follow-up duration was 5.5 years, with no late complications identified. Of the patients successfully managed nonoperatively, 92% had their follow-up at our institution; 74% underwent subsequent imaging, and none resulted in intervention or alteration of management plan. Pediatric BSI can be managed in adult trauma centers with success rates of nonoperative management comparable to dedicated children's hospitals. Routine follow-up imaging is not necessary. Overall splenic injury salvage rate in our experience was 100%. Therapeutic/epidemiologic study, level IV.

Direct traumatic carotid cavernous fistula: angiographic classification and treatment strategies. Study of 172 cases.

PubMed

Chi, Cuong Tran; Nguyen, Dang; Duc, Vo Tan; Chau, Huynh Hong; Son, Vo Tan

2014-01-01

We report our experience in treatment of traumatic direct carotid cavernous fistula (CCF) via endovascular intervention. We hereof recommend an additional classification system for type A CCF and suggest respective treatment strategies. Only type A CCF patients (Barrow's classification) would be recruited for the study. Based on the angiographic characteristics of the CCF, we classified type A CCF into three subtypes including small size, medium size and large size fistula depending on whether there was presence of the anterior carotid artery (ACA) and/or middle carotid artery (MCA). Angiograms with opacification of both ACA and MCA were categorized as small size fistula. Angiograms with opacification of either ACA or MCA were categorized as medium size fistula and those without opacification of neither ACA nor MCA were classified as large size fiatula. After the confirm angiogram, endovascular embolization would be performed impromptu using detachable balloon, coils or both. All cases were followed up for complication and effect after the embolization. A total of 172 direct traumatic CCF patients were enrolled. The small size fistula was accountant for 12.8% (22 cases), medium size 35.5% (61 cases) and large size fistula accountant for 51.7% (89 cases). The successful rate of fistula occlusion under endovascular embolization was 94% with preservation of the carotid artery in 70%. For the treatment of each subtype, a total of 21/22 cases of the small size fistulas were successfully treated using coils alone. The other single case of small fistula was defaulted. Most of the medium and large size fistulas were cured using detachable balloons. When the fistula sealing could not be obtained using detachable balloon, coils were added to affirm the embolization of the cavernous sinus via venous access. There were about 2.9% of patient experienced direct carotid artery puncture and 0.6% puncture after carotid artery cut-down exposure. About 30% of cases experienced sacrifice of the parent vessels and it was associated with sizes of the fistula. Total severe complication was about 2.4% which included 1 death (0.6%) due to vagal shock; 1 transient hemiparesis post-sacrifice occlusion of the carotid artery but the patient had recovered after 3 months; 1 acute thrombus embolism and the patient was completely saved with recombinant tissue plaminogen activator (rTPA); 1 balloon dislodgement then got stuck at the anterior communicating artery but the patient was asymptomatic. Endovascular intervention as the treatment of direct traumatic CCF had high cure rate and low complication with its ability to preserve the carotid artery. It also can supply flexible accesses to the fistulous site with various alternative embolic materials. The new classification of type A CCF based on angiographic features was helpful for planning for the embolization. Coil should be considered as the first embolic material for small size fistula meanwhile detachable balloons was suggested as the first-choice embolic agent for the medium and large size fistula.

Reliability of classification for post-traumatic ankle osteoarthritis.

PubMed

Claessen, Femke M A P; Meijer, Diederik T; van den Bekerom, Michel P J; Gevers Deynoot, Barend D J; Mallee, Wouter H; Doornberg, Job N; van Dijk, C Niek

2016-04-01

The purpose of this study was to identify the most reliable classification system for clinical outcome studies to categorize post-traumatic-fracture-osteoarthritis. A total of 118 orthopaedic surgeons and residents-gathered in the Ankle Platform Study Collaborative Science of Variation Group-evaluated 128 anteroposterior and lateral radiographs of patients after a bi- or trimalleolar ankle fracture on a Web-based platform in order to rate post-traumatic osteoarthritis according to the classification systems coined by (1) van Dijk, (2) Kellgren, and (3) Takakura. Reliability was evaluated with the use of the Siegel and Castellan's multirater kappa measure. Differences between classification systems were compared using the two-sample Z-test. Interobserver agreement of surgeons who participated in the survey was fair for the van Dijk osteoarthritis scale (k = 0.24), and poor for the Takakura (k = 0.19) and the Kellgren systems (k = 0.18) according to the categorical rating of Landis and Koch. This difference in one categorical rating was found to be significant (p < 0.001, CI 0.046-0.053) with the high numbers of observers and cases available. This study documents fair interobserver agreement for the van Dijk osteoarthritis scale, and poor interobserver agreement for the Takakura and Kellgren osteoarthritis classification systems. Because of the low interobserver agreement for the van Dijk, Kellgren, and Takakura classification systems, those systems cannot be used for clinical decision-making. Development of diagnostic criteria on basis of consecutive patients, Level II.

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

Support Vector Machines for Hyperspectral Remote Sensing Classification

NASA Technical Reports Server (NTRS)

Gualtieri, J. Anthony; Cromp, R. F.

1998-01-01

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

75 FR 69143 - Postal Rate and Classification Changes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-10

...This document addresses a recently-filed Postal Service request for three postal rate and classification changes. One change will affect certain senders of First-Class Mail Presort and Automation Letters. Another change will affect Standard Mail and High Density milers. The third change affects the Move Update Charge threshold. This document provides details about the anticipated changes and addresses procedural steps associated with this filing.

Bibliographic control of audiovisuals: analysis of a cataloging project using OCLC.

PubMed

Curtis, J A; Davison, F M

1985-04-01

The staff of the Quillen-Dishner College of Medicine Library cataloged 702 audiovisual titles between July 1, 1982, and June 30, 1983, using the OCLC database. This paper discusses the library's audiovisual collection and describes the method and scope of a study conducted during this project, the cataloging standards and conventions adopted, the assignment and use of NLM classification, the provision of summaries for programs, and the amount of staff time expended in cataloging typical items. An analysis of the use of OCLC for this project resulted in the following findings: the rate of successful searches for audiovisual copy was 82.4%; the error rate for records used was 41.9%; modifications were required in every record used; the Library of Congress and seven member institutions provided 62.8% of the records used. It was concluded that the effort to establish bibliographic control of audiovisuals is not widespread and that expanded and improved audiovisual cataloging by the Library of Congress and the National Library of Medicine would substantially contribute to that goal.

The Children's Sleep Comic: Psychometrics of a Self-rating Instrument for Childhood Insomnia.

PubMed

Schwerdtle, Barbara; Kanis, Julia; Kübler, Andrea; Schlarb, Angelika A

2016-02-01

The Children's Sleep Comic is a standardized self-report questionnaire for assessing insomnia in children ages 5-11 years. The goal of the present study is to introduce a revised version of this measure and to present psychometrics and a cut-off score. Therefore, the revised Children's Sleep Comic, the Sleep Self Report, the Children's Sleep Habits Questionnaire, and the Child Behavior Checklist were applied to a sample of 393 children and their parents. Of the parents who participated voluntarily, a subsample (n = 176) was interviewed on the phone to diagnose their children with sleep disorders according to the International Classification of Sleep Disorders, if applicable. The results indicated that the Children's Sleep Comic is a reliable self-rating instrument for diagnosing childhood insomnia. Internal consistency was α = 0.83; and convergent and divergent validity were adequate. The child-friendly format can foster a good therapeutic relationship, and thus establish the basis for successful intervention.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Intraoperative cochlear nerve mapping with the mobile cochlear nerve compound action potential tracer in vestibular schwannoma surgery.

