Accurate crop classification using hierarchical genetic fuzzy rule-based systems
NASA Astrophysics Data System (ADS)
Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.
2014-10-01
This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.
Accurate Arabic Script Language/Dialect Classification
2014-01-01
Army Research Laboratory Accurate Arabic Script Language/Dialect Classification by Stephen C. Tratz ARL-TR-6761 January 2014 Approved for public...1197 ARL-TR-6761 January 2014 Accurate Arabic Script Language/Dialect Classification Stephen C. Tratz Computational and Information Sciences...Include area code) Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 January 2014 Final Accurate Arabic Script Language/Dialect Classification
Fainsinger, Robin L; Nekolaichuk, Cheryl L
2008-06-01
The purpose of this paper is to provide an overview of the development of a "TNM" cancer pain classification system for advanced cancer patients, the Edmonton Classification System for Cancer Pain (ECS-CP). Until we have a common international language to discuss cancer pain, understanding differences in clinical and research experience in opioid rotation and use remains problematic. The complexity of the cancer pain experience presents unique challenges for the classification of pain. To date, no universally accepted pain classification measure can accurately predict the complexity of pain management, particularly for patients with cancer pain that is difficult to treat. In response to this gap in clinical assessment, the Edmonton Staging System (ESS), a classification system for cancer pain, was developed. Difficulties in definitions and interpretation of some aspects of the ESS restricted acceptance and widespread use. Construct, inter-rater reliability, and predictive validity evidence have contributed to the development of the ECS-CP. The five features of the ECS-CP--Pain Mechanism, Incident Pain, Psychological Distress, Addictive Behavior and Cognitive Function--have demonstrated value in predicting pain management complexity. The development of a standardized classification system that is comprehensive, prognostic and simple to use could provide a common language for clinical management and research of cancer pain. An international study to assess the inter-rater reliability and predictive value of the ECS-CP is currently in progress.
Changing Patient Classification System for Hospital Reimbursement in Romania
Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian
2010-01-01
Aim To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Methods Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). Results The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians’ knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Conclusion Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care. PMID:20564769
Changing patient classification system for hospital reimbursement in Romania.
Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian
2010-06-01
To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians' knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case-mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case-mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care.
Overview of classification systems in peripheral artery disease.
Hardman, Rulon L; Jazaeri, Omid; Yi, J; Smith, M; Gupta, Rajan
2014-12-01
Peripheral artery disease (PAD), secondary to atherosclerotic disease, is currently the leading cause of morbidity and mortality in the western world. While PAD is common, it is estimated that the majority of patients with PAD are undiagnosed and undertreated. The challenge to the treatment of PAD is to accurately diagnose the symptoms and determine treatment for each patient. The varied presentations of peripheral vascular disease have led to numerous classification schemes throughout the literature. Consistent grading of patients leads to both objective criteria for treating patients and a baseline for clinical follow-up. Reproducible classification systems are also important in clinical trials and when comparing medical, surgical, and endovascular treatment paradigms. This article reviews the various classification systems for PAD and advantages to each system.
NASA Astrophysics Data System (ADS)
Skrzypek, N.; Warren, S. J.; Faherty, J. K.; Mortlock, D. J.; Burgasser, A. J.; Hewett, P. C.
2015-02-01
Aims: We present a method, named photo-type, to identify and accurately classify L and T dwarfs onto the standard spectral classification system using photometry alone. This enables the creation of large and deep homogeneous samples of these objects efficiently, without the need for spectroscopy. Methods: We created a catalogue of point sources with photometry in 8 bands, ranging from 0.75 to 4.6 μm, selected from an area of 3344 deg2, by combining SDSS, UKIDSS LAS, and WISE data. Sources with 13.0
Accurate mobile malware detection and classification in the cloud.
Wang, Xiaolei; Yang, Yuexiang; Zeng, Yingzhi
2015-01-01
As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.
Accurate, Rapid Taxonomic Classification of Fungal Large-Subunit rRNA Genes
Liu, Kuan-Liang; Porras-Alfaro, Andrea; Eichorst, Stephanie A.
2012-01-01
Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of fungal classification. The lack of an accurate and robust classification tool trained by a validated sequence database for taxonomic placement of fungal LSU genes is a severe limitation in taxonomic analysis of fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 fungal LSU gene fragments, we determined the performance characteristics of a naïve Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naïve Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naïve Bayesian classifier provides an effective and rapid means to classify fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project (http://rdp.cme.msu.edu/classifier/classifier.jsp). PMID:22194300
Neuromuscular disease classification system
NASA Astrophysics Data System (ADS)
Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen
2013-06-01
Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.
Acuity systems dialogue and patient classification system essentials.
Harper, Kelle; McCully, Crystal
2007-01-01
Obtaining resources for quality patient care is a major responsibility of nurse leaders and requires accurate information in the political world of budgeting. Patient classification systems (PCS) assist nurse managers in controlling cost and improving patient care while appropriately using financial resources. This paper communicates acuity systems development, background, flaws, and components while discussing a few tools currently available. It also disseminates the development of a new acuity tool, the Patient Classification System. The PCS tool, developed in a small rural hospital, uses 5 broad concepts: (1) medications, (2) complicated procedures, (3) education, (4) psychosocial issues, and (5) complicated intravenous medications. These concepts embrace a 4-tiered scale that differentiates significant patient characteristics and assists in staffing measures for equality in patient staffing and improving quality of care and performance. Data obtained through use of the PCS can be used by nurse leaders to effectively and objectively lobby for appropriate patient care resources. Two questionnaires distributed to registered nurses on a medical-surgical unit evaluated the nurses' opinion of the 5 concepts and the importance for establishing patient acuity for in-patient care. Interrater reliability among nurses was 87% with the author's acuity tool.
Nursing Classification Systems
Henry, Suzanne Bakken; Mead, Charles N.
1997-01-01
Abstract Our premise is that from the perspective of maximum flexibility of data usage by computer-based record (CPR) systems, existing nursing classification systems are necessary, but not sufficient, for representing important aspects of “what nurses do.” In particular, we have focused our attention on those classification systems that represent nurses' clinical activities through the abstraction of activities into categories of nursing interventions. In this theoretical paper, we argue that taxonomic, combinatorial vocabularies capable of coding atomic-level nursing activities are required to effectively capture in a reproducible and reversible manner the clinical decisions and actions of nurses, and that, without such vocabularies and associated grammars, potentially important clinical process data is lost during the encoding process. Existing nursing intervention classification systems do not fulfill these criteria. As background to our argument, we first present an overview of the content, methods, and evaluation criteria used in previous studies whose focus has been to evaluate the effectiveness of existing coding and classification systems. Next, using the Ingenerf typology of taxonomic vocabularies, we categorize the formal type and structure of three existing nursing intervention classification systems—Nursing Interventions Classification, Omaha System, and Home Health Care Classification. Third, we use records from home care patients to show examples of lossy data transformation, the loss of potentially significant atomic data, resulting from encoding using each of the three systems. Last, we provide an example of the application of a formal representation methodology (conceptual graphs) which we believe could be used as a model to build the required combinatorial, taxonomic vocabulary for representing nursing interventions. PMID:9147341
MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering
Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu
2009-01-01
Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors. PMID:19698124
NASA Astrophysics Data System (ADS)
Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y.; Drake, Steven K.; Gucek, Marjan; Sacks, David B.; Yu, Yi-Kuo
2018-06-01
Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.
Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y; Drake, Steven K; Gucek, Marjan; Sacks, David B; Yu, Yi-Kuo
2018-06-05
Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.
NASA Astrophysics Data System (ADS)
Lin, Y.; Chen, X.
2016-12-01
Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.
Aircraft Operations Classification System
NASA Technical Reports Server (NTRS)
Harlow, Charles; Zhu, Weihong
2001-01-01
Accurate data is important in the aviation planning process. In this project we consider systems for measuring aircraft activity at airports. This would include determining the type of aircraft such as jet, helicopter, single engine, and multiengine propeller. Some of the issues involved in deploying technologies for monitoring aircraft operations are cost, reliability, and accuracy. In addition, the system must be field portable and acceptable at airports. A comparison of technologies was conducted and it was decided that an aircraft monitoring system should be based upon acoustic technology. A multimedia relational database was established for the study. The information contained in the database consists of airport information, runway information, acoustic records, photographic records, a description of the event (takeoff, landing), aircraft type, and environmental information. We extracted features from the time signal and the frequency content of the signal. A multi-layer feed-forward neural network was chosen as the classifier. Training and testing results were obtained. We were able to obtain classification results of over 90 percent for training and testing for takeoff events.
Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.
Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried; De Vos, Winnok H
2017-01-01
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.
Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification
Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried
2017-01-01
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows. PMID:28125723
NASA Astrophysics Data System (ADS)
Manteiga, M.; Carricajo, I.; Rodríguez, A.; Dafonte, C.; Arcay, B.
2009-02-01
Astrophysics is evolving toward a more rational use of costly observational data by intelligently exploiting the large terrestrial and spatial astronomical databases. In this paper, we present a study showing the suitability of an expert system to perform the classification of stellar spectra in the Morgan and Keenan (MK) system. Using the formalism of artificial intelligence for the development of such a system, we propose a rules' base that contains classification criteria and confidence grades, all integrated in an inference engine that emulates human reasoning by means of a hierarchical decision rules tree that also considers the uncertainty factors associated with rules. Our main objective is to illustrate the formulation and development of such a system for an astrophysical classification problem. An extensive spectral database of MK standard spectra has been collected and used as a reference to determine the spectral indexes that are suitable for classification in the MK system. It is shown that by considering 30 spectral indexes and associating them with uncertainty factors, we can find an accurate diagnose in MK types of a particular spectrum. The system was evaluated against the NOAO-INDO-US spectral catalog.
Cognitive-motivational deficits in ADHD: development of a classification system.
Gupta, Rashmi; Kar, Bhoomika R; Srinivasan, Narayanan
2011-01-01
The classification systems developed so far to detect attention deficit/hyperactivity disorder (ADHD) do not have high sensitivity and specificity. We have developed a classification system based on several neuropsychological tests that measure cognitive-motivational functions that are specifically impaired in ADHD children. A total of 240 (120 ADHD children and 120 healthy controls) children in the age range of 6-9 years and 32 Oppositional Defiant Disorder (ODD) children (aged 9 years) participated in the study. Stop-Signal, Task-Switching, Attentional Network, and Choice Delay tests were administered to all the participants. Receiver operating characteristic (ROC) analysis indicated that percentage choice of long-delay reward best classified the ADHD children from healthy controls. Single parameters were not helpful in making a differential classification of ADHD with ODD. Multinominal logistic regression (MLR) was performed with multiple parameters (data fusion) that produced improved overall classification accuracy. A combination of stop-signal reaction time, posterror-slowing, mean delay, switch cost, and percentage choice of long-delay reward produced an overall classification accuracy of 97.8%; with internal validation, the overall accuracy was 92.2%. Combining parameters from different tests of control functions not only enabled us to accurately classify ADHD children from healthy controls but also in making a differential classification with ODD. These results have implications for the theories of ADHD.
Characteristics of a global classification system for perinatal deaths: a Delphi consensus study.
Wojcieszek, Aleena M; Reinebrant, Hanna E; Leisher, Susannah Hopkins; Allanson, Emma; Coory, Michael; Erwich, Jan Jaap; Frøen, J Frederik; Gardosi, Jason; Gordijn, Sanne; Gulmezoglu, Metin; Heazell, Alexander E P; Korteweg, Fleurisca J; McClure, Elizabeth; Pattinson, Robert; Silver, Robert M; Smith, Gordon; Teoh, Zheyi; Tunçalp, Özge; Flenady, Vicki
2016-08-15
Despite the global burden of perinatal deaths, there is currently no single, globally-acceptable classification system for perinatal deaths. Instead, multiple, disparate systems are in use world-wide. This inconsistency hinders accurate estimates of causes of death and impedes effective prevention strategies. The World Health Organisation (WHO) is developing a globally-acceptable classification approach for perinatal deaths. To inform this work, we sought to establish a consensus on the important characteristics of such a system. A group of international experts in the classification of perinatal deaths were identified and invited to join an expert panel to develop a list of important characteristics of a quality global classification system for perinatal death. A Delphi consensus methodology was used to reach agreement. Three rounds of consultation were undertaken using a purpose built on-line survey. Round one sought suggested characteristics for subsequent scoring and selection in rounds two and three. The panel of experts agreed on a total of 17 important characteristics for a globally-acceptable perinatal death classification system. Of these, 10 relate to the structural design of the system and 7 relate to the functional aspects and use of the system. This study serves as formative work towards the development of a globally-acceptable approach for the classification of the causes of perinatal deaths. The list of functional and structural characteristics identified should be taken into consideration when designing and developing such a system.
Reliability of intracerebral hemorrhage classification systems: A systematic review.
Rannikmäe, Kristiina; Woodfield, Rebecca; Anderson, Craig S; Charidimou, Andreas; Chiewvit, Pipat; Greenberg, Steven M; Jeng, Jiann-Shing; Meretoja, Atte; Palm, Frederic; Putaala, Jukka; Rinkel, Gabriel Je; Rosand, Jonathan; Rost, Natalia S; Strbian, Daniel; Tatlisumak, Turgut; Tsai, Chung-Fen; Wermer, Marieke Jh; Werring, David; Yeh, Shin-Joe; Al-Shahi Salman, Rustam; Sudlow, Cathie Lm
2016-08-01
Accurately distinguishing non-traumatic intracerebral hemorrhage (ICH) subtypes is important since they may have different risk factors, causal pathways, management, and prognosis. We systematically assessed the inter- and intra-rater reliability of ICH classification systems. We sought all available reliability assessments of anatomical and mechanistic ICH classification systems from electronic databases and personal contacts until October 2014. We assessed included studies' characteristics, reporting quality and potential for bias; summarized reliability with kappa value forest plots; and performed meta-analyses of the proportion of cases classified into each subtype. We included 8 of 2152 studies identified. Inter- and intra-rater reliabilities were substantial to perfect for anatomical and mechanistic systems (inter-rater kappa values: anatomical 0.78-0.97 [six studies, 518 cases], mechanistic 0.89-0.93 [three studies, 510 cases]; intra-rater kappas: anatomical 0.80-1 [three studies, 137 cases], mechanistic 0.92-0.93 [two studies, 368 cases]). Reporting quality varied but no study fulfilled all criteria and none was free from potential bias. All reliability studies were performed with experienced raters in specialist centers. Proportions of ICH subtypes were largely consistent with previous reports suggesting that included studies are appropriately representative. Reliability of existing classification systems appears excellent but is unknown outside specialist centers with experienced raters. Future reliability comparisons should be facilitated by studies following recently published reporting guidelines. © 2016 World Stroke Organization.
A novel risk classification system for 30-day mortality in children undergoing surgery
Walter, Arianne I.; Jones, Tamekia L.; Huang, Eunice Y.; Davis, Robert L.
2018-01-01
A simple, objective and accurate way of grouping children undergoing surgery into clinically relevant risk groups is needed. The purpose of this study, is to develop and validate a preoperative risk classification system for postsurgical 30-day mortality for children undergoing a wide variety of operations. The National Surgical Quality Improvement Project-Pediatric participant use file data for calendar years 2012–2014 was analyzed to determine preoperative variables most associated with death within 30 days of operation (D30). Risk groups were created using classification tree analysis based on these preoperative variables. The resulting risk groups were validated using 2015 data, and applied to neonates and higher risk CPT codes to determine validity in high-risk subpopulations. A five-level risk classification was found to be most accurate. The preoperative need for ventilation, oxygen support, inotropic support, sepsis, the need for emergent surgery and a do not resuscitate order defined non-overlapping groups with observed rates of D30 that vary from 0.075% (Very Low Risk) to 38.6% (Very High Risk). When CPT codes where death was never observed are eliminated or when the system is applied to neonates, the groupings remained predictive of death in an ordinal manner. PMID:29351327
Waltho, Daniel; Hatchell, Alexandra; Thoma, Achilleas
2017-03-01
Gynecomastia is a common deformity of the male breast, where certain cases warrant surgical management. There are several surgical options, which vary depending on the breast characteristics. To guide surgical management, several classification systems for gynecomastia have been proposed. A systematic review was performed to (1) identify all classification systems for the surgical management of gynecomastia, and (2) determine the adequacy of these classification systems to appropriately categorize the condition for surgical decision-making. The search yielded 1012 articles, and 11 articles were included in the review. Eleven classification systems in total were ascertained, and a total of 10 unique features were identified: (1) breast size, (2) skin redundancy, (3) breast ptosis, (4) tissue predominance, (5) upper abdominal laxity, (6) breast tuberosity, (7) nipple malposition, (8) chest shape, (9) absence of sternal notch, and (10) breast skin elasticity. On average, classification systems included two or three of these features. Breast size and ptosis were the most commonly included features. Based on their review of the current classification systems, the authors believe the ideal classification system should be universal and cater to all causes of gynecomastia; be surgically useful and easy to use; and should include a comprehensive set of clinically appropriate patient-related features, such as breast size, breast ptosis, tissue predominance, and skin redundancy. None of the current classification systems appears to fulfill these criteria.
42 CFR 412.620 - Patient classification system.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 42 Public Health 2 2010-10-01 2010-10-01 false Patient classification system. 412.620 Section 412... Inpatient Rehabilitation Hospitals and Rehabilitation Units § 412.620 Patient classification system. (a) Classification methodology. (1) A patient classification system is used to classify patients in inpatient...
42 CFR 412.620 - Patient classification system.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 42 Public Health 2 2011-10-01 2011-10-01 false Patient classification system. 412.620 Section 412... Inpatient Rehabilitation Hospitals and Rehabilitation Units § 412.620 Patient classification system. (a) Classification methodology. (1) A patient classification system is used to classify patients in inpatient...
Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.
Oh, Sang-Il; Kang, Hang-Bong
2017-01-22
To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.
Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems
Oh, Sang-Il; Kang, Hang-Bong
2017-01-01
To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742
Obtaining Accurate Probabilities Using Classifier Calibration
ERIC Educational Resources Information Center
Pakdaman Naeini, Mahdi
2016-01-01
Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…
The development of a classification system for maternity models of care.
Donnolley, Natasha; Butler-Henderson, Kerryn; Chapman, Michael; Sullivan, Elizabeth
2016-08-01
A lack of standard terminology or means to identify and define models of maternity care in Australia has prevented accurate evaluations of outcomes for mothers and babies in different models of maternity care. As part of the Commonwealth-funded National Maternity Data Development Project, a classification system was developed utilising a data set specification that defines characteristics of models of maternity care. The Maternity Care Classification System or MaCCS was developed using a participatory action research design that built upon the published and grey literature. The study identified the characteristics that differentiate models of care and classifies models into eleven different Major Model Categories. The MaCCS will enable individual health services, local health districts (networks), jurisdictional and national health authorities to make better informed decisions for planning, policy development and delivery of maternity services in Australia. © The Author(s) 2016.
NASA Astrophysics Data System (ADS)
Bechet, P.; Mitran, R.; Munteanu, M.
2013-08-01
Non-contact methods for the assessment of vital signs are of great interest for specialists due to the benefits obtained in both medical and special applications, such as those for surveillance, monitoring, and search and rescue. This paper investigates the possibility of implementing a digital processing algorithm based on the MUSIC (Multiple Signal Classification) parametric spectral estimation in order to reduce the observation time needed to accurately measure the heart rate. It demonstrates that, by proper dimensioning the signal subspace, the MUSIC algorithm can be optimized in order to accurately assess the heart rate during an 8-28 s time interval. The validation of the processing algorithm performance was achieved by minimizing the mean error of the heart rate after performing simultaneous comparative measurements on several subjects. In order to calculate the error the reference value of heart rate was measured using a classic measurement system through direct contact.
Comparing drug classification systems.
Mahoney, Anne; Evans, Jonathan
2008-11-06
An essential quality of drug classification systems is the ability to assign medications to a structured hierarchy for categories such as mechanism of action, physiological effects, and therapeutic indications. No single classification system can meet all of these needs; however, there should be consistency among those that group by the same underlying principals. We discovered discrepancies in how drugs with multiple therapeutic indications are classified among four widely used schemas.
Ibrahim, E F; Forrest, N P; Forester, A
2015-10-01
Misinterpretation of the Rockwood classification system for acromioclavicular joint (ACJ) separations has resulted in a trend towards using unilateral radiographs for grading. Further, the use of weighted views to 'unmask' a grade III injury has fallen out of favour. Recent evidence suggests that many radiographic grade III injuries represent only a partial injury to the stabilising ligaments. This study aimed to determine (1) whether accurate classification is possible on unilateral radiographs and (2) the efficacy of weighted bilateral radiographs in unmasking higher-grade injuries. Complete bilateral non-weighted and weighted sets of radiographs for patients presenting with an acromioclavicular separation over a 10-year period were analysed retrospectively, and they were graded I-VI according to Rockwood's criteria. Comparison was made between grading based on (1) a single antero-posterior (AP) view of the injured side, (2) bilateral non-weighted views and (3) bilateral weighted views. Radiographic measurements for cases that changed grade after weighted views were statistically compared to see if this could have been predicted beforehand. Fifty-nine sets of radiographs on 59 patients (48 male, mean age of 33 years) were included. Compared with unilateral radiographs, non-weighted bilateral comparison films resulted in a grade change for 44 patients (74.5%). Twenty-eight of 56 patients initially graded as I, II or III were upgraded to grade V and two of three initial grade V patients were downgraded to grade III. The addition of a weighted view further upgraded 10 patients to grade V. No grade II injury was changed to grade III and no injury of any severity was downgraded by a weighted view. Grade III injuries upgraded on weighted views had a significantly greater baseline median percentage coracoclavicular distance increase than those that were not upgraded (80.7% vs. 55.4%, p=0.015). However, no cut-off point for this value could be identified to predict an
Proximal humeral fracture classification systems revisited.
Majed, Addie; Macleod, Iain; Bull, Anthony M J; Zyto, Karol; Resch, Herbert; Hertel, Ralph; Reilly, Peter; Emery, Roger J H
2011-10-01
This study evaluated several classification systems and expert surgeons' anatomic understanding of these complex injuries based on a consecutive series of patients. We hypothesized that current proximal humeral fracture classification systems, regardless of imaging methods, are not sufficiently reliable to aid clinical management of these injuries. Complex fractures in 96 consecutive patients were investigated by generation of rapid sequence prototyping models from computed tomography Digital Imaging and Communications in Medicine (DICOM) imaging data. Four independent senior observers were asked to classify each model using 4 classification systems: Neer, AO, Codman-Hertel, and a prototype classification system by Resch. Interobserver and intraobserver κ coefficient values were calculated for the overall classification system and for selected classification items. The κ coefficient values for the interobserver reliability were 0.33 for Neer, 0.11 for AO, 0.44 for Codman-Hertel, and 0.15 for Resch. Interobserver reliability κ coefficient values were 0.32 for the number of fragments and 0.30 for the anatomic segment involved using the Neer system, 0.30 for the AO type (A, B, C), and 0.53, 0.48, and 0.08 for the Resch impaction/distraction, varus/valgus and flexion/extension subgroups, respectively. Three-part fractures showed low reliability for the Neer and AO systems. Currently available evidence suggests fracture classifications in use have poor intra- and inter-observer reliability despite the modality of imaging used thus making treating these injuries difficult as weak as affecting scientific research as well. This study was undertaken to evaluate the reliability of several systems using rapid sequence prototype models. Overall interobserver κ values represented slight to moderate agreement. The most reliable interobserver scores were found with the Codman-Hertel classification, followed by elements of Resch's trial system. The AO system had the lowest
42 CFR 412.513 - Patient classification system.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 42 Public Health 2 2010-10-01 2010-10-01 false Patient classification system. 412.513 Section 412... Long-Term Care Hospitals § 412.513 Patient classification system. (a) Classification methodology. CMS...-DRGs. (1) The classification of a particular discharge is based, as appropriate, on the patient's age...
42 CFR 412.513 - Patient classification system.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 42 Public Health 2 2011-10-01 2011-10-01 false Patient classification system. 412.513 Section 412... Long-Term Care Hospitals § 412.513 Patient classification system. (a) Classification methodology. CMS...-DRGs. (1) The classification of a particular discharge is based, as appropriate, on the patient's age...
Sharma, Manish; Goyal, Deepanshu; Achuth, P V; Acharya, U Rajendra
2018-07-01
Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique. Copyright © 2018 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Zhao, Xiao-mei; Xie, Dong-fan; Li, Qi
2015-02-01
With the development of intelligent transport system, advanced information feedback strategies have been developed to reduce traffic congestion and enhance the capacity. However, previous strategies provide accurate information to travelers and our simulation results show that accurate information brings negative effects, especially in delay case. Because travelers prefer to the best condition route with accurate information, and delayed information cannot reflect current traffic condition but past. Then travelers make wrong routing decisions, causing the decrease of the capacity and the increase of oscillations and the system deviating from the equilibrium. To avoid the negative effect, bounded rationality is taken into account by introducing a boundedly rational threshold BR. When difference between two routes is less than the BR, routes have equal probability to be chosen. The bounded rationality is helpful to improve the efficiency in terms of capacity, oscillation and the gap deviating from the system equilibrium.
Prototype Expert System for Climate Classification.
ERIC Educational Resources Information Center
Harris, Clay
Many students find climate classification laborious and time-consuming, and through their lack of repetition fail to grasp the details of classification. This paper describes an expert system for climate classification that is being developed at Middle Tennessee State University. Topics include: (1) an introduction to the nature of classification,…
An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.
Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan
2015-01-01
A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the
Casemix classification systems.
Fetter, R B
1999-01-01
The idea of using casemix classification to manage hospital services is not new, but has been limited by available technology. It was not until after the introduction of Medicare in the United States in 1965 that serious attempts were made to measure hospital production in order to contain spiralling costs. This resulted in a system of casemix classification known as diagnosis related groups (DRGs). This paper traces the development of DRGs and their evolution from the initial version to the All Patient Refined DRGs developed in 1991.
Classification systems for natural resource management
Kleckner, Richard L.
1981-01-01
Resource managers employ various types of resource classification systems in their management activities such as inventory, mapping, and data analysis. Classification is the ordering or arranging of objects into groups or sets on the basis of their relationships, and as such, provide the resource managers with a structure for organizing their needed information. In addition of conforming to certain logical principles, resource classifications should be flexible, widely applicable to a variety of environmental conditions, and useable with minimal training. The process of classification may be approached from the bottom up (aggregation) or the top down (subdivision) or a combination of both, depending on the purpose of the classification. Most resource classification systems in use today focus on a single resource and are used for a single, limited purpose. However, resource managers now must employ the concept of multiple use in their management activities. What they need is an integrated, ecologically based approach to resource classification which would fulfill multiple-use mandates. In an effort to achieve resource-data compatibility and data sharing among Federal agencies, and interagency agreement has been signed by five Federal agencies to coordinate and cooperate in the area of resource classification and inventory.
Wong, Wai Keat; Shetty, Subhaschandra
2017-08-01
Parotidectomy remains the mainstay of treatment for both benign and malignant lesions of the parotid gland. There exists a wide range of possible surgical options in parotidectomy in terms of extent of parotid tissue removed. There is increasing need for uniformity of terminology resulting from growing interest in modifications of the conventional parotidectomy. It is, therefore, of paramount importance for a standardized classification system in describing extent of parotidectomy. Recently, the European Salivary Gland Society (ESGS) proposed a novel classification system for parotidectomy. The aim of this study is to evaluate this system. A classification system proposed by the ESGS was critically re-evaluated and modified to increase its accuracy and its acceptability. Modifications mainly focused on subdividing Levels I and II into IA, IB, IIA, and IIB. From June 2006 to June 2016, 126 patients underwent 130 parotidectomies at our hospital. The classification system was tested in that cohort of patient. While the ESGS classification system is comprehensive, it does not cover all possibilities. The addition of Sublevels IA, IB, IIA, and IIB may help to address some of the clinical situations seen and is clinically relevant. We aim to test the modified classification system for partial parotidectomy to address some of the challenges mentioned.
Federal Register 2010, 2011, 2012, 2013, 2014
2013-03-26
...-AM78 Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System... 2007 North American Industry Classification System (NAICS) codes currently used in Federal Wage System... (OPM) issued a final rule (73 FR 45853) to update the 2002 North American Industry Classification...
ABCD classification system: a novel classification for subaxial cervical spine injuries.
Shousha, Mootaz
2014-04-20
The classification system was derived through a retrospective analysis of 73 consecutive cases of subaxial cervical spine injury as well as thorough literature review. To define a new classification system for subaxial cervical spine injuries. There exist several methods to classify subaxial cervical spine injuries but no single system has emerged as clearly superior to the others. On the basis of a 2-column anatomical model, the first part of the proposed classification is an anatomical description of the injury. It delivers the information whether the injury is bony, ligamentous, or a combined one. The first 4 alphabetical letters have been used for simplicity. Each column is represented by an alphabetical letter from A to D. Each letter has a radiological meaning (A = Absent injury, B = Bony lesion, C = Combined bony and ligamentous, D = Disc or ligamentous injury).The second part of the classification is represented by 3 modifiers. These are the neurological status of the patient (N), the degree of spinal canal stenosis (S), and the degree of instability (I). For simplicity, each modifier was graded in an ascending pattern of severity from zero to 2. The last part is optional and denotes which radiological examination has been used to define the injury type. The new ABCD classification was applicable for all patients. The most common type was anterior ligamentous and posterior combined injury "DC" (37.9%), followed by "DD" injury in 12% of the cases. Through this work a new classification for cervical spine injuries is proposed. The aim is to establish criteria for a common language in description of cervical injuries aiming for simplification, especially for junior residents. Each letter and each sign has a meaning to deliver the largest amount of information. Both the radiological as well as the clinical data are represented in this scheme. However, further evaluation of this classification is needed. 3.
Classification of close binary systems by Svechnikov
NASA Astrophysics Data System (ADS)
Dryomova, G. N.
The paper presents the historical overview of classification schemes of eclipsing variable stars with the foreground of advantages of the classification scheme by Svechnikov being widely appreciated for Close Binary Systems due to simplicity of classification criteria and brevity.
A statistical approach to root system classification
Bodner, Gernot; Leitner, Daniel; Nakhforoosh, Alireza; Sobotik, Monika; Moder, Karl; Kaul, Hans-Peter
2013-01-01
Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for “plant functional type” identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential. PMID:23914200
A statistical approach to root system classification.
Bodner, Gernot; Leitner, Daniel; Nakhforoosh, Alireza; Sobotik, Monika; Moder, Karl; Kaul, Hans-Peter
2013-01-01
Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for "plant functional type" identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential.
Extensions to the Speech Disorders Classification System (SDCS)
ERIC Educational Resources Information Center
Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.
2010-01-01
This report describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). Part I describes a classification extension to the SDCS to differentiate motor speech disorders from speech delay and to differentiate among three sub-types of motor speech disorders.…
NASA Astrophysics Data System (ADS)
Shyu, Mei-Ling; Sainani, Varsha
The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.
An evaluation of classification systems for stillbirth
Flenady, Vicki; Frøen, J Frederik; Pinar, Halit; Torabi, Rozbeh; Saastad, Eli; Guyon, Grace; Russell, Laurie; Charles, Adrian; Harrison, Catherine; Chauke, Lawrence; Pattinson, Robert; Koshy, Rachel; Bahrin, Safiah; Gardener, Glenn; Day, Katie; Petersson, Karin; Gordon, Adrienne; Gilshenan, Kristen
2009-01-01
Background Audit and classification of stillbirths is an essential part of clinical practice and a crucial step towards stillbirth prevention. Due to the limitations of the ICD system and lack of an international approach to an acceptable solution, numerous disparate classification systems have emerged. We assessed the performance of six contemporary systems to inform the development of an internationally accepted approach. Methods We evaluated the following systems: Amended Aberdeen, Extended Wigglesworth; PSANZ-PDC, ReCoDe, Tulip and CODAC. Nine teams from 7 countries applied the classification systems to cohorts of stillbirths from their regions using 857 stillbirth cases. The main outcome measures were: the ability to retain the important information about the death using the InfoKeep rating; the ease of use according to the Ease rating (both measures used a five-point scale with a score <2 considered unsatisfactory); inter-observer agreement and the proportion of unexplained stillbirths. A randomly selected subset of 100 stillbirths was used to assess inter-observer agreement. Results InfoKeep scores were significantly different across the classifications (p ≤ 0.01) due to low scores for Wigglesworth and Aberdeen. CODAC received the highest mean (SD) score of 3.40 (0.73) followed by PSANZ-PDC, ReCoDe and Tulip [2.77 (1.00), 2.36 (1.21), 1.92 (1.24) respectively]. Wigglesworth and Aberdeen resulted in a high proportion of unexplained stillbirths and CODAC and Tulip the lowest. While Ease scores were different (p ≤ 0.01), all systems received satisfactory scores; CODAC received the highest score. Aberdeen and Wigglesworth showed poor agreement with kappas of 0.35 and 0.25 respectively. Tulip performed best with a kappa of 0.74. The remainder had good to fair agreement. Conclusion The Extended Wigglesworth and Amended Aberdeen systems cannot be recommended for classification of stillbirths. Overall, CODAC performed best with PSANZ-PDC and ReCoDe performing
An evaluation of classification systems for stillbirth.