PubMed

Watanabe, Nobuyuki; Ishii, Takuya; Fujitsu, Kazuhiko; Kaku, Shogo; Ichikawa, Teruo; Miyahara, Kosuke; Okada, Tomu; Tanino, Shin; Uriu, Yasuhiro; Murayama, Yuichi

2018-05-18

OBJECTIVE The authors describe the usefulness and limitations of the cochlear nerve compound action potential (CNAP) mobile tracer (MCT) that they developed to aid in cochlear nerve mapping during vestibular schwannoma surgery (VSS) for hearing preservation. METHODS This MCT device requires no more than 2 seconds for stable placement on the nerve to obtain the CNAP and thus is able to trace the cochlear nerve instantaneously. Simultaneous bipolar and monopolar recording is possible. The authors present the outcomes of 18 consecutive patients who underwent preoperative useful hearing (defined as class I or II of the Gardner-Robertson classification system) and underwent hearing-preservation VSS with the use of the MCT. Mapping was considered successful when it was possible to detect and trace the cochlear nerve. RESULTS Mapping of the cochlear nerve was successful in 13 of 18 patients (72.2%), and useful hearing was preserved in 11 patients (61.1%). Among 8 patients with large tumors (Koos grade 3 or 4), the rate of successful mapping was 62.5% (5 patients). The rate of hearing preservation in patients with large tumors was 50% (4 patients). CONCLUSIONS In addition to microsurgical presumption of the arrangement of each nerve, frequent probing on and around an unidentified nerve and comparison of each waveform are advisable with the use of both more sensitive monopolar and more location-specific bipolar MCT. MCT proved to be useful in cochlear nerve mapping and may consequently be helpful in hearing preservation. The authors discuss some limitations and problems with this device.

The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

DTIC Science & Technology

2013-03-01

fMRI data (e.g. Kamitami & Tong, 2005). This approach has been remarkably successful in classifying mental workload in complex tasks (Berka, et al...1991). These previous studies relied upon spectral comparison rather than classification. In previous research examining the stability of fMRI ...chose to focus on electrophysiology, as the collection conditions may be more carefully controlled across days than fMRI and it is more amenable to

Crop identification from radar imagery of the Huntington County, Indiana test site

NASA Technical Reports Server (NTRS)

Batlivala, P. P.; Ulaby, F. T. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Like polarization was successful in discriminating corn and soybeans; however, pasture and woods were consistently confused as soybeans and corn, respectively. The probability of correct classification was about 65%. The cross polarization component (highest for woods and lowest for pasture) helped in separating the woods from corn, and pasture from soybeans, and when used with the like polarization component, the probability of correct classification increased to 74%.

Performance of Activity Classification Algorithms in Free-living Older Adults

PubMed Central

Sasaki, Jeffer Eidi; Hickey, Amanda; Staudenmayer, John; John, Dinesh; Kent, Jane A.; Freedson, Patty S.

2015-01-01

Purpose To compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Methods Thirty-five older adults (21F and 14M ; 70.8 ± 4.9 y) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, and ankle). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore the GT3X+ in free-living settings and were directly observed for 2-3 hours. Time- and frequency- domain features from acceleration signals of each monitor were used to train Random Forest (RF) and Support Vector Machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on lab data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20 s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Results Overall classification accuracy rates for the algorithms developed from lab data were between 49% (wrist) to 55% (ankle) for the SVMLab algorithms, and 49% (wrist) to 54% (ankle) for RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Conclusion Our algorithms developed on free-living accelerometer data were more accurate in classifying activity type in free-living older adults than our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine-learning algorithms in older adults. PMID:26673129

Performance of Activity Classification Algorithms in Free-Living Older Adults.

PubMed

Sasaki, Jeffer Eidi; Hickey, Amanda M; Staudenmayer, John W; John, Dinesh; Kent, Jane A; Freedson, Patty S

2016-05-01

The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults.

Attachment-based classifications of children's family drawings: psychometric properties and relations with children's adjustment in kindergarten.

PubMed

Pianta, R C; Longmaid, K; Ferguson, J E

1999-06-01

Investigated an attachment-based theoretical framework and classification system, introduced by Kaplan and Main (1986), for interpreting children's family drawings. This study concentrated on the psychometric properties of the system and the relation between drawings classified using this system and teacher ratings of classroom social-emotional and behavioral functioning, controlling for child age, ethnic status, intelligence, and fine motor skills. This nonclinical sample consisted of 200 kindergarten children of diverse racial and socioeconomic status (SES). Limited support for reliability of this classification system was obtained. Kappas for overall classifications of drawings (e.g., secure) exceeded .80 and mean kappa for discrete drawing features (e.g., figures with smiles) was .82. Coders' endorsement of the presence of certain discrete drawing features predicted their overall classification at 82.5% accuracy. Drawing classification was related to teacher ratings of classroom functioning independent of child age, sex, race, SES, intelligence, and fine motor skills (with p values for the multivariate effects ranging from .043-.001). Results are discussed in terms of the psychometric properties of this system for classifying children's representations of family and the limitations of family drawing techniques for young children.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

The generalization ability of online SVM classification based on Markov sampling.

PubMed

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

ERIC Educational Resources Information Center

Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

2010-01-01

The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

Aptitude and Trait Predictors of Manned and Unmanned Aircraft Pilot Job Performance

DTIC Science & Technology

2016-04-22

actually fly RPAs. To address this gap, the present study evaluated pre-accession trait (Big Five personality domains) and aptitude (spatial...knowledge, and personality traits that predict successful job performance for manned aircraft pilots also predict successful job performance for RPA...aptitude and personality traits , job performance, remotely-piloted aircraft, unmanned aircraft systems 16. SECURITY CLASSIFICATION OF: 17

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

PubMed Central

Song, Le; Epps, Julien

2007-01-01

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach. PMID:18364986

The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.

Integrating human and machine intelligence in galaxy morphology classification tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Applications of Wavelet Transform and Fuzzy Neural Network on Power Quality Recognition

NASA Astrophysics Data System (ADS)

Liao, Chiung-Chou; Yang, Hong-Tzer; Lin, Ying-Chun

2008-10-01

The wavelet transform coefficients (WTCs) contain plenty of information needed for transient event identification of power quality (PQ) events. However, adopting WTCs directly has the drawbacks of taking a longer time and too much memory for the recognition system. To solve the abovementioned recognition problems and to effectively reduce the number of features representing power transients, spectrum energies of WTCs in different scales are calculated by Parseval's Theorem. Through the proposed approach, features of the original power signals can be reserved and not influenced by occurring points of PQ events. The fuzzy neural classification systems are then used for signal recognition and fuzzy rule construction. Success rates of recognizing PQ events from noise-riding signals are proven to be feasible in power system applications in this paper.

Online signature recognition using principal component analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

2016-12-01

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

Forecasting Flare Activity Using Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Hernandez, T.

2017-12-01

Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.

PubMed

Torija, Antonio J; Ruiz, Diego P

2012-10-01

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400 Hz and 1-2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%. Copyright © 2012 Elsevier B.V. All rights reserved.

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection.

PubMed

Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

2017-06-12

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection †

PubMed Central

Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

2017-01-01

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities. PMID:28604628

Classification of breast tissue in mammograms using efficient coding.

PubMed

Costa, Daniel D; Campos, Lúcio F; Barros, Allan K

2011-06-24

Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

Rectal surgery for endometriosis--should we be aggressive?

PubMed

Varol, Nesrin; Maher, Peter; Healey, Martin; Woods, Rod; Wood, Carl; Hill, David; Lolatgis, Nick; Tsaltas, Jim

2003-05-01

To assess the outcome of aggressive but conservative laparoscopic surgery in the treatment of severe endometriosis involving the rectum. Retrospective study (Canadian Task Force classification III). Endosurgery unit of a tertiary referral center. One hundred sixty-nine women. Laparoscopy or laparotomy. The procedure was completed successfully laparoscopically in 145 (86%) and by laparotomy in 24 women (14%). The rate of preoperative symptoms was higher in 25 women who underwent bowel resection compared with those who had other bowel surgery. In addition to bowel surgery, excision of uterosacral ligaments, adhesiolysis, excision of endometrioma, and oophorectomy were the four most commonly performed procedures. At 35-month follow-up 61 patients (36%) required further surgery for pain. The average time between primary and repeat surgery was 16 months. This second operation was performed by laparoscopy in over three-fourths of the women. Overall recurrent endometriosis was found in 26 patients (15%). Overall morbidity associated with all surgery was 12.4%. Surgery for endometriosis of the cul-de-sac and bowel involves some of the most difficult dissections encountered, but it can be accomplished successfully with the low postoperative morbidity typical of laparoscopy.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

NASA Astrophysics Data System (ADS)

Briard, Jennie G.; Fernandez, Michael; de Luna, Phil; Woo, Tom. K.; Ben, Robert N.

2016-05-01

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

PubMed Central

Briard, Jennie G.; Fernandez, Michael; De Luna, Phil; Woo, Tom. K.; Ben, Robert N.

2016-01-01

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

PubMed

Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

2016-05-24

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

PubMed

Yu, Kezi; Quirk, J Gerald; Djurić, Petar M

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

PubMed Central

Yu, Kezi; Quirk, J. Gerald

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting. PMID:28953927

Classification and correlates of eating disorders among Blacks: findings from the National Survey of American Life.

PubMed

Taylor, Jacquelyn Y; Caldwell, Cleopatra Howard; Baser, Raymond E; Matusko, Niki; Faison, Nakesha; Jackson, James S

2013-02-01

To assess classification adjustments and examine correlates of eating disorders among Blacks. The National Survey of American Life (NSAL) was conducted from 2001-2003 and consisted of adults (n=5,191) and adolescents (n=1,170). The World Mental Health Composite International Diagnostic Interview (WMH-CIDI-World Health Organization 2004-modified) and DSM-IV-TR eating disorder criteria were used. Sixty-six percent of African American and 59% Caribbean Black adults were overweight or obese, while 30% and 29% of adolescents were overweight or obese. Although lifetime rates of anorexia nervosa and bulimia nervosa were low, binge eating disorder was high for both ethnic groups among adults and adolescents. Eliminating certain classification criteria resulted in higher rates of eating disorders for all groups. Culturally sensitive criteria should be incorporated into future versions of Diagnostic Statistical Manual (DSM) classifications for eating disorders that consider within-group ethnic variations.

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

PubMed

Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

2016-06-01

Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18; Z score, 4.00; P < .001; peak voxel r = 0.73). Furthermore, the analysis of treatment effects revealed a increase in hippocampal volume in the ECT sample (MNI coordinates x = -28, y = -9, z = -18; Z score, 7.81; P < .001) that was missing in the medication-only sample. A relatively small degree of structural impairment in the subgenual cingulate cortex before therapy seems to be associated with successful treatment with ECT. In the future, neuroimaging techniques could prove to be promising tools for predicting the individual therapeutic effectiveness of ECT.

Multicenter study of endoscopic preoperative biliary drainage for malignant hilar biliary obstruction: E-POD hilar study.

PubMed

Nakai, Yousuke; Yamamoto, Ryuichi; Matsuyama, Masato; Sakai, Yuji; Takayama, Yukiko; Ushio, Jun; Ito, Yukiko; Kitamura, Katsuya; Ryozawa, Shomei; Imamura, Tsunao; Tsuchida, Kouhei; Hayama, Jo; Itoi, Takao; Kawaguchi, Yoshiaki; Yoshida, Yu; Sugimori, Kazuya; Shimura, Kenji; Mizuide, Masafumi; Iwai, Tomohisa; Nishikawa, Ko; Yagioka, Hiroshi; Nagahama, Masatsugu; Toda, Nobuo; Saito, Tomotaka; Yasuda, Ichiro; Hirano, Kenji; Togawa, Osamu; Nakamura, Kenji; Maetani, Iruru; Sasahira, Naoki; Isayama, Hiroyuki

2018-05-01

Endoscopic nasobiliary drainage (ENBD) is often recommended in preoperative biliary drainage (PBD) for hilar malignant biliary obstruction (MBO), but endoscopic biliary stent (EBS) is also used in the clinical practice. We conducted this large-scale multicenter study to compare ENBD and EBS in this setting. A total of 374 cases undergoing PBD including 281 ENBD and 76 EBS for hilar MBO in 29 centers were retrospectively studied. Extrahepatic cholangiocarcinoma (ECC) accounted for 69.8% and Bismuth-Corlette classification was III or more in 58.8% of the study population. Endoscopic PBD was technically successful in 94.6%, and adverse event rate was 21.9%. The rate of post-endoscopic retrograde cholangiopancreatography pancreatitis was 16.0%, and non-endoscopic sphincterotomy was the only risk factor (odds ratio [OR] 2.51). Preoperative re-intervention was performed in 61.5%: planned re-interventions in 48.4% and unplanned re-interventions in 31.0%. Percutaneous transhepatic biliary drainage was placed in 6.4% at the time of surgery. The risk factors for unplanned procedures were ECC (OR 2.64) and total bilirubin ≥ 10 mg/dL (OR 2.18). In surgically resected cases, prognostic factors were ECC (hazard ratio [HR] 0.57), predraiange magnetic resonance cholangiopancreatography (HR 1.62) and unplanned re-interventions (HR 1.81). EBS was not associated with increased adverse events, unplanned re-interventions, or a poor prognosis. Our retrospective analysis did not demonstrate the advantage of ENBD over EBS as the initial PBD for resectable hilar MBO. Although the technical success rate of endoscopic PBD was high, its re-intervention rate was not negligible, and unplanned re-intervention was associated with a poor prognosis in resected hilar MBO. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Systematic Review of Results of Kissing Stents in the Treatment of Aortoiliac Occlusive Disease.