Flenady, Vicki; Frøen, J Frederik; Pinar, Halit; Torabi, Rozbeh; Saastad, Eli; Guyon, Grace; Russell, Laurie; Charles, Adrian; Harrison, Catherine; Chauke, Lawrence; Pattinson, Robert; Koshy, Rachel; Bahrin, Safiah; Gardener, Glenn; Day, Katie; Petersson, Karin; Gordon, Adrienne; Gilshenan, Kristen
2009-06-19
Audit and classification of stillbirths is an essential part of clinical practice and a crucial step towards stillbirth prevention. Due to the limitations of the ICD system and lack of an international approach to an acceptable solution, numerous disparate classification systems have emerged. We assessed the performance of six contemporary systems to inform the development of an internationally accepted approach. We evaluated the following systems: Amended Aberdeen, Extended Wigglesworth; PSANZ-PDC, ReCoDe, Tulip and CODAC. Nine teams from 7 countries applied the classification systems to cohorts of stillbirths from their regions using 857 stillbirth cases. The main outcome measures were: the ability to retain the important information about the death using the InfoKeep rating; the ease of use according to the Ease rating (both measures used a five-point scale with a score <2 considered unsatisfactory); inter-observer agreement and the proportion of unexplained stillbirths. A randomly selected subset of 100 stillbirths was used to assess inter-observer agreement. InfoKeep scores were significantly different across the classifications (p < or = 0.01) due to low scores for Wigglesworth and Aberdeen. CODAC received the highest mean (SD) score of 3.40 (0.73) followed by PSANZ-PDC, ReCoDe and Tulip [2.77 (1.00), 2.36 (1.21), 1.92 (1.24) respectively]. Wigglesworth and Aberdeen resulted in a high proportion of unexplained stillbirths and CODAC and Tulip the lowest. While Ease scores were different (p < or = 0.01), all systems received satisfactory scores; CODAC received the highest score. Aberdeen and Wigglesworth showed poor agreement with kappas of 0.35 and 0.25 respectively. Tulip performed best with a kappa of 0.74. The remainder had good to fair agreement. The Extended Wigglesworth and Amended Aberdeen systems cannot be recommended for classification of stillbirths. Overall, CODAC performed best with PSANZ-PDC and ReCoDe performing well. Tulip was shown to have the
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.
A Confidence Paradigm for Classification Systems
2008-09-01
methodology to determine how much confi- dence one should have in a classifier output. This research proposes a framework to determine the level of...theoretical framework that attempts to unite the viewpoints of the classification system developer (or engineer) and the classification system user (or...operating point. An algorithm is developed that minimizes a “confidence” measure called Binned Error in the Posterior ( BEP ). Then, we prove that training a
Designing and Implementation of River Classification Assistant Management System
NASA Astrophysics Data System (ADS)
Zhao, Yinjun; Jiang, Wenyuan; Yang, Rujun; Yang, Nan; Liu, Haiyan
2018-03-01
In an earlier publication, we proposed a new Decision Classifier (DCF) for Chinese river classification based on their structures. To expand, enhance and promote the application of the DCF, we build a computer system to support river classification named River Classification Assistant Management System. Based on ArcEngine and ArcServer platform, this system implements many functions such as data management, extraction of river network, river classification, and results publication under combining Client / Server with Browser / Server framework.
Creating a Canonical Scientific and Technical Information Classification System for NCSTRL+
NASA Technical Reports Server (NTRS)
Tiffany, Melissa E.; Nelson, Michael L.
1998-01-01
The purpose of this paper is to describe the new subject classification system for the NCSTRL+ project. NCSTRL+ is a canonical digital library (DL) based on the Networked Computer Science Technical Report Library (NCSTRL). The current NCSTRL+ classification system uses the NASA Scientific and Technical (STI) subject classifications, which has a bias towards the aerospace, aeronautics, and engineering disciplines. Examination of other scientific and technical information classification systems showed similar discipline-centric weaknesses. Traditional, library-oriented classification systems represented all disciplines, but were too generalized to serve the needs of a scientific and technically oriented digital library. Lack of a suitable existing classification system led to the creation of a lightweight, balanced, general classification system that allows the mapping of more specialized classification schemes into the new framework. We have developed the following classification system to give equal weight to all STI disciplines, while being compact and lightweight.
Automotive System for Remote Surface Classification.
Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail
2017-04-01
In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.
Automotive System for Remote Surface Classification
Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail
2017-01-01
In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions. PMID:28368297
Discriminant forest classification method and system
Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.
2012-11-06
A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.
Common occupational classification system - revision 3
DOE Office of Scientific and Technical Information (OSTI.GOV)
Stahlman, E.J.; Lewis, R.E.
1996-05-01
Workforce planning has become an increasing concern within the DOE community as the Office of Environmental Restoration and Waste Management (ER/WM or EM) seeks to consolidate and refocus its activities and the Office of Defense Programs (DP) closes production sites. Attempts to manage the growth and skills mix of the EM workforce while retaining the critical skills of the DP workforce have been difficult due to the lack of a consistent set of occupational titles and definitions across the complex. Two reasons for this difficulty may be cited. First, classification systems commonly used in industry often fail to cover inmore » sufficient depth the unique demands of DOE`s nuclear energy and research community. Second, the government practice of contracting the operation of government facilities to the private sector has introduced numerous contractor-specific classification schemes to the DOE complex. As a result, sites/contractors report their workforce needs using unique classification systems. It becomes difficult, therefore, to roll these data up to the national level necessary to support strategic planning and analysis. The Common Occupational Classification System (COCS) is designed to overcome these workforce planning barriers. The COCS is based on earlier workforce planning activities and the input of technical, workforce planning, and human resource managers from across the DOE complex. It provides a set of mutually-exclusive occupation titles and definitions that cover the broad range of activities present in the DOE complex. The COCS is not a required record-keeping or data management guide. Neither is it intended to replace contractor/DOE-specific classification systems. Instead, the system provides a consistent, high- level, functional structure of occupations to which contractors can crosswalk (map) their job titles.« less
Intra- and Interobserver Reliability of Three Classification Systems for Hallux Rigidus.
Dillard, Sarita; Schilero, Christina; Chiang, Sharon; Pham, Peter
2018-04-18
There are over ten classification systems currently used in the staging of hallux rigidus. This results in confusion and inconsistency with radiographic interpretation and treatment. The reliability of hallux rigidus classification systems has not yet been tested. The purpose of this study was to evaluate intra- and interobserver reliability using three commonly used classifications for hallux rigidus. Twenty-one plain radiograph sets were presented to ten ACFAS board-certified foot and ankle surgeons. Each physician classified each radiograph based on clinical experience and knowledge according to the Regnauld, Roukis, and Hattrup and Johnson classification systems. The two-way mixed single-measure consistency intraclass correlation was used to calculate intra- and interrater reliability. The intrarater reliability of individual sets for the Roukis and Hattrup and Johnson classification systems was "fair to good" (Roukis, 0.62±0.19; Hattrup and Johnson, 0.62±0.28), whereas the intrarater reliability of individual sets for the Regnauld system bordered between "fair to good" and "poor" (0.43±0.24). The interrater reliability of the mean classification was "excellent" for all three classification systems. Conclusions Reliable and reproducible classification systems are essential for treatment and prognostic implications in hallux rigidus. In our study, Roukis classification system had the best intrarater reliability. Although there are various classification systems for hallux rigidus, our results indicate that all three of these classification systems show reliability and reproducibility.
U.S. Geological Survey Library classification system
Sasscer, R. Scott
2000-01-01
The U.S. Geological Survey Library classification system has been designed for earth science libraries. It is a tool for assigning call numbers to earth science and allied pure science materials in order to collect these materials into related subject groups on the library shelves and arrange them alphabetically by author and title. The classification can be used as a retrieval system to access materials through the subject and geographic numbers.The classification scheme has been developed over the years since 1904 to meet the ever-changing needs of increased specialization and the development of new areas of research in the earth sciences. The system contains seven schedules: Subject scheduleGeological survey schedule Earth science periodical scheduleGovernment document periodical scheduleGeneral science periodical schedule Earth science map schedule Geographic schedule Introduction provides detailed instructions on the construction of call numbers for works falling into the framework of the classification schedules.The tables following the introduction can be quickly accessed through the use of the newly expanded subject index.The purpose of this publication is to provide the earth science community with a classification and retrieval system for earth science materials, to offer sufficient explanation of its structure and use, and to enable library staff and clientele to classify or access research materials in a library collection.
Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James
2018-01-01
Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus
A drone detection with aircraft classification based on a camera array
NASA Astrophysics Data System (ADS)
Liu, Hao; Qu, Fangchao; Liu, Yingjian; Zhao, Wei; Chen, Yitong
2018-03-01
In recent years, because of the rapid popularity of drones, many people have begun to operate drones, bringing a range of security issues to sensitive areas such as airports and military locus. It is one of the important ways to solve these problems by realizing fine-grained classification and providing the fast and accurate detection of different models of drone. The main challenges of fine-grained classification are that: (1) there are various types of drones, and the models are more complex and diverse. (2) the recognition test is fast and accurate, in addition, the existing methods are not efficient. In this paper, we propose a fine-grained drone detection system based on the high resolution camera array. The system can quickly and accurately recognize the detection of fine grained drone based on hd camera.
DNA methylation-based classification of central nervous system tumours.
Capper, David; Jones, David T W; Sill, Martin; Hovestadt, Volker; Schrimpf, Daniel; Sturm, Dominik; Koelsche, Christian; Sahm, Felix; Chavez, Lukas; Reuss, David E; Kratz, Annekathrin; Wefers, Annika K; Huang, Kristin; Pajtler, Kristian W; Schweizer, Leonille; Stichel, Damian; Olar, Adriana; Engel, Nils W; Lindenberg, Kerstin; Harter, Patrick N; Braczynski, Anne K; Plate, Karl H; Dohmen, Hildegard; Garvalov, Boyan K; Coras, Roland; Hölsken, Annett; Hewer, Ekkehard; Bewerunge-Hudler, Melanie; Schick, Matthias; Fischer, Roger; Beschorner, Rudi; Schittenhelm, Jens; Staszewski, Ori; Wani, Khalida; Varlet, Pascale; Pages, Melanie; Temming, Petra; Lohmann, Dietmar; Selt, Florian; Witt, Hendrik; Milde, Till; Witt, Olaf; Aronica, Eleonora; Giangaspero, Felice; Rushing, Elisabeth; Scheurlen, Wolfram; Geisenberger, Christoph; Rodriguez, Fausto J; Becker, Albert; Preusser, Matthias; Haberler, Christine; Bjerkvig, Rolf; Cryan, Jane; Farrell, Michael; Deckert, Martina; Hench, Jürgen; Frank, Stephan; Serrano, Jonathan; Kannan, Kasthuri; Tsirigos, Aristotelis; Brück, Wolfgang; Hofer, Silvia; Brehmer, Stefanie; Seiz-Rosenhagen, Marcel; Hänggi, Daniel; Hans, Volkmar; Rozsnoki, Stephanie; Hansford, Jordan R; Kohlhof, Patricia; Kristensen, Bjarne W; Lechner, Matt; Lopes, Beatriz; Mawrin, Christian; Ketter, Ralf; Kulozik, Andreas; Khatib, Ziad; Heppner, Frank; Koch, Arend; Jouvet, Anne; Keohane, Catherine; Mühleisen, Helmut; Mueller, Wolf; Pohl, Ute; Prinz, Marco; Benner, Axel; Zapatka, Marc; Gottardo, Nicholas G; Driever, Pablo Hernáiz; Kramm, Christof M; Müller, Hermann L; Rutkowski, Stefan; von Hoff, Katja; Frühwald, Michael C; Gnekow, Astrid; Fleischhack, Gudrun; Tippelt, Stephan; Calaminus, Gabriele; Monoranu, Camelia-Maria; Perry, Arie; Jones, Chris; Jacques, Thomas S; Radlwimmer, Bernhard; Gessi, Marco; Pietsch, Torsten; Schramm, Johannes; Schackert, Gabriele; Westphal, Manfred; Reifenberger, Guido; Wesseling, Pieter; Weller, Michael; Collins, Vincent Peter; Blümcke, Ingmar; Bendszus, Martin; Debus, Jürgen; Huang, Annie; Jabado, Nada; Northcott, Paul A; Paulus, Werner; Gajjar, Amar; Robinson, Giles W; Taylor, Michael D; Jaunmuktane, Zane; Ryzhova, Marina; Platten, Michael; Unterberg, Andreas; Wick, Wolfgang; Karajannis, Matthias A; Mittelbronn, Michel; Acker, Till; Hartmann, Christian; Aldape, Kenneth; Schüller, Ulrich; Buslei, Rolf; Lichter, Peter; Kool, Marcel; Herold-Mende, Christel; Ellison, David W; Hasselblatt, Martin; Snuderl, Matija; Brandner, Sebastian; Korshunov, Andrey; von Deimling, Andreas; Pfister, Stefan M
2018-03-22
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.
A new tree classification system for southern hardwoods
James S. Meadows; Daniel A. Jr. Skojac
2008-01-01
A new tree classification system for southern hardwoods is described. The new system is based on the Putnam tree classification system, originally developed by Putnam et al., 1960, Management ond inventory of southern hardwoods, Agriculture Handbook 181, US For. Sew., Washington, DC, which consists of four tree classes: (1) preferred growing stock, (2) reserve growing...
The Transporter Classification Database: recent advances.
Saier, Milton H; Yen, Ming Ren; Noto, Keith; Tamang, Dorjee G; Elkan, Charles
2009-01-01
The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users.
A Global Classification System for Catchment Hydrology
NASA Astrophysics Data System (ADS)
Woods, R. A.
2004-05-01
It is a shocking state of affairs - there is no underpinning scientific taxonomy of catchments. There are widely used global classification systems for climate, river morphology, lakes and wetlands, but for river catchments there exists only a plethora of inconsistent, incomplete regional schemes. By proceeding without a common taxonomy for catchments, freshwater science has missed one of its key developmental stages, and has leapt from definition of phenomena to experiments, theories and models, without the theoretical framework of a classification. I propose the development of a global hierarchical classification system for physical aspects of river catchments, to help underpin physical science in the freshwater environment and provide a solid foundation for classification of river ecosystems. Such a classification scheme can open completely new vistas in hydrology: for example it will be possible to (i) rationally transfer experimental knowledge of hydrological processes between basins anywhere in the world, provided they belong to the same class; (ii) perform meaningful meta-analyses in order to reconcile studies that show inconsistent results (iii) generate new testable hypotheses which involve locations worldwide.
Berlth, Felix; Bollschweiler, Elfriede; Drebber, Uta; Hoelscher, Arnulf H; Moenig, Stefan
2014-01-01
Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer. PMID:24914328
Al-Masni, Mohammed A; Al-Antari, Mugahed A; Park, Jeong-Min; Gi, Geon; Kim, Tae-Yeon; Rivera, Patricio; Valarezo, Edwin; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong
2018-04-01
Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. Copyright © 2018 Elsevier B.V. All rights reserved.
The Comprehensive AOCMF Classification System: Mandible Fractures- Level 2 Tutorial
Cornelius, Carl-Peter; Audigé, Laurent; Kunz, Christoph; Rudderman, Randal; Buitrago-Téllez, Carlos H.; Frodel, John; Prein, Joachim
2014-01-01
This tutorial outlines the details of the AOCMF image-based classification system for fractures of the mandible at the precision level 2 allowing description of their topographical distribution. A short introduction about the anatomy is made. Mandibular fractures are classified by the anatomic regions involved. For this purpose, the mandible is delineated into an array of nine regions identified by letters: the symphysis/parasymphysis region anteriorly, two body regions on each lateral side, combined angle and ascending ramus regions, and finally the condylar and coronoid processes. A precise definition of the demarcation lines between these regions is given for the unambiguous allocation of fractures. Four transition zones allow an accurate topographic assignment if fractures end up in or run across the borders of anatomic regions. These zones are defined between angle/ramus and body, and between body and symphysis/parasymphysis. A fracture is classified as “confined” as long as it is located within a region, in contrast to a fracture being “nonconfined” when it extents to an adjoining region. Illustrations and case examples of mandible fractures are presented to become familiar with the classification procedure in daily routine. PMID:25489388
42 CFR 412.10 - Changes in the DRG classification system.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 42 Public Health 2 2010-10-01 2010-10-01 false Changes in the DRG classification system. 412.10... § 412.10 Changes in the DRG classification system. (a) General rule. CMS issues changes in the DRG classification system in a Federal Register notice at least annually. Except as specified in paragraphs (c) and...
42 CFR 412.10 - Changes in the DRG classification system.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 42 Public Health 2 2011-10-01 2011-10-01 false Changes in the DRG classification system. 412.10... § 412.10 Changes in the DRG classification system. (a) General rule. CMS issues changes in the DRG classification system in a Federal Register notice at least annually. Except as specified in paragraphs (c) and...
Towards a consensus on a hearing preservation classification system.
Skarzynski, Henryk; van de Heyning, P; Agrawal, S; Arauz, S L; Atlas, M; Baumgartner, W; Caversaccio, M; de Bodt, M; Gavilan, J; Godey, B; Green, K; Gstoettner, W; Hagen, R; Han, D M; Kameswaran, M; Karltorp, E; Kompis, M; Kuzovkov, V; Lassaletta, L; Levevre, F; Li, Y; Manikoth, M; Martin, J; Mlynski, R; Mueller, J; O'Driscoll, M; Parnes, L; Prentiss, S; Pulibalathingal, S; Raine, C H; Rajan, G; Rajeswaran, R; Rivas, J A; Rivas, A; Skarzynski, P H; Sprinzl, G; Staecker, H; Stephan, K; Usami, S; Yanov, Y; Zernotti, M E; Zimmermann, K; Lorens, A; Mertens, G
2013-01-01
The comprehensive Hearing Preservation classification system presented in this paper is suitable for use for all cochlear implant users with measurable pre-operative residual hearing. If adopted as a universal reporting standard, as it was designed to be, it should prove highly beneficial by enabling future studies to quickly and easily compare the results of previous studies and meta-analyze their data. To develop a comprehensive Hearing Preservation classification system suitable for use for all cochlear implant users with measurable pre-operative residual hearing. The HEARRING group discussed and reviewed a number of different propositions of a HP classification systems and reviewed critical appraisals to develop a qualitative system in accordance with the prerequisites. The Hearing Preservation Classification System proposed herein fulfills the following necessary criteria: 1) classification is independent from users' initial hearing, 2) it is appropriate for all cochlear implant users with measurable pre-operative residual hearing, 3) it covers the whole range of pure tone average from 0 to 120 dB; 4) it is easy to use and easy to understand.
Analysis of biliary anatomy according to different classification systems.
Deka, Pranjal; Islam, Mahibul; Jindal, Deepti; Kumar, Niteen; Arora, Ankur; Negi, Sanjay Singh
2014-01-01
Variations in biliary anatomy are common, and different classifications have been described. These classification systems have not been compared to each other in a single cohort. We report such variations in biliary anatomy on magnetic resonance cholangiopancreatography (MRCP) using six different classification systems. In 299 patients undergoing MRCP for various indications, biliary anatomy was classified as described by Couinaud (1957), Huang (1996), Karakas (2008), Choi (2003), Champetier (1994), and Ohkubo (2004). Correlation with direct cholangiography and vascular anatomy was done. Bile duct dimensions were measured. Cystic duct junction and pancreaticobiliary ductal junction (PBDJ) were classified. Normal biliary anatomy was noted in 57.8 %. The most common variants were Couinaud type D2, Choi type 3A, Huang type A1, Champetier type a, Ohkubo types D and J, and Karakas type 2a. The Ohkubo classification was the most appropriate; 3.1 % of right ducts and 6.3 % of left ducts with variant anatomy could not be classified using the Ohkubo classification. There was a good agreement between MRCP and direct cholangiography (ĸ = 0.9). Anomalous PBDJ was noted in 8.7 %. Variant biliary anatomy was not associated with gender (p = 0.194) or variant vascular anatomy (p = 0.24). Although each classification system has its merits and demerits, some anatomical variations cannot be classified using any of the previously described classifications. The Ohkubo classification system is the most applicable as it considers most clinically relevant variations pertinent to hepatobiliary surgery.
The 7th lung cancer TNM classification and staging system: Review of the changes and implications.
Mirsadraee, Saeed; Oswal, Dilip; Alizadeh, Yalda; Caulo, Andrea; van Beek, Edwin
2012-04-28
Lung cancer is the most common cause of death from cancer in males, accounting for more than 1.4 million deaths in 2008. It is a growing concern in China, Asia and Africa as well. Accurate staging of the disease is an important part of the management as it provides estimation of patient's prognosis and identifies treatment sterategies. It also helps to build a database for future staging projects. A major revision of lung cancer staging has been announced with effect from January 2010. The new classification is based on a larger surgical and non-surgical cohort of patients, and thus more accurate in terms of outcome prediction compared to the previous classification. There are several original papers regarding this new classification which give comprehensive description of the methodology, the changes in the staging and the statistical analysis. This overview is a simplified description of the changes in the new classification and their potential impact on patients' treatment and prognosis.
FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data
NASA Technical Reports Server (NTRS)
Strahler, A. H.; Franklin, J.; Woodcook, C. E.; Logan, T. L.
1981-01-01
Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.
Annotation and Classification of CRISPR-Cas Systems
Makarova, Kira S.; Koonin, Eugene V.
2018-01-01
The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) is a prokaryotic adaptive immune system that is represented in most archaea and many bacteria. Among the currently known prokaryotic defense systems, the CRISPR-Cas genomic loci show unprecedented complexity and diversity. Classification of CRISPR-Cas variants that would capture their evolutionary relationships to the maximum possible extent is essential for comparative genomic and functional characterization of this theoretically and practically important system of adaptive immunity. To this end, a multipronged approach has been developed that combines phylogenetic analysis of the conserved Cas proteins with comparison of gene repertoires and arrangements in CRISPR-Cas loci. This approach led to the current classification of CRISPR-Cas systems into three distinct types and ten subtypes for each of which signature genes have been identified. Comparative genomic analysis of the CRISPR-Cas systems in new archaeal and bacterial genomes performed over the 3 years elapsed since the development of this classification makes it clear that new types and subtypes of CRISPR-Cas need to be introduced. Moreover, this classification system captures only part of the complexity of CRISPR-Cas organization and evolution, due to the intrinsic modularity and evolutionary mobility of these immunity systems, resulting in numerous recombinant variants. Moreover, most of the cas genes evolve rapidly, complicating the family assignment for many Cas proteins and the use of family profiles for the recognition of CRISPR-Cas subtype signatures. Further progress in the comparative analysis of CRISPR-Cas systems requires integration of the most sensitive sequence comparison tools, protein structure comparison, and refined approaches for comparison of gene neighborhoods. PMID:25981466
Annotation and Classification of CRISPR-Cas Systems.
Makarova, Kira S; Koonin, Eugene V
2015-01-01
The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) is a prokaryotic adaptive immune system that is represented in most archaea and many bacteria. Among the currently known prokaryotic defense systems, the CRISPR-Cas genomic loci show unprecedented complexity and diversity. Classification of CRISPR-Cas variants that would capture their evolutionary relationships to the maximum possible extent is essential for comparative genomic and functional characterization of this theoretically and practically important system of adaptive immunity. To this end, a multipronged approach has been developed that combines phylogenetic analysis of the conserved Cas proteins with comparison of gene repertoires and arrangements in CRISPR-Cas loci. This approach led to the current classification of CRISPR-Cas systems into three distinct types and ten subtypes for each of which signature genes have been identified. Comparative genomic analysis of the CRISPR-Cas systems in new archaeal and bacterial genomes performed over the 3 years elapsed since the development of this classification makes it clear that new types and subtypes of CRISPR-Cas need to be introduced. Moreover, this classification system captures only part of the complexity of CRISPR-Cas organization and evolution, due to the intrinsic modularity and evolutionary mobility of these immunity systems, resulting in numerous recombinant variants. Moreover, most of the cas genes evolve rapidly, complicating the family assignment for many Cas proteins and the use of family profiles for the recognition of CRISPR-Cas subtype signatures. Further progress in the comparative analysis of CRISPR-Cas systems requires integration of the most sensitive sequence comparison tools, protein structure comparison, and refined approaches for comparison of gene neighborhoods.
Use of Deo's classification system on rock : final report.
DOT National Transportation Integrated Search
1983-01-01
A shale from a construction site on Route 23 in Wise County, Virginia, was classified using Deo's classification system, and the usefulness of the classification system was evaluated. In addition, rock that had previously been used in the development...
CLASSIFICATION FRAMEWORK FOR COASTAL SYSTEMS
U.S. Environmental Protection Agency. Classification Framework for Coastal Systems. EPA/600/R-04/061. U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, RI, Gulf Ecology Division, Gulf Bree...
U.S. Geological Survey Library classification system
Sasscer, R. Scott
1992-01-01
The U.S. Geological Survey library classification system has been designed for an earth science library. It is a tool for assigning classification numbers to earth science and allied pure science library materials in order to collect these materials into related subject groups on the library shelves and arrange them alphabetically by author and title. It can also be used as a retrieval system to access these materials through the subject and visible geographic classification numbers.The classification scheme has been developed over the years since 1904 to meet the ever-changing needs of increased specialization and new areas of study in the earth sciences.This system contains seven schedules:Subject scheduleGeological survey scheduleEarth science periodical scheduleGovernment documents periodical scheduleGeneral science periodical scheduleEarth science maps scheduleGeographic scheduleA geographic number, from the geographic schedule, is distinguished from other numbers in the system in that it is always enclosed in parentheses; for example, (200) is the geographic number for the United States.The geographic number is used in conjunction with the six other previously listed schedules, and it represents slightly different nuances of meanings, in respect to geographic locale, for each schedule.When used with a subject number, the geographic number indicates the country, state, province, or region in which the research was made. The subject number, 203, geology, when combined with the geographic number, (200), for example 203(200), is the classification number for library materials on the geology of the United States.The geographic number, combined with the capital letter G, for example, G(211), is the classification number for an earth science periodical issued by a geological association or university geology department in the State of Maine.When the letter S is combined with a geographic number, for example, S(276), it represents a general science periodical for a
The Bellevue Classification System: nursing's voice upon the library shelves*†
Mages, Keith C
2011-01-01
This article examines the inspiration, construction, and meaning of the Bellevue Classification System (BCS), created during the 1930s for use in the Bellevue School of Nursing Library. Nursing instructor Ann Doyle, with assistance from librarian Mary Casamajor, designed the BCS after consulting with library leaders and examining leading contemporary classification systems, including the Dewey Decimal Classification and Library of Congress, Ballard, and National Health Library classification systems. A close textual reading of the classes, subclasses, and subdivisions of these classification systems against those of the resulting BCS, reveals Doyle's belief that the BCS was created not only to organize the literature, but also to promote the burgeoning intellectualism and professionalism of early twentieth-century American nursing. PMID:21243054
Classification of Radiological Changes in Burst Fractures
Şentürk, Salim; Öğrenci, Ahmet; Gürçay, Ahmet Gürhan; Abdioğlu, Ahmet Atilla; Yaman, Onur; Özer, Ali Fahir
2018-01-01
AIM: Burst fractures can occur with different radiological images after high energy. We aimed to simplify radiological staging of burst fractures. METHODS: Eighty patients whom exposed spinal trauma and had burst fracture were evaluated concerning age, sex, fracture segment, neurological deficit, secondary organ injury and radiological changes that occurred. RESULTS: We performed a new classification in burst fractures at radiological images. CONCLUSIONS: According to this classification system, secondary organ injury and neurological deficit can be an indicator of energy exposure. If energy is high, the clinical status will be worse. Thus, we can get an idea about the likelihood of neurological deficit and secondary organ injuries. This classification has simplified the radiological staging of burst fractures and is a classification that gives a very accurate idea about the neurological condition. PMID:29531604
Kopka, Michaela; Fourman, Mitchell; Soni, Ashish; Cordle, Andrew C; Lin, Albert
2017-09-01
The Walch classification is the most recognized means of assessing glenoid wear in preoperative planning for shoulder arthroplasty. This classification relies on advanced imaging, which is more expensive and less practical than plain radiographs. The purpose of this study was to determine whether the Walch classification could be accurately applied to x-ray images compared with magnetic resonance imaging (MRI) as the gold standard. We hypothesized that x-ray images cannot adequately replace advanced imaging in the evaluation of glenoid wear. Preoperative axillary x-ray images and MRI scans of 50 patients assessed for shoulder arthroplasty were independently reviewed by 5 raters. Glenoid wear was individually classified according to the Walch classification using each imaging modality. The raters then collectively reviewed the MRI scans and assigned a consensus classification to serve as the gold standard. The κ coefficient was used to determine interobserver agreement for x-ray images and independent MRI reads, as well as the agreement between x-ray images and consensus MRI. The inter-rater agreement for x-ray images and MRIs was "moderate" (κ = 0.42 and κ = 0.47, respectively) for the 5-category Walch classification (A1, A2, B1, B2, C) and "moderate" (κ = 0.54 and κ = 0.59, respectively) for the 3-category Walch classification (A, B, C). The agreement between x-ray images and consensus MRI was much lower: "fair-to-moderate" (κ = 0.21-0.51) for the 5-category and "moderate" (κ = 0.36-0.60) for the 3-category Walch classification. The inter-rater agreement between x-ray images and consensus MRI is "fair-to-moderate." This is lower than the previously reported reliability of the Walch classification using computed tomography scans. Accordingly, x-ray images are inferior to advanced imaging when assessing glenoid wear. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights
Accurate vehicle classification including motorcycles using piezoelectric sensors.
DOT National Transportation Integrated Search
2013-03-01
State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifiers currently employ inductive loo...
Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.
Chen, Peng-Jen; Lin, Meng-Chiung; Lai, Mei-Ju; Lin, Jung-Chun; Lu, Henry Horng-Shing; Tseng, Vincent S
2018-02-01
Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as
Wong, Gerard; Leckie, Christopher; Kowalczyk, Adam
2012-01-15
Feature selection is a key concept in machine learning for microarray datasets, where features represented by probesets are typically several orders of magnitude larger than the available sample size. Computational tractability is a key challenge for feature selection algorithms in handling very high-dimensional datasets beyond a hundred thousand features, such as in datasets produced on single nucleotide polymorphism microarrays. In this article, we present a novel feature set reduction approach that enables scalable feature selection on datasets with hundreds of thousands of features and beyond. Our approach enables more efficient handling of higher resolution datasets to achieve better disease subtype classification of samples for potentially more accurate diagnosis and prognosis, which allows clinicians to make more informed decisions in regards to patient treatment options. We applied our feature set reduction approach to several publicly available cancer single nucleotide polymorphism (SNP) array datasets and evaluated its performance in terms of its multiclass predictive classification accuracy over different cancer subtypes, its speedup in execution as well as its scalability with respect to sample size and array resolution. Feature Set Reduction (FSR) was able to reduce the dimensions of an SNP array dataset by more than two orders of magnitude while achieving at least equal, and in most cases superior predictive classification performance over that achieved on features selected by existing feature selection methods alone. An examination of the biological relevance of frequently selected features from FSR-reduced feature sets revealed strong enrichment in association with cancer. FSR was implemented in MATLAB R2010b and is available at http://ww2.cs.mu.oz.au/~gwong/FSR.
Refining Landsat classification results using digital terrain data
Miller, Wayne A.; Shasby, Mark
1982-01-01
Scientists at the U.S. Geological Survey's Earth Resources Observation systems (EROS) Data Center have recently completed two land-cover mapping projects in which digital terrain data were used to refine Landsat classification results. Digital ter rain data were incorporated into the Landsat classification process using two different procedures that required developing decision criteria either subjectively or quantitatively. The subjective procedure was used in a vegetation mapping project in Arizona, and the quantitative procedure was used in a forest-fuels mapping project in Montana. By incorporating digital terrain data into the Landsat classification process, more spatially accurate landcover maps were produced for both projects.