PubMed

Groot Jebbink, Erik; Holewijn, Suzanne; Slump, Cornelis H; Lardenoije, Jan-Willem; Reijnen, Michel M P J

2017-07-01

Endovascular treatment of aortoiliac occlusive disease (AIOD) involving the aortic bifurcation is challenging. The gold standard is open surgery with patency rates up to 90% at 5 years, but has considerable morbidity and mortality. The kissing stent (KS) technique was introduced as an alternative. The goal of this review is to give an overview of the current results and role of the KS technique in AIOD treatment. The Cochrane guidelines were used to assure a systematic method. A search query designed in the Scopus search interface was used to identify relevant studies. Abstracts from the search were screened against the inclusion and exclusion criteria. During full-text reading, methodological quality was scored using a critical review list tailored to the topic of AIOD. Thereafter, study data were extracted and pooled for further analysis. In total, 143 abstracts were retrieved using Scopus, 116 were rejected and 7 more were rejected after full-text screening. One study was included after cross referencing. Twenty-one studies presented 1,390 patients. Rutherford classification 1/2/3 was the indication in 76.2% of patients, and 48.4% of the lesions were classified as Trans-Atlantic Inter-Society Consensus C or D. The technical success rate was 98.7%, and the complication rate was 10.8%. Clinical improvement at 30 days was achieved in 89.9%. Primary patency at 12, 24, and 60 months was 89.3%, 78.6%, and 69.0%, respectively. KS treatment of AIOD yields acceptable mid-term results, with high technical success rates and mostly minor complications occur. The long-term patency cannot yet match that of open surgery, underlining the need for further research that provides insight into factors related to reocclusion. Copyright © 2017 Elsevier Inc. All rights reserved.

Columbia Classification Algorithm of Suicide Assessment (C-CASA): classification of suicidal events in the FDA's pediatric suicidal risk analysis of antidepressants.

PubMed

Posner, Kelly; Oquendo, Maria A; Gould, Madelyn; Stanley, Barbara; Davies, Mark

2007-07-01

To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antidepressants conducted by the Food and Drug Administration (FDA). Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of "suicide attempt," a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA's audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs.

Columbia Classification Algorithm of Suicide Assessment (C-CASA): Classification of Suicidal Events in the FDA’s Pediatric Suicidal Risk Analysis of Antidepressants

PubMed Central

Posner, Kelly; Oquendo, Maria A.; Gould, Madelyn; Stanley, Barbara; Davies, Mark

2013-01-01

Objective To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antide-pressants conducted by the Food and Drug Administration (FDA). Method Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Results Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of “suicide attempt,” a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Conclusions Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA’s audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs. PMID:17606655

The Application of Remote Sensing Techniques to Urban Data Acquisition

NASA Technical Reports Server (NTRS)

Horton, F. E.

1971-01-01

The application of remote sensing techniques useful in acquiring data concerning housing quality is discussed. Conclusions reached from the investigation were: (1) Use of individuals with a higher degree of training in photointerpretation should significantly increase the percentage of successful classifications. (2) Small area classification of urban housing quality can definitely be accomplished via high resolution aerial photography. Such surveys, at the levels of accuracy demonstrated, can be of major utility in quick look surveys. (3) Survey costs should be significantly reduced.

Deployment and Performance of the NASA D3R During the GPM OLYMPEx Field Campaign

NASA Technical Reports Server (NTRS)

Chandrasekar, V.; Beauchamp, Robert M.; Chen, Haonan; Vega, Manuel; Schwaller, Mathew; Willie, Delbert; Dabrowski, Aaron; Kumar, Mohit; Petersen, Walter; Wolff, David

2016-01-01

The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.

Neuro-classification of multi-type Landsat Thematic Mapper data

NASA Technical Reports Server (NTRS)

Zhuang, Xin; Engel, Bernard A.; Fernandez, R. N.; Johannsen, Chris J.

1991-01-01

Neural networks have been successful in image classification and have shown potential for classifying remotely sensed data. This paper presents classifications of multitype Landsat Thematic Mapper (TM) data using neural networks. The Landsat TM Image for March 23, 1987 with accompanying ground observation data for a study area In Miami County, Indiana, U.S.A. was utilized to assess recognition of crop residues. Principal components and spectral ratio transformations were performed on the TM data. In addition, a layer of the geographic information system (GIS) for the study site was incorporated to generate GIS-enhanced TM data. This paper discusses (1) the performance of neuro-classification on each type of data, (2) how neural networks recognized each type of data as a new image and (3) comparisons of the results for each type of data obtained using neural networks, maximum likelihood, and minimum distance classifiers.

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?

PubMed

Bozkurt, Selen; Bostanci, Asli; Turhan, Murat

2017-08-11

The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination. In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used. Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model. Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.

Classification of stillbirths is an ongoing dilemma.

PubMed

Nappi, Luigi; Trezza, Federica; Bufo, Pantaleo; Riezzo, Irene; Turillazzi, Emanuela; Borghi, Chiara; Bonaccorsi, Gloria; Scutiero, Gennaro; Fineschi, Vittorio; Greco, Pantaleo

2016-10-01

To compare different classification systems in a cohort of stillbirths undergoing a comprehensive workup; to establish whether a particular classification system is most suitable and useful in determining cause of death, purporting the lowest percentage of unexplained death. Cases of stillbirth at gestational age 22-41 weeks occurring at the Department of Gynecology and Obstetrics of Foggia University during a 4 year period were collected. The World Health Organization (WHO) diagnosis of stillbirth was used. All the data collection was based on the recommendations of an Italian diagnostic workup for stillbirth. Two expert obstetricians reviewed all cases and classified causes according to five classification systems. Relevant Condition at Death (ReCoDe) and Causes Of Death and Associated Conditions (CODAC) classification systems performed best in retaining information. The ReCoDe system provided the lowest rate of unexplained stillbirth (14%) compared to de Galan-Roosen (16%), CODAC (16%), Tulip (18%), Wigglesworth (62%). Classification of stillbirth is influenced by the multiplicity of possible causes and factors related to fetal death. Fetal autopsy, placental histology and cytogenetic analysis are strongly recommended to have a complete diagnostic evaluation. Commonly employed classification systems performed differently in our experience, the most satisfactory being the ReCoDe. Given the rate of "unexplained" cases, none can be considered optimal and further efforts are necessary to work out a clinically useful system.

[A systematic review of worldwide natural history models of colorectal cancer: classification, transition rate and a recommendation for developing Chinese population-specific model].

PubMed

Li, Z F; Huang, H Y; Shi, J F; Guo, C G; Zou, S M; Liu, C C; Wang, Y; Wang, L; Zhu, S L; Wu, S L; Dai, M