Railroad Classification Yard Technology Manual: Volume II : Yard Computer Systems
DOT National Transportation Integrated Search
1981-08-01
This volume (Volume II) of the Railroad Classification Yard Technology Manual documents the railroad classification yard computer systems methodology. The subjects covered are: functional description of process control and inventory computer systems,...
Low-cost real-time automatic wheel classification system
NASA Astrophysics Data System (ADS)
Shabestari, Behrouz N.; Miller, John W. V.; Wedding, Victoria
1992-11-01
This paper describes the design and implementation of a low-cost machine vision system for identifying various types of automotive wheels which are manufactured in several styles and sizes. In this application, a variety of wheels travel on a conveyor in random order through a number of processing steps. One of these processes requires the identification of the wheel type which was performed manually by an operator. A vision system was designed to provide the required identification. The system consisted of an annular illumination source, a CCD TV camera, frame grabber, and 386-compatible computer. Statistical pattern recognition techniques were used to provide robust classification as well as a simple means for adding new wheel designs to the system. Maintenance of the system can be performed by plant personnel with minimal training. The basic steps for identification include image acquisition, segmentation of the regions of interest, extraction of selected features, and classification. The vision system has been installed in a plant and has proven to be extremely effective. The system properly identifies the wheels correctly up to 30 wheels per minute regardless of rotational orientation in the camera's field of view. Correct classification can even be achieved if a portion of the wheel is blocked off from the camera. Significant cost savings have been achieved by a reduction in scrap associated with incorrect manual classification as well as a reduction of labor in a tedious task.
Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data
Ahmed, Moiz; Mehmood, Nadeem; Mehmood, Amir; Rizwan, Kashif
2017-01-01
Objectives Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. Methods We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. Results To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. Conclusions In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios. PMID:28875049
Goossen, W T; Epping, P J; Abraham, I L
1996-03-01
The development of nursing information systems (NIS) is often hampered by the fact that nursing lacks a unified nursing terminology and classification system. Currently there exist various initiatives in this area. We address the question as to how current initiatives in the development of nursing terminology and classification systems can contribute towards the development of NIS. First, the rationale behind the formalization of nursing knowledge is discussed. Next, using a framework for nursing information processing, the most important developments in the field of nursing on formalization, terminology and classification are critically reviewed. The initiatives discussed include nursing terminology projects in several countries, and the International Classification of Nursing Practice. Suggestions for further developments in the area are discussed. Finally, implications for NIS are presented, as well as the relationships of these components to other sections of an integrated computerized patient record.
IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.
Grigore, Mariana; Avram, Alina
2015-01-01
Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.
Barnado, April; Casey, Carolyn; Carroll, Robert J; Wheless, Lee; Denny, Joshua C; Crofford, Leslie J
2017-05-01
To study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision (ICD-9), Clinical Modification codes, laboratory testing, and medications to identify SLE patients. We used Vanderbilt's Synthetic Derivative, a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least 1 SLE ICD-9 code (710.0), yielding 5,959 individuals. To create a training set, 200 subjects were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive antinuclear antibody (ANA), ever use of medications, and a keyword of "lupus" in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5,759 subjects. The algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥1:40), and ever use of both disease-modifying antirheumatic drugs and steroids, while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes. We developed and validated the first EHR algorithm that incorporates laboratory values and medications with the SLE ICD-9 code to identify patients with SLE accurately. © 2016, American College of Rheumatology.
NASA Astrophysics Data System (ADS)
Su, Y.; Ong, E. T.; Lee, K. H.
2002-05-01
The past decade has seen an accelerated growth of technology in the field of microelectromechanical systems (MEMS). The development of MEMS products has generated the need for efficient analytical and simulation methods for minimizing the requirement for actual prototyping. The boundary element method is widely used in the electrostatic analysis for MEMS devices. However, singular elements are needed to accurately capture the behavior at singular regions, such as sharp corners and edges, where standard elements fail to give an accurate result. The manual classification of boundary elements based on their singularity conditions is an immensely laborious task, especially when the boundary element model is large. This process can be automated by querying the geometric model of the MEMS device for convex edges based on geometric information of the model. The associated nodes of the boundary elements on these edges can then be retrieved. The whole process is implemented in the MSC/PATRAN platform using the Patran Command Language (the source code is available as supplementary data in the electronic version of this journal issue).
Towards a Collaborative Intelligent Tutoring System Classification Scheme
ERIC Educational Resources Information Center
Harsley, Rachel
2014-01-01
This paper presents a novel classification scheme for Collaborative Intelligent Tutoring Systems (CITS), an emergent research field. The three emergent classifications of CITS are unstructured, semi-structured, and fully structured. While all three types of CITS offer opportunities to improve student learning gains, the full extent to which these…
High accurate time system of the Low Latitude Meridian Circle.
NASA Astrophysics Data System (ADS)
Yang, Jing; Wang, Feng; Li, Zhiming
In order to obtain the high accurate time signal for the Low Latitude Meridian Circle (LLMC), a new GPS accurate time system is developed which include GPS, 1 MC frequency source and self-made clock system. The second signal of GPS is synchronously used in the clock system and information can be collected by a computer automatically. The difficulty of the cancellation of the time keeper can be overcomed by using this system.
The functional therapeutic chemical classification system.
Croset, Samuel; Overington, John P; Rebholz-Schuhmann, Dietrich
2014-03-15
Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds. In order to computationally generate repositioning hypotheses, we used the Web Ontology Language to formally define the semantics of over 20 000 terms with axioms to correctly denote various modes of action (MoA). Based on an integration of public data, we have automatically assigned over a thousand of approved drugs into these MoA categories. The resulting new resource is called the Functional Therapeutic Chemical Classification System and was further evaluated against the content of the traditional Anatomical Therapeutic Chemical Classification System. We illustrate how the new classification can be used to generate drug repurposing hypotheses, using Alzheimers disease as a use-case. https://www.ebi.ac.uk/chembl/ftc; https://github.com/loopasam/ftc. croset@ebi.ac.uk Supplementary data are available at Bioinformatics online.
Strudwick, Gillian; Hardiker, Nicholas R
2016-10-01
In the era of evidenced based healthcare, nursing is required to demonstrate that care provided by nurses is associated with optimal patient outcomes, and a high degree of quality and safety. The use of standardized nursing terminologies and classification systems are a way that nursing documentation can be leveraged to generate evidence related to nursing practice. Several widely-reported nursing specific terminologies and classifications systems currently exist including the Clinical Care Classification System, International Classification for Nursing Practice(®), Nursing Intervention Classification, Nursing Outcome Classification, Omaha System, Perioperative Nursing Data Set and NANDA International. However, the influence of these systems on demonstrating the value of nursing and the professions' impact on quality, safety and patient outcomes in published research is relatively unknown. This paper seeks to understand the use of standardized nursing terminology and classification systems in published research, using the International Classification for Nursing Practice(®) as a case study. A systematic review of international published empirical studies on, or using, the International Classification for Nursing Practice(®) were completed using Medline and the Cumulative Index for Nursing and Allied Health Literature. Since 2006, 38 studies have been published on the International Classification for Nursing Practice(®). The main objectives of the published studies have been to validate the appropriateness of the classification system for particular care areas or populations, further develop the classification system, or utilize it to support the generation of new nursing knowledge. To date, most studies have focused on the classification system itself, and a lesser number of studies have used the system to generate information about the outcomes of nursing practice. Based on the published literature that features the International Classification for Nursing
Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza
2017-04-01
Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Error Detection in Mechanized Classification Systems
ERIC Educational Resources Information Center
Hoyle, W. G.
1976-01-01
When documentary material is indexed by a mechanized classification system, and the results judged by trained professionals, the number of documents in disagreement, after suitable adjustment, defines the error rate of the system. In a test case disagreement was 22 percent and, of this 22 percent, the computer correctly identified two-thirds of…
A Classification of Designated Logic Systems
1988-02-01
Introduction to Logic. New York: Macmillan Publishing, 1972. Grimaldi , Ralph P . Discrete and Combinatorial Mathematics. Reading: Addison-Wesley...sound basis for understanding non-classical logic systems. I would like to thank the Air Force Institute of Technology for funding this research. vi p ...6 p ILLUSTRATIONS Figure Page 1. Two Classifications of Designated Logic Systems 13 2. Two Partitions of Two-valued Logic Systems 14 3. Two
Automated classification of articular cartilage surfaces based on surface texture.
Stachowiak, G P; Stachowiak, G W; Podsiadlo, P
2006-11-01
In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.
Progressive Classification Using Support Vector Machines
NASA Technical Reports Server (NTRS)
Wagstaff, Kiri; Kocurek, Michael
2009-01-01
An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user
Modified Mahalanobis Taguchi System for Imbalance Data Classification
2017-01-01
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA). PMID:28811820
Reverse Shoulder Arthroplasty Prosthesis Design Classification System.
Routman, Howard D; Flurin, Pierre-Henri; Wright, Thomas W; Zuckerman, Joseph D; Hamilton, Matthew A; Roche, Christopher P
2015-12-01
Multiple different reverse total shoulder arthroplasty (rTSA) prosthesis designs are available in the global marketplace for surgeons to perform this growing procedure. Subtle differences in rTSA prosthesis design parameters have been shown to have significant biomechanical impact and clinical consequences. We propose an rTSA prosthesis design classification system to objectively identify and categorize different designs based upon their specific glenoid and humeral prosthetic characteristics for the purpose of standardizing nomenclature that will help the orthopaedic surgeon determine which combination of design configurations best suit a given clinical scenario. The impact of each prosthesis classification type on shoulder muscle length and deltoid wrapping are also described to illustrate how each prosthesis classification type impacts these biomechanical parameters.
van der Heijden, Martijn; Dikkers, Frederik G; Halmos, Gyorgy B
2015-12-01
Laryngomalacia is the most common cause of dyspnea and stridor in newborn infants. Laryngomalacia is a dynamic change of the upper airway based on abnormally pliable supraglottic structures, which causes upper airway obstruction. In the past, different classification systems have been introduced. Until now no classification system is widely accepted and applied. Our goal is to provide a simple and complete classification system based on systematic literature search and our experiences. Retrospective cohort study with literature review. All patients with laryngomalacia under the age of 5 at time of diagnosis were included. Photo and video documentation was used to confirm diagnosis and characteristics of dynamic airway change. Outcome was compared with available classification systems in literature. Eighty-five patients were included. In contrast to other classification systems, only three typical different dynamic changes have been identified in our series. Two existing classification systems covered 100% of our findings, but there was an unnecessary overlap between different types in most of the systems. Based on our finding, we propose a new a classification system for laryngomalacia, which is purely based on dynamic airway changes. The groningen laryngomalacia classification is a new, simplified classification system with three types, based on purely dynamic laryngeal changes, tested in a tertiary referral center: Type 1: inward collapse of arytenoids cartilages, Type 2: medial displacement of aryepiglottic folds, and Type 3: posterocaudal displacement of epiglottis against the posterior pharyngeal wall. © 2015 Wiley Periodicals, Inc.
Second Language Writing Classification System Based on Word-Alignment Distribution
ERIC Educational Resources Information Center
Kotani, Katsunori; Yoshimi, Takehiko
2010-01-01
The present paper introduces an automatic classification system for assisting second language (L2) writing evaluation. This system, which classifies sentences written by L2 learners as either native speaker-like or learner-like sentences, is constructed by machine learning algorithms using word-alignment distributions as classification features…
Classification of Children Intelligence with Fuzzy Logic Method
NASA Astrophysics Data System (ADS)
Syahminan; ika Hidayati, Permata
2018-04-01
Intelligence of children s An Important Thing To Know The Parents Early on. Typing Can be done With a Child’s intelligence Grouping Dominant Characteristics Of each Type of Intelligence. To Make it easier for Parents in Determining The type of Children’s intelligence And How to Overcome them, for It Created A Classification System Intelligence Grouping Children By Using Fuzzy logic method For determination Of a Child’s degree of intelligence type. From the analysis We concluded that The presence of Intelligence Classification systems Pendulum Children With Fuzzy Logic Method Of determining The type of The Child’s intelligence Can be Done in a way That is easier And The results More accurate Conclusions Than Manual tests.
K, Jalal Deen; R, Ganesan; A, Merline
2017-07-27
Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License
K, Jalal Deen; R, Ganesan; A, Merline
2017-01-01
Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127
IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW
Grigore, Mariana; Avram, Alina
2015-01-01
Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112
Classification System and Information Services in the Library of SAO RAS
NASA Astrophysics Data System (ADS)
Shvedova, G. S.
The classification system used at SAO RAS is described. It includes both special determinants from UDC (Universal Decimal Classification) and newer tables with astronomical terms from the Library-Bibliographical Classification (LBC). The classification tables are continually modified, and new astronomical terms are introduced. At the present time the information services of the scientists is fulfilled with the help of the Abstract Journal Astronomy, Astronomy and Astrophysics Abstracts, catalogues and card indexes of the library. Based on our classification system and The Astronomy Thesaurus completed by R.M. Shobbrook and R.R. Shobbrook the development of a database for the library has been started, which allows prompt service of the observatory's staff members.
Designing a Classification System for Internet Offenders: Doing Cognitive Distortions
ERIC Educational Resources Information Center
Hundersmarck, Steven F.; Durkin, Keith F.; Delong, Ronald L.
2007-01-01
Televised features such as NBC's "To Catch a Predator" have highlighted the growing problem posed by Internet sexual predators. This paper reports on the authors' attempts in designing a classification system for Internet offenders. The classification system was designed based on existing theory, understanding the nature of Internet offenders and…
NASA Astrophysics Data System (ADS)
McClanahan, James Patrick
Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw in-situ (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (>95
An updated evolutionary classification of CRISPR–Cas systems
Makarova, Kira S.; Wolf, Yuri I.; Alkhnbashi, Omer S.; Costa, Fabrizio; Shah, Shiraz A.; Saunders, Sita J.; Barrangou, Rodolphe; Brouns, Stan J. J.; Charpentier, Emmanuelle; Haft, Daniel H.; Horvath, Philippe; Moineau, Sylvain; Mojica, Francisco J. M.; Terns, Rebecca M.; Terns, Michael P.; White, Malcolm F.; Yakunin, Alexander F.; Garrett, Roger A.; van der Oost, John; Backofen, Rolf; Koonin, Eugene V.
2017-01-01
The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized. PMID:26411297
Refining Time-Activity Classification of Human Subjects Using the Global Positioning System
Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun
2016-01-01
Background Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Methods Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Results Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. Conclusions The random forests classification model can achieve high accuracy for the four major time
Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.
Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun
2016-01-01
Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well
a Gsa-Svm Hybrid System for Classification of Binary Problems
NASA Astrophysics Data System (ADS)
Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan
2011-06-01
This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.
A system for analysis and classification of voice communications
NASA Technical Reports Server (NTRS)
Older, H. J.; Jenney, L. L.; Garland, L.
1973-01-01
A method for analysis and classification of verbal communications typically associated with manned space missions or simulations was developed. The study was carried out in two phases. Phase 1 was devoted to identification of crew tasks and activities which require voice communication for accomplishment or reporting. Phase 2 entailed development of a message classification system and a preliminary test of its feasibility. The classification system permits voice communications to be analyzed to three progressively more specific levels of detail and to be described in terms of message content, purpose, and the participants in the information exchange. A coding technique was devised to allow messages to be recorded by an eight-digit number.
Classification System for the Sudden Unexpected Infant Death Case Registry and its Application
Shapiro-Mendoza, Carrie K.; Camperlengo, Lena; Ludvigsen, Rebecca; Cottengim, Carri; Anderson, Robert N.; Andrew, Thomas; Covington, Theresa; Hauck, Fern R.; Kemp, James; MacDorman, Marian
2015-01-01
Sudden unexpected infant deaths (SUID) accounted for 1 in 3 postneonatal deaths in 2010. Sudden infant death syndrome and accidental sleep-related suffocation are among the most frequently reported types of SUID. The causes of these SUID usually are not obvious before a medico-legal investigation and may remain unexplained even after investigation. Lack of consistent investigation practices and an autopsy marker make it difficult to distinguish sudden infant death syndrome from other SUID. Standardized categories might assist in differentiating SUID subtypes and allow for more accurate monitoring of the magnitude of SUID, as well as an enhanced ability to characterize the highest risk groups. To capture information about the extent to which cases are thoroughly investigated and how factors like unsafe sleep may contribute to deaths, CDC created a multistate SUID Case Registry in 2009. As part of the registry, the Centers for Disease Control and Prevention developed a classification system that recognizes the uncertainty about how suffocation or asphyxiation may contribute to death and that accounts for unknown and incomplete information about the death scene and autopsy. This report describes the classification system, including its definitions and decision-making algorithm, and applies the system to 436 US SUID cases that occurred in 2011 and were reported to the registry. These categories, although not replacing official cause-of-death determinations, allow local and state programs to track SUID subtypes, creating a valuable tool to identify gaps in investigation and inform SUID reduction strategies. PMID:24913798
Evaluation of Urinary Tract Dilation Classification System for Grading Postnatal Hydronephrosis.
Hodhod, Amr; Capolicchio, John-Paul; Jednak, Roman; El-Sherif, Eid; El-Doray, Abd El-Alim; El-Sherbiny, Mohamed
2016-03-01
We assessed the reliability and validity of the Urinary Tract Dilation classification system as a new grading system for postnatal hydronephrosis. We retrospectively reviewed charts of patients who presented with hydronephrosis from 2008 to 2013. We included patients diagnosed prenatally and those with hydronephrosis discovered incidentally during the first year of life. We excluded cases involving urinary tract infection, neurogenic bladder and chromosomal anomalies, those associated with extraurinary congenital malformations and those with followup of less than 24 months without resolution. Hydronephrosis was graded postnatally using the Society for Fetal Urology system, and then the management protocol was chosen. All units were regraded using the Urinary Tract Dilation classification system and compared to the Society for Fetal Urology system to assess reliability. Univariate and multivariate analyses were performed to assess the validity of the Urinary Tract Dilation classification system in predicting hydronephrosis resolution and surgical intervention. A total of 490 patients (730 renal units) were eligible to participate. The Urinary Tract Dilation classification system was reliable in the assessment of hydronephrosis (parallel forms 0.92). Hydronephrosis resolved in 357 units (49%), and 86 units (12%) were managed by surgical intervention. The remainder of renal units demonstrated stable or improved hydronephrosis. Multivariate analysis revealed that the likelihood of surgical intervention was predicted independently by Urinary Tract Dilation classification system risk group, while Society for Fetal Urology grades were predictive of likelihood of resolution. The Urinary Tract Dilation classification system is reliable for evaluation of postnatal hydronephrosis and is valid in predicting surgical intervention. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
CHANGING OUR DIAGNOSTIC PARADIGM: MOVEMENT SYSTEM DIAGNOSTIC CLASSIFICATION
Kamonseki, Danilo H.; Staker, Justin L.; Lawrence, Rebekah L.; Braman, Jonathan P.
2017-01-01
Proper diagnosis is a first step in applying best available treatments, and prognosticating outcomes for clients. Currently, the majority of musculoskeletal diagnoses are classified according to pathoanatomy. However, the majority of physical therapy treatments are applied toward movement system impairments or pain. While advocated within the physical therapy profession for over thirty years, diagnostic classification within a movement system framework has not been uniformly developed or adopted. We propose a basic framework and rationale for application of a movement system diagnostic classification for atraumatic shoulder pain conditions, as a case for the broader development of movement system diagnostic labels. Shifting our diagnostic paradigm has potential to enhance communication, improve educational efficiency, facilitate research, directly link to function, improve clinical care, and accelerate preventive interventions. PMID:29158950
Validation of a new classification system for skin tears.
LeBlanc, Kimberly; Baranoski, Sharon; Holloway, Samantha; Langemo, Diane
2013-06-01
The aim of this study was to validate and establish reliability of the International Skin Tear classification system. A consensus panel of 12 internationally recognized key opinion leaders convened in 2011 to establish consensus statements on the prevention, prediction, assessment, and treatment of skin tears. Subsequently, a new skin tear classification system was proposed. The system was then tested for interrater and intrarater reliability between the experts before being tested more widely on a sample of 327 individuals from the United States, Canada, and Europe. The results of the study indicated a substantial level of agreement for the expert panel (Fleiss κ = 0.619; 2-month follow-up = 0.653). Intrarater reliability was high (Cohen κ = 0.877). Interrater reliability was moderate (Fleiss κ = 0.555) for healthcare professionals (n = 303) and fair for non-health professionals (Fleiss κ = 0.338; n = 24). This international study established the reliability and validity of a new classification system for skin tears.
Vegetation classification system for California: user's guide
Serena C. Hunter; Timothy E. Paysen
1986-01-01
The Vegetation Classification System for California is an unbiased system of defining and naming units of vegetation. The concept was devised by an interagency, interdisciplinary team (Paysen and others 1980, 1982). The system derives its uniqueness from its impartiality to any particular agency or resource discipline, thus providing a long-needed link between diverse...
Idkaidek, Nasir M.
2013-01-01
The aim of this commentary is to investigate the interplay of Biopharmaceutics Classification System (BCS), Biopharmaceutics Drug Disposition Classification System (BDDCS) and Salivary Excretion Classification System (SECS). BCS first classified drugs based on permeability and solubility for the purpose of predicting oral drug absorption. Then BDDCS linked permeability with hepatic metabolism and classified drugs based on metabolism and solubility for the purpose of predicting oral drug disposition. On the other hand, SECS classified drugs based on permeability and protein binding for the purpose of predicting the salivary excretion of drugs. The role of metabolism, rather than permeability, on salivary excretion is investigated and the results are not in agreement with BDDCS. Conclusion The proposed Salivary Excretion Classification System (SECS) can be used as a guide for drug salivary excretion based on permeability (not metabolism) and protein binding. PMID:24493977
A systematic literature review of automated clinical coding and classification systems.
Stanfill, Mary H; Williams, Margaret; Fenton, Susan H; Jenders, Robert A; Hersh, William R
2010-01-01
Clinical coding and classification processes transform natural language descriptions in clinical text into data that can subsequently be used for clinical care, research, and other purposes. This systematic literature review examined studies that evaluated all types of automated coding and classification systems to determine the performance of such systems. Studies indexed in Medline or other relevant databases prior to March 2009 were considered. The 113 studies included in this review show that automated tools exist for a variety of coding and classification purposes, focus on various healthcare specialties, and handle a wide variety of clinical document types. Automated coding and classification systems themselves are not generalizable, nor are the results of the studies evaluating them. Published research shows these systems hold promise, but these data must be considered in context, with performance relative to the complexity of the task and the desired outcome.
A new classification system for congenital laryngeal cysts.
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.
Tsimmerman, Ia S
2008-01-01
The new International Classification of Chronic Pancreatitis (designated as M-ANNHEIM) proposed by a group of German specialists in late 2007 is reviewed. All its sections are subjected to analysis (risk group categories, clinical stages and phases, variants of clinical course, diagnostic criteria for "established" and "suspected" pancreatitis, instrumental methods and functional tests used in the diagnosis, evaluation of the severity of the disease using a scoring system, stages of elimination of pain syndrome). The new classification is compared with the earlier classification proposed by the author. Its merits and demerits are discussed.
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.
Cause of and factors associated with stillbirth: a systematic review of classification systems.
Aminu, Mamuda; Bar-Zeev, Sarah; van den Broek, Nynke
2017-05-01
An estimated 2.6 million stillbirths occur worldwide each year. A standardized classification system setting out possible cause of death and contributing factors is useful to help obtain comparative data across different settings. We undertook a systematic review of stillbirth classification systems to highlight their strengths and weaknesses for practitioners and policymakers. We conducted a systematic search and review of the literature to identify the classification systems used to aggregate information for stillbirth and perinatal deaths. Narrative synthesis was used to compare the range and depth of information required to apply the systems, and the different categories provided for cause of and factors contributing to stillbirth. A total of 118 documents were screened; 31 classification systems were included, of which six were designed specifically for stillbirth, 14 for perinatal death, three systems included neonatal deaths and two included infant deaths. Most (27/31) were developed in and first tested using data obtained from high-income settings. All systems required information from clinical records. One-third of the classification systems (11/31) included information obtained from histology or autopsy. The percentage where cause of death remained unknown ranged from 0.39% using the Nordic-Baltic classification to 46.4% using the Keeling system. Over time, classification systems have become more complex. The success of application is dependent on the availability of detailed clinical information and laboratory investigations. Systems that adopt a layered approach allow for classification of cause of death to a broad as well as to a more detailed level. © 2017 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).
A systematic literature review of automated clinical coding and classification systems
Williams, Margaret; Fenton, Susan H; Jenders, Robert A; Hersh, William R
2010-01-01
Clinical coding and classification processes transform natural language descriptions in clinical text into data that can subsequently be used for clinical care, research, and other purposes. This systematic literature review examined studies that evaluated all types of automated coding and classification systems to determine the performance of such systems. Studies indexed in Medline or other relevant databases prior to March 2009 were considered. The 113 studies included in this review show that automated tools exist for a variety of coding and classification purposes, focus on various healthcare specialties, and handle a wide variety of clinical document types. Automated coding and classification systems themselves are not generalizable, nor are the results of the studies evaluating them. Published research shows these systems hold promise, but these data must be considered in context, with performance relative to the complexity of the task and the desired outcome. PMID:20962126
A Systematic Classification for HVAC Systems and Components
DOE Office of Scientific and Technical Information (OSTI.GOV)
Li, Han; Chen, Yan; Zhang, Jian
Depending on the application, the complexity of an HVAC system can range from a small fan coil unit to a large centralized air conditioning system with primary and secondary distribution loops, and central plant components. Currently, the taxonomy of HVAC systems and the components has various aspects, which can get quite complex because of the various components and system configurations. For example, based on cooling and heating medium delivered to terminal units, systems can be classified as either air systems, water systems or air-water systems. In addition, some of the system names might be commonly used in a confusing manner,more » such as “unitary system” vs. “packaged system.” Without a systematic classification, these components and system terminology can be confusing to understand or differentiate from each other, and it creates ambiguity in communication, interpretation, and documentation. It is valuable to organize and classify HVAC systems and components so that they can be easily understood and used in a consistent manner. This paper aims to develop a systematic classification of HVAC systems and components. First, we summarize the HVAC component information and definitions based on published literature, such as ASHRAE handbooks, regulations, and rating standards. Then, we identify common HVAC system types and map them to the collected components in a meaningful way. Classification charts are generated and described based on the component information. Six main categories are identified for the HVAC components and equipment, i.e., heating and cooling production, heat extraction and rejection, air handling process, distribution system, terminal use, and stand-alone system. Components for each main category are further analyzed and classified in detail. More than fifty system names are identified and grouped based on their characteristics. The result from this paper will be helpful for education, communication, and systems and component
A System for Heart Sounds Classification
Redlarski, Grzegorz; Gradolewski, Dawid; Palkowski, Aleksander
2014-01-01
The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability. PMID:25393113
Krause, Fabian G; Di Silvestro, Matthew; Penner, Murray J; Wing, Kevin J; Glazebrook, Mark A; Daniels, Timothy R; Lau, Johnny T C; Younger, Alastair S E
2012-02-01
End-stage ankle arthritis is operatively treated with numerous designs of total ankle replacement and different techniques for ankle fusion. For superior comparison of these procedures, outcome research requires a classification system to stratify patients appropriately. A postoperative 4-type classification system was designed by 6 fellowship-trained foot and ankle surgeons. Four surgeons reviewed blinded patient profiles and radiographs on 2 occasions to determine the interobserver and intraobserver reliability of the classification. Excellent interobserver reliability (κ = .89) and intraobserver reproducibility (κ = .87) were demonstrated for the postoperative classification system. In conclusion, the postoperative Canadian Orthopaedic Foot and Ankle Society (COFAS) end-stage ankle arthritis classification system appears to be a valid tool to evaluate the outcome of patients operated for end-stage ankle arthritis.
Raster Vs. Point Cloud LiDAR Data Classification
NASA Astrophysics Data System (ADS)
El-Ashmawy, N.; Shaker, A.
2014-09-01
Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the
5 CFR 9701.231 - Conversion of positions and employees to the DHS classification system.
Code of Federal Regulations, 2011 CFR
2011-01-01
... the DHS classification system. 9701.231 Section 9701.231 Administrative Personnel DEPARTMENT OF... MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Classification Transitional Provisions § 9701.231 Conversion of positions and employees to the DHS classification system. (a) This...
Classifications of central solar domestic hot water systems
NASA Astrophysics Data System (ADS)
Guo, J. Y.; Hao, B.; Peng, C.; Wang, S. S.
2016-08-01
Currently, there are many means by which to classify solar domestic hot water systems, which are often categorized according to their scope of supply, solar collector positions, and type of heat storage tank. However, the lack of systematic and scientific classification as well as the general disregard of the thermal performance of the auxiliary heat source is important to DHW systems. Thus, the primary focus of this paper is to determine a classification system for solar domestic hot water systems based on the positions of the solar collector and auxiliary heating device, both respectively and in combination. Field-testing data regarding many central solar DHW systems demonstrates that the position of the auxiliary heat source clearly reflects the operational energy consumption. The consumption of collective auxiliary heating hot water system is much higher than individual auxiliary heating hot water system. In addition, costs are significantly reduced by the separation of the heat storage tank and the auxiliary heating device.
Automatic grade classification of Barretts Esophagus through feature enhancement
NASA Astrophysics Data System (ADS)
Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid
2017-03-01
Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.
Menon, K Venugopal; Kumar, Dinesh; Thomas, Tessamma
2014-02-01
Study Design Preliminary evaluation of new tool. Objective To ascertain whether the newly developed content-based image retrieval (CBIR) software can be used successfully to retrieve images of similar cases of adolescent idiopathic scoliosis (AIS) from a database to help plan treatment without adhering to a classification scheme. Methods Sixty-two operated cases of AIS were entered into the newly developed CBIR database. Five new cases of different curve patterns were used as query images. The images were fed into the CBIR database that retrieved similar images from the existing cases. These were analyzed by a senior surgeon for conformity to the query image. Results Within the limits of variability set for the query system, all the resultant images conformed to the query image. One case had no similar match in the series. The other four retrieved several images that were matching with the query. No matching case was left out in the series. The postoperative images were then analyzed to check for surgical strategies. Broad guidelines for treatment could be derived from the results. More precise query settings, inclusion of bending films, and a larger database will enhance accurate retrieval and better decision making. Conclusion The CBIR system is an effective tool for accurate documentation and retrieval of scoliosis images. Broad guidelines for surgical strategies can be made from the postoperative images of the existing cases without adhering to any classification scheme.
Contextual classification of multispectral image data: Approximate algorithm
NASA Technical Reports Server (NTRS)
Tilton, J. C. (Principal Investigator)
1980-01-01
An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive. Classifications that are nearly as accurate are produced.
Interrater reliability of a Pilates movement-based classification system.
Yu, Kwan Kenny; Tulloch, Evelyn; Hendrick, Paul
2015-01-01
To determine the interrater reliability for identification of a specific movement pattern using a Pilates Classification system. Videos of 5 subjects performing specific movement tasks were sent to raters trained in the DMA-CP classification system. Ninety-six raters completed the survey. Interrater reliability for the detection of a directional bias was excellent (Pi = 0.92, and K(free) = 0.89). Interrater reliability for classifying an individual into a specific subgroup was moderate (Pi = 0.64, K(free) = 0.55) however raters who had completed levels 1-4 of the DMA-CP training and reported using the assessment daily demonstrated excellent reliability (Pi = 0.89 and K(free) = 0.87). The reliability of the classification system demonstrated almost perfect agreement in determining the existence of a specific movement pattern and classifying into a subgroup for experienced raters. There was a trend for greater reliability associated with increased levels of training and experience of the raters. Copyright © 2014 Elsevier Ltd. All rights reserved.
Centrifuge: rapid and sensitive classification of metagenomic sequences
Song, Li; Breitwieser, Florian P.
2016-01-01
Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. PMID:27852649
Genome-Wide Comparative Gene Family Classification
Frech, Christian; Chen, Nansheng
2010-01-01
Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221
Classification framework for partially observed dynamical systems
NASA Astrophysics Data System (ADS)
Shen, Yuan; Tino, Peter; Tsaneva-Atanasova, Krasimira
2017-04-01
We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using point estimates of model parameters to represent individual data items, we employ posterior distributions over model parameters, thus taking into account in a principled manner the uncertainty due to both the generative (observational and/or dynamic noise) and observation (sampling in time) processes. We evaluate the framework on two test beds: a biological pathway model and a stochastic double-well system. Crucially, we show that the classification performance is not impaired when the model structure used for inferring posterior distributions is much more simple than the observation-generating model structure, provided the reduced-complexity inferential model structure captures the essential characteristics needed for the given classification task.