2017-02-10

Objective: To review the worldwide studies on natural history models among colorectal cancer (CRC), and to inform building a Chinese population-specific CRC model and developing a platform for further evaluation of CRC screening and other interventions in population in China. Methods: A structured literature search process was conducted in PubMed and the target publication dates were from January 1995 to December 2014. Information about classification systems on both colorectal cancer and precancer on corresponding transition rate, were extracted and summarized. Indicators were mainly expressed by the medians and ranges of annual progression or regression rate. Results: A total of 24 studies were extracted from 1 022 studies, most were from America ( n =9), but 2 from China including 1 from the mainland area, mainly based on Markov model ( n =22). Classification systems for adenomas included progression risk ( n =9) and the sizes of adenoma ( n =13, divided into two ways) as follows: 1) Based on studies where adenoma was risk-dependent, the median annual transition rates, from ' normal status' to ' non-advanced adenoma', 'non-advanced' to ' advanced' and ' advanced adenoma' to CRC were 0.016 0 (range: 0.002 2-0.020 0), 0.020 (range: 0.002-0.177) and 0.044 (range: 0.005-0.063), respectively. 2) Median annual transition rates, based on studies where adenoma were classified by sizes, into <10 mm and ≥10 mm ( n =7), from ' normal' to adenoma <10 mm, from adenoma <10 mm to adenoma ≥10 mm and adenoma ≥ 10 mm to CRC, were 0.016 7 (range: 0.015 0-0.037 0), 0.020 (range: 0.015-0.035) and 0.040 0 (range: 0.008 5-0.050 0), respectively. 3) Median annual transition rates, based on studies where adenoma, were classified by sizes into diminutive (≤5 mm), small (6-9 mm) and large adenoma (≥10 mm) ( n =6), from ' normal' to diminutive adenoma,'diminutive' to ' small','small' to ' large', and large adenoma to CRC were 0.013 (range: 0.009-0.019), 0.043 (range: 0.020-0.085), 0.044 (range: 0.020-0.125) and 0.033 5 (range: 0.030-0.040), respectively. Staging system of CRC mainly included LRD (localized/regional/distant, n =10), Dukes' ( n =7) and TNM ( n =3). When using the LRD classification, the median annual transition rates from ' localized' to ' regional' and ' regional' to 'distant' were 0.28 (range: 0.20-0.33) and 0.40 (range: 0.24-0.63), respectively. Under the Dukes' classification, the median annual transition rates appeared as 0.583 (range: 0.050-0.910), 0.656 (range: 0.280-0.720) and 0.830 (range: 0.630-0.865) from Dukes' A to B, B to C and C to Dukes' D, respectively. Again, when using the TNM classification, very limited transition rate was reported. Serrated pathway was only described in one study. Conclusions: Studies on the natural history model of colorectal cancer was still limited worldwide. Adenoma seemed the most common status setting for precancer model, and the risk-dependent classification for adenoma was consistent with the most commonly used system in clinical practice as well as major cancer screening programs in China. Since the staging systems of cancers varied, and shortage of transition rates based on TNM classification (commonly used in China), there will be a challenge for building Chinese population-specific natural history model of colorectal cancer, information from other classification systems could be conditionally applied.

Drug safety: Pregnancy rating classifications and controversies.

PubMed

Wilmer, Erin; Chai, Sandy; Kroumpouzos, George

2016-01-01

This contribution consolidates data on international pregnancy rating classifications, including the former US Food and Drug Administration (FDA), Swedish, and Australian classification systems, as well as the evidence-based medicine system, and discusses discrepancies among them. It reviews the new Pregnancy and Lactation Labeling Rule (PLLR) that replaced the former FDA labeling system with narrative-based labeling requirements. PLLR emphasizes on human data and highlights pregnancy exposure registry information. In this context, the review discusses important data on the safety of most medications used in the management of skin disease in pregnancy. There are also discussions of controversies relevant to the safety of certain dermatologic medications during gestation. Copyright © 2016 Elsevier Inc. All rights reserved.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.

PubMed

Assawamakin, Anunchai; Prueksaaroon, Supakit; Kulawonganunchai, Supasak; Shaw, Philip James; Varavithya, Vara; Ruangrajitpakorn, Taneth; Tongsima, Sissades

2013-01-01

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Prognostic factors of non-functioning pancreatic neuroendocrine tumor revisited: The value of WHO 2010 classification.

PubMed

Bu, Jiyoung; Youn, Sangmin; Kwon, Wooil; Jang, Kee Taek; Han, Sanghyup; Han, Sunjong; You, Younghun; Heo, Jin Seok; Choi, Seong Ho; Choi, Dong Wook

2018-02-01

Various factors have been reported as prognostic factors of non-functional pancreatic neuroendocrine tumors (NF-pNETs). There remains some controversy as to the factors which might actually serve to successfully prognosticate future manifestation and diagnosis of NF-pNETs. As well, consensus regarding management strategy has never been achieved. The aim of this study is to further investigate potential prognostic factors using a large single-center cohort to help determine the management strategy of NF-pNETs. During the time period 1995 through 2013, 166 patients with NF-pNETs who underwent surgery in Samsung Medical Center were entered in a prospective database, and those factors thought to represent predictors of prognosis were tested in uni- and multivariate models. The median follow-up time was 46.5 months; there was a maximum follow-up period of 217 months. The five-year overall survival and disease-free survival rates were 88.5% and 77.0%, respectively. The 2010 WHO classification was found to be the only prognostic factor which affects overall survival and disease-free survival in multivariate analysis. Also, pathologic tumor size and preoperative image tumor size correlated strongly with the WHO grades ( p <0.001, and p <0.001). Our study demonstrates that 2010 WHO classification represents a valuable prognostic factor of NF-pNETs and tumor size on preoperative image correlated with WHO grade. In view of the foregoing, the preoperative image size is thought to represent a reasonable reference with regard to determination and development of treatment strategy of NF-pNETs.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Neural Network Classification of Receiver Functions as a Step Towards Automatic Crustal Parameter Determination

NASA Astrophysics Data System (ADS)

Jemberie, A.; Dugda, M. T.; Reusch, D.; Nyblade, A.

2006-12-01

Neural networks are decision making mathematical/engineering tools, which if trained properly, can do jobs automatically (and objectively) that normally require particular expertise and/or tedious repetition. Here we explore two techniques from the field of artificial neural networks (ANNs) that seek to reduce the time requirements and increase the objectivity of quality control (QC) and Event Identification (EI) on seismic datasets. We explore to apply the multiplayer Feed Forward (FF) Artificial Neural Networks (ANN) and Self- Organizing Maps (SOM) in combination with Hk stacking of receiver functions in an attempt to test the extent of the usefulness of automatic classification of receiver functions for crustal parameter determination. Feed- forward ANNs (FFNNs) are a supervised classification tool while self-organizing maps (SOMs) are able to provide unsupervised classification of large, complex geophysical data sets into a fixed number of distinct generalized patterns or modes. Hk stacking is a methodology that is used to stack receiver functions based on the relative arrival times of P-to-S converted phase and next two reverberations to determine crustal thickness H and Vp-to-Vs ratio (k). We use receiver functions from teleseismic events recorded by the 2000- 2002 Ethiopia Broadband Seismic Experiment. Preliminary results of applying FFNN neural network and Hk stacking of receiver functions for automatic receiver functions classification as a step towards an effort of automatic crustal parameter determination look encouraging. After training a FFNN neural network, the network could classify the best receiver functions from bad ones with a success rate of about 75 to 95%. Applying H? stacking on the receiver functions classified by this FFNN as the best receiver functions, we could obtain crustal thickness and Vp/Vs ratio of 31±4 km and 1.75±0.05, respectively, for the crust beneath station ARBA in the Main Ethiopian Rift. To make comparison, we applied Hk stacking on the receiver functions which we ourselves classified as the best set and found that the crustal thickness and Vp/Vs ratio are 31±2 km and 1.75±0.02, respectively.

Influence of nuclei segmentation on breast cancer malignancy classification

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

Validation of a new classification for periprosthetic shoulder fractures.