Evolution and classification of the CRISPR-Cas systems
S. Makarova, Kira; H. Haft, Daniel; Barrangou, Rodolphe; J. J. Brouns, Stan; Charpentier, Emmanuelle; Horvath, Philippe; Moineau, Sylvain; J. M. Mojica, Francisco; I. Wolf, Yuri; Yakunin, Alexander F.; van der Oost, John; V. Koonin, Eugene
2012-01-01
The CRISPR–Cas (clustered regularly interspaced short palindromic repeats–CRISPR-associated proteins) modules are adaptive immunity systems that are present in many archaea and bacteria. These defence systems are encoded by operons that have an extraordinarily diverse architecture and a high rate of evolution for both the cas genes and the unique spacer content. Here, we provide an updated analysis of the evolutionary relationships between CRISPR–Cas systems and Cas proteins. Three major types of CRISPR–Cas system are delineated, with a further division into several subtypes and a few chimeric variants. Given the complexity of the genomic architectures and the extremely dynamic evolution of the CRISPR–Cas systems, a unified classification of these systems should be based on multiple criteria. Accordingly, we propose a `polythetic' classification that integrates the phylogenies of the most common cas genes, the sequence and organization of the CRISPR repeats and the architecture of the CRISPR–cas loci. PMID:21552286
The history of female genital tract malformation classifications and proposal of an updated system.
Acién, Pedro; Acién, Maribel I
2011-01-01
A correct classification of malformations of the female genital tract is essential to prevent unnecessary and inadequate surgical operations and to compare reproductive results. An ideal classification system should be based on aetiopathogenesis and should suggest the appropriate therapeutic strategy. We conducted a systematic review of relevant articles found in PubMed, Scopus, Scirus and ISI webknowledge, and analysis of historical collections of 'female genital malformations' and 'classifications'. Of 124 full-text articles assessed for eligibility, 64 were included because they contained original general, partial or modified classifications. All the existing classifications were analysed and grouped. The unification of terms and concepts was also analysed. Traditionally, malformations of the female genital tract have been catalogued and classified as Müllerian malformations due to agenesis, lack of fusion, the absence of resorption and lack of posterior development of the Müllerian ducts. The American Fertility Society classification of the late 1980s included seven basic groups of malformations also considering the Müllerian development and the relationship of the malformations to fertility. Other classifications are based on different aspects: functional, defects in vertical fusion, embryological or anatomical (Vagina, Cervix, Uterus, Adnex and Associated Malformation: VCUAM classification). However, an embryological-clinical classification system seems to be the most appropriate. Accepting the need for a new classification system of genitourinary malformations that considers the experience gained from the application of the current classification systems, the aetiopathogenesis and that also suggests the appropriate treatment, we proposed an update of our embryological-clinical classification as a new system with six groups of female genitourinary anomalies.
Classification of Instructional Programs: 2000 Edition.
ERIC Educational Resources Information Center
Morgan, Robert L.; Hunt, E. Stephen
This third revision of the Classification of Instructional Programs (CIP) updates and modifies education program classifications, providing a taxonomic scheme that supports the accurate tracking, assessment, and reporting of field of study and program completions activity. This edition has also been adopted as the standard field of study taxonomy…
Enhancing communication by using the Coordinated Care Classification System.
O'Neal, P V; Kozeny, D K; Garland, P P; Gaunt, S M; Gordon, S C
1998-07-01
Because of the changes in our healthcare system, some clinical nurse specialists (CNSs) are having to expand their traditional roles of clinician, educator, consultant, leader, and researcher to include case management activities. The CNSs at Promina Gwinnett Health System in Lawrenceville, Georgia, have combined CNS and case manager activities and have adopted the title "CNS/Outcomes Coordinator." The CNS/Outcomes Coordinator is responsible for coordinating patient care, promoting team collaboration, and facilitating communication. To inform the healthcare team of the CNS/Outcomes Coordinator's patient responsibilities, the CNS/Outcomes Coordinators developed a Coordinated Care Classification System. This article describes how coordinating patient care, promoting team collaboration, and facilitating communication can be enhanced by the use of a classification system.
New decision support tool for acute lymphoblastic leukemia classification
NASA Astrophysics Data System (ADS)
Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.
2012-03-01
In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.
Flying insect detection and classification with inexpensive sensors.
Chen, Yanping; Why, Adena; Batista, Gustavo; Mafra-Neto, Agenor; Keogh, Eamonn
2014-10-15
An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect's flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.
Flying Insect Detection and Classification with Inexpensive Sensors
Chen, Yanping; Why, Adena; Batista, Gustavo; Mafra-Neto, Agenor; Keogh, Eamonn
2014-01-01
An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered. PMID:25350921
A proposal of criteria for the classification of systemic sclerosis.
Nadashkevich, Oleg; Davis, Paul; Fritzler, Marvin J
2004-11-01
Sensitive and specific criteria for the classification of systemic sclerosis are required by clinicians and investigators to achieve higher quality clinical studies and approaches to therapy. A clinical study of systemic sclerosis patients in Europe and Canada led to a set of criteria that achieve high sensitivity and specificity. Both clinical and laboratory investigations of patients with systemic sclerosis, related conditions and diseases with clinical features that can be mistaken as part of the systemic sclerosis spectrum were undertaken. Laboratory investigations included the detection of autoantibodies to centromere proteins, Scl-70 (topoisomerase I), and fibrillarin (U3-RNP). Based on the investigation of 269 systemic sclerosis patients and 720 patients presenting with related and confounding conditions, the following set of criteria for the classification of systemic sclerosis was proposed: 1) autoantibodies to: centromere proteins, Scl-70 (topo I), fibrillarin; 2) bibasilar pulmonary fibrosis; 3) contractures of the digital joints or prayer sign; 4) dermal thickening proximal to the wrists; 5) calcinosis cutis; 6) Raynaud's phenomenon; 7) esophageal distal hypomotility or reflux-esophagitis; 8) sclerodactyly or non-pitting digital edema; 9) teleangiectasias. The classification of definite SSc requires at least three of the above criteria. Criteria for the classification of systemic sclerosis have been proposed. Preliminary testing has defined the sensitivity and specificity of these criteria as high as 99% and 100%, respectively. Testing and validation of the proposed criteria by other clinical centers is required.
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
Blastocyst classification systems used in Latin America: is a consensus possible?
Puga-Torres, Tatiana; Blum-Rojas, Xavier; Blum-Narváez, Medardo
2017-01-01
Objective To identify different blastocyst classification systems used by embryologists in Latin American countries and evaluate the possibility of establishing a consensus among these countries. Methods An E-mail survey was carried out through the Latin American Network of Assisted Reproduction (REDLARA) aimed at embryologists from assisted reproduction centers in Latin countries. Results Sixty surveys were collected from 12 Latin American countries, of which 66.7% had >10years of professional practice as embryologists. Seven different blastocyst classification systems were reported, of which 5 have previously been described in the literature. Conclusion Although the group of embryologists surveyed use different blastocyst classification systems, most in this group consider that the embryo score system should be unified in their countries as well as in the region. PMID:28837032
DOT National Transportation Integrated Search
1996-02-01
This study reviewed the low volume road (LVR) classifications in Kansas in conjunction with the State A, B, C, D, E road classification system and addressed alignment of these differences. As an extension to the State system, an F, G, H classificatio...
Development of a definition, classification system, and model for cultural geology
NASA Astrophysics Data System (ADS)
Mitchell, Lloyd W., III
The concept for this study is based upon a personal interest by the author, an American Indian, in promoting cultural perspectives in undergraduate college teaching and learning environments. Most academicians recognize that merged fields can enhance undergraduate curricula. However, conflict may occur when instructors attempt to merge social science fields such as history or philosophy with geoscience fields such as mining and geomorphology. For example, ideologies of Earth structures derived from scientific methodologies may conflict with historical and spiritual understandings of Earth structures held by American Indians. Specifically, this study addresses the problem of how to combine cultural studies with the geosciences into a new merged academic discipline called cultural geology. This study further attempts to develop the merged field of cultural geology using an approach consisting of three research foci: a definition, a classification system, and a model. Literature reviews were conducted for all three foci. Additionally, to better understand merged fields, a literature review was conducted specifically for academic fields that merged social and physical sciences. Methodologies concentrated on the three research foci: definition, classification system, and model. The definition was derived via a two-step process. The first step, developing keyword hierarchical ranking structures, was followed by creating and analyzing semantic word meaning lists. The classification system was developed by reviewing 102 classification systems and incorporating selected components into a system framework. The cultural geology model was created also utilizing a two-step process. A literature review of scientific models was conducted. Then, the definition and classification system were incorporated into a model felt to reflect the realm of cultural geology. A course syllabus was then developed that incorporated the resulting definition, classification system, and model. This
Lie-Hamilton systems on the plane: Properties, classification and applications
NASA Astrophysics Data System (ADS)
Ballesteros, A.; Blasco, A.; Herranz, F. J.; de Lucas, J.; Sardón, C.
2015-04-01
We study Lie-Hamilton systems on the plane, i.e. systems of first-order differential equations describing the integral curves of a t-dependent vector field taking values in a finite-dimensional real Lie algebra of planar Hamiltonian vector fields with respect to a Poisson structure. We start with the local classification of finite-dimensional real Lie algebras of vector fields on the plane obtained in González-López, Kamran, and Olver (1992) [23] and we interpret their results as a local classification of Lie systems. By determining which of these real Lie algebras consist of Hamiltonian vector fields relative to a Poisson structure, we provide the complete local classification of Lie-Hamilton systems on the plane. We present and study through our results new Lie-Hamilton systems of interest which are used to investigate relevant non-autonomous differential equations, e.g. we get explicit local diffeomorphisms between such systems. We also analyse biomathematical models, the Milne-Pinney equations, second-order Kummer-Schwarz equations, complex Riccati equations and Buchdahl equations.
The development of a recreational land classification system
Robert W. Reinhardt; Diana Gould
1995-01-01
The New York State Office of Parks, Recreation, and Historic Preservation has had a land classification system in place since 1972. This system was developed during the creation of the first Statewide Comprehensive Outdoor Recreation Plan. This system has become the backbone of the Park Master Planning Process. During the past few years the New York State Park System...
Hyun, S; Park, H A
2002-06-01
Nursing language plays an important role in describing and defining nursing phenomena and nursing actions. There are numerous vocabularies describing nursing diagnoses, interventions and outcomes in nursing. However, the lack of a standardized unified nursing language is considered a problem for further development of the discipline of nursing. In an effort to unify the nursing languages, the International Council of Nurses (ICN) has proposed the International Classification for Nursing Practice (ICNP) as a unified nursing language system. The purpose of this study was to evaluate the inclusiveness and expressiveness of the ICNP terms by cross-mapping them with the existing nursing terminologies, specifically the North American Nursing Diagnosis Association (NANDA) taxonomy I, the Omaha System, the Home Health Care Classification (HHCC) and the Nursing Interventions Classification (NIC). Nine hundred and seventy-four terms from these four classifications were cross-mapped with the ICNP terms. This was performed in accordance with the Guidelines for Composing a Nursing Diagnosis and Guidelines for Composing a Nursing Intervention, which were suggested by the ICNP development team. An expert group verified the results. The ICNP Phenomena Classification described 87.5% of the NANDA diagnoses, 89.7% of the HHCC diagnoses and 72.7% of the Omaha System problem classification scheme. The ICNP Action Classification described 79.4% of the NIC interventions, 80.6% of the HHCC interventions and 71.4% of the Omaha System intervention scheme. The results of this study suggest that the ICNP has a sound starting structure for a unified nursing language system and can be used to describe most of the existing terminologies. Recommendations for the addition of terms to the ICNP are provided.
Moon, Ki-Ho
2012-01-01
Introduction: Radiographic pathology of severe osteoarthritis of the knee (OAK) such as severe osteophyte at tibial spine (TS), compartment narrowing, marginal osteophyte, and subchondral sclerosis is well known. Kellgren-Lawrence grading system, which is widely used to diagnose OAK, describes narrowing-marginal osteophyte in 4-grades but uses osteophyte at TS only as evidence of OAK without detailed-grading. However, kinematically the knee employs medial TS as an axis while medial and lateral compartments carry the load, suggesting that early OAK would occur sooner at TS than at compartment. Then, Kellgren-Lawrence system may be inadequate to diagnose early-stage OAK manifested as a subtle osteophyte at TS without narrowing-marginal osteophyte. This undiagnosed-OAK will deteriorate becoming a contributing factor in an increasing incidence of OAK. Methods: This study developed a radiographic OAK-marker based on both osteophyte at TS and compartment narrowing-marginal osteophyte and graded as normal, mild, moderate, and severe. With this marker, both knee radiographs of 1,728 patients with knee pain were analyzed. Results: Among 611 early-stage mild OAK, 562 or 92% started at TS and 49 or 8% at compartment. It suggests the initial development site of OAK, helping develop new site-specific radiographic classification system of OAK accurately to diagnose all severity of OAK at early, intermediate, or late-stage. It showed that Kellgren-Lawrence system missed 92.0% of early-stage mild OAK from diagnosis. Conclusions: A subtle osteophyte at TS is the earliest radiographic sign of OAK. A new radiographic classification system of OAK was suggested for accurate diagnosis of all OAK in severity and at stage. PMID:23675278
Moon, Ki-Ho
2012-12-01
Radiographic pathology of severe osteoarthritis of the knee (OAK) such as severe osteophyte at tibial spine (TS), compartment narrowing, marginal osteophyte, and subchondral sclerosis is well known. Kellgren-Lawrence grading system, which is widely used to diagnose OAK, describes narrowing-marginal osteophyte in 4-grades but uses osteophyte at TS only as evidence of OAK without detailed-grading. However, kinematically the knee employs medial TS as an axis while medial and lateral compartments carry the load, suggesting that early OAK would occur sooner at TS than at compartment. Then, Kellgren-Lawrence system may be inadequate to diagnose early-stage OAK manifested as a subtle osteophyte at TS without narrowing-marginal osteophyte. This undiagnosed-OAK will deteriorate becoming a contributing factor in an increasing incidence of OAK. This study developed a radiographic OAK-marker based on both osteophyte at TS and compartment narrowing-marginal osteophyte and graded as normal, mild, moderate, and severe. With this marker, both knee radiographs of 1,728 patients with knee pain were analyzed. Among 611 early-stage mild OAK, 562 or 92% started at TS and 49 or 8% at compartment. It suggests the initial development site of OAK, helping develop new site-specific radiographic classification system of OAK accurately to diagnose all severity of OAK at early, intermediate, or late-stage. It showed that Kellgren-Lawrence system missed 92.0% of early-stage mild OAK from diagnosis. A subtle osteophyte at TS is the earliest radiographic sign of OAK. A new radiographic classification system of OAK was suggested for accurate diagnosis of all OAK in severity and at stage.
An Automatic Vehicle Classification System.
1981-07-01
addi- tion, various portions of the system design can be used by other vehicle study projects, e.g. for projects concerned with vehicle speed or for...traffic study projects that require an axle counter or vehicle height indicator. A *4 UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE(W1en Data Enrerod...optoelectronic components as the basis for detection. Factors of vehicle length, height, and number of axles are used as identification characteristics. In
A system of vegetation classification applied to Hawaii
Michael G. Buck; Timothy E. Paysen
1984-01-01
A classification system for use in describing vegetation has been developed for Hawaii. Physiognomic and taxonomic criteria are used for a hierarchical stratification of vegetation in which the system categories are Formation, Subformation, Series, Association, and Phase. The System applies to local resource management activities and serves as a framework for resource...
Global Stress Classification System for Materials Used in Solar Energy Applications
NASA Astrophysics Data System (ADS)
Slamova, Karolina; Schill, Christian; Herrmann, Jan; Datta, Pawan; Chih Wang, Chien
2016-08-01
Depending on the geographical location, the individual or combined impact of environmental stress factors and corresponding performance losses for solar applications varies significantly. Therefore, as a strategy to reduce investment risks and operating and maintenance costs, it is necessary to adapt the materials and components of solar energy systems specifically to regional environmental conditions. The project «GloBe Solar» supports this strategy by focusing on the development of a global stress classification system for materials in solar energy applications. The aim of this classification system is to assist in the identification of the individual stress conditions for every location on the earth's surface. The stress classification system could serve as a decision support tool for the industry (manufacturers, investors, lenders and project developers) and help to improve knowledge and services that can provide higher confidence to solar power systems.
Validation of a new classification system for interprosthetic femoral fractures.
Pires, Robinson Esteves Santos; Silveira, Marcelo Peixoto Sena; Resende, Alessandra Regina da Silva; Junior, Egidio Oliveira Santana; Campos, Tulio Vinicius Oliveira; Santos, Leandro Emilio Nascimento; Balbachevsky, Daniel; Andrade, Marco Antônio Percope de
2017-07-01
Interprosthetic femoral fracture (IFF) incidence is gradually increasing as the population is progressively ageing. However, treatment remains challenging due to several contributing factors, such as poor bone quality, patient comorbidities, small interprosthetic fragment, and prostheses instability. An effective and specific classification system is essential to optimize treatment management, therefore diminishing complication rates. This study aims to validate a previously described classification system for interprosthetic femoral fractures. Copyright © 2017 Elsevier Ltd. All rights reserved.
The 2010 Standard Occupational Classification (SOC): A Classification System Gets an Update
ERIC Educational Resources Information Center
Emmel, Alissa; Cosca, Theresa
2010-01-01
Making sense of occupational data isn't always easy. But the task is less daunting when the data are well organized. For Federal occupational statistics, the Standard Occupational Classification (SOC) system establishes that organization. And a recent revision to the SOC means that the data will be current, in addition to being well organized. The…
A patch-based convolutional neural network for remote sensing image classification.
Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di
2017-11-01
Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.
Accurate positioning based on acoustic and optical sensors
NASA Astrophysics Data System (ADS)
Cai, Kerong; Deng, Jiahao; Guo, Hualing
2009-11-01
Unattended laser target designator (ULTD) was designed to partly take the place of conventional LTDs for accurate positioning and laser marking. Analyzed the precision, accuracy and errors of acoustic sensor array, the requirements of laser generator, and the technology of image analysis and tracking, the major system modules were determined. The target's classification, velocity and position can be measured by sensors, and then coded laser beam will be emitted intelligently to mark the excellent position at the excellent time. The conclusion shows that, ULTD can not only avoid security threats, be deployed massively, and accomplish battle damage assessment (BDA), but also be fit for information-based warfare.
The Australian experience in dental classification.
Mahoney, Greg
2008-01-01
The Australian Defence Health Service uses a disease-risk management strategy to achieve two goals: first, to identify Australian Defence Force (ADF) members who are at high risk of developing an adverse health event, and second, to deliver intervention strategies efficiently so that maximum benefits for health within the ADF are achieved with the least cost. The present dental classification system utilized by the ADF, while an excellent dental triage tool, has been found not to be predictive of an ADF member having an adverse dental event in the following 12-month period. Clearly, there is a need for further research to establish a predictive risk-based dental classification system. This risk assessment must be sensitive enough to accurately estimate the probability that an ADF member will experience dental pain, dysfunction, or other adverse dental events within a forthcoming period, typically 12 months. Furthermore, there needs to be better epidemiological data collected in the field to assist in the research.
Nippita, T A; Khambalia, A Z; Seeho, S K; Trevena, J A; Patterson, J A; Ford, J B; Morris, J M; Roberts, C L
2015-09-01
A lack of reproducible methods for classifying women having an induction of labour (IOL) has led to controversies regarding IOL and related maternal and perinatal health outcomes. To evaluate articles that classify IOL and to develop a novel IOL classification system. Electronic searches using CINAHL, EMBASE, WEB of KNOWLEDGE, and reference lists. Two reviewers independently assessed studies that classified women having an IOL. For the systematic review, data were extracted on study characteristics, quality, and results. Pre-specified criteria were used for evaluation. A multidisciplinary collaboration developed a new classification system using a clinically logical model and stakeholder feedback, demonstrating applicability in a population cohort of 909 702 maternities in New South Wales, Australia, over the period 2002-2011. All seven studies included in the systematic review categorised women according to the presence or absence of varying medical indications for IOL. Evaluation identified uncertainties or deficiencies across all studies, related to the criteria of total inclusivity, reproducibility, clinical utility, implementability, and data availability. A classification system of ten groups was developed based on parity, previous caesarean, gestational age, number, and presentation of the fetus. Nulliparous and parous women at full term were the largest groups (21.2 and 24.5%, respectively), and accounted for the highest proportion of all IOL (20.7 and 21.5%, respectively). Current methods of classifying women undertaking IOL based on medical indications are inadequate. We propose a classification system that has the attributes of simplicity and clarity, uses information that is readily and reliably collected, and enables the standard characterisation of populations of women having an IOL across and within jurisdictions. © 2015 Royal College of Obstetricians and Gynaecologists.
Post-treatment glenoid classification system for total shoulder arthroplasty.
Churchill, R Sean
2012-04-01
Over the past 10 years, numerous advancements in glenoid preparation and resurfacing have occurred. Current glenoid classification systems are either focused solely on the patient's preoperative glenoid bone configuration or on the available glenoid bone stock in revision arthroplasty cases. While these systems provide value in preoperative planning, they fail to properly classify the surgical reconstruction completed. A literature review of common bone preparation methods and sources of glenoid prosthetic failure was performed. Based upon this review, a classification system for grading the status of the glenoid after prosthetic implantation was developed. A 6 category, post-treatment, glenoid classification system is proposed: type 0: no reaming; type I: glenoid reaming into but not through the subchondral bone; type II: glenoid reaming which perforates through <50% of the subchondral bone surface area; type III: glenoid reaming which perforates through >50% of the subchondral bone surface area; type IV: use of structural bone graft; and type V: use of a posterior augmented glenoid prosthesis. Types I-III are further subdivided into subtype A which have 100% bone support of the prosthesis, and subtype B which have a region of unsupported prosthesis. The classification system proposed addresses the surgical management of the glenoid during prosthetic replacement. This unique approach to classifying the glenoid following surgical intervention will allow direct follow-up comparison of similarly treated glenoid replacements. Future multicenter studies, possibly through joint registry databases, could then determine the long-term efficacy of the various glenoid preparation methods. Copyright © 2012 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Mosby, Inc. All rights reserved.
Non-Linear Dynamical Classification of Short Time Series of the Rössler System in High Noise Regimes
Lainscsek, Claudia; Weyhenmeyer, Jonathan; Hernandez, Manuel E.; Poizner, Howard; Sejnowski, Terrence J.
2013-01-01
Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson’s disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to −30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A′ under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data. PMID
Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail
2017-01-01
Introduction: Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. Aim: The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. Methods: first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. Results: There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. Conclusion: The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research. PMID:28883671
Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail
2017-06-01
Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research.
Gender classification system in uncontrolled environments
NASA Astrophysics Data System (ADS)
Zeng, Pingping; Zhang, Yu-Jin; Duan, Fei
2011-01-01
Most face analysis systems available today perform mainly on restricted databases of images in terms of size, age, illumination. In addition, it is frequently assumed that all images are frontal and unconcealed. Actually, in a non-guided real-time supervision, the face pictures taken may often be partially covered and with head rotation less or more. In this paper, a special system supposed to be used in real-time surveillance with un-calibrated camera and non-guided photography is described. It mainly consists of five parts: face detection, non-face filtering, best-angle face selection, texture normalization, and gender classification. Emphases are focused on non-face filtering and best-angle face selection parts as well as texture normalization. Best-angle faces are figured out by PCA reconstruction, which equals to an implicit face alignment and results in a huge increase of the accuracy for gender classification. Dynamic skin model and a masked PCA reconstruction algorithm are applied to filter out faces detected in error. In order to fully include facial-texture and shape-outline features, a hybrid feature that is a combination of Gabor wavelet and PHoG (pyramid histogram of gradients) was proposed to equitable inner texture and outer contour. Comparative study on the effects of different non-face filtering and texture masking methods in the context of gender classification by SVM is reported through experiments on a set of UT (a company name) face images, a large number of internet images and CAS (Chinese Academy of Sciences) face database. Some encouraging results are obtained.
A generalized baleen whale call detection and classification system.
Baumgartner, Mark F; Mussoline, Sarah E
2011-05-01
Passive acoustic monitoring allows the assessment of marine mammal occurrence and distribution at greater temporal and spatial scales than is now possible with traditional visual surveys. However, the large volume of acoustic data and the lengthy and laborious task of manually analyzing these data have hindered broad application of this technique. To overcome these limitations, a generalized automated detection and classification system (DCS) was developed to efficiently and accurately identify low-frequency baleen whale calls. The DCS (1) accounts for persistent narrowband and transient broadband noise, (2) characterizes temporal variation of dominant call frequencies via pitch-tracking, and (3) classifies calls based on attributes of the resulting pitch tracks using quadratic discriminant function analysis (QDFA). Automated detections of sei whale (Balaenoptera borealis) downsweep calls and North Atlantic right whale (Eubalaena glacialis) upcalls were evaluated using recordings collected in the southwestern Gulf of Maine during the spring seasons of 2006 and 2007. The accuracy of the DCS was similar to that of a human analyst: variability in differences between the DCS and an analyst was similar to that between independent analysts, and temporal variability in call rates was similar among the DCS and several analysts.
[Research progress in molecular classification of gastric cancer].
Zhou, Menglong; Li, Guichao; Zhang, Zhen
2016-09-25
Gastric cancer(GC) is a highly heterogeneous malignancy. The present widely used histopathological classifications have gradually failed to meet the needs of individualized diagnosis and treatment. Development of technologies such as microarray and next-generation sequencing (NGS) has allowed GC to be studied at the molecular level. Mechanisms about tumorigenesis and progression of GC can be elucidated in the aspects of gene mutations, chromosomal alterations, transcriptional and epigenetic changes, on the basis of which GC can be divided into several subtypes. The classifications of Tan's, Lei's, TCGA and ACRG are relatively comprehensive. Especially the TCGA and ACRG classifications have large sample size and abundant molecular profiling data, thus, the genomic characteristics of GC can be depicted more accurately. However, significant differences between both classifications still exist so that they cannot be substituted for each other. So far there is no widely accepted molecular classification of GC. Compared with TCGA classification, ACRG system may have more clinical significance in Chinese GC patients since the samples are mostly from Asian population and show better association with prognosis. The molecular classification of GC may provide the theoretical and experimental basis for early diagnosis, therapeutic efficacy prediction and treatment stratification while their clinical application is still limited. Future work should involve the application of molecular classifications in the clinical settings for improving the medical management of GC.
Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules
Winters-Hilt, Stephen; Vercoutere, Wenonah; DeGuzman, Veronica S.; Deamer, David; Akeson, Mark; Haussler, David
2003-01-01
We introduce a computational method for classification of individual DNA molecules measured by an α-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection. PMID:12547778
A classification of large amplitude oscillations of a spring-pendulum system
NASA Technical Reports Server (NTRS)
Broucke, R.
1977-01-01
We present a detailed classification of large amplitude oscillations of a non-integrable autonomous system with two degrees of freedom: the spring pendulum system. The classification is made with the method of invariant curves. The results show the importance of three types of motion: periodic, quasi-periodic and semi-ergodic. The numerical results are given for nine different values of the energy constant.
Gordijn, Sanne J; Korteweg, Fleurisca J; Erwich, Jan Jaap H M; Holm, Jozien P; van Diem, Mariet Th; Bergman, Klasien A; Timmer, Albertus
2009-06-01
Many classification systems for perinatal mortality are available, all with their own strengths and weaknesses: none of them has been universally accepted. We present a systematic multilayered approach for the analysis of perinatal mortality based on information related to the moment of death, the conditions associated with death and the underlying cause of death, using a combination of representatives of existing classification systems. We compared the existing classification systems regarding their definition of the perinatal period, level of complexity, inclusion of maternal, foetal and/or placental factors and whether they focus at a clinical or pathological viewpoint. Furthermore, we allocated the classification systems to one of three categories: 'when', 'what' or 'why', dependent on whether the allocation of the individual cases of perinatal mortality is based on the moment of death ('when'), the clinical conditions associated with death ('what'), or the underlying cause of death ('why'). A multilayered approach for the analysis and classification of perinatal mortality is possible by using combinations of existing systems; for example the Wigglesworth or Nordic Baltic ('when'), ReCoDe ('what') and Tulip ('why') classification systems. This approach is useful not only for in depth analysis of perinatal mortality in the developed world but also for analysis of perinatal mortality in the developing countries, where resources to investigate death are often limited.
A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.
Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian
2017-01-01
Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.
Tepeler, Abdulkadir; Resorlu, Berkan; Sahin, Tolga; Sarikaya, Selcuk; Bayindir, Mirze; Oguz, Ural; Armagan, Abdullah; Unsal, Ali
2014-02-01
To review our experience with ureteroscopy (URS) in the treatment of ureteral calculi and stratify intraoperative complications of URS according to the modified Satava classification system. We performed a retrospective analysis of 1,208 patients (672 males and 536 females), with a mean age of 43.1 years (range 1-78), who underwent ureteroscopic procedures for removal of ureteral stones. Intraoperative complications were recorded according to modified Satava classification system. Grade 1 complications included incidents without consequences for the patient; grade 2 complications, which are treated intraoperatively with endoscopic surgery (grade 2a) or required endoscopic re-treatment (grade 2b); and grade 3 complications included incidents requiring open or laparoscopic surgery. The stones were completely removed in 1,067 (88.3%) patients after primary procedure by either simple extraction or after fragmentation. The overall incidence of intraoperative complications was 12.6%. The most common complications were proximal stone migration (3.9%), mucosal injury (2.8%), bleeding (1.9%), inability to reach stone (1.8%), malfunctioning or breakage of instruments (0.8%), ureteral perforation (0.8%) and ureteral avulsion (0.16%). According to modified Satava classification system, there were 4.5% grade 1; 4.4% grade 2a; 3.2% grade 2b; and 0.57% grade 3 complications. We think that modified Satava classification is a quick and simple system for describing the severity of intraoperative URS complications and this grading system will facilitate a better comparison for the surgical outcomes obtained from different centers.
VizieR Online Data Catalog: LAMOST-Kepler MKCLASS spectral classification (Gray+, 2016)
NASA Astrophysics Data System (ADS)
Gray, R. O.; Corbally, C. J.; De Cat, P.; Fu, J. N.; Ren, A. B.; Shi, J. R.; Luo, A. L.; Zhang, H. T.; Wu, Y.; Cao, Z.; Li, G.; Zhang, Y.; Hou, Y.; Wang, Y.
2016-07-01
The data for the LAMOST-Kepler project are supplied by the Large Sky Area Multi Object Fiber Spectroscopic Telescope (LAMOST, also known as the Guo Shou Jing Telescope). This unique astronomical instrument is located at the Xinglong observatory in China, and combines a large aperture (4 m) telescope with a 5° circular field of view (Wang et al. 1996ApOpt..35.5155W). Our role in this project is to supply accurate two-dimensional spectral types for the observed targets. The large number of spectra obtained for this project (101086) makes traditional visual classification techniques impractical, so we have utilized the MKCLASS code to perform these classifications. The MKCLASS code (Gray & Corbally 2014AJ....147...80G, v1.07 http://www.appstate.edu/~grayro/mkclass/), an expert system designed to classify blue-violet spectra on the MK Classification system, was employed to produce the spectral classifications reported in this paper. MKCLASS was designed to reproduce the steps skilled human classifiers employ in the classification process. (2 data files).
[Classification of memory systems: a revision].
Agrest, M
2001-12-01
The present paper exposes the arguments against considering memory as a monolytic entity and how is it to be divided into several systems in order to understand its operation. Historically this division was acknowledge by different authors but in the last few decades it received the confirmation from the scientific research. The most accepted taxonomy establishes the existence of two major memory systems: declarative and non declarative memory. The article also presents the arguments for and against this kind of division, as well as an alternative classification in five major systems: procedural, perceptual representation, semantic, primary and episodic.
On a production system using default reasoning for pattern classification
NASA Technical Reports Server (NTRS)
Barry, Matthew R.; Lowe, Carlyle M.
1990-01-01
This paper addresses an unconventional application of a production system to a problem involving belief specialization. The production system reduces a large quantity of low-level descriptions into just a few higher-level descriptions that encompass the problem space in a more tractable fashion. This classification process utilizes a set of descriptions generated by combining the component hierarchy of a physical system with the semantics of the terminology employed in its operation. The paper describes an application of this process in a program, constructed in C and CLIPS, that classifies signatures of electromechanical system configurations. The program compares two independent classifications, describing the actual and expected system configurations, in order to generate a set of contradictions between the two.
Rough set classification based on quantum logic
NASA Astrophysics Data System (ADS)
Hassan, Yasser F.
2017-11-01
By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.
Classification of cardiac patient states using artificial neural networks
Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM
2003-01-01
Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222
Update on diabetes classification.
Thomas, Celeste C; Philipson, Louis H
2015-01-01
This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.
Robson, Michael; Murphy, Martina; Byrne, Fionnuala
2015-10-01
Quality assurance in labor and delivery is needed. The method must be simple and consistent, and be of universal value. It needs to be clinically relevant, robust, and prospective, and must incorporate epidemiological variables. The 10-Group Classification System (TGCS) is a simple method providing a common starting point for further detailed analysis within which all perinatal events and outcomes can be measured and compared. The system is demonstrated in the present paper using data for 2013 from the National Maternity Hospital in Dublin, Ireland. Interpretation of the classification can be easily taught. The standard table can provide much insight into the philosophy of care in the population of women studied and also provide information on data quality. With standardization of audit of events and outcomes, any differences in either sizes of groups, events or outcomes can be explained only by poor data collection, significant epidemiological variables, or differences in practice. In April 2015, WHO proposed that the TGCS (also known as the Robson classification) is used as a global standard for assessing, monitoring, and comparing cesarean delivery rates within and between healthcare facilities. Copyright © 2015. Published by Elsevier Ireland Ltd.
Perceptual and Acoustic Reliability Estimates for the Speech Disorders Classification System (SDCS)
ERIC Educational Resources Information Center
Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.
2010-01-01
A companion paper describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). The SDCS uses perceptual and acoustic data reduction methods to obtain information on a speaker's speech, prosody, and voice. The present paper provides reliability estimates for…
Accurate phylogenetic classification of DNA fragments based onsequence composition
DOE Office of Scientific and Technical Information (OSTI.GOV)
McHardy, Alice C.; Garcia Martin, Hector; Tsirigos, Aristotelis
2006-05-01
Metagenome studies have retrieved vast amounts of sequenceout of a variety of environments, leading to novel discoveries and greatinsights into the uncultured microbial world. Except for very simplecommunities, diversity makes sequence assembly and analysis a verychallenging problem. To understand the structure a 5 nd function ofmicrobial communities, a taxonomic characterization of the obtainedsequence fragments is highly desirable, yet currently limited mostly tothose sequences that contain phylogenetic marker genes. We show that forclades at the rank of domain down to genus, sequence composition allowsthe very accurate phylogenetic 10 characterization of genomic sequence.We developed a composition-based classifier, PhyloPythia, for de novophylogenetic sequencemore » characterization and have trained it on adata setof 340 genomes. By extensive evaluation experiments we show that themethodis accurate across all taxonomic ranks considered, even forsequences that originate fromnovel organisms and are as short as 1kb.Application to two metagenome datasets 15 obtained from samples ofphosphorus-removing sludge showed that the method allows the accurateclassification at genus level of most sequence fragments from thedominant populations, while at the same time correctly characterizingeven larger parts of the samples at higher taxonomic levels.« less
Nasal Septal Deviations: A Systematic Review of Classification Systems.
Teixeira, Jeffrey; Certal, Victor; Chang, Edward T; Camacho, Macario
2016-01-01
Objective. To systematically review the international literature for internal nasal septal deviation classification systems and summarize them for clinical and research purposes. Data Sources. Four databases (including PubMed/MEDLINE) were systematically searched through December 16, 2015. Methods. Systematic review, adhering to PRISMA. Results. After removal of duplicates, this study screened 952 articles for relevance. A final comprehensive review of 50 articles identified that 15 of these articles met the eligibility criteria. The classification systems defined in these articles included C-shaped, S-shaped, reverse C-shaped, and reverse S-shaped descriptions of the septal deviation in both the cephalocaudal and anteroposterior dimensions. Additional studies reported use of computed tomography and categorized deviation based on predefined locations. Three studies graded the severity of septal deviations based on the amount of deflection. The systems defined in the literature also included an evaluation of nasal septal spurs and perforations. Conclusion. This systematic review ascertained that the majority of the currently published classification systems for internal nasal septal deviations can be summarized by C-shaped or reverse C-shaped, as well as S-shaped or reverse S-shaped deviations in the anteroposterior and cephalocaudal dimensions. For imaging studies, predefined points have been defined along the septum. Common terminology can facilitate future research.
Prostate segmentation by sparse representation based classification
Gao, Yaozong; Liao, Shu; Shen, Dinggang
2012-01-01
Purpose: The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. Methods: To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by
Prostate segmentation by sparse representation based classification.
Gao, Yaozong; Liao, Shu; Shen, Dinggang
2012-10-01
The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by the elastic net in
Sugawara, Kotaro; Yamashita, Hiroharu; Uemura, Yukari; Mitsui, Takashi; Yagi, Koichi; Nishida, Masato; Aikou, Susumu; Mori, Kazuhiko; Nomura, Sachiyo; Seto, Yasuyuki
2017-10-01
The current eighth tumor node metastasis lymph node category pathologic lymph node staging system for esophageal squamous cell carcinoma is based solely on the number of metastatic nodes and does not consider anatomic distribution. We aimed to assess the prognostic capability of the eighth tumor node metastasis pathologic lymph node staging system (numeric-based) compared with the 11th Japan Esophageal Society (topography-based) pathologic lymph node staging system in patients with esophageal squamous cell carcinoma. We retrospectively reviewed the clinical records of 289 patients with esophageal squamous cell carcinoma who underwent esophagectomy with extended lymph node dissection during the period from January 2006 through June 2016. We compared discrimination abilities for overall survival, recurrence-free survival, and cancer-specific survival between these 2 staging systems using C-statistics. The median number of dissected and metastatic nodes was 61 (25% to 75% quartile range, 45 to 79) and 1 (25% to 75% quartile range, 0 to 3), respectively. The eighth tumor node metastasis pathologic lymph node staging system had a greater ability to accurately determine overall survival (C-statistics: tumor node metastasis classification, 0.69, 95% confidence interval, 0.62-0.76; Japan Esophageal Society classification; 0.65, 95% confidence interval, 0.58-0.71; P = .014) and cancer-specific survival (C-statistics: tumor node metastasis classification, 0.78, 95% confidence interval, 0.70-0.87; Japan Esophageal Society classification; 0.72, 95% confidence interval, 0.64-0.80; P = .018). Rates of total recurrence rose as the eighth tumor node metastasis pathologic lymph node stage increased, while stratification of patients according to the topography-based node classification system was not feasible. Numeric nodal staging is an essential tool for stratifying the oncologic outcomes of patients with esophageal squamous cell carcinoma even in the cohort in which adequate
A vegetation classification system applied to southern California
Timothy E. Paysen; Jeanine A. Derby; Hugh Black; Vernon C. Bleich; John W. Mincks
1980-01-01
A classification system for use in describing vegetation has been developed and is being applied to southern California. It is based upon a hierarchical stratification of vegetation, using physiognomic and taxonomic criteria. The system categories are Formation, Subformation. Series, Association, and Phase. Formations, Subformations, and Series have been specified for...
[Construction of biopharmaceutics classification system of Chinese materia medica].
Liu, Yang; Wei, Li; Dong, Ling; Zhu, Mei-Ling; Tang, Ming-Min; Zhang, Lei
2014-12-01
Based on the characteristics of multicomponent of traditional Chinese medicine and drawing lessons from the concepts, methods and techniques of biopharmaceutics classification system (BCS) in chemical field, this study comes up with the science framework of biopharmaceutics classification system of Chinese materia medica (CMMBCS). Using the different comparison method of multicomponent level and the CMMBCS method of overall traditional Chinese medicine, the study constructs the method process while setting forth academic thoughts and analyzing theory. The basic role of this system is clear to reveal the interaction and the related absorption mechanism of multicomponent in traditional Chinese medicine. It also provides new ideas and methods for improving the quality of Chinese materia medica and the development of new drug research.
An Expert System for Classifying Stars on the MK Spectral Classification System
NASA Astrophysics Data System (ADS)
Corbally, Christopher J.; Gray, R. O.
2013-01-01
We will describe an expert computer system designed to classify stellar spectra on the MK Spectral Classification system employing methods similar to those of humans who make direct comparison with the MK classification standards. Like an expert human classifier, MKCLASS first comes up with a rough spectral type, and then refines that type by direct comparison with MK standards drawn from a standards library using spectral criteria appropriate to the spectral class. Certain common spectral-type peculiarities can also be detected by the program. The program is also capable of identifying WD spectra and carbon stars and giving appropriate (but currently approximate) spectral types on the relevant systems. We will show comparisons between spectral types (including luminosity types) performed by MKCLASS and humans. The program currently is capable of competent classifications in the violet-green region, but plans are underway to extend the spectral criteria into the red and near-infrared regions. Two standard libraries with resolutions of 1.8 and 3.6Å are now available, but a higher-resolution standard library, using the new spectrograph on the Vatican Advanced Technology Telescope, is currently under preparation. Once that library is available, MKCLASS and the spectral libraries will be made available to the astronomical community.
Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.
de Moura, Karina de O A; Balbinot, Alexandre
2018-05-01
A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method
Impact of Information based Classification on Network Epidemics
Mishra, Bimal Kumar; Haldar, Kaushik; Sinha, Durgesh Nandini
2016-01-01
Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results. PMID:27329348
Comparison of wheat classification accuracy using different classifiers of the image-100 system
NASA Technical Reports Server (NTRS)
Dejesusparada, N. (Principal Investigator); Chen, S. C.; Moreira, M. A.; Delima, A. M.
1981-01-01
Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics.
Recognizing systemic sclerosis: comparative analysis of various sets of classification criteria
Romanowska-Próchnicka, Katarzyna; Olesińska, Marzena
2016-01-01
Systemic sclerosis is a complex disease characterized by autoimmunity, vasculopathy and tissue fibrosis. Although most patients present with some degree of skin sclerosis, which is a distinguishing hallmark, the clinical presentation vary greatly complicating the diagnosis. In this regard, new classification criteria were jointly published in 2013 by American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR). A recent major development in the classification criteria is improved sensitivity, particularly for detecting early disease. The new criteria allow more cases to be classified as having systemic sclerosis (SSc), which leads to earlier treatment. Moreover it is clinically beneficial in preventing the disease progression with its irreversible fibrosis and organ damage. The aim of this review is to give insight into new classification criteria and current trends in the diagnosis of systemic sclerosis. PMID:28115780
Centrifuge: rapid and sensitive classification of metagenomic sequences.
Kim, Daehwan; Song, Li; Breitwieser, Florian P; Salzberg, Steven L
2016-12-01
Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. © 2016 Kim et al.; Published by Cold Spring Harbor Laboratory Press.
Himmelmann, Kate; Horber, Veronka; De La Cruz, Javier; Horridge, Karen; Mejaski-Bosnjak, Vlatka; Hollody, Katalin; Krägeloh-Mann, Ingeborg
2017-01-01
To develop and evaluate a classification system for magnetic resonance imaging (MRI) findings of children with cerebral palsy (CP) that can be used in CP registers. The classification system was based on pathogenic patterns occurring in different periods of brain development. The MRI classification system (MRICS) consists of five main groups: maldevelopments, predominant white matter injury, predominant grey matter injury, miscellaneous, and normal findings. A detailed manual for the descriptions of these patterns was developed, including test cases (www.scpenetwork.eu/en/my-scpe/rtm/neuroimaging/cp-neuroimaging/). A literature review was performed and MRICS was compared with other classification systems. An exercise was carried out to check applicability and interrater reliability. Professionals working with children with CP or in CP registers were invited to participate in the exercise and chose to classify either 18 MRIs or MRI reports of children with CP. Classification systems in the literature were compatible with MRICS and harmonization possible. Interrater reliability was found to be good overall (k=0.69; 0.54-0.82) among the 41 participants and very good (k=0.81; 0.74-0.92) using the classification based on imaging reports. Surveillance of Cerebral Palsy in Europe (SCPE) proposes the MRICS as a reliable tool. Together with its manual it is simple to apply for CP registers. © 2016 Mac Keith Press.
ISE-based sensor array system for classification of foodstuffs
NASA Astrophysics Data System (ADS)
Ciosek, Patrycja; Sobanski, Tomasz; Augustyniak, Ewa; Wróblewski, Wojciech
2006-01-01
A system composed of an array of polymeric membrane ion-selective electrodes and a pattern recognition block—a so-called 'electronic tongue'—was used for the classification of liquid samples: milk, fruit juice and tonic. The task of this system was to automatically recognize a brand of the product. To analyze the measurement set-up responses various non-parametric classifiers such as k-nearest neighbours, a feedforward neural network and a probabilistic neural network were used. In order to enhance the classification ability of the system, standard model solutions of salts were measured (in order to take into account any variation in time of the working parameters of the sensors). This system was capable of recognizing the brand of the products with accuracy ranging from 68% to 100% (in the case of the best classifier).
R-parametrization and its role in classification of linear multivariable feedback systems
NASA Technical Reports Server (NTRS)
Chen, Robert T. N.
1988-01-01
A classification of all the compensators that stabilize a given general plant in a linear, time-invariant multi-input, multi-output feedback system is developed. This classification, along with the associated necessary and sufficient conditions for stability of the feedback system, is achieved through the introduction of a new parameterization, referred to as R-Parameterization, which is a dual of the familiar Q-Parameterization. The classification is made to the stability conditions of the compensators and the plant by themselves; and necessary and sufficient conditions are based on the stability of Q and R themselves.
St-Jean, Audray; Meziou, Salma; Ayotte, Pierre; Lucas, Michel
2017-11-22
Little is known about the suitability of three commonly used body mass index (BMI) classification systems for Indigenous youth. We estimated overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three BMI classification systems, assessed the level of agreement between them, and evaluated their accuracy through body fat and cardiometabolic risk factors. Data on 288 youth (aged 8-17 years) were collected. Overweight and obesity prevalence were estimated with Centers for Disease Control and Prevention (CDC), International Obesity Task Force (IOTF) and World Health Organization (WHO) criteria. Agreement was measured with weighted kappa (κw). Associations with body fat and cardiometabolic risk factors were evaluated by analysis of variance. Obesity prevalence was 42.7% with IOTF, 47.2% with CDC, and 49.3% with WHO criteria. Agreement was almost perfect between IOTF and CDC (κw = 0.93), IOTF and WHO (κw = 0.91), and WHO and CDC (κw = 0.94). Means of body fat and cardiometabolic risk factors were significantly higher (P trend < 0.001) from normal weight to obesity, regardless of the system used. Youth considered overweight by IOTF but obese by CDC or WHO exhibited less severe clinical obesity. IOTF seems to be more accurate in identifying obesity in Cree youth.
Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds.
Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M; Bloom, Peter H; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd
2017-01-01
Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.
Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds
Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd
2017-01-01
Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159
Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds
Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael J.; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd
2017-01-01
Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.
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.
Natural fracture systems on planetary surfaces: Genetic classification and pattern randomness
NASA Technical Reports Server (NTRS)
Rossbacher, Lisa A.
1987-01-01
One method for classifying natural fracture systems is by fracture genesis. This approach involves the physics of the formation process, and it has been used most frequently in attempts to predict subsurface fractures and petroleum reservoir productivity. This classification system can also be applied to larger fracture systems on any planetary surface. One problem in applying this classification system to planetary surfaces is that it was developed for ralatively small-scale fractures that would influence porosity, particularly as observed in a core sample. Planetary studies also require consideration of large-scale fractures. Nevertheless, this system offers some valuable perspectives on fracture systems of any size.
42 CFR 412.620 - Patient classification system.
Code of Federal Regulations, 2013 CFR
2013-10-01
... weighting factors to reflect changes in— (1) Treatment patterns; (2) Technology; (3) Number of discharges... 42 Public Health 2 2013-10-01 2013-10-01 false Patient classification system. 412.620 Section 412... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix...
42 CFR 412.620 - Patient classification system.
Code of Federal Regulations, 2012 CFR
2012-10-01
... weighting factors to reflect changes in— (1) Treatment patterns; (2) Technology; (3) Number of discharges... 42 Public Health 2 2012-10-01 2012-10-01 false Patient classification system. 412.620 Section 412... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix...
42 CFR 412.620 - Patient classification system.
Code of Federal Regulations, 2014 CFR
2014-10-01
... weighting factors to reflect changes in— (1) Treatment patterns; (2) Technology; (3) Number of discharges... 42 Public Health 2 2014-10-01 2014-10-01 false Patient classification system. 412.620 Section 412... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix...
Submerged Object Detection and Classification System
1993-04-16
example of this type of system is a conventional sonar device wherein a highly directional beam of sonic energy periodically radiates from a...scanning transducer which in turn operates as a receiver to detect echoes reflected from any object within the path of 15 propagation. Sonar devices...classification, which requires relatively high frequency signals. Sonar devices also have the shortcoming of sensing background noise generated by
Lainscsek, Claudia; Weyhenmeyer, Jonathan; Hernandez, Manuel E; Poizner, Howard; Sejnowski, Terrence J
2013-01-01
Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson's disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to -30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A' under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.
Intelligence system based classification approach for medical disease diagnosis
NASA Astrophysics Data System (ADS)
Sagir, Abdu Masanawa; Sathasivam, Saratha
2017-08-01
The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.
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.
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.
A Model Assessment and Classification System for Men and Women in Correctional Institutions.
ERIC Educational Resources Information Center
Hellervik, Lowell W.; And Others
The report describes a manpower assessment and classification system for criminal offenders directed towards making practical training and job classification decisions. The model is not concerned with custody classifications except as they affect occupational/training possibilities. The model combines traditional procedures of vocational…
Injuries from Intimate Partner and Sexual Violence: Significance and Classification Systems
Sommers, Marilyn S.; Brunner, Lillian S.; Brown, Kathleen M.; Buschur, Carole; Everett, Janine S.; Fargo, Jamison D.; Fisher, Bonnie S.; Hinkle, Christina; Zink, Therese M.
2012-01-01
While intimate partner violence (IPV) and sexual violence (SV) are highly associated with injury, the healthcare and legal significance of these injuries is controversial. Purpose: Herein we propose to explore the significance of injury in IPV and SV and examine the current status of injury classification systems from the perspectives of the healthcare and criminal justice systems. We will review current injury classification systems and suggest a typology of injury that could be tested empirically. Findings: Within the published literature, we found that no commonly-accepted injury typology exists. While nuanced and controversial issues surround the role of injury detection in the sexual assault forensic examination, enough evidence exists to support the continued pursuance of a scientific approach to injury classification. We propose an injury typology that is measureable, is applicable to the healthcare setting and criminal justice system, and allows us to use uses a matrix approach that includes a severity score, anatomic location, and injury type. We suggest a typology that might be used for further empirical testing on the validity and reliability of IPV and SV injury data. Conclusion: We recommend that the community of scientists concerned about IPV and SV develop a more rigorous injury classification system that will improve the quality of forensic evidence proffered and decisions made throughout the criminal justice process. PMID:22687765
Injuries from intimate partner and sexual violence: Significance and classification systems.
Sommers, Marilyn S; Brown, Kathleen M; Buschur, Carole; Everett, Janine S; Fargo, Jamison D; Fisher, Bonnie S; Hinkle, Christina; Zink, Therese M
2012-07-01
While intimate partner violence (IPV) and sexual violence (SV) are highly associated with injury, the healthcare and legal significance of these injuries is controversial. Herein we propose to explore the significance of injury in IPV and SV and examine the current status of injury classification systems from the perspectives of the healthcare and criminal justice systems. We will review current injury classification systems and suggest a typology of injury that could be tested empirically. Within the published literature, we found that no commonly accepted injury typology exists. While nuanced and controversial issues surround the role of injury detection in the sexual assault forensic examination, enough evidence exists to support the continued pursuance of a scientific approach to injury classification. We propose an injury typology that is measurable, is applicable to the healthcare setting and criminal justice system, and allows us to use uses a matrix approach that includes a severity score, anatomic location, and injury type. We suggest a typology that might be used for further empirical testing on the validity and reliability of IPV and SV injury data. We recommend that the community of scientists concerned about IPV and SV develop a more rigorous injury classification system that will improve the quality of forensic evidence proffered and decisions made throughout the criminal justice process. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
Teaching sexual history-taking skills using the Sexual Events Classification System.
Fidler, Donald C; Petri, Justin Daniel; Chapman, Mark
2010-01-01
The authors review the literature about educational programs for teaching sexual history-taking skills and describe novel techniques for teaching these skills. Psychiatric residents enrolled in a brief sexual history-taking course that included instruction on the Sexual Events Classification System, feedback on residents' video-recorded interviews with simulated patients, discussion of videos that simulated bad interviews, simulated patients, and a competency scoring form to score a video of a simulated interview. After the course, residents completed an anonymous survey to assess the usefulness of the experience. After the course, most residents felt more comfortable taking sexual histories. They described the Sexual Events Classification System and simulated interviews as practical methods for teaching sexual history-taking skills. The Sexual Events Classification System and simulated patient experiences may serve as a practical model for teaching sexual history-taking skills to general psychiatric residents.
NASA Astrophysics Data System (ADS)
Land, Walker H., Jr.; Lewis, Michael; Sadik, Omowunmi; Wong, Lut; Wanekaya, Adam; Gonzalez, Richard J.; Balan, Arun
2004-04-01
This paper extends the classification approaches described in reference [1] in the following way: (1.) developing and evaluating a new method for evolving organophosphate nerve agent Support Vector Machine (SVM) classifiers using Evolutionary Programming, (2.) conducting research experiments using a larger database of organophosphate nerve agents, and (3.) upgrading the architecture to an object-based grid system for evaluating the classification of EP derived SVMs. Due to the increased threats of chemical and biological weapons of mass destruction (WMD) by international terrorist organizations, a significant effort is underway to develop tools that can be used to detect and effectively combat biochemical warfare. This paper reports the integration of multi-array sensors with Support Vector Machines (SVMs) for the detection of organophosphates nerve agents using a grid computing system called Legion. Grid computing is the use of large collections of heterogeneous, distributed resources (including machines, databases, devices, and users) to support large-scale computations and wide-area data access. Finally, preliminary results using EP derived support vector machines designed to operate on distributed systems have provided accurate classification results. In addition, distributed training time architectures are 50 times faster when compared to standard iterative training time methods.
Automatic classification of blank substrate defects
NASA Astrophysics Data System (ADS)
Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati
2014-10-01
Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask
Spectral Classification in the MK System of 167 Northern HD Stars
NASA Astrophysics Data System (ADS)
Jensen, K. S.
1981-09-01
Spectral classifications in the MK system of 167 northern HD stars are presented. The spectra (102 A/mm at Hγ, width 0.60 mm) are from objective prism plates obtained with the Schmidt telescope of the CUO, Brorfelde. Most of the stars have no previous MK classification.
Protein classification based on text document classification techniques.
Cheng, Betty Yee Man; Carbonell, Jaime G; Klein-Seetharaman, Judith
2005-03-01
The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM) using alignment-based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naive Bayes classifiers with chi-square feature selection on counts of n-grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naive Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively. Copyright 2005 Wiley-Liss, Inc.
Shankar, Vijay; Reo, Nicholas V; Paliy, Oleg
2015-12-09
We previously showed that stool samples of pre-adolescent and adolescent US children diagnosed with diarrhea-predominant IBS (IBS-D) had different compositions of microbiota and metabolites compared to healthy age-matched controls. Here we explored whether observed fecal microbiota and metabolite differences between these two adolescent populations can be used to discriminate between IBS and health. We constructed individual microbiota- and metabolite-based sample classification models based on the partial least squares multivariate analysis and then applied a Bayesian approach to integrate individual models into a single classifier. The resulting combined classification achieved 84 % accuracy of correct sample group assignment and 86 % prediction for IBS-D in cross-validation tests. The performance of the cumulative classification model was further validated by the de novo analysis of stool samples from a small independent IBS-D cohort. High-throughput microbial and metabolite profiling of subject stool samples can be used to facilitate IBS diagnosis.
Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System
Balbinot, Alexandre
2018-01-01
A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method
A Biochar Classification System and Associated Test Methods
DOE Office of Scientific and Technical Information (OSTI.GOV)
Camps-Arbestain, Marta; Amonette, James E.; Singh, Balwant
2015-02-18
In this chapter, a biochar classification system related to its use as soil amendment is proposed. This document builds upon previous work and constrains its scope to materials with properties that satisfy the criteria for biochar as defined by either the International Biochar Initiative (IBI) Biochar Standards or the European Biochar Community (EBC) Standards, and it is intended to minimise the need for testing in addition to those required according to the above-mentioned standards. The classification system envisions enabling stakeholders and commercial entities to (i) identify the most suitable biochar to fulfil the requirements for a particular soil and/or land-use,more » and (ii) distinguish the application of biochar for specific niches (e.g., soilless agriculture). It is based on the best current knowledge and the intention is to periodically review and update the document based on new data and knowledge that become available in the scientific literature. The main thrust of this classification system is based on the direct or indirect beneficial effects that biochar provides from its application to soil. We have classified the potential beneficial effects of biochar application to soils into five categories with their corresponding classes, where applicable: (i) carbon (C) storage value, (ii) fertiliser value, (iii) liming value, (iv) particle-size, and (v) use in soil-less agriculture. A summary of recommended test methods is provided at the end of the chapter.« less
DOT National Transportation Integrated Search
1980-02-01
The report describes the development of an AGT classification structure. Five classes are defined based on three system characteristics: service type, minimum travelling unit capacity, and maximum operating velocity. The five classes defined are: Per...
General classification handbook for floodplain vegetation in large river systems
Dieck, Jennifer J.; Ruhser, Janis; Hoy, Erin E.; Robinson, Larry R.
2015-01-01
This handbook describes the General Wetland Vegetation Classification System developed as part of the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration (UMRR) Program, Long Term Resource Monitoring (LTRM) element. The UMRR is a cooperative effort between the U.S. Army Corps of Engineers, U.S. Geological Survey, U.S. Fish and Wildlife Service, and the states of Illinois, Iowa, Minnesota, Missouri, and Wisconsin. The classification system consists of 31 general map classes and has been used to create systemic vegetation data layers throughout the diverse Upper Mississippi River System (UMRS), which includes the commercially navigable reaches of the Mississippi River from Minneapolis, Minnesota, in the north to Cairo, Illinois, in the south, the Illinois River, and navigable portions of the Kaskaskia, Black, St. Croix, and Minnesota Rivers. In addition, this handbook describes the evolution of the General Wetland Vegetation Classification System, discusses the process of creating a vegetation data layer, and describes each of the 31 map classes in detail. The handbook also acts as a pictorial guide to each of the map classes as they may appear in the field, as well as on color-infrared imagery. This version is an update to the original handbook published in 2004.
ERIC Educational Resources Information Center
Clark, Ginny; Grady, M. Tim
The objective of this study was to determine if the Annehurst Curriculum Classification System (ACCS) learner characteristics and curriculum materials classifications among elementary mathematics students, can be used as the dimensions of an aptitude-treatment interaction analysis. The subjects were 34 fourth and fifth graders in three open-space…
A land use and land cover classification system for use with remote sensor data
Anderson, James R.; Hardy, Ernest E.; Roach, John T.; Witmer, Richard E.
1976-01-01
The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in U.S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.
Asynchronous Data-Driven Classification of Weapon Systems
2009-10-01
Classification of Weapon SystemsF Xin Jin† Kushal Mukherjee† Shalabh Gupta† Asok Ray † Shashi Phoha† Thyagaraju Damarla‡ xuj103@psu.edu kum162@psu.edu szg107...Orlando, FL. [8] A. Ray , “Symbolic dynamic analysis of complex systems for anomaly detection,” Signal Processing, vol. 84, no. 7, pp. 1115–1130, July...2004. [9] S. Gupta and A. Ray , “Symbolic dynamic filtering for data-driven pat- tern recognition,” PATTERN RECOGNITION: Theory and Application
Development of the Gross Motor Function Classification System (1997)
ERIC Educational Resources Information Center
Morris, Christopher
2008-01-01
To address the need for a standardized system to classify the gross motor function of children with cerebral palsy, the authors developed a five-level classification system analogous to the staging and grading systems used in medicine. Nominal group process and Delphi survey consensus methods were used to examine content validity and revise the…
Analysis and application of classification methods of complex carbonate reservoirs
NASA Astrophysics Data System (ADS)
Li, Xiongyan; Qin, Ruibao; Ping, Haitao; Wei, Dan; Liu, Xiaomei
2018-06-01
There are abundant carbonate reservoirs from the Cenozoic to Mesozoic era in the Middle East. Due to variation in sedimentary environment and diagenetic process of carbonate reservoirs, several porosity types coexist in carbonate reservoirs. As a result, because of the complex lithologies and pore types as well as the impact of microfractures, the pore structure is very complicated. Therefore, it is difficult to accurately calculate the reservoir parameters. In order to accurately evaluate carbonate reservoirs, based on the pore structure evaluation of carbonate reservoirs, the classification methods of carbonate reservoirs are analyzed based on capillary pressure curves and flow units. Based on the capillary pressure curves, although the carbonate reservoirs can be classified, the relationship between porosity and permeability after classification is not ideal. On the basis of the flow units, the high-precision functional relationship between porosity and permeability after classification can be established. Therefore, the carbonate reservoirs can be quantitatively evaluated based on the classification of flow units. In the dolomite reservoirs, the average absolute error of calculated permeability decreases from 15.13 to 7.44 mD. Similarly, the average absolute error of calculated permeability of limestone reservoirs is reduced from 20.33 to 7.37 mD. Only by accurately characterizing pore structures and classifying reservoir types, reservoir parameters could be calculated accurately. Therefore, characterizing pore structures and classifying reservoir types are very important to accurate evaluation of complex carbonate reservoirs in the Middle East.
A proposal for the annotation of recurrent colorectal cancer: the 'Sheffield classification'.
Majeed, A W; Shorthouse, A J; Blakeborough, A; Bird, N C
2011-11-01
Current classification systems of large bowel cancer only refer to metastatic disease as M0, M1 or Mx. Recurrent colorectal cancer primarily occurs in the liver, lungs, nodes or peritoneum. The management of each of these sites of recurrence has made significant advances and each is a subspecialty in its own right. The aim of this paper was to devise a classification system which accurately describes the site and extent of metastatic spread. An amendment of the current system is proposed in which liver, lung and peritoneal metastases are annotated by 'Liv 0,1', 'Pul 0,1' and 'Per 0,1' in describing the primary presentation. These are then subclassified, taking into account the chronology, size, number and geographical distribution of metastatic disease or logoregional recurrence and its K-Ras status. This discussion document proposes a classification system which is logical and simple to use. We plan to validate it prospectively. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.
Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.
Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu
2016-10-20
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.
Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System
Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu
2016-01-01
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596
A thyroid nodule classification method based on TI-RADS
NASA Astrophysics Data System (ADS)
Wang, Hao; Yang, Yang; Peng, Bo; Chen, Qin
2017-07-01
Thyroid Imaging Reporting and Data System(TI-RADS) is a valuable tool for differentiating the benign and the malignant thyroid nodules. In clinic, doctors can determine the extent of being benign or malignant in terms of different classes by using TI-RADS. Classification represents the degree of malignancy of thyroid nodules. TI-RADS as a classification standard can be used to guide the ultrasonic doctor to examine thyroid nodules more accurately and reliably. In this paper, we aim to classify the thyroid nodules with the help of TI-RADS. To this end, four ultrasound signs, i.e., cystic and solid, echo pattern, boundary feature and calcification of thyroid nodules are extracted and converted into feature vectors. Then semi-supervised fuzzy C-means ensemble (SS-FCME) model is applied to obtain the classification results. The experimental results demonstrate that the proposed method can help doctors diagnose the thyroid nodules effectively.
Development of an ecological classification system for the Wayne National Forest
David M. Hix; Andrea M. Chech
1993-01-01
In 1991, a collaborative research project was initiated to create an ecological classification system for the Wayne National Forest of southeastern Ohio. The work focuses on the ecological land type (ELT) level of ecosystem classification. The most common ELTs are being identified and described using information from intensive field sampling and multivariate data...
Halitosis: a new definition and classification.
Aydin, M; Harvey-Woodworth, C N
2014-07-11
There is no universally accepted, precise definition, nor standardisation in terminology and classification of halitosis. To propose a new definition, free from subjective descriptions (faecal, fish odour, etc), one-time sulphide detector readings and organoleptic estimation of odour levels, and excludes temporary exogenous odours (for example, from dietary sources). Some terms previously used in the literature are revised. A new aetiologic classification is proposed, dividing pathologic halitosis into Type 1 (oral), Type 2 (airway), Type 3 (gastroesophageal), Type 4 (blood-borne) and Type 5 (subjective). In reality, any halitosis complaint is potentially the sum of these types in any combination, superimposed on the Type 0 (physiologic odour) present in health. This system allows for multiple diagnoses in the same patient, reflecting the multifactorial nature of the complaint. It represents the most accurate model to understand halitosis and forms an efficient and logical basis for clinical management of the complaint.