PubMed

Kirchhoff, Chlodwig; Beirer, Marc; Brunner, Ulrich; Buchholz, Arne; Biberthaler, Peter; Crönlein, Moritz

2018-06-01

Successful treatment of periprosthetic shoulder fractures depends on the right strategy, starting with a well-structured classification of the fracture. Unfortunately, clinically relevant factors for treatment planning are missing in the pre-existing classifications. Therefore, the aim of the present study was to describe a new specific classification system for periprosthetic shoulder fractures including a structured treatment algorithm for this important fragility fracture issue. The classification was established, focussing on five relevant items, naming the prosthesis type, the fracture localisation, the rotator cuff status, the anatomical fracture region and the stability of the implant. After considering each single item, the individual treatment concept can be assessed in one last step. To evaluate the introduced classification, a retrospective analysis of pre- and post-operative data of patients, treated with periprosthetic shoulder fractures, was conducted by two board certified trauma surgery consultants. The data of 19 patients (8 male, 11 female) with a mean age of 74 ± five years have been analysed in our study. The suggested treatment algorithm was proven to be reliable, detected by good clinical outcome in 15 of 16 (94%) cases, where the suggested treatment was maintained. Only one case resulted in poor outcome due to post-operative wound infection and had to be revised. The newly developed six-step classification is easy to utilise and extends the pre-existing classification systems in terms of clinically-relevant information. This classification should serve as a simple tool for the surgeon to consider the optimal treatment for his patients.

Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment

NASA Astrophysics Data System (ADS)

Naidoo, L.; Cho, M. A.; Mathieu, R.; Asner, G.

2012-04-01

The accurate classification and mapping of individual trees at species level in the savanna ecosystem can provide numerous benefits for the managerial authorities. Such benefits include the mapping of economically useful tree species, which are a key source of food production and fuel wood for the local communities, and of problematic alien invasive and bush encroaching species, which can threaten the integrity of the environment and livelihoods of the local communities. Species level mapping is particularly challenging in African savannas which are complex, heterogeneous, and open environments with high intra-species spectral variability due to differences in geology, topography, rainfall, herbivory and human impacts within relatively short distances. Savanna vegetation are also highly irregular in canopy and crown shape, height and other structural dimensions with a combination of open grassland patches and dense woody thicket - a stark contrast to the more homogeneous forest vegetation. This study classified eight common savanna tree species in the Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. The most important predictors, which were found to play an important role in the different classification models and contributed to the success of the hybrid dataset model when combined, were species tree height; NDVI; the chlorophyll b wavelength (466 nm) and a selection of raw, continuum removed and Spectral Angle Mapper (SAM) bands. It was also concluded that the hybrid predictor dataset Random Forest model yielded the highest classification accuracy and prediction success for the eight savanna tree species with an overall classification accuracy of 87.68% and KHAT value of 0.843.

A New Tool for Classifying Small Solar System Objects

NASA Astrophysics Data System (ADS)

Desfosses, Ryan; Arel, D.; Walker, M. E.; Ziffer, J.; Harvell, T.; Campins, H.; Fernandez, Y. R.

2011-05-01

An artificial intelligence program, AutoClass, which was developed by NASA's Artificial Intelligence Branch, uses Bayesian classification theory to automatically choose the most probable classification distribution to describe a dataset. To investigate its usefulness to the Planetary Science community, we tested its ability to reproduce the taxonomic classes as defined by Tholen and Barucci (1989). Of the 406 asteroids from the Eight Color Asteroid Survey (ECAS) we chose for our test, 346 were firmly classified and all but 3 (<1%) were classified by Autoclass as they had been in the previous classification system (Walker et al., 2011). We are now applying it to larger datasets to improve the taxonomy of currently unclassified objects. Having demonstrated AutoClass's ability to recreate existing classification effectively, we extended this work to investigations of albedo-based classification systems. To determine how predictive albedo can be, we used data from the Infrared Astronomical Satellite (IRAS) database in conjunction with the large Sloan Digital Sky Survey (SDSS), which contains color and position data for over 200,000 classified and unclassified asteroids (Ivesic et al., 2001). To judge our success we compared our results with a similar approach to classifying objects using IRAS albedo and asteroid color by Tedesco et al. (1989). Understanding the distribution of the taxonomic classes is important to understanding the history and evolution of our Solar System. AutoClass's success in categorizing ECAS, IRAS and SDSS asteroidal data highlights its potential to scan large domains for natural classes in small solar system objects. Based upon our AutoClass results, we intend to make testable predictions about asteroids observed with the Wide-field Infrared Survey Explorer (WISE).

Effects of organisational-level interventions at work on employees' health: a systematic review.

PubMed

Montano, Diego; Hoven, Hanno; Siegrist, Johannes

2014-02-08

Organisational-level workplace interventions are thought to produce more sustainable effects on the health of employees than interventions targeting individual behaviours. However, scientific evidence from intervention studies does not fully support this notion. It is therefore important to explore conditions of positive health effects by systematically reviewing available studies. We set out to evaluate the effectiveness of 39 health-related intervention studies targeting a variety of working conditions. Systematic review. Organisational-level workplace interventions aiming at improving employees' health were identified in electronic databases and manual searches. The appraisal of studies was adapted from the Cochrane Back Review Group guidelines. To improve comparability of the widely varying studies we classified the interventions according to the main approaches towards modifying working conditions. Based on this classification we applied a logistic regression model to estimate significant intervention effects. 39 intervention studies published between 1993 and 2012 were included. In terms of methodology the majority of interventions were of medium quality, and four studies only had a high level of evidence. About half of the studies (19) reported significant effects. There was a marginally significant probability of reporting effects among interventions targeting several organisational-level modifications simultaneously (Odds ratio (OR) 2.71; 95% CI 0.94-11.12), compared to those targeting one dimension only. Despite the heterogeneity of the 39 organisational-level workplace interventions underlying this review, we were able to compare their effects by applying broad classification categories. Success rates were higher among more comprehensive interventions tackling material, organisational and work-time related conditions simultaneously. To increase the number of successful organisational-level interventions in the future, commonly reported obstacles against the implementation process should be addressed in developing these studies.

Effects of organisational-level interventions at work on employees’ health: a systematic review

PubMed Central

2014-01-01

Background Organisational-level workplace interventions are thought to produce more sustainable effects on the health of employees than interventions targeting individual behaviours. However, scientific evidence from intervention studies does not fully support this notion. It is therefore important to explore conditions of positive health effects by systematically reviewing available studies. We set out to evaluate the effectiveness of 39 health-related intervention studies targeting a variety of working conditions. Methods Systematic review. Organisational-level workplace interventions aiming at improving employees’ health were identified in electronic databases and manual searches. The appraisal of studies was adapted from the Cochrane Back Review Group guidelines. To improve comparability of the widely varying studies we classified the interventions according to the main approaches towards modifying working conditions. Based on this classification we applied a logistic regression model to estimate significant intervention effects. Results 39 intervention studies published between 1993 and 2012 were included. In terms of methodology the majority of interventions were of medium quality, and four studies only had a high level of evidence. About half of the studies (19) reported significant effects. There was a marginally significant probability of reporting effects among interventions targeting several organisational-level modifications simultaneously (Odds ratio (OR) 2.71; 95% CI 0.94-11.12), compared to those targeting one dimension only. Conclusions Despite the heterogeneity of the 39 organisational-level workplace interventions underlying this review, we were able to compare their effects by applying broad classification categories. Success rates were higher among more comprehensive interventions tackling material, organisational and work-time related conditions simultaneously. To increase the number of successful organisational-level interventions in the future, commonly reported obstacles against the implementation process should be addressed in developing these studies. PMID:24507447

Application of classification-tree methods to identify nitrate sources in ground water

USGS Publications Warehouse

Spruill, T.B.; Showers, W.J.; Howe, S.S.