Hosseinpour-Feizi, Hojjat; Soleimanpour, Jafar; Sales, Jafar Ganjpour; Arzroumchilar, Ali
2011-01-01
The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems. The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance. A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems. Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification's priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method.
Seismic event classification system
Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William
1994-01-01
In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.
Velidedeoglu, Mehmet; Arikan, Akif Enes; Uludag, Sezgin Server; Olgun, Deniz Cebi; Kilic, Fahrettin; Kapan, Metin
2015-05-01
Due to being a severe complication, iatrogenic bile duct injury is still a challenging issue for surgeons in gallbladder surgery. However, a commonly accepted classification describing the type of injury has not been available yet. This study aims to evaluate ability of six current classification systems to discriminate bile duct injury patterns. Twelve patients, who were referred to our clinic because of iatrogenic bile duct injury after laparoscopic cholecystectomy were reviewed retrospectively. We described type of injury for each patient according to current six different classifications. 9 patients underwent definitive biliary reconstruction. Bismuth, Strasberg-Bismuth, Stewart-Way and Neuhaus classifications do not consider vascular involvement, Siewert system does, but only for the tangential lesions without structural loss of duct and lesion with a structural defect of hepatic or common bile duct. Siewert, Neuhaus and Stewart-Way systems do not discriminate between lesions at or above bifurcation of the hepatic duct. The Hannover classification may resolve the missing aspects of other systems by describing additional vascular involvement and location of the lesion at or above bifurcation.
Classification of Unmanned Aircraft Systems. UAS Classification/Categorization for Certification
NASA Technical Reports Server (NTRS)
2004-01-01
Category, class, and type designations are primary means to identify appropriate aircraft certification basis, operating rules/limitations, and pilot qualifications to operate in the National Airspace System (NAS). The question is whether UAS fit into existing aircraft categories or classes, or are unique enough to justify the creation of a new category/class. In addition, the characteristics or capabilities, which define when an UAS becomes a regulated aircraft, must also be decided. This issue focuses on UAS classification for certification purposes. Several approaches have been considered for classifying UAS. They basically group into either using a weight/mass basis, or a safety risk basis, factoring in the performance of the UAS, including where the UAS would operate. Under existing standards, aircraft must have a Type Certificate and Certificate of Airworthiness, in order to be used for "compensation or hire", a major difference from model aircraft. Newer technologies may make it possible for very small UAS to conduct commercial services, but that is left for a future discussion to extend the regulated aircraft to a lower level. The Access 5 position is that UAS are aircraft and should be regulated above the weight threshold differentiating them from model airplanes. The recommended classification grouping is summarized in a chart.
Code of Federal Regulations, 2012 CFR
2012-01-01
... System biogeographic classification scheme and estuarine typologies. 921.3 Section 921.3 Commerce and... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... classification scheme based on regional variations in the nation's coastal zone has been developed. The...
Code of Federal Regulations, 2013 CFR
2013-01-01
... System biogeographic classification scheme and estuarine typologies. 921.3 Section 921.3 Commerce and... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... classification scheme based on regional variations in the nation's coastal zone has been developed. The...
Code of Federal Regulations, 2010 CFR
2010-01-01
... System biogeographic classification scheme and estuarine typologies. 921.3 Section 921.3 Commerce and... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... classification scheme based on regional variations in the nation's coastal zone has been developed. The...
Code of Federal Regulations, 2014 CFR
2014-01-01
... System biogeographic classification scheme and estuarine typologies. 921.3 Section 921.3 Commerce and... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... classification scheme based on regional variations in the nation's coastal zone has been developed. The...
Code of Federal Regulations, 2011 CFR
2011-01-01
... System biogeographic classification scheme and estuarine typologies. 921.3 Section 921.3 Commerce and... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... classification scheme based on regional variations in the nation's coastal zone has been developed. The...
A detailed procedure for the use of small-scale photography in land use classification
NASA Technical Reports Server (NTRS)
Vegas, P. L.
1974-01-01
A procedure developed to produce accurate land use maps from available high-altitude, small-scale photography in a cost-effective manner is presented. An alternative procedure, for use when the capability for updating the resultant land use map is not required, is also presented. The technical approach is discussed in detail, and personnel and equipment needs are analyzed. Accuracy percentages are listed, and costs are cited. The experiment land use classification categories are explained, and a proposed national land use classification system is recommended.
Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.
Li, Linyi; Xu, Tingbao; Chen, Yun
2017-01-01
In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.
Scalable metagenomic taxonomy classification using a reference genome database
Ames, Sasha K.; Hysom, David A.; Gardner, Shea N.; Lloyd, G. Scott; Gokhale, Maya B.; Allen, Jonathan E.
2013-01-01
Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23828782
Scanning electron microscope automatic defect classification of process induced defects
NASA Astrophysics Data System (ADS)
Wolfe, Scott; McGarvey, Steve
2017-03-01
With the integration of high speed Scanning Electron Microscope (SEM) based Automated Defect Redetection (ADR) in both high volume semiconductor manufacturing and Research and Development (R and D), the need for reliable SEM Automated Defect Classification (ADC) has grown tremendously in the past few years. In many high volume manufacturing facilities and R and D operations, defect inspection is performed on EBeam (EB), Bright Field (BF) or Dark Field (DF) defect inspection equipment. A comma separated value (CSV) file is created by both the patterned and non-patterned defect inspection tools. The defect inspection result file contains a list of the inspection anomalies detected during the inspection tools' examination of each structure, or the examination of an entire wafers surface for non-patterned applications. This file is imported into the Defect Review Scanning Electron Microscope (DRSEM). Following the defect inspection result file import, the DRSEM automatically moves the wafer to each defect coordinate and performs ADR. During ADR the DRSEM operates in a reference mode, capturing a SEM image at the exact position of the anomalies coordinates and capturing a SEM image of a reference location in the center of the wafer. A Defect reference image is created based on the Reference image minus the Defect image. The exact coordinates of the defect is calculated based on the calculated defect position and the anomalies stage coordinate calculated when the high magnification SEM defect image is captured. The captured SEM image is processed through either DRSEM ADC binning, exporting to a Yield Analysis System (YAS), or a combination of both. Process Engineers, Yield Analysis Engineers or Failure Analysis Engineers will manually review the captured images to insure that either the YAS defect binning is accurately classifying the defects or that the DRSEM defect binning is accurately classifying the defects. This paper is an exploration of the feasibility of the
NASA Astrophysics Data System (ADS)
Zhu, Jun; Chen, Lijun; Ma, Lantao; Li, Dejian; Jiang, Wei; Pan, Lihong; Shen, Huiting; Jia, Hongmin; Hsiang, Chingyun; Cheng, Guojie; Ling, Li; Chen, Shijie; Wang, Jun; Liao, Wenkui; Zhang, Gary
2014-04-01
Defect review is a time consuming job. Human error makes result inconsistent. The defects located on don't care area would not hurt the yield and no need to review them such as defects on dark area. However, critical area defects can impact yield dramatically and need more attention to review them such as defects on clear area. With decrease in integrated circuit dimensions, mask defects are always thousands detected during inspection even more. Traditional manual or simple classification approaches are unable to meet efficient and accuracy requirement. This paper focuses on automatic defect management and classification solution using image output of Lasertec inspection equipment and Anchor pattern centric image process technology. The number of mask defect found during an inspection is always in the range of thousands or even more. This system can handle large number defects with quick and accurate defect classification result. Our experiment includes Die to Die and Single Die modes. The classification accuracy can reach 87.4% and 93.3%. No critical or printable defects are missing in our test cases. The missing classification defects are 0.25% and 0.24% in Die to Die mode and Single Die mode. This kind of missing rate is encouraging and acceptable to apply on production line. The result can be output and reloaded back to inspection machine to have further review. This step helps users to validate some unsure defects with clear and magnification images when captured images can't provide enough information to make judgment. This system effectively reduces expensive inline defect review time. As a fully inline automated defect management solution, the system could be compatible with current inspection approach and integrated with optical simulation even scoring function and guide wafer level defect inspection.
NASA Astrophysics Data System (ADS)
Dondeyne, Stefaan; Juilleret, Jérôme; Vancampenhout, Karen; Deckers, Jozef; Hissler, Christophe
2017-04-01
Classification of soils in both World Reference Base for soil resources (WRB) and Soil Taxonomy hinges on the identification of diagnostic horizons and characteristics. However as these features often occur within the first 100 cm, these classification systems convey little information on subsoil characteristics. An integrated knowledge of the soil, soil-to-substratum and deeper substratum continuum is required when dealing with environmental issues such as vegetation ecology, water quality or the Critical Zone in general. Therefore, we recently proposed a classification system of the subsolum complementing current soil classification systems. By reflecting on the structure of the subsoil classification system which is inspired by WRB, we aim at fostering a discussion on some potential future developments of WRB. For classifying the subsolum we define Regolite, Saprolite, Saprock and Bedrock as four Subsolum Reference Groups each corresponding to different weathering stages of the subsoil. Principal qualifiers can be used to categorize intergrades of these Subsoil Reference Groups while morphologic and lithologic characteristics can be presented with supplementary qualifiers. We argue that adopting a low hierarchical structure - akin to WRB and in contrast to a strong hierarchical structure as in Soil Taxonomy - offers the advantage of having an open classification system avoiding the need for a priori knowledge of all possible combinations which may be encountered in the field. Just as in WRB we also propose to use principal and supplementary qualifiers as a second level of classification. However, in contrast to WRB we propose to reserve the principal qualifiers for intergrades and to regroup the supplementary qualifiers into thematic categories (morphologic or lithologic). Structuring the qualifiers in this manner should facilitate the integration and handling of both soil and subsoil classification units into soil information systems and calls for paying
London 2012 Paralympic swimming: passive drag and the classification system.
Oh, Yim-Taek; Burkett, Brendan; Osborough, Conor; Formosa, Danielle; Payton, Carl
2013-09-01
The key difference between the Olympic and Paralympic Games is the use of classification systems within Paralympic sports to provide a fair competition for athletes with a range of physical disabilities. In 2009, the International Paralympic Committee mandated the development of new, evidence-based classification systems. This study aims to assess objectively the swimming classification system by determining the relationship between passive drag and level of swimming-specific impairment, as defined by the current swimming class. Data were collected on participants at the London 2012 Paralympic Games. The passive drag force of 113 swimmers (classes 3-14) was measured using an electro-mechanical towing device and load cell. Swimmers were towed on the surface of a swimming pool at 1.5 m/s while holding their most streamlined position. Passive drag ranged from 24.9 to 82.8 N; the normalised drag (drag/mass) ranged from 0.45 to 1.86 N/kg. Significant negative associations were found between drag and the swimming class (τ = -0.41, p < 0.01) and normalised drag and the swimming class (τ = -0.60, p < 0.01). The mean difference in drag between adjacent classes was inconsistent, ranging from 0 N (6 vs 7) to 11.9 N (5 vs 6). Reciprocal Ponderal Index (a measure of slenderness) correlated moderately with normalised drag (r(P) = -0.40, p < 0.01). Although swimmers with the lowest swimming class experienced the highest passive drag and vice versa, the inconsistent difference in mean passive drag between adjacent classes indicates that the current classification system does not always differentiate clearly between swimming groups.
Free classification of American English dialects by native and non-native listeners
Clopper, Cynthia G.; Bradlow, Ann R.
2009-01-01
Most second language acquisition research focuses on linguistic structures, and less research has examined the acquisition of sociolinguistic patterns. The current study explored the perceptual classification of regional dialects of American English by native and non-native listeners using a free classification task. Results revealed similar classification strategies for the native and non-native listeners. However, the native listeners were more accurate overall than the non-native listeners. In addition, the non-native listeners were less able to make use of constellations of cues to accurately classify the talkers by dialect. However, the non-native listeners were able to attend to cues that were either phonologically or sociolinguistically relevant in their native language. These results suggest that non-native listeners can use information in the speech signal to classify talkers by regional dialect, but that their lack of signal-independent cultural knowledge about variation in the second language leads to less accurate classification performance. PMID:20161400
Multiple Sparse Representations Classification
Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik
2015-01-01
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and
ERIC Educational Resources Information Center
McKinlay, John
Despite some inroads by the Library of Congress Classification and short-lived experimentation with Universal Decimal Classification and Bliss Classification, Dewey Decimal Classification, with its ability in recent editions to be hospitable to local needs, remains the most widely used classification system in Australia. Although supplemented at…
Effects of stress typicality during speeded grammatical classification.
Arciuli, Joanne; Cupples, Linda
2003-01-01
The experiments reported here were designed to investigate the influence of stress typicality during speeded grammatical classification of disyllabic English words by native and non-native speakers. Trochaic nouns and iambic gram verbs were considered to be typically stressed, whereas iambic nouns and trochaic verbs were considered to be atypically stressed. Experiments 1a and 2a showed that while native speakers classified typically stressed words individual more quickly and more accurately than atypically stressed words during differences reading, there were no overall effects during classification of spoken stimuli. However, a subgroup of native speakers with high error rates did show a significant effect during classification of spoken stimuli. Experiments 1b and 2b showed that non-native speakers classified typically stressed words more quickly and more accurately than atypically stressed words during reading. Typically stressed words were classified more accurately than atypically stressed words when the stimuli were spoken. Importantly, there was a significant relationship between error rates, vocabulary size and the size of the stress typicality effect in each experiment. We conclude that participants use information about lexical stress to help them distinguish between disyllabic nouns and verbs during speeded grammatical classification. This is especially so for individuals with a limited vocabulary who lack other knowledge (e.g., semantic knowledge) about the differences between these grammatical categories.
Waring, R; Knight, R
2013-01-01
Children with speech sound disorders (SSD) form a heterogeneous group who differ in terms of the severity of their condition, underlying cause, speech errors, involvement of other aspects of the linguistic system and treatment response. To date there is no universal and agreed-upon classification system. Instead, a number of theoretically differing classification systems have been proposed based on either an aetiological (medical) approach, a descriptive-linguistic approach or a processing approach. To describe and review the supporting evidence, and to provide a critical evaluation of the current childhood SSD classification systems. Descriptions of the major specific approaches to classification are reviewed and research papers supporting the reliability and validity of the systems are evaluated. Three specific paediatric SSD classification systems; the aetiologic-based Speech Disorders Classification System, the descriptive-linguistic Differential Diagnosis system, and the processing-based Psycholinguistic Framework are identified as potentially useful in classifying children with SSD into homogeneous subgroups. The Differential Diagnosis system has a growing body of empirical support from clinical population studies, across language error pattern studies and treatment efficacy studies. The Speech Disorders Classification System is currently a research tool with eight proposed subgroups. The Psycholinguistic Framework is a potential bridge to linking cause and surface level speech errors. There is a need for a universally agreed-upon classification system that is useful to clinicians and researchers. The resulting classification system needs to be robust, reliable and valid. A universal classification system would allow for improved tailoring of treatments to subgroups of SSD which may, in turn, lead to improved treatment efficacy. © 2012 Royal College of Speech and Language Therapists.
Development of municipal solid waste classification in Korea based on fossil carbon fraction.
Lee, Jeongwoo; Kang, Seongmin; Kim, Seungjin; Kim, Ki-Hyun; Jeon, Eui-Chan
2015-10-01
Environmental problems and climate change arising from waste incineration are taken quite seriously in the world. In Korea, the waste disposal methods are largely classified into landfill, incineration, recycling, etc. and the amount of incinerated waste has risen by 24.5% from 2002. In the analysis of CO₂emissions estimations of waste incinerators fossil carbon content are main factor by the IPCC. FCF differs depending on the characteristics of waste in each country, and a wide range of default values are proposed by the IPCC. This study conducted research on the existing classifications of the IPCC and Korean waste classification systems based on FCF for accurate greenhouse gas emissions estimation of waste incineration. The characteristics possible for sorting were classified according to FCF and form. The characteristics sorted according to fossil carbon fraction were paper, textiles, rubber, and leather. Paper was classified into pure paper and processed paper; textiles were classified into cotton and synthetic fibers; and rubber and leather were classified into artificial and natural. The analysis of FCF was implemented by collecting representative samples from each classification group, by applying the 14C method, and using AMS equipment. And the analysis values were compared with the default values proposed by the IPCC. In this study of garden and park waste and plastics, the differences were within the range of the IPCC default values or the differences were negligible. However, coated paper, synthetic textiles, natural rubber, synthetic rubber, artificial leather, and other wastes showed differences of over 10% in FCF content. IPCC is comprised of largely 9 types of qualitative classifications, in emissions estimation a great difference can occur from the combined characteristics according with the existing IPCC classification system by using the minutely classified waste characteristics as in this study. Fossil carbon fraction (FCF) differs depending
48 CFR 19.303 - Determining North American Industry Classification System codes and size standards.
Code of Federal Regulations, 2014 CFR
2014-10-01
... Industry Classification System codes and size standards. 19.303 Section 19.303 Federal Acquisition... of Small Business Status for Small Business Programs 19.303 Determining North American Industry... North American Industry Classification System (NAICS) code and related small business size standard and...
Hosseinpour-Feizi, Hojjat; Soleimanpour, Jafar; Sales, Jafar Ganjpour; Arzroumchilar, Ali
2011-01-01
Purpose The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems. Methods The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance. Results A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems. Conclusion Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification’s priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method. PMID:22267934
On the Implementation of a Land Cover Classification System for SAR Images Using Khoros
NASA Technical Reports Server (NTRS)
Medina Revera, Edwin J.; Espinosa, Ramon Vasquez
1997-01-01
The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.
An Automated Slide Classification System at Georgia Tech
ERIC Educational Resources Information Center
LoPresti, Maryellen
1973-01-01
The Georgia Tech Architecture Library slide collection is being revolutionized by adapting the Santa Cruz Slide Classification System. The slide catalog record is being transferred inexpensively to tapes and updated by the computer. Computer programs print out indexes in any of fifteen different sort fields. (Author)
The development of Version 2 of the AN-SNAP casemix classification system.
Green, Janette; Gordon, Robert
2007-04-01
This paper presents the results of a recent review of the Australian National Sub-acute and Non-acute Patient (AN-SNAP) classification system. The AN-SNAP system was developed by the Centre for Health Service Development, University of Wollongong in 1997. The review was conducted between August 2005 and September 2006. Four clinical sub-committees comprising more than 50 clinicians from sub-acute services across New South Wales as well as representatives from Queensland and the Australian Capital Territory were established to develop a set of proposals to be considered for incorporation into Version 2 of the classification. It is proposed that the final AN-SNAP Version 2 classification will be available for implementation from 1 July 2007.
[CT morphometry for calcaneal fractures and comparison of the Zwipp and Sanders classifications].
Andermahr, J; Jesch, A B; Helling, H J; Jubel, A; Fischbach, R; Rehm, K E
2002-01-01
The aim of the study is to correlate the CT-morphological changes of fractured calcaneus and the classifications of Zwipp and Sanders with the clinical outcome. In a retrospective clinical study, the preoperative CT scans of 75 calcaneal fractures were analysed. The morphometry of the fractures was determined by measuring height, length diameter and calcaneo-cuboidal angle in comparison to the intact contralateral side. At a mean of 38 months after trauma 44 patients were clinically followed-up. The data of CT image morphometry were correlated with the severity of fracture classified by Zwipp or Sanders as well as with the functional outcome. There was a good correlation between the fracture classifications and the morphometric data. Both fracture classifying systems have a predictive impact for functional outcome. The more exacting and accurate Zwipp classification considers the most important cofactors like involvement of the calcaneo-cuboidal joint, soft tissue damage, additional fractures etc. The Sanders classification is easier to use during clinical routine. The Zwipp classification includes more relevant cofactors (fracture of the calcaneo-cuboidal-joint, soft tissue swelling, etc.) and presents a higher correlation to the choice of therapy. Both classification systems present a prognostic impact concerning the clinical outcome.
Resource utilization groups. A patient classification system for long-term care.
Fries, B E; Cooney, L M
1985-02-01
The ability to understand, control, manage, regulate, and reimburse nursing home care has been hampered by the unavailability of a classification system of long-term care patients. A study of 1,469 patients in Connecticut nursing homes has resulted in such a classification system that clusters patients with similar relative needs for resources, in particular, for nursing time. The nine groups formed can be used to develop a case-mix profile of the relative care needs of these patients, and their development demonstrates that only a few measures of the functional status of patients, rather than diagnosis or psychosocial/behavioral problems, are sufficient to form such a system.
Seismic event classification system
Dowla, F.U.; Jarpe, S.P.; Maurer, W.
1994-12-13
In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 48 Federal Acquisition Regulations System 3 2014-10-01 2014-10-01 false Determining North American Industry Classification System (NAICS) codes and size standards. 219.303 Section 219.303 Federal... Determining North American Industry Classification System (NAICS) codes and size standards. Contracting...
Code of Federal Regulations, 2013 CFR
2013-10-01
... 48 Federal Acquisition Regulations System 3 2013-10-01 2013-10-01 false Determining North American Industry Classification System (NAICS) codes and size standards. 219.303 Section 219.303 Federal... Determining North American Industry Classification System (NAICS) codes and size standards. Contracting...
Wangensteen, Arnlaug; Tol, Johannes L; Roemer, Frank W; Bahr, Roald; Dijkstra, H Paul; Crema, Michel D; Farooq, Abdulaziz; Guermazi, Ali
2017-04-01
To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Male athletes (n=40) with clinical diagnosis of acute hamstring injury and MRI ≤5days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. We observed 'substantial' to 'almost perfect' intra- (κ range 0.65-1.00) and interrater reliability (κ range 0.77-1.00) with percentage agreement 83-100% and 88-100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range -0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated 'substantial' to 'almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear. Copyright © 2017 Elsevier B.V. All rights reserved.
Fuel Characteristic Classification System version 3.0: technical documentation
Susan J. Prichard; David V. Sandberg; Roger D. Ottmar; Ellen Eberhardt; Anne Andreu; Paige Eagle; Kjell Swedin
2013-01-01
The Fuel Characteristic Classification System (FCCS) is a software module that records wildland fuel characteristics and calculates potential fire behavior and hazard potentials based on input environmental variables. The FCCS 3.0 is housed within the Integrated Fuels Treatment Decision Support System (Joint Fire Science Program 2012). It can also be run from command...
Novel classification system of rib fractures observed in infants.
Love, Jennifer C; Derrick, Sharon M; Wiersema, Jason M; Pinto, Deborrah C; Greeley, Christopher; Donaruma-Kwoh, Marcella; Bista, Bibek
2013-03-01
Rib fractures are considered highly suspicious for nonaccidental injury in the pediatric clinical literature; however, a rib fracture classification system has not been developed. As an aid and impetus for rib fracture research, we developed a concise schema for classifying rib fracture types and fracture location that is applicable to infants. The system defined four fracture types (sternal end, buckle, transverse, and oblique) and four regions of the rib (posterior, posterolateral, anterolateral, and anterior). It was applied to all rib fractures observed during 85 consecutive infant autopsies. Rib fractures were found in 24 (28%) of the cases. A total of 158 rib fractures were identified. The proposed schema was adequate to classify 153 (97%) of the observed fractures. The results indicate that the classification system is sufficiently robust to classify rib fractures typically observed in infants and should be used by researchers investigating infant rib fractures. © 2013 American Academy of Forensic Sciences.
Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng
2007-01-01
Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.
Auto-simultaneous laser treatment and Ohshiro's classification of laser treatment
NASA Astrophysics Data System (ADS)
Ohshiro, Toshio
2005-07-01
" survival threshold, then this is low reactive-level laser therapy (LLLT). All three of these classifications can occur simultaneously in the one target, and fall under the umbrella of laser treatment (LT). LT is further subdivided into three main types: mono-type LT (Mo-LT, treatment with a single laser system; multi-type LT (Mu-LT, treatment with multiple laser systems); and concomitant LT (Cc-LT), laser treatment in combination, each of which is further subdivided by tissue reaction to give an accurate, treatment-based categorization of laser treatment. When this effect-based classification is combined with and illustrated by the appropriate laser apple pattern, an accurate and simple method of classifying laser/tissue reactions by the reaction, rather than by the laser used to produce the reaction, is achieved. Examples will be given to illustrate the author"s new approach to this important concept.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Determining North American Industry Classification System (NAICS) codes and size standards. 19.303 Section 19.303 Federal Acquisition... Classification System (NAICS) codes and size standards. (a) The contracting officer shall determine the...
Code of Federal Regulations, 2011 CFR
2011-10-01
... 48 Federal Acquisition Regulations System 3 2011-10-01 2011-10-01 false Determining North American Industry Classification System (NAICS) codes and size standards. 219.303 Section 219.303 Federal... Programs 219.303 Determining North American Industry Classification System (NAICS) codes and size standards...
Code of Federal Regulations, 2012 CFR
2012-10-01
... 48 Federal Acquisition Regulations System 3 2012-10-01 2012-10-01 false Determining North American Industry Classification System (NAICS) codes and size standards. 219.303 Section 219.303 Federal... Programs 219.303 Determining North American Industry Classification System (NAICS) codes and size standards...
Robust tissue classification for reproducible wound assessment in telemedicine environments
NASA Astrophysics Data System (ADS)
Wannous, Hazem; Treuillet, Sylvie; Lucas, Yves
2010-04-01
In telemedicine environments, a standardized and reproducible assessment of wounds, using a simple free-handled digital camera, is an essential requirement. However, to ensure robust tissue classification, particular attention must be paid to the complete design of the color processing chain. We introduce the key steps including color correction, merging of expert labeling, and segmentation-driven classification based on support vector machines. The tool thus developed ensures stability under lighting condition, viewpoint, and camera changes, to achieve accurate and robust classification of skin tissues. Clinical tests demonstrate that such an advanced tool, which forms part of a complete 3-D and color wound assessment system, significantly improves the monitoring of the healing process. It achieves an overlap score of 79.3 against 69.1% for a single expert, after mapping on the medical reference developed from the image labeling by a college of experts.
Calibration Techniques for Accurate Measurements by Underwater Camera Systems
Shortis, Mark
2015-01-01
Calibration of a camera system is essential to ensure that image measurements result in accurate estimates of locations and dimensions within the object space. In the underwater environment, the calibration must implicitly or explicitly model and compensate for the refractive effects of waterproof housings and the water medium. This paper reviews the different approaches to the calibration of underwater camera systems in theoretical and practical terms. The accuracy, reliability, validation and stability of underwater camera system calibration are also discussed. Samples of results from published reports are provided to demonstrate the range of possible accuracies for the measurements produced by underwater camera systems. PMID:26690172
Fernandes, Melissa A; Verstraete, Sofia G; Garnett, Elizabeth A; Heyman, Melvin B
2016-02-01
The aim of the study was to investigate the value of microscopic findings in the classification of pediatric Crohn disease (CD) by determining whether classification of disease changes significantly with inclusion of histologic findings. Sixty patients were randomly selected from a cohort of patients studied at the Pediatric Inflammatory Bowel Disease Clinic at the University of California, San Francisco Benioff Children's Hospital. Two physicians independently reviewed the electronic health records of the included patients to determine the Paris classification for each patient by adhering to present guidelines and then by including microscopic findings. Macroscopic and combined disease location classifications were discordant in 34 (56.6%), with no statistically significant differences between groups. Interobserver agreement was higher in the combined classification (κ = 0.73, 95% confidence interval 0.65-0.82) as opposed to when classification was limited to macroscopic findings (κ = 0.53, 95% confidence interval 0.40-0.58). When evaluating the proximal upper gastrointestinal tract (Paris L4a), the interobserver agreement was better in macroscopic compared with the combined classification. Disease extent classifications differed significantly when comparing isolated macroscopic findings (Paris classification) with the combined scheme that included microscopy. Further studies are needed to determine which scheme provides more accurate representation of disease extent.
Land use/cover classification in the Brazilian Amazon using satellite images.
Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira
2012-09-01
Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.
Land use/cover classification in the Brazilian Amazon using satellite images
Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira
2013-01-01
Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353
An alternative respiratory sounds classification system utilizing artificial neural networks.
Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen
2015-01-01
Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.
Koo, Henry; Leveridge, Mike; Thompson, Charles; Zdero, Rad; Bhandari, Mohit; Kreder, Hans J; Stephen, David; McKee, Michael D; Schemitsch, Emil H
2008-07-01
The purpose of this study was to measure interobserver reliability of 2 classification systems of pelvic ring fractures and to determine whether computed tomography (CT) improves reliability. The reliability of several radiographic findings was also tested. Thirty patients taken from a database at a Level I trauma facility were reviewed. For each patient, 3 radiographs (AP pelvis, inlet, and outlet) and CT scans were available. Six different reviewers (pelvic and acetabular specialist, orthopaedic traumatologist, or orthopaedic trainee) classified the injury according to Young-Burgess and Tile classification systems after reviewing plain radiographs and then after CT scans. The Kappa coefficient was used to determine interobserver reliability of these classification systems before and after CT scan. For plain radiographs, overall Kappa values for the Young-Burgess and Tile classification systems were 0.72 and 0.30, respectively. For CT scan and plain radiographs, the overall Kappa values for the Young-Burgess and Tile classification systems were 0.63 and 0.33, respectively. The pelvis/acetabular surgeons demonstrated the highest level of agreement using both classification systems. For individual questions, the addition of CT did significantly improve reviewer interpretation of fracture stability. The pre-CT and post-CT Kappa values for fracture stability were 0.59 and 0.93, respectively. The CT scan can improve the reliability of assessment of pelvic stability because of its ability to identify anatomical features of injury. The Young-Burgess system may be optimal for the learning surgeon. The Tile classification system is more beneficial for specialists in pelvic and acetabular surgery.
Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef
2010-01-01
In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.
Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.
Li, Yuqian; Cornelis, Bruno; Dusa, Alexandra; Vanmeerbeeck, Geert; Vercruysse, Dries; Sohn, Erik; Blaszkiewicz, Kamil; Prodanov, Dimiter; Schelkens, Peter; Lagae, Liesbet
2018-05-01
Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
Optimization of the ANFIS using a genetic algorithm for physical work rate classification.
Habibi, Ehsanollah; Salehi, Mina; Yadegarfar, Ghasem; Taheri, Ali
2018-03-13
Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.
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.
Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features
Xu, Tingbao; Chen, Yun
2017-01-01
In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440
A Functional-Phylogenetic Classification System for Transmembrane Solute Transporters
Saier, Milton H.
2000-01-01
A comprehensive classification system for transmembrane molecular transporters has been developed and recently approved by the transport panel of the nomenclature committee of the International Union of Biochemistry and Molecular Biology. This system is based on (i) transporter class and subclass (mode of transport and energy coupling mechanism), (ii) protein phylogenetic family and subfamily, and (iii) substrate specificity. Almost all of the more than 250 identified families of transporters include members that function exclusively in transport. Channels (115 families), secondary active transporters (uniporters, symporters, and antiporters) (78 families), primary active transporters (23 families), group translocators (6 families), and transport proteins of ill-defined function or of unknown mechanism (51 families) constitute distinct categories. Transport mode and energy coupling prove to be relatively immutable characteristics and therefore provide primary bases for classification. Phylogenetic grouping reflects structure, function, mechanism, and often substrate specificity and therefore provides a reliable secondary basis for classification. Substrate specificity and polarity of transport prove to be more readily altered during evolutionary history and therefore provide a tertiary basis for classification. With very few exceptions, a phylogenetic family of transporters includes members that function by a single transport mode and energy coupling mechanism, although a variety of substrates may be transported, sometimes with either inwardly or outwardly directed polarity. In this review, I provide cross-referencing of well-characterized constituent transporters according to (i) transport mode, (ii) energy coupling mechanism, (iii) phylogenetic grouping, and (iv) substrates transported. The structural features and distribution of recognized family members throughout the living world are also evaluated. The tabulations should facilitate familial and functional
NASA Astrophysics Data System (ADS)
Lahmiri, Salim; Shmuel, Amir
2017-11-01
Diabetic retinopathy is a disease that can cause a loss of vision. An early and accurate diagnosis helps to improve treatment of the disease and prognosis. One of the earliest characteristics of diabetic retinopathy is the appearance of retinal hemorrhages. The purpose of this study is to design a fully automated system for the detection of hemorrhages in a retinal image. In the first stage of our proposed system, a retinal image is processed with variational mode decomposition (VMD) to obtain the first variational mode, which captures the high frequency components of the original image. In the second stage, four texture descriptors are extracted from the first variational mode. Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages. Experimental results showed evidence of the effectiveness of the proposed system for detection of hemorrhages in the retina, since a perfect detection rate was achieved. Our proposed system for detecting diabetic retinopathy is simple and easy to implement. It requires only short processing time, and it yields higher accuracy in comparison with previously proposed methods for detecting diabetic retinopathy.