2002-01-01

A study was conducted to determine if nitrate sources in ground water (fertilizer on crops, fertilizer on golf courses, irrigation spray from hog (Sus scrofa) wastes, and leachate from poultry litter and septic systems) could be classified with 80% or greater success. Two statistical classification-tree models were devised from 48 water samples containing nitrate from five source categories. Model I was constructed by evaluating 32 variables and selecting four primary predictor variables (??15N, nitrate to ammonia ratio, sodium to potassium ratio, and zinc) to identify nitrate sources. A ??15N value of nitrate plus potassium 18.2 indicated inorganic or soil organic N. A nitrate to ammonia ratio 575 indicated nitrate from golf courses. A sodium to potassium ratio 3.2 indicated spray or poultry wastes. A value for zinc 2.8 indicated poultry wastes. Model 2 was devised by using all variables except ??15N. This model also included four variables (sodium plus potassium, nitrate to ammonia ratio, calcium to magnesium ratio, and sodium to potassium ratio) to distinguish categories. Both models were able to distinguish all five source categories with better than 80% overall success and with 71 to 100% success in individual categories using the learning samples. Seventeen water samples that were not used in model development were tested using Model 2 for three categories, and all were correctly classified. Classification-tree models show great potential in identifying sources of contamination and variables important in the source-identification process.

48 CFR 22.406-4 - Apprentices and trainees.

Code of Federal Regulations, 2011 CFR

2011-10-01

... contractor's employment and payment records of apprentices and trainees made available pursuant to the clause... reject the classification and require the contractor to pay the affected employees at the rates applicable to the classification of the work actually performed. ...

48 CFR 22.406-4 - Apprentices and trainees.

Code of Federal Regulations, 2010 CFR

2010-10-01

... contractor's employment and payment records of apprentices and trainees made available pursuant to the clause... reject the classification and require the contractor to pay the affected employees at the rates applicable to the classification of the work actually performed. ...

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

Automatic Classification of High Resolution Satellite Imagery - a Case Study for Urban Areas in the Kingdom of Saudi Arabia

NASA Astrophysics Data System (ADS)

Maas, A.; Alrajhi, M.; Alobeid, A.; Heipke, C.

2017-05-01

Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results.

Applying matching pursuit decomposition time-frequency processing to UGS footstep classification

NASA Astrophysics Data System (ADS)

Larsen, Brett W.; Chung, Hugh; Dominguez, Alfonso; Sciacca, Jacob; Kovvali, Narayan; Papandreou-Suppappola, Antonia; Allee, David R.

2013-06-01

The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.

Comparison of rates of reported adverse events associated with i.v. iron products in the United States.

PubMed

Bailie, George R

2012-02-15

An analysis of reported adverse events (AEs) among patients using i.v. iron products, including the newer agent ferumoxytol, is presented. All AE reports to the Food and Drug Administration (FDA) citing iron sucrose, ferric gluconate, high- and low-molecular-weight iron dextran products, or ferumoxytol from October 2009 through June 2010 were evaluated. The rates of various classifications of reported AEs were calculated on a per-unit-sold basis and, for comparison of products supplied in different unit sizes, also in terms of 100-mg dose equivalents (DEq) of iron. A total of 197 reported AEs were identified (a cumulative rate of 14.1 AEs per million units sold). The rates of all AE classifications combined ranged from 5.25 to 746 per million units sold for iron sucrose and ferumoxytol, respectively; using the other method of calculation, the rates ranged from 5.24 per million DEq (iron sucrose) to 147 per million DEq (ferumoxytol). Relative to iron sucrose and sodium ferric gluconate, ferumoxytol was associated with significantly elevated risks of death (odds ratio [OR], 475 and 156, respectively; p < 0.0001), serious nonfatal AEs (OR, 263 and 121, respectively; p < 0.0001), and all evaluated AE classifications combined (OR, 142 and 109, respectively; p < 0.05). Analysis of reports submitted to FDA revealed large differences among i.v. iron products in reported deaths, serious AEs, other major AEs, and other AEs. Iron sucrose and sodium ferric gluconate were associated with much lower rates of AEs per million units sold than iron dextran or ferumoxytol, which were associated with the highest rates of all reported AE classifications.

Random whole metagenomic sequencing for forensic discrimination of soils.

PubMed

Khodakova, Anastasia S; Smith, Renee J; Burgoyne, Leigh; Abarno, Damien; Linacre, Adrian

2014-01-01

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data.

PubMed

Shah, M; Marchand, M; Corbeil, J

2012-01-01

One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of the well-known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with a much smaller number of genes while giving competitive classification accuracy but also having tight risk guarantees on future performance, unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.

Diagnostic Performance of MR Elastography and Vibration-controlled Transient Elastography in the Detection of Hepatic Fibrosis in Patients with Severe to Morbid Obesity

PubMed Central

Chen, Jun; Yin, Meng; Talwalkar, Jayant A.; Oudry, Jennifer; Glaser, Kevin J.; Smyrk, Thomas C.; Miette, Véronique; Sandrin, Laurent

2017-01-01

Purpose To evaluate the diagnostic performance and examination success rate of magnetic resonance (MR) elastography and vibration-controlled transient elastography (VCTE) in the detection of hepatic fibrosis in patients with severe to morbid obesity. Materials and Methods This prospective and HIPAA-compliant study was approved by the institutional review board. A total of 111 patients (71 women, 40 men) participated. Written informed consent was obtained from all patients. Patients underwent MR elastography with two readers and VCTE with three observers to acquire liver stiffness measurements for liver fibrosis assessment. The results were compared with those from liver biopsy. Each pathology specimen was evaluated by two hepatopathologists according to the METAVIR scoring system or Brunt classification when appropriate. All imaging observers were blinded to the biopsy results, and all hepatopathologists were blinded to the imaging results. Examination success rate, interobserver agreement, and diagnostic accuracy for fibrosis detection were assessed. Results In this obese patient population (mean body mass index = 40.3 kg/m2; 95% confidence interval [CI]: 38.7 kg/m2, 41.8 kg/m2]), the examination success rate was 95.8% (92 of 96 patients) for MR elastography and 81.3% (78 of 96 patients) or 88.5% (85 of 96 patients) for VCTE. Interobserver agreement was higher with MR elastography than with biopsy (intraclass correlation coefficient, 0.95 vs 0.89). In patients with successful MR elastography and VCTE examinations (excluding unreliable VCTE examinations), both MR elastography and VCTE had excellent diagnostic accuracy in the detection of clinically significant hepatic fibrosis (stage F2–F4) (mean area under the curve: 0.93 [95% CI: 0.85, 0.97] vs 0.91 [95% CI: 0.83, 0.96]; P = .551). Conclusion In this obese patient population, both MR elastography and VCTE had excellent diagnostic performance for assessing hepatic fibrosis; MR elastography was more technically reliable than VCTE and had a higher interobserver agreement than liver biopsy. © RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on January 25, 2017. PMID:27861111

Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT)

PubMed Central

Murugappan, Murugappan; Murugappan, Subbulakshmi; Zheng, Bong Siao

2013-01-01

[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness − 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems. PMID:24259846

Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT).

PubMed

Murugappan, Murugappan; Murugappan, Subbulakshmi; Zheng, Bong Siao

2013-07-01

[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness - 50.28%; happiness - 79.03%; fear - 77.78%; disgust - 88.69%; and neutral - 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems.

Assessment of target detection limits in hyperspectral data

NASA Astrophysics Data System (ADS)

Gross, W.; Boehler, J.; Schilling, H.; Middelmann, W.; Weyermann, J.; Wellig, P.; Oechslin, R.; Kneubuehler, M.

2015-10-01

Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband sensors. The comparably low spatial resolution is compensated by the fact that small targets, even below image resolution, can still be classified. The goal of this paper is to determine the target size to spatial resolution ratio for successful classification of different target and background materials. Airborne hyperspectral data is used to simulate data with known mixture ratios and to estimate the detection threshold for given false alarm rates. The data was collected in July 2014 over Greding, Germany, using airborne aisaEAGLE and aisaHAWK hyperspectral sensors. On the ground, various target materials were placed on natural background. The targets were four quadratic molton patches with an edge length of 7 meters in the colors black, white, grey and green. Also, two different types of polyethylene (camouflage nets) with an edge length of approximately 5.5 meters were deployed. Synthetic data is generated from the original data using spectral mixtures. Target signatures are linearly combined with different background materials in specific ratios. The simulated mixtures are appended to the original data and the target areas are removed for evaluation. Commonly used classification algorithms, e.g. Matched Filtering, Adaptive Cosine Estimator are used to determine the detection limit. Fixed false alarm rates are employed to find and analyze certain regions where false alarms usually occur first. A combination of 18 targets and 12 backgrounds is analyzed for three VNIR and two SWIR data sets of the same area.

Direct Stenting in Patients with Acute Lower Limb Arterial Occlusions: Immediate and Long-Term Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Galanakis, Nikolaos; Kontopodis, Nikolaos; Peteinarakis, Ioannis

PurposeThe purpose of this study is to accentuate the efficacy of direct stenting (stent placement without predilatation of the lesion) in patients with acute lower limb arterial ischemia (ALLI).Materials and MethodsBetween January 2010 and September 2015, 16 patients (11 men and 5 women) underwent direct stenting of acute arterial occlusions. All patients had contraindication for surgical revascularization or catheter-directed thrombolysis. According to SVS/ISCVS Classification, six patients had IIa and ten patients IIb ALLI. The occlusions were located in CIA, EIA, SFA, or popliteal artery. Mean follow-up time with clinical examination and color Duplex ultrasonography was 37.6 months (range 1–72). We analyzedmore » the technical and clinical outcomes of the procedures, as well the complications and patency rates.ResultsTechnical success was achieved in all patients (16/16) and there was significant clinical improvement in 15 patients. There was neither distal embolization nor procedure-related complications. During the 6 years of follow-up, four patients died due to non-procedure-related causes and there were two minor and one major amputations. The primary patency rates and the amputation-free survival rates were 93.7 and 87% at 1 year, 75.2 and 71.2% at 3 years, and 75.2 and 62.3%, respectively, at 6 years.ConclusionsDirect stenting may be a valuable alternative procedure for acute arterial occlusions in selected cases with high technical success and significant clinical improvement.Level of EvidenceLevel 4, Case Series.« less

Artificial Neural Network applied to lightning flashes

NASA Astrophysics Data System (ADS)

Gin, R. B.; Guedes, D.; Bianchi, R.

2013-05-01

The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a success rate of 90%. The videos used in this experiment were acquired by seven video cameras installed in São Bernardo do Campo, Brazil, that continuously recorded lightning events during the summer. The cameras were disposed in a 360 loop, recording all data at a time resolution of 33ms. During this period, several convective storms were recorded.

The PLATINO study: description of the distribution, stability, and mortality according to the Global Initiative for Chronic Obstructive Lung Disease classification from 2007 to 2017.

PubMed

Menezes, Ana M; Wehrmeister, Fernando C; Perez-Padilla, Rogelio; Viana, Karynna P; Soares, Claudia; Müllerova, Hana; Valdivia, Gonzalo; Jardim, José R; Montes de Oca, Maria

2017-01-01

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) report provides a framework for classifying COPD reflecting the impacts of disease on patients and for targeting treatment recommendations. The GOLD 2017 introduced a new classification with 16 subgroups based on a composite of spirometry and symptoms/exacerbations. Data from the population-based PLATINO study, collected at baseline and at follow-up, in three sites in Latin America were analyzed to compare the following: 1) the distribution of COPD patients according to GOLD 2007, 2013, and 2017; 2) the stability of the 2007 and 2013 classifications; and 3) the mortality rate over time stratified by GOLD 2007, 2013, and 2017. Of the 524 COPD patients evaluated, most of them were classified as Grade I or II (GOLD 2007) and Group A or B (GOLD 2013), with ≈70% of those classified as Group A in GOLD 2013 also classified as Grade I in GOLD 2007 and the highest percentage (41%) in Group D (2013) classified as Grade III (2007). According to GOLD 2017, among patients with Grade I airflow limitation, 69% of them were categorized into Group A, whereas Grade IV patients were more evenly distributed among Groups A-D. Most of the patients classified by GOLD 2007 remained in the same airflow limitation group at the follow-up; a greater temporal variability was observed with GOLD 2013 classification. Incidence-mortality rate in patients classified by GOLD 2007 was positively associated with increasing severity of airflow obstruction; for GOLD 2013 and GOLD 2017 (Groups A-D), highest mortality rates were observed in Groups C and D. No clear pattern was observed for mortality across the GOLD 2017 subgroups. The PLATINO study data suggest that GOLD 2007 classification shows more stability over time compared with GOLD 2013. No clear patterns with respect to the distribution of patients or incidence-mortality rates were observed according to GOLD 2013/2017 classification.

Learning to Classify with Possible Sensor Failures

DTIC Science & Technology

2014-05-04

SVMs), have demonstrated good classification performance when the training data is representative of the test data [1, 2, 3]. However, in many real...Detection of people and animals using non- imaging sensors,” Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on, pp...classification methods in terms of both classification accuracy and anomaly detection rate using 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13

Classification of speech and language profiles in 4-year old children with cerebral palsy: A prospective preliminary study

PubMed Central

Hustad, Katherine C.; Gorton, Kristin; Lee, Jimin

2010-01-01

Purpose Little is known about the speech and language abilities of children with cerebral palsy (CP) and there is currently no system for classifying speech and language profiles. Such a system would have epidemiological value and would have the potential to advance the development of interventions that improve outcomes. In this study, we propose and test a preliminary speech and language classification system by quantifying how well speech and language data differentiate among children classified into different hypothesized profile groups. Method Speech and language assessment data were collected in a laboratory setting from 34 children with CP (18 males; 16 females) who were a mean age of 54 months (SD 1.8 months). Measures of interest were vowel area, speech rate, language comprehension scores, and speech intelligibility ratings. Results Canonical discriminant function analysis showed that three functions accounted for 100% of the variance among profile groups, with speech variables accounting for 93% of the variance. Classification agreement varied from 74% to 97% using four different classification paradigms. Conclusions Results provide preliminary support for the classification of speech and language abilities of children with CP into four initial profile groups. Further research is necessary to validate the full classification system. PMID:20643795