Filler particle size and composite resin classification systems.
Lang, B R; Jaarda, M; Wang, R F
1992-11-01
The currently used composite resin classification systems need review if they are to continue to serve as descriptives and quantitative parameters denoting the filler particle content of these materials. Examination of the particles in 12 composite resins using a technique of washing the filler particles from the matrix of the composite resin was presented as yet another method of grouping composites according to filler particle content. Light microscopic examination of the filler particles that remained provided a separation of the 12 materials into four easily distinguished groups based on filler particle sizes. The wear of the 12 composite resins determined in a previous study was examined in relation to the classification of the materials by the currently available systems. The wear values were also examined using the groupings of the materials according to their filler particle sizes as determined by separating the particles from the matrix by the washing technique. Grouping composites on the basis of the filler particle sizes found after washing was easily correlated with wear and supported the suggestion that composites with smaller filler particles wear less.
Classification of Microarray Data Using Kernel Fuzzy Inference System
Kumar Rath, Santanu
2014-01-01
The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543
Advances in Spectral-Spatial Classification of Hyperspectral Images
NASA Technical Reports Server (NTRS)
Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.
2012-01-01
Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
Advances in Spectral-Spatial Classification of Hyperspectral Images
NASA Technical Reports Server (NTRS)
Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.
2012-01-01
Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
ERIC Educational Resources Information Center
Matarazzo, Bridget B.; Clemans, Tracy A.; Silverman, Morton M.; Brenner, Lisa A.
2013-01-01
The lack of a standardized nomenclature for suicide-related thoughts and behaviors prompted the Centers for Disease Control and Prevention, with the Veterans Integrated Service Network 19 Mental Illness Research Education and Clinical Center, to create the Self-Directed Violence Classification System (SDVCS). SDVCS has been adopted by the…
Extensions to the Speech Disorders Classification System (SDCS)
Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.
2010-01-01
This report describes three extensions to a classification system for pediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). Part I describes a classification extension to the SDCS to differentiate motor speech disorders from speech delay and to differentiate among three subtypes of motor speech disorders. Part II describes the Madison Speech Assessment Protocol (MSAP), an approximately two-hour battery of 25 measures that includes 15 speech tests and tasks. Part III describes the Competence, Precision, and Stability Analytics (CPSA) framework, a current set of approximately 90 perceptual- and acoustic-based indices of speech, prosody, and voice used to quantify and classify subtypes of Speech Sound Disorders (SSD). A companion paper, Shriberg, Fourakis, et al. (2010) provides reliability estimates for the perceptual and acoustic data reduction methods used in the SDCS. The agreement estimates in the companion paper support the reliability of SDCS methods and illustrate the complementary roles of perceptual and acoustic methods in diagnostic analyses of SSD of unknown origin. Examples of research using the extensions to the SDCS described in the present report include diagnostic findings for a sample of youth with motor speech disorders associated with galactosemia (Shriberg, Potter, & Strand, 2010) and a test of the hypothesis of apraxia of speech in a group of children with autism spectrum disorders (Shriberg, Paul, Black, & van Santen, 2010). All SDCS methods and reference databases running in the PEPPER (Programs to Examine Phonetic and Phonologic Evaluation Records; [Shriberg, Allen, McSweeny, & Wilson, 2001]) environment will be disseminated without cost when complete. PMID:20831378
Perroca, Marcia Galan; Ek, Anna-Christina
2007-07-01
Although patient classification tools have been used in Sweden since the 1980s, few studies have examined how they are utilized and monitored. This paper investigates the patient classification systems implemented in hospitals in the country as well as the level of satisfaction of nurses with the implemented instrument. A postal survey method was used in which a total of 128 questionnaires were sent to nurse managers. Twenty-three hospitals were identified with patient classification systems currently in operation. The Zebra and Beakta systems are the most commonly used instruments. Nurse managers appear to be satisfied with the patient classification systems in use on their wards as a whole except for their inability to measure the quality of care provided, the time spent to use the instruments and the fact that the administration do not estimate nursing staff requirements using the system.
The International Neuroblastoma Risk Group (INRG) Classification System: An INRG Task Force Report
Cohn, Susan L.; Pearson, Andrew D.J.; London, Wendy B.; Monclair, Tom; Ambros, Peter F.; Brodeur, Garrett M.; Faldum, Andreas; Hero, Barbara; Iehara, Tomoko; Machin, David; Mosseri, Veronique; Simon, Thorsten; Garaventa, Alberto; Castel, Victoria; Matthay, Katherine K.
2009-01-01
Purpose Because current approaches to risk classification and treatment stratification for children with neuroblastoma (NB) vary greatly throughout the world, it is difficult to directly compare risk-based clinical trials. The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Patients and Methods The statistical and clinical significance of 13 potential prognostic factors were analyzed in a cohort of 8,800 children diagnosed with NB between 1990 and 2002 from North America and Australia (Children's Oncology Group), Europe (International Society of Pediatric Oncology Europe Neuroblastoma Group and German Pediatric Oncology and Hematology Group), and Japan. Survival tree regression analyses using event-free survival (EFS) as the primary end point were performed to test the prognostic significance of the 13 factors. Results Stage, age, histologic category, grade of tumor differentiation, the status of the MYCN oncogene, chromosome 11q status, and DNA ploidy were the most highly statistically significant and clinically relevant factors. A new staging system (INRG Staging System) based on clinical criteria and tumor imaging was developed for the INRG Classification System. The optimal age cutoff was determined to be between 15 and 19 months, and 18 months was selected for the classification system. Sixteen pretreatment groups were defined on the basis of clinical criteria and statistically significantly different EFS of the cohort stratified by the INRG criteria. Patients with 5-year EFS more than 85%, more than 75% to ≤ 85%, ≥ 50% to ≤ 75%, or less than 50% were classified as very low risk, low risk, intermediate risk, or high risk, respectively. Conclusion By defining homogenous pretreatment patient cohorts, the INRG classification system will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world and the
Automatic Fault Characterization via Abnormality-Enhanced Classification
DOE Office of Scientific and Technical Information (OSTI.GOV)
Bronevetsky, G; Laguna, I; de Supinski, B R
Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help tomore » identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.« less
A systematic review of definitions and classification systems of adjacent segment pathology.
Kraemer, Paul; Fehlings, Michael G; Hashimoto, Robin; Lee, Michael J; Anderson, Paul A; Chapman, Jens R; Raich, Annie; Norvell, Daniel C
2012-10-15
Systematic review. To undertake a systematic review to determine how "adjacent segment degeneration," "adjacent segment disease," or clinical pathological processes that serve as surrogates for adjacent segment pathology are classified and defined in the peer-reviewed literature. Adjacent segment degeneration and adjacent segment disease are terms referring to degenerative changes known to occur after reconstructive spine surgery, most commonly at an immediately adjacent functional spinal unit. These can include disc degeneration, instability, spinal stenosis, facet degeneration, and deformity. The true incidence and clinical impact of degenerative changes at the adjacent segment is unclear because there is lack of a universally accepted classification system that rigorously addresses clinical and radiological issues. A systematic review of the English language literature was undertaken and articles were classified using the Grades of Recommendation Assessment, Development, and Evaluation criteria. RESULTS.: Seven classification systems of spinal degeneration, including degeneration at the adjacent segment, were identified. None have been evaluated for reliability or validity specific to patients with degeneration at the adjacent segment. The ways in which terms related to adjacent segment "degeneration" or "disease" are defined in the peer-reviewed literature are highly variable. On the basis of the systematic review presented in this article, no formal classification system for either cervical or thoracolumbar adjacent segment disorders currently exists. No recommendations regarding the use of current classification of degeneration at any segments can be made based on the available literature. A new comprehensive definition for adjacent segment pathology (ASP, the now preferred terminology) has been proposed in this Focus Issue, which reflects the diverse pathology observed at functional spinal units adjacent to previous spinal reconstruction and balances
Image-based fall detection and classification of a user with a walking support system
NASA Astrophysics Data System (ADS)
Taghvaei, Sajjad; Kosuge, Kazuhiro
2017-10-01
The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user's depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called "RT Walker") by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user's motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.
ERIC Educational Resources Information Center
Clary, Renee; Wandersee, James
2013-01-01
In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…
NASA Astrophysics Data System (ADS)
Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith
2014-05-01
An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.
Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith
2014-05-01
An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.
Exercise-Associated Collapse in Endurance Events: A Classification System.
ERIC Educational Resources Information Center
Roberts, William O.
1989-01-01
Describes a classification system devised for exercise-associated collapse in endurance events based on casualties observed at six Twin Cities Marathons. Major diagnostic criteria are body temperature and mental status. Management protocol includes fluid and fuel replacement, temperature correction, and leg cramp treatment. (Author/SM)
The Comprehensive AOCMF Classification System: Condylar Process Fractures - Level 3 Tutorial.
Neff, Andreas; Cornelius, Carl-Peter; Rasse, Michael; Torre, Daniel Dalla; Audigé, Laurent
2014-12-01
This tutorial outlines the detailed system for fractures of the condylar process at the precision level 3 and is organized in a sequence of sections dealing with the description of the classification system within topographical subdivisions along with rules for fracture coding and a series of case examples with clinical imaging. Basically, the condylar process comprises three fracture levels and is subdivided into the head region, the condylar neck, and the condylar base. Fractures of the condylar head show typical fracture lines either within the lateral pole zone, which may lead to loss of vertical height, or medially to the pole zone, with the latter ones usually not compromising the vertical condyle to fossa relation. In condylar head fractures, the morphology is further described by the presence of minor or major fragmentation, the vertical apposition of fragments at the plane of the head fracture, the displacement of the condylar head with regard to the fossa including a potential distortion of the condylar head congruency resulting in dystopic condyle to fossa relations and the presence or absence of a loss of vertical ramus height. A specific vertical fracture pattern extending from the head to the neck or base subregion is considered. Fractures of the condylar neck and base can be differentiated according to a newly introduced one-third to two-thirds rule with regard to the proportion of the fracture line above and below the level of the sigmoid notch, which is presented in the classification article, and are basically subdivided according to the presence or absence of displacement or dislocation. In both condylar neck and base fractures, the classification is again based on the above mentioned parameters such as fragmentation, displacement of the condylar head with regard to the fossa, including dystopic condyle to fossa relations and loss of vertical ramus height, that is, according to the measurement of the condylar process. In addition, the
Classification of male lower torso for underwear design
NASA Astrophysics Data System (ADS)
Cheng, Z.; Kuzmichev, V. E.
2017-10-01
By means of scanning technology we have got new information about the morphology of male bodies and have redistricted the classification of men’s underwear by adopting one to consumer demands. To build the new classification in accordance with male body characteristic factors of lower torso, we make the method of underwear designing which allow to get the accurate and convenience for consumers products.
Two-tier tissue decomposition for histopathological image representation and classification.
Gultekin, Tunc; Koyuncu, Can Fahrettin; Sokmensuer, Cenk; Gunduz-Demir, Cigdem
2015-01-01
In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.
A Highly Accurate Face Recognition System Using Filtering Correlation
NASA Astrophysics Data System (ADS)
Watanabe, Eriko; Ishikawa, Sayuri; Kodate, Kashiko
2007-09-01
The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (S-FARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 × 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera.
A Novel Classification System for Injuries After Electronic Cigarette Explosions.
Patterson, Scott B; Beckett, Allison R; Lintner, Alicia; Leahey, Carly; Greer, Ashley; Brevard, Sidney B; Simmons, Jon D; Kahn, Steven A
Electronic cigarettes (e-cigarettes) contain lithium batteries that have been known to explode and/or cause fires that have resulted in burn injury. The purpose of this article is to present a case study, review injuries caused by e-cigarettes, and present a novel classification system from the newly emerging patterns of burns. A case study was presented and online media reports for e-cigarette burns were queried with search terms "e-cigarette burns" and "electronic cigarette burns." The reports and injury patterns were tabulated. Analysis was then performed to create a novel classification system based on the distinct injury patterns seen in the study. Two patients were seen at our regional burn center after e-cigarette burns. One had an injury to his thigh and penis that required operative intervention after ignition of this device in his pocket. The second had a facial burn and corneal abrasions when the device exploded while he was inhaling vapor. The Internet search and case studies resulted in 26 cases for evaluation. The burn patterns were divided in direct injury from the device igniting and indirect injury when the device caused a house or car fire. A numerical classification was created: direct injury: type 1 (hand injury) 7 cases, type 2 (face injury) 8 cases, type 3 (waist/groin injury) 11 cases, and type 5a (inhalation injury from using device) 2 cases; indirect injury: type 4 (house fire injury) 7 cases and type 5b (inhalation injury from fire started by the device) 4 cases. Multiple e-cigarette injuries are occurring in the United States and distinct patterns of burns are emerging. The classification system developed in this article will aid in further study and future regulation of these dangerous devices.
Wauters, Lauri D J; Miguel-Moragas, Joan San; Mommaerts, Maurice Y
2015-11-01
To gain insight into the methodology of different computer-aided design-computer-aided manufacturing (CAD-CAM) applications for the reconstruction of cranio-maxillo-facial (CMF) defects. We reviewed and analyzed the available literature pertaining to CAD-CAM for use in CMF reconstruction. We proposed a classification system of the techniques of implant and cutting, drilling, and/or guiding template design and manufacturing. The system consisted of 4 classes (I-IV). These classes combine techniques used for both the implant and template to most accurately describe the methodology used. Our classification system can be widely applied. It should facilitate communication and immediate understanding of the methodology of CAD-CAM applications for the reconstruction of CMF defects.
Claessen, Femke M A P; van den Ende, Kimberly I M; Doornberg, Job N; Guitton, Thierry G; Eygendaal, Denise; van den Bekerom, Michel P J
2015-10-01
The radiographic appearance of osteochondritis dissecans (OCD) of the humeral capitellum varies according to the stage of the lesion. It is important to evaluate the stage of OCD lesion carefully to guide treatment. We compared the interobserver reliability of currently used classification systems for OCD of the humeral capitellum to identify the most reliable classification system. Thirty-two musculoskeletal radiologists and orthopaedic surgeons specialized in elbow surgery from several countries evaluated anteroposterior and lateral radiographs and corresponding computed tomography (CT) scans of 22 patients to classify the stage of OCD of the humeral capitellum according to the classification systems developed by (1) Minami, (2) Berndt and Harty, (3) Ferkel and Sgaglione, and (4) Anderson on a Web-based study platform including a Digital Imaging and Communications in Medicine viewer. Magnetic resonance imaging was not evaluated as part of this study. We measured agreement among observers using the Siegel and Castellan multirater κ. All OCD classification systems, except for Berndt and Harty, which had poor agreement among observers (κ = 0.20), had fair interobserver agreement: κ was 0.27 for the Minami, 0.23 for Anderson, and 0.22 for Ferkel and Sgaglione classifications. The Minami Classification was significantly more reliable than the other classifications (P < .001). The Minami Classification was the most reliable for classifying different stages of OCD of the humeral capitellum. However, it is unclear whether radiographic evidence of OCD of the humeral capitellum, as categorized by the Minami Classification, guides treatment in clinical practice as a result of this fair agreement. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.
Classification of Dynamical Diffusion States in Single Molecule Tracking Microscopy
Bosch, Peter J.; Kanger, Johannes S.; Subramaniam, Vinod
2014-01-01
Single molecule tracking of membrane proteins by fluorescence microscopy is a promising method to investigate dynamic processes in live cells. Translating the trajectories of proteins to biological implications, such as protein interactions, requires the classification of protein motion within the trajectories. Spatial information of protein motion may reveal where the protein interacts with cellular structures, because binding of proteins to such structures often alters their diffusion speed. For dynamic diffusion systems, we provide an analytical framework to determine in which diffusion state a molecule is residing during the course of its trajectory. We compare different methods for the quantification of motion to utilize this framework for the classification of two diffusion states (two populations with different diffusion speed). We found that a gyration quantification method and a Bayesian statistics-based method are the most accurate in diffusion-state classification for realistic experimentally obtained datasets, of which the gyration method is much less computationally demanding. After classification of the diffusion, the lifetime of the states can be determined, and images of the diffusion states can be reconstructed at high resolution. Simulations validate these applications. We apply the classification and its applications to experimental data to demonstrate the potential of this approach to obtain further insights into the dynamics of cell membrane proteins. PMID:25099798
Classification of forest land attributes using multi-source remotely sensed data
NASA Astrophysics Data System (ADS)
Pippuri, Inka; Suvanto, Aki; Maltamo, Matti; Korhonen, Kari T.; Pitkänen, Juho; Packalen, Petteri
2016-02-01
The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008-2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.
Classification of cognitive systems dedicated to data sharing
NASA Astrophysics Data System (ADS)
Ogiela, Lidia; Ogiela, Marek R.
2017-08-01
In this paper will be presented classification of new cognitive information systems dedicated to cryptographic data splitting and sharing processes. Cognitive processes of semantic data analysis and interpretation, will be used to describe new classes of intelligent information and vision systems. In addition, cryptographic data splitting algorithms and cryptographic threshold schemes will be used to improve processes of secure and efficient information management with application of such cognitive systems. The utility of the proposed cognitive sharing procedures and distributed data sharing algorithms will be also presented. A few possible application of cognitive approaches for visual information management and encryption will be also described.
Boyd, Theonia K.; Wright, Colleen A.; Odendaal, Hein J.; Elliott, Amy J.; Sens, Mary Ann; Folkerth, Rebecca D.; Roberts, Drucilla J.; Kinney, Hannah C.
2017-01-01
OBJECTIVE Describe the classification system for the assignment of the cause of death for stillbirth in the Safe Passage Study, an international, multi-institutional, prospective analysis conducted by the NIAAA/NICHD funded PASS Network (The Prenatal Alcohol in SIDS and Stillbirth (PASS) Research Network). The study mission is to determine the role of prenatal alcohol and/or cigarette smoke exposure in adverse pregnancy outcomes, including stillbirth, in a high-risk cohort of 12,000 maternal/fetal dyads. METHODS The PASS Network classification system is based upon 5 ‘sites of origin’ for cause of stillbirth (Fetal, Placental, Maternal, External/Environmental, or Undetermined), further subdivided into mechanism subcategories (e.g., Placental Perfusion Failure). Both site of origin and mechanism stratification are employed to assign an ultimate cause of death. Each PASS stillbirth (n=19) in the feasibility study was assigned a cause of death, and status of sporadic versus recurrent. Adjudication involved review of the maternal and obstetrical records, and fetal autopsy and placental findings, with complete consensus in each case. Two published classification systems, i.e., INCODE and ReCoDe, were used for comparison. RESULTS Causes of stillbirth classified were: fetal (n=5, 26%), placental (n=10, 53%), external (n=1, 5%), and undetermined (n=3, 16%). Nine cases (47%) had placental causes of death due to maternal disorders that carry recurrence risks. There was complete agreement for the cause of death across the three classification systems in 26% of cases, and a combination of partial or complete agreement in 68% of cases. Complete vs. partial agreements were predicated upon the classification schemes used for comparison. CONCLUSIONS The proposed PASS system is a user-friendly classification system that provides comparable information to previously published systems. Advantages include its simplicity, mechanistic formulations, tight clinicopathologic integration
Peker, Musa; Şen, Baha; Gürüler, Hüseyin
2015-02-01
The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed parallelization method yielded high accurate classification results in a faster time.
Multiple-frequency continuous wave ultrasonic system for accurate distance measurement
NASA Astrophysics Data System (ADS)
Huang, C. F.; Young, M. S.; Li, Y. C.
1999-02-01
A highly accurate multiple-frequency continuous wave ultrasonic range-measuring system for use in air is described. The proposed system uses a method heretofore applied to radio frequency distance measurement but not to air-based ultrasonic systems. The method presented here is based upon the comparative phase shifts generated by three continuous ultrasonic waves of different but closely spaced frequencies. In the test embodiment to confirm concept feasibility, two low cost 40 kHz ultrasonic transducers are set face to face and used to transmit and receive ultrasound. Individual frequencies are transmitted serially, each generating its own phase shift. For any given frequency, the transmitter/receiver distance modulates the phase shift between the transmitted and received signals. Comparison of the phase shifts allows a highly accurate evaluation of target distance. A single-chip microcomputer-based multiple-frequency continuous wave generator and phase detector was designed to record and compute the phase shift information and the resulting distance, which is then sent to either a LCD or a PC. The PC is necessary only for calibration of the system, which can be run independently after calibration. Experiments were conducted to test the performance of the whole system. Experimentally, ranging accuracy was found to be within ±0.05 mm, with a range of over 1.5 m. The main advantages of this ultrasonic range measurement system are high resolution, low cost, narrow bandwidth requirements, and ease of implementation.
An ecological classification system for the central hardwoods region: The Hoosier National Forest
James E. Van Kley; George R. Parker
1993-01-01
This study, a multifactor ecological classification system, using vegetation, soil characteristics, and physiography, was developed for the landscape of the Hoosier National Forest in Southern Indiana. Measurements of ground flora, saplings, and canopy trees from selected stands older than 80 years were subjected to TWINSPAN classification and DECORANA ordination....
Gender and cultural issues in psychiatric nosological classification systems.
van de Water, Tanya; Suliman, Sharain; Seedat, Soraya
2016-08-01
Much has changed since the two dominant mental health nosological systems, the International Classification of Diseases (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM), were first published in 1900 and 1952, respectively. Despite numerous modifications to stay up to date with scientific and cultural changes (eg, exclusion of homosexuality as a disorder) and to improve the cultural sensitivity of psychiatric diagnoses, the ICD and DSM have only recently renewed attempts at harmonization. Previous nosological iterations demonstrate the oscillation in the importance placed on the biological focus, highlighting the tension between a gender- and culture-free nosology (solely biological) and a contextually relevant understanding of mental illness. In light of the release of the DSM 5, future nosological systems, such as the ICD 11, scheduled for release in 2017, and the Research Development Criteria (RDoC), can learn from history and apply critiques. This article aims to critically consider gender and culture in previous editions of the ICD and DSM to inform forthcoming classifications.
Classification of weld defect based on information fusion technology for radiographic testing system
DOE Office of Scientific and Technical Information (OSTI.GOV)
Jiang, Hongquan; Liang, Zeming, E-mail: heavenlzm@126.com; Gao, Jianmin
Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster–Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defectmore » feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.« less
Jiang, Hongquan; Liang, Zeming; Gao, Jianmin; Dang, Changying
2016-03-01
Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster-Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defect feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.
Classification of spatially unresolved objects
NASA Technical Reports Server (NTRS)
Nalepka, R. F.; Horwitz, H. M.; Hyde, P. D.; Morgenstern, J. P.
1972-01-01
A proportion estimation technique for classification of multispectral scanner images is reported that uses data point averaging to extract and compute estimated proportions for a single average data point to classify spatial unresolved areas. Example extraction calculations of spectral signatures for bare soil, weeds, alfalfa, and barley prove quite accurate.
2009-11-01
Equation Chapter 1 Section 1 A MAPPING FROM THE HUMAN FACTORS ANALYSIS AND CLASSIFICATION SYSTEM (DOD...OMB control number. 1. REPORT DATE NOV 2009 2. REPORT TYPE 3. DATES COVERED 4. TITLE AND SUBTITLE A Mapping from the Human Factors Analysis ...7 The Human Factors Analysis and Classification System .................................................. 7 Mapping of DoD
HIDECKER, MARY JO COOLEY; PANETH, NIGEL; ROSENBAUM, PETER L; KENT, RAYMOND D; LILLIE, JANET; EULENBERG, JOHN B; CHESTER, KEN; JOHNSON, BRENDA; MICHALSEN, LAUREN; EVATT, MORGAN; TAYLOR, KARA
2011-01-01
Aim The purpose of this study was to create and validate a Communication Function Classification System (CFCS) for children with cerebral palsy (CP) that can be used by a wide variety of individuals who are interested in CP. This paper reports the content validity, interrater reliability, and test–retest reliability of the CFCS for children with CP. Method An 11-member development team created comprehensive descriptions of the CFCS levels, and four nominal groups comprising 27 participants critiqued these levels. Within a Delphi survey, 112 participants commented on the clarity and usefulness of the CFCS. Interrater reliability was completed by 61 professionals and 68 parents/relatives who classified 69 children with CP aged 2 to 18 years. Test–retest reliability was completed by 48 professionals who allowed at least 2 weeks between classifications. The participants who assessed the CFCS were all relevant stakeholders: adults with CP, parents of children with CP, educators, occupational therapists, physical therapists, physicians, and speech–language pathologists. Results The interrater reliability of the CFCS was 0.66 between two professionals and 0.49 between a parent and a professional. Professional interrater reliability improved to 0.77 for classification of children older than 4 years. The test–retest reliability was 0.82. Interpretation The CFCS demonstrates content validity and shows very good test–retest reliability, good professional interrater reliability, and moderate parent–professional interrater reliability. Combining the CFCS with the Gross Motor Function Classification System and the Manual Ability Classification System contributes to a functional performance view of daily life for individuals with CP, in accordance with the World Health Organization’s International Classification of Functioning, Disability and Health. PMID:21707596
Towns, Megan; Rosenbaum, Peter; Palisano, Robert; Wright, F Virginia
2018-02-01
This literature review addressed four questions. (1) In which populations other than cerebral palsy (CP) has the Gross Motor Function Classification System (GMFCS) been applied? (2) In what types of study, and why was it used? (3) How was it modified to facilitate these applications? (4) What justifications and evidence of psychometric adequacy were used to support its application? A search of PubMed, MEDLINE, and Embase databases (January 1997 to April 2017) using the terms: 'GMFCS' OR 'Gross Motor Function Classification System' yielded 2499 articles. 118 met inclusion criteria and reported children/adults with 133 health conditions/clinical descriptions other than CP. Three broad GMFCS applications were observed: as a categorization tool, independent variable, or outcome measure. While the GMFCS is widely used for children with health conditions/clinical description other than CP, researchers rarely provided adequate justification for these uses. We offer recommendations for development/validation of other condition-specific classification systems and discuss the potential need for a generic gross motor function classification system. The Gross Motor Function Classification System should not be used outside cerebral palsy or as an outcome measure. The authors provide recommendations for development and validation of condition-specific or generic classification systems. © 2017 Mac Keith Press.
Shubham, Divya; Kawthalkar, Anjali S
2018-05-01
To assess the feasibility of the PALM-COEIN system for the classification of abnormal uterine bleeding (AUB) in low-resource settings and to suggest modifications. A prospective study was conducted among women with AUB who were admitted to the gynecology ward of a tertiary care hospital and research center in central India between November 2014 and October 2016. All patients were managed as per department protocols. The causes of AUB were classified before treatment using the PALM-COEIN system (classification I) and on the basis of the histopathology reports of the hysterectomy specimens (classification II); the results were compared using classification II as the gold standard. The study included 200 women with AUB; hysterectomy was performed in 174 women. Preoperative classification of AUB per the PALM-COEIN system was correct in 130 (65.0%) women. Adenomyosis (evaluated by transvaginal ultrasonography) and endometrial hyperplasia (evaluated by endometrial curettage) were underdiagnosed. The PALM-COEIN classification system helps in deciding the best treatment modality for women with AUB on a case-by-case basis. The incorporation of suggested modifications will further strengthen its utility as a pretreatment classification system in low-resource settings. © 2017 International Federation of Gynecology and Obstetrics.
An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems
NASA Astrophysics Data System (ADS)
Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim
2017-03-01
Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.
Autoclass: An automatic classification system
NASA Technical Reports Server (NTRS)
Stutz, John; Cheeseman, Peter; Hanson, Robin
1991-01-01
The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.
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.
Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel
2015-07-01
Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.
The revised WHO dengue case classification: does the system need to be modified?
Hadinegoro, Sri Rezeki S
2012-05-01
There has been considerable debate regarding the value of both the 1997 and 2009 World Health Organization (WHO) dengue case classification criteria for its diagnosis and management. Differentiation between classic dengue fever (DF) and dengue haemorrhagic fever (DHF) or severe dengue is a key aspect of dengue case classification. The geographic expansion of dengue and its increased incidence in older age groups have contributed to the limited applicability of the 1997 case definitions. Clinical experience of dengue suggests that the illness presents as a spectrum of disease instead of distinct phases. However, despite the rigid grouping of dengue into DF, DHF and dengue shock syndrome (DSS), overlap between the different manifestations has often been observed, which has affected clinical management and triage of patients. The findings of the DENCO study evaluating the 1997 case definitions formed the basis of the revised 2009 WHO case definitions, which classified the illness into dengue with and without warning signs and severe dengue. Although the revised scheme is more sensitive to the diagnosis of severe dengue, and beneficial to triage and case management, there remain issues with its applicability. It is considered by many to be too broad, requiring more specific definition of warning signs. Quantitative research into the predictive value of these warning signs on patient outcomes and the cost-effectiveness of the new classification system is required to ascertain whether the new classification system requires further modification, or whether elements of both classification systems can be combined.
Stygoregions – a promising approach to a bioregional classification of groundwater systems
Stein, Heide; Griebler, Christian; Berkhoff, Sven; Matzke, Dirk; Fuchs, Andreas; Hahn, Hans Jürgen
2012-01-01
Linked to diverse biological processes, groundwater ecosystems deliver essential services to mankind, the most important of which is the provision of drinking water. In contrast to surface waters, ecological aspects of groundwater systems are ignored by the current European Union and national legislation. Groundwater management and protection measures refer exclusively to its good physicochemical and quantitative status. Current initiatives in developing ecologically sound integrative assessment schemes by taking groundwater fauna into account depend on the initial classification of subsurface bioregions. In a large scale survey, the regional and biogeographical distribution patterns of groundwater dwelling invertebrates were examined for many parts of Germany. Following an exploratory approach, our results underline that the distribution patterns of invertebrates in groundwater are not in accordance with any existing bioregional classification system established for surface habitats. In consequence, we propose to develope a new classification scheme for groundwater ecosystems based on stygoregions. PMID:22993698
Learning accurate very fast decision trees from uncertain data streams
NASA Astrophysics Data System (ADS)
Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo
2015-12-01
Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.
Classification of time series patterns from complex dynamic systems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Schryver, J.C.; Rao, N.
1998-07-01
An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately,more » the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.« less
Swords, Michael P; Alton, Timothy B; Holt, Sarah; Sangeorzan, Bruce J; Shank, John R; Benirschke, Stephen K
2014-10-01
There are several published computed tomography (CT) classification systems for calcaneus fractures, each validated by a different standard. The goal of this study was to measure which system would best predict clinical outcomes as measured by a widely used and validated musculoskeletal health status questionnaire. Forty-nine patients with isolated intra-articular joint depression calcaneus fractures more than 2 years after treatment were identified. All had preoperative CT studies and were treated with open reduction and plate fixation using a lateral extensile approach. Four different blinded reviewers classified injuries according to the CT classification systems of Crosby and Fitzgibbons, Eastwood, and Sanders. Functional outcomes evaluated with a Musculoskeletal Functional Assessment (MFA). The mean follow-up was 4.3 years. The mean MFA score was 15.7 (SD = 11.6), which is not significantly different from published values for midfoot injuries, hindfoot injuries, or both, 1 year after injury (mean = 22.1, SD = 18.4). The classification systems of Crosby and Fitzgibbons, Eastwood, and Sanders, the number of fragments of the posterior facet, and payer status were not significantly associated with outcome as determined by the MFA. The Sanders classification trended toward significance. Anterior process comminution and surgeon's overall impression of severity were significantly associated with functional outcome. The amount of anterior process comminution was an important determinant of functional outcome with increasing anterior process comminution significantly associated with worsened functional outcome (P = .04). In addition, the surgeon's overall impression of severity of injury was predictive of functional outcome (P = .02), as determined by MFA. Level III, comparative series. © The Author(s) 2014.
Amini, Michael H; Sykes, Joshua B; Olson, Stephen T; Smith, Richard A; Mauck, Benjamin M; Azar, Frederick M; Throckmorton, Thomas W
2015-03-01
The severity of elbow arthritis is one of many factors that surgeons must evaluate when considering treatment options for a given patient. Elbow surgeons have historically used the Broberg and Morrey (BM) and Hastings and Rettig (HR) classification systems to radiographically stage the severity of post-traumatic arthritis (PTA) and primary osteoarthritis (OA). We proposed to compare the intraobserver and interobserver reliability between systems for patients with either PTA or OA. The radiographs of 45 patients were evaluated at least 2 weeks apart by 6 evaluators of different levels of training. Intraobserver and interobserver reliability were calculated by Spearman correlation coefficients with 95% confidence intervals. Agreement was considered almost perfect for coefficients >0.80 and substantial for coefficients of 0.61 to 0.80. In patients with both PTA and OA, intraobserver reliability and interobserver reliability were substantial, with no difference between classification systems. There were no significant differences in intraobserver or interobserver reliability between attending physicians and trainees for either classification system (all P > .10). The presence of fracture implants did not affect reliability in the BM system but did substantially worsen reliability in the HR system (intraobserver P = .04 and interobserver P = .001). The BM and HR classifications both showed substantial intraobserver and interobserver reliability for PTA and OA. Training level differences did not affect reliability for either system. Both trainees and fellowship-trained surgeons may easily and reliably apply each classification system to the evaluation of primary elbow OA and PTA, although the HR system was less reliable in the presence of fracture implants. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.
Simple and accurate sum rules for highly relativistic systems
NASA Astrophysics Data System (ADS)
Cohen, Scott M.
2005-03-01
In this paper, I consider the Bethe and Thomas-Reiche-Kuhn sum rules, which together form the foundation of Bethe's theory of energy loss from fast charged particles to matter. For nonrelativistic target systems, the use of closure leads directly to simple expressions for these quantities. In the case of relativistic systems, on the other hand, the calculation of sum rules is fraught with difficulties. Various perturbative approaches have been used over the years to obtain relativistic corrections, but these methods fail badly when the system in question is very strongly bound. Here, I present an approach that leads to relatively simple expressions yielding accurate sums, even for highly relativistic many-electron systems. I also offer an explanation for the difference between relativistic and nonrelativistic sum rules in terms of the Zitterbewegung of the electrons.
Matthews, R; Turner, P J; McDonald, N J; Ermolaev, K; Manus, T; Shelby, R A; Steindorf, M
2008-01-01
This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.
A proposed ecosystem services classification system to support green accounting
There are a multitude of actual or envisioned, complete or incomplete, ecosystem service classification systems being proposed to support Green Accounting. Green Accounting is generally thought to be the formal accounting attempt to factor environmental production into National ...
Bidra, Avinash S; Jacob, Rhonda F; Taylor, Thomas D
2012-04-01
Maxillectomy defects are complex and involve a number of anatomic structures. Several maxillectomy defect classifications have been proposed with no universal acceptance among surgeons and prosthodontists. Established criteria for describing the maxillectomy defect are lacking. This systematic review aimed to evaluate classification systems in the available literature, to provide a critical appraisal, and to identify the criteria necessary for a universal description of maxillectomy and midfacial defects. An electronic search of the English language literature between the periods of 1974 and June 2011 was performed by using PubMed, Scopus, and Cochrane databases with predetermined inclusion criteria. Key terms included in the search were maxillectomy classification, maxillary resection classification, maxillary removal classification, maxillary reconstruction classification, midfacial defect classification, and midfacial reconstruction classification. This was supplemented by a manual search of selected journals. After application of predetermined exclusion criteria, the final list of articles was reviewed in-depth to provide a critical appraisal and identify criteria for a universal description of a maxillectomy defect. The electronic database search yielded 261 titles. Systematic application of inclusion and exclusion criteria resulted in identification of 14 maxillectomy and midfacial defect classification systems. From these articles, 6 different criteria were identified as necessary for a universal description of a maxillectomy defect. Multiple deficiencies were noted in each classification system. Though most articles described the superior-inferior extent of the defect, only a small number of articles described the anterior-posterior and medial-lateral extent of the defect. Few articles listed dental status and soft palate involvement when describing maxillectomy defects. No classification system has accurately described the maxillectomy defect, based on
Evaluation of a wetland classification system devised for ...
The manuscript is part of an FY14 RAP product: "Functional Assessment of Alaska Peatlands in Cook Inlet Basin: A report to Region 10". This report included this technical information product which is a manuscript that has now been fully revised, reviewed and published in a scientific peer-reviewed publication with open access (doi:10.1007/s11273-016-9504-0). The journal article scientific abstract is as follows: "Several wetland classification schemes are now commonly used to describe wetlands in the contiguous United States to meet local, regional, and national regulatory requirements. However, these established systems have proven to be insufficient to meet the needs of land managers in Alaska. The wetlands of this northern region are predominantly peatlands, which are not adequately treated by the nationally-used systems, which have few, if any, peatland classes. A new system was therefore devised to classify wetlands in the rapidly urbanizing Cook Inlet Basin of southcentral Alaska, USA. The Cook Inlet Classification (CIC) is based on seven geomorphic and six hydrologic components that incorporate the environmental gradients responsible for the primary sources of variation in peatland ecosystems. The geomorphic and hydrologic components have the added advantage of being detectable on remote sensing imagery, which facilitates regional mapping across large tracts of inaccessible terrain. Three different quantitative measures were used to evaluate the robu
NASA Astrophysics Data System (ADS)
Schmalz, M.; Ritter, G.
Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and
DeepPap: Deep Convolutional Networks for Cervical Cell Classification.
Zhang, Ling; Le Lu; Nogues, Isabella; Summers, Ronald M; Liu, Shaoxiong; Yao, Jianhua
2017-11-01
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.
An evidence-based diagnostic classification system for low back pain
Vining, Robert; Potocki, Eric; Seidman, Michael; Morgenthal, A. Paige
2013-01-01
Introduction: While clinicians generally accept that musculoskeletal low back pain (LBP) can arise from specific tissues, it remains difficult to confirm specific sources. Methods: Based on evidence supported by diagnostic utility studies, doctors of chiropractic functioning as members of a research clinic created a diagnostic classification system, corresponding exam and checklist based on strength of evidence, and in-office efficiency. Results: The diagnostic classification system contains one screening category, two pain categories: Nociceptive, Neuropathic, one functional evaluation category, and one category for unknown or poorly defined diagnoses. Nociceptive and neuropathic pain categories are each divided into 4 subcategories. Conclusion: This article describes and discusses the strength of evidence surrounding diagnostic categories for an in-office, clinical exam and checklist tool for LBP diagnosis. The use of a standardized tool for diagnosing low back pain in clinical and research settings is encouraged. PMID:23997245
ERIC Educational Resources Information Center
Harris, Christopher
2013-01-01
In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…
[Diversity and classification system of weed community in Harbin City, China].
Chen, Xiao-Shuang; Liang, Hong; Song, Kun; Da, Liang-Jun
2014-08-01
To analyze the causes of weed community diversity and their strategies of adaption to the high heterogeneity of urban habitats, weed communities in the central urban area of Harbin, China were studied, and a classification system was established for the weed communities. There were 175 weed species, belonging to 128 genera and 38 families. The heterogeneous urban habitats and species' temporal niche differentiation resulted in the highly diversified weed communities. The high proportions of mono-species dominance and annual species dominance communities were their response to the unstable urban habitats under human disturbances with high intensities and frequencies. A four-level classification system was established in terms of plant species and habitat conditions. Within this system, the identified 1763 weed communities could be categorized into two types of life form, 5 types of dormancy form, 22 community groups, and 119 dominance communities.
[GRADE system: classification of quality of evidence and strength of recommendation].
Aguayo-Albasini, José Luis; Flores-Pastor, Benito; Soria-Aledo, Víctor
2014-02-01
The acquisition and classification of scientific evidence, and subsequent formulation of recommendations constitute the basis for the development of clinical practice guidelines. There are several systems for the classification of evidence and strength of recommendations; the most commonly used nowadays is the Grading of Recommendations, Assessment, Development and Evaluation system (GRADE). The GRADE system initially classifies the evidence into high or low, coming from experimental or observational studies; subsequently and following a series of considerations, the evidence is classified into high, moderate, low or very low. The strength of recommendations is based not only on the quality of the evidence, but also on a series of factors such as the risk/benefit balance, values and preferences of the patients and professionals, and the use of resources or costs. Copyright © 2013 AEC. Published by Elsevier Espana. All rights reserved.
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.
Algorithmic Classification of Five Characteristic Types of Paraphasias.
Fergadiotis, Gerasimos; Gorman, Kyle; Bedrick, Steven
2016-12-01
This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes.
The Comprehensive AOCMF Classification System: Condylar Process Fractures - Level 3 Tutorial
Neff, Andreas; Cornelius, Carl-Peter; Rasse, Michael; Torre, Daniel Dalla; Audigé, Laurent
2014-01-01
This tutorial outlines the detailed system for fractures of the condylar process at the precision level 3 and is organized in a sequence of sections dealing with the description of the classification system within topographical subdivisions along with rules for fracture coding and a series of case examples with clinical imaging. Basically, the condylar process comprises three fracture levels and is subdivided into the head region, the condylar neck, and the condylar base. Fractures of the condylar head show typical fracture lines either within the lateral pole zone, which may lead to loss of vertical height, or medially to the pole zone, with the latter ones usually not compromising the vertical condyle to fossa relation. In condylar head fractures, the morphology is further described by the presence of minor or major fragmentation, the vertical apposition of fragments at the plane of the head fracture, the displacement of the condylar head with regard to the fossa including a potential distortion of the condylar head congruency resulting in dystopic condyle to fossa relations and the presence or absence of a loss of vertical ramus height. A specific vertical fracture pattern extending from the head to the neck or base subregion is considered. Fractures of the condylar neck and base can be differentiated according to a newly introduced one-third to two-thirds rule with regard to the proportion of the fracture line above and below the level of the sigmoid notch, which is presented in the classification article, and are basically subdivided according to the presence or absence of displacement or dislocation. In both condylar neck and base fractures, the classification is again based on the above mentioned parameters such as fragmentation, displacement of the condylar head with regard to the fossa, including dystopic condyle to fossa relations and loss of vertical ramus height, that is, according to the measurement of the condylar process. In addition, the
Leisher, Susannah Hopkins; Teoh, Zheyi; Reinebrant, Hanna; Allanson, Emma; Blencowe, Hannah; Erwich, Jan Jaap; Frøen, J Frederik; Gardosi, Jason; Gordijn, Sanne; Gülmezoglu, A Metin; Heazell, Alexander E P; Korteweg, Fleurisca; Lawn, Joy; McClure, Elizabeth M; Pattinson, Robert; Smith, Gordon C S; Tunçalp, Ӧzge; Wojcieszek, Aleena M; Flenady, Vicki
2016-09-15
To reduce the burden of 5.3 million stillbirths and neonatal deaths annually, an understanding of causes of deaths is critical. A systematic review identified 81 systems for classification of causes of stillbirth (SB) and neonatal death (NND) between 2009 and 2014. The large number of systems hampers efforts to understand and prevent these deaths. This study aimed to assess the alignment of current classification systems with expert-identified characteristics for a globally effective classification system. Eighty-one classification systems were assessed for alignment with 17 characteristics previously identified through expert consensus as necessary for an effective global system. Data were extracted independently by two authors. Systems were assessed against each characteristic and weighted and unweighted scores assigned to each. Subgroup analyses were undertaken by system use, setting, type of death included and type of characteristic. None of the 81 systems were aligned with more than 9 of the 17 characteristics; most (82 %) were aligned with four or fewer. On average, systems were aligned with 19 % of characteristics. The most aligned system (Frøen 2009-Codac) still had an unweighted score of only 9/17. Alignment with individual characteristics ranged from 0 to 49 %. Alignment was somewhat higher for widely used as compared to less used systems (22 % v 17 %), systems used only in high income countries as compared to only in low and middle income countries (20 % vs 16 %), and systems including both SB and NND (23 %) as compared to NND-only (15 %) and SB-only systems (13 %). Alignment was higher with characteristics assessing structure (23 %) than function (15 %). There is an unmet need for a system exhibiting all the characteristics of a globally effective system as defined by experts in the use of systems, as none of the 81 contemporary classification systems assessed was highly aligned with these characteristics. A particular concern in terms of
NASA Astrophysics Data System (ADS)
Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher
2012-10-01
Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.
Early warning, warning or alarm systems for natural hazards? A generic classification.
NASA Astrophysics Data System (ADS)
Sättele, Martina; Bründl, Michael; Straub, Daniel
2013-04-01
Early warning, warning and alarm systems have gained popularity in recent years as cost-efficient measures for dangerous natural hazard processes such as floods, storms, rock and snow avalanches, debris flows, rock and ice falls, landslides, flash floods, glacier lake outburst floods, forest fires and even earthquakes. These systems can generate information before an event causes loss of property and life. In this way, they mainly mitigate the overall risk by reducing the presence probability of endangered objects. These systems are typically prototypes tailored to specific project needs. Despite their importance there is no recognised system classification. This contribution classifies warning and alarm systems into three classes: i) threshold systems, ii) expert systems and iii) model-based expert systems. The result is a generic classification, which takes the characteristics of the natural hazard process itself and the related monitoring possibilities into account. The choice of the monitoring parameters directly determines the system's lead time. The classification of 52 active systems moreover revealed typical system characteristics for each system class. i) Threshold systems monitor dynamic process parameters of ongoing events (e.g. water level of a debris flow) and incorporate minor lead times. They have a local geographical coverage and a predefined threshold determines if an alarm is automatically activated to warn endangered objects, authorities and system operators. ii) Expert systems monitor direct changes in the variable disposition (e.g crack opening before a rock avalanche) or trigger events (e.g. heavy rain) at a local scale before the main event starts and thus offer extended lead times. The final alarm decision incorporates human, model and organisational related factors. iii) Model-based expert systems monitor indirect changes in the variable disposition (e.g. snow temperature, height or solar radiation that influence the occurrence probability
Conflicts in wound classification of neonatal operations.
Vu, Lan T; Nobuhara, Kerilyn K; Lee, Hanmin; Farmer, Diana L
2009-06-01
This study sought to determine the reliability of wound classification guidelines when applied to neonatal operations. This study is a cross-sectional web-based survey of pediatric surgeons. From a random sample of 22 neonatal operations, participants classified each operation as "clean," "clean-contaminated," "contaminated," or "dirty or infected," and specified duration of perioperative antibiotics as "none," "single preoperative," "24 hours," or ">24 hours." Unweighted kappa score was calculated to estimate interrater reliability. Overall interrater reliability for wound classification was poor (kappa = 0.30). The following operations were classified as clean: pyloromyotomy, resection of sequestration, resection of sacrococcygeal teratoma, oophorectomy, and immediate repair of omphalocele; as clean-contaminated: Ladd procedure, bowel resection for midgut volvulus and meconium peritonitis, fistula ligation of tracheoesophageal fistula, primary esophageal anastomosis of esophageal atresia, thoracic lobectomy, staged closure of gastroschisis, delayed repair and primary closure of omphalocele, perineal anoplasty and diverting colostomy for imperforate anus, anal pull-through for Hirschsprung disease, and colostomy closure; and as dirty: perforated necrotizing enterocolitis. There is poor consensus on how neonatal operations are classified based on contamination. An improved classification system will provide more accurate risk assessment for development of surgical site infections and identify neonates who would benefit from antibiotic prophylaxis.
Xiang, Kun; Li, Yinglei; Ford, William; Land, Walker; Schaffer, J David; Congdon, Robert; Zhang, Jing; Sadik, Omowunmi
2016-02-21
We hereby report the design and implementation of an Autonomous Microbial Cell Culture and Classification (AMC(3)) system for rapid detection of food pathogens. Traditional food testing methods require multistep procedures and long incubation period, and are thus prone to human error. AMC(3) introduces a "one click approach" to the detection and classification of pathogenic bacteria. Once the cultured materials are prepared, all operations are automatic. AMC(3) is an integrated sensor array platform in a microbial fuel cell system composed of a multi-potentiostat, an automated data collection system (Python program, Yocto Maxi-coupler electromechanical relay module) and a powerful classification program. The classification scheme consists of Probabilistic Neural Network (PNN), Support Vector Machines (SVM) and General Regression Neural Network (GRNN) oracle-based system. Differential Pulse Voltammetry (DPV) is performed on standard samples or unknown samples. Then, using preset feature extractions and quality control, accepted data are analyzed by the intelligent classification system. In a typical use, thirty-two extracted features were analyzed to correctly classify the following pathogens: Escherichia coli ATCC#25922, Escherichia coli ATCC#11775, and Staphylococcus epidermidis ATCC#12228. 85.4% accuracy range was recorded for unknown samples, and within a shorter time period than the industry standard of 24 hours.
Large-scale gene function analysis with the PANTHER classification system.
Mi, Huaiyu; Muruganujan, Anushya; Casagrande, John T; Thomas, Paul D
2013-08-01
The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs). Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways. The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests. It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. In the 2013 release of PANTHER (v.8.0), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability. This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system.
NASA Astrophysics Data System (ADS)
Khan, Asif; Ryoo, Chang-Kyung; Kim, Heung Soo
2017-04-01
This paper presents a comparative study of different classification algorithms for the classification of various types of inter-ply delaminations in smart composite laminates. Improved layerwise theory is used to model delamination at different interfaces along the thickness and longitudinal directions of the smart composite laminate. The input-output data obtained through surface bonded piezoelectric sensor and actuator is analyzed by the system identification algorithm to get the system parameters. The identified parameters for the healthy and delaminated structure are supplied as input data to the classification algorithms. The classification algorithms considered in this study are ZeroR, Classification via regression, Naïve Bayes, Multilayer Perceptron, Sequential Minimal Optimization, Multiclass-Classifier, and Decision tree (J48). The open source software of Waikato Environment for Knowledge Analysis (WEKA) is used to evaluate the classification performance of the classifiers mentioned above via 75-25 holdout and leave-one-sample-out cross-validation regarding classification accuracy, precision, recall, kappa statistic and ROC Area.
Comparing Features for Classification of MEG Responses to Motor Imagery.
Halme, Hanna-Leena; Parkkonen, Lauri
2016-01-01
Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction
Comparing Features for Classification of MEG Responses to Motor Imagery
Halme, Hanna-Leena; Parkkonen, Lauri
2016-01-01
Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single
Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems
NASA Technical Reports Server (NTRS)
Hearn, Tristan A.
2015-01-01
This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.
A machine learning approach for viral genome classification.
Remita, Mohamed Amine; Halioui, Ahmed; Malick Diouara, Abou Abdallah; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré
2017-04-11
Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .
The ESHRE/ESGE consensus on the classification of female genital tract congenital anomalies.
Grimbizis, Grigoris F; Gordts, Stephan; Di Spiezio Sardo, Attilio; Brucker, Sara; De Angelis, Carlo; Gergolet, Marco; Li, Tin-Chiu; Tanos, Vasilios; Brölmann, Hans; Gianaroli, Luca; Campo, Rudi
2013-08-01
What classification system is more suitable for the accurate, clear, simple and related to the clinical management categorization of female genital anomalies? The new ESHRE/ESGE classification system of female genital anomalies is presented. Congenital malformations of the female genital tract are common miscellaneous deviations from normal anatomy with health and reproductive consequences. Until now, three systems have been proposed for their categorization but all of them are associated with serious limitations. The European Society of Human Reproduction and Embryology (ESHRE) and the European Society for Gynaecological Endoscopy (ESGE) have established a common Working Group, under the name CONUTA (CONgenital UTerine Anomalies), with the goal of developing a new updated classification system. A scientific committee (SC) has been appointed to run the project, looking also for consensus within the scientists working in the field. The new system is designed and developed based on (i) scientific research through critical review of current proposals and preparation of an initial proposal for discussion between the experts, (ii) consensus measurement among the experts through the use of the DELPHI procedure and (iii) consensus development by the SC, taking into account the results of the DELPHI procedure and the comments of the experts. Almost 90 participants took part in the process of development of the ESHRE/ESGE classification system, contributing with their structured answers and comments. The ESHRE/ESGE classification system is based on anatomy. Anomalies are classified into the following main classes, expressing uterine anatomical deviations deriving from the same embryological origin: U0, normal uterus; U1, dysmorphic uterus; U2, septate uterus; U3, bicorporeal uterus; U4, hemi-uterus; U5, aplastic uterus; U6, for still unclassified cases. Main classes have been divided into sub-classes expressing anatomical varieties with clinical significance. Cervical and
NASA Astrophysics Data System (ADS)
Jürgens, Björn; Herrero-Solana, Victor
2017-04-01
Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.
A vegetation classification system for use in California: its conceptual basis
Timothy E. Paysen; Jeanine A. Derby; C. Eugene Conrad
1982-01-01
A taxonomic Vegetation Classification System proposed for use in California is designed to simplify interdisciplinary communication about vegetation. The system structure is an aggregative plant community hierarchy at four levels of precision--the Association, Series, Subformation, and Formation. A flexible Phase category links specific resource management concerns to...
Single-trial EEG RSVP classification using convolutional neural networks
NASA Astrophysics Data System (ADS)
Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William
2016-05-01
Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.
NASA Astrophysics Data System (ADS)
Haaf, Ezra; Barthel, Roland
2016-04-01
When assessing hydrogeological conditions at the regional scale, the analyst is often confronted with uncertainty of structures, inputs and processes while having to base inference on scarce and patchy data. Haaf and Barthel (2015) proposed a concept for handling this predicament by developing a groundwater systems classification framework, where information is transferred from similar, but well-explored and better understood to poorly described systems. The concept is based on the central hypothesis that similar systems react similarly to the same inputs and vice versa. It is conceptually related to PUB (Prediction in ungauged basins) where organization of systems and processes by quantitative methods is intended and used to improve understanding and prediction. Furthermore, using the framework it is expected that regional conceptual and numerical models can be checked or enriched by ensemble generated data from neighborhood-based estimators. In a first step, groundwater hydrographs from a large dataset in Southern Germany are compared in an effort to identify structural similarity in groundwater dynamics. A number of approaches to group hydrographs, mostly based on a similarity measure - which have previously only been used in local-scale studies, can be found in the literature. These are tested alongside different global feature extraction techniques. The resulting classifications are then compared to a visual "expert assessment"-based classification which serves as a reference. A ranking of the classification methods is carried out and differences shown. Selected groups from the classifications are related to geological descriptors. Here we present the most promising results from a comparison of classifications based on series correlation, different series distances and series features, such as the coefficients of the discrete Fourier transform and the intrinsic mode functions of empirical mode decomposition. Additionally, we show examples of classes
Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis
NASA Technical Reports Server (NTRS)
Tarabalka, Yuliya; Tilton, James C.
2012-01-01
A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.
Rowe, A. K.; Hirnschall, G.; Lambrechts, T.; Bryce, J.
1999-01-01
Differences in the terms used to classify diseases in the Integrated Management of Childhood Illness (IMCI) guidelines and for health information system (HIS) disease surveillance could easily create confusion among health care workers. If the equivalent terms in the two classifications are not clear to health workers who are following the guidelines, they may have problems in performing the dual activities of case management and disease surveillance. These difficulties could adversely affect an individual's performance as well as the overall effectiveness of the IMCI strategy or HIS surveillance, or both. We interviewed key informants to determine the effect of these differences between the IMCI and HIS classifications on the countries that were implementing the IMCI guidelines. Four general approaches for addressing the problem were identified: translating the IMCI classifications into HIS classifications; changing the HIS list to include the IMCI classifications; using both the IMCI and HIS classification systems at the time of consultations; and doing nothing. No single approach can satisfy the needs of all countries. However, if the short-term or medium-term goal of IMCI planners is to find a solution that will reduce the problem for health workers and is also easy to implement, the approach most likely to succeed is translation of IMCI classifications into HIS classifications. Where feasible, a modification of the health information system to include the IMCI classifications may also be considered. PMID:10680246
Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS.
Hsieh, Sen-Yung; Tseng, Chiao-Li; Lee, Yun-Shien; Kuo, An-Jing; Sun, Chien-Feng; Lin, Yen-Hsiu; Chen, Jen-Kun
2008-02-01
Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.
A new classification system for all-ceramic and ceramic-like restorative materials.
Gracis, Stefano; Thompson, Van P; Ferencz, Jonathan L; Silva, Nelson R F A; Bonfante, Estevam A
2015-01-01
Classification systems for all-ceramic materials are useful for communication and educational purposes and warrant continuous revisions and updates to incorporate new materials. This article proposes a classification system for ceramic and ceramic-like restorative materials in an attempt to systematize and include a new class of materials. This new classification system categorizes ceramic restorative materials into three families: (1) glass-matrix ceramics, (2) polycrystalline ceramics, and (3) resin-matrix ceramics. Subfamilies are described in each group along with their composition, allowing for newly developed materials to be placed into the already existing main families. The criteria used to differentiate ceramic materials are based on the phase or phases present in their chemical composition. Thus, an all-ceramic material is classified according to whether a glass-matrix phase is present (glass-matrix ceramics) or absent (polycrystalline ceramics) or whether the material contains an organic matrix highly filled with ceramic particles (resin-matrix ceramics). Also presented are the manufacturers' clinical indications for the different materials and an overview of the different fabrication methods and whether they are used as framework materials or monolithic solutions. Current developments in ceramic materials not yet available to the dental market are discussed.
Di Spiezio Sardo, A; Campo, R; Gordts, S; Spinelli, M; Cosimato, C; Tanos, V; Brucker, S; Li, T C; Gergolet, M; De Angelis, C; Gianaroli, L; Grimbizis, G
2015-05-01
How comprehensive is the recently published European Society of Human Reproduction and Embryology (ESHRE)/European Society for Gynaecological Endoscopy (ESGE) classification system of female genital anomalies? The ESHRE/ESGE classification provides a comprehensive description and categorization of almost all of the currently known anomalies that could not be classified properly with the American Fertility Society (AFS) system. Until now, the more accepted classification system, namely that of the AFS, is associated with serious limitations in effective categorization of female genital anomalies. Many cases published in the literature could not be properly classified using the AFS system, yet a clear and accurate classification is a prerequisite for treatment. The CONUTA (CONgenital UTerine Anomalies) ESHRE/ESGE group conducted a systematic review of the literature to examine if those types of anomalies that could not be properly classified with the AFS system could be effectively classified with the use of the new ESHRE/ESGE system. An electronic literature search through Medline, Embase and Cochrane library was carried out from January 1988 to January 2014. Three participants independently screened, selected articles of potential interest and finally extracted data from all the included studies. Any disagreement was discussed and resolved after consultation with a fourth reviewer and the results were assessed independently and approved by all members of the CONUTA group. Among the 143 articles assessed in detail, 120 were finally selected reporting 140 cases that could not properly fit into a specific class of the AFS system. Those 140 cases were clustered in 39 different types of anomalies. The congenital anomaly involved a single organ in 12 (30.8%) out of the 39 types of anomalies, while multiple organs and/or segments of Müllerian ducts (complex anomaly) were involved in 27 (69.2%) types. Uterus was the organ most frequently involved (30/39: 76.9%), followed
Automated cloud classification with a fuzzy logic expert system
NASA Technical Reports Server (NTRS)
Tovinkere, Vasanth; Baum, Bryan A.
1993-01-01
An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.
ERIC Educational Resources Information Center
Williams, Florence M.
This report addresses the feasibility of changing the classification of library materials in the Chicago Public School libraries from the Dewey Decimal classification system (DDC) to the Library of Congress system (LC), thus patterning the city school libraries after the Chicago Public Library and strengthening the existing close relationship…
Classification of deadlift biomechanics with wearable inertial measurement units.
O'Reilly, Martin A; Whelan, Darragh F; Ward, Tomas E; Delahunt, Eamonn; Caulfield, Brian M
2017-06-14
The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes. Copyright © 2017 Elsevier Ltd. All rights reserved.
Reliability of a four-column classification for tibial plateau fractures.
Martínez-Rondanelli, Alfredo; Escobar-González, Sara Sofía; Henao-Alzate, Alejandro; Martínez-Cano, Juan Pablo
2017-09-01
A four-column classification system offers a different way of evaluating tibial plateau fractures. The aim of this study is to compare the intra-observer and inter-observer reliability between four-column and classic classifications. This is a reliability study, which included patients presenting with tibial plateau fractures between January 2013 and September 2015 in a level-1 trauma centre. Four orthopaedic surgeons blindly classified each fracture according to four different classifications: AO, Schatzker, Duparc and four-column. Kappa, intra-observer and inter-observer concordance were calculated for the reliability analysis. Forty-nine patients were included. The mean age was 39 ± 14.2 years, with no gender predominance (men: 51%; women: 49%), and 67% of the fractures included at least one of the posterior columns. The intra-observer and inter-observer concordance were calculated for each classification: four-column (84%/79%), Schatzker (60%/71%), AO (50%/59%) and Duparc (48%/58%), with a statistically significant difference among them (p = 0.001/p = 0.003). Kappa coefficient for intr-aobserver and inter-observer evaluations: Schatzker 0.48/0.39, four-column 0.61/0.34, Duparc 0.37/0.23, and AO 0.34/0.11. The proposed four-column classification showed the highest intra and inter-observer agreement. When taking into account the agreement that occurs by chance, Schatzker classification showed the highest inter-observer kappa, but again the four-column had the highest intra-observer kappa value. The proposed classification is a more inclusive classification for the posteromedial and posterolateral fractures. We suggest, therefore, that it be used in addition to one of the classic classifications in order to better understand the fracture pattern, as it allows more attention to be paid to the posterior columns, it improves the surgical planning and allows the surgical approach to be chosen more accurately.
An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
Wang, Xiaopu; Xiong, Yan; Huang, Wenchao
2016-01-01
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360∘ duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user’s motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances. PMID:27801866
An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones.
Wang, Xiaopu; Xiong, Yan; Huang, Wenchao
2016-10-29
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360 ∘ duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user's motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances.
The Comprehensive AOCMF Classification System: Midface Fractures - Level 3 Tutorial
Cornelius, Carl-Peter; Audigé, Laurent; Kunz, Christoph; Buitrago-Téllez, Carlos H.; Rudderman, Randal; Prein, Joachim
2014-01-01
This tutorial outlines the details of the AOCMF image-based classification system for fractures of the midface at the precision level 3. The topography of the different midface regions (central midface—upper central midface, intermediate central midface, lower central midface—incorporating the naso-orbito-ethmoid region; lateral midface—zygoma and zygomatic arch, palate) is subdivided in much greater detail than in level 2 going beyond the Le Fort fracture types and its analogs. The level 3 midface classification system is presented along with guidelines to precisely delineate the fracture patterns in these specific subregions. It is easy to plot common fracture entities, such as nasal and naso-orbito-ethmoid, and their variants due to the refined structural layout of the subregions. As a key attribute, this focused approach permits to document the occurrence of fragmentation (i.e., single vs. multiple fracture lines), displacement, and bone loss. Moreover, the preinjury dental state and the degree of alveolar atrophy in edentulous maxillary regions can be recorded. On the basis of these individual features, tooth injuries, periodontal trauma, and fracture involvement of the alveolar process can be assessed. Coding rules are given to set up a distinctive formula for typical midface fractures and their combinations. The instructions and illustrations are elucidated by a series of radiographic imaging examples. A critical appraisal of the design of this level 3 midface classification is made. PMID:25489392
The fuelbed: a key element of the Fuel Characteristic Classification System.
Cynthia L. Riccardi; Roger D. Ottmar; David V. Sandberg; Anne Andreu; Ella Elman; Karen Kopper; Jennifer Long
2007-01-01
Wildland fuelbed characteristics are temporally and spatially complex and can vary widely across regions. To capture this variability, we designed the Fuel Characteristic Classification System (FCCS), a national system to create fuelbeds and classify those fuelbeds for their capacity to support fire and consume fuels. This paper describes the structure of the fuelbeds...
Classification Systems Used in Illinois Public Community College Learning Resource Centers.
ERIC Educational Resources Information Center
Johnson, Catherine J.
Using a survey to identify the classification systems employed in Illinois public community college instructional materials centers, this study considered three questions: Was there a trend developing in the use of the Library of Congress (LC) system? What factors influenced this trend? Was this trend more evident in large centers than in small…
A novel processed food classification system applied to Australian food composition databases.
O'Halloran, S A; Lacy, K E; Grimes, C A; Woods, J; Campbell, K J; Nowson, C A
2017-08-01
The extent of food processing can affect the nutritional quality of foodstuffs. Categorising foods by the level of processing emphasises the differences in nutritional quality between foods within the same food group and is likely useful for determining dietary processed food consumption. The present study aimed to categorise foods within Australian food composition databases according to the level of food processing using a processed food classification system, as well as assess the variation in the levels of processing within food groups. A processed foods classification system was applied to food and beverage items contained within Australian Food and Nutrient (AUSNUT) 2007 (n = 3874) and AUSNUT 2011-13 (n = 5740). The proportion of Minimally Processed (MP), Processed Culinary Ingredients (PCI) Processed (P) and Ultra Processed (ULP) by AUSNUT food group and the overall proportion of the four processed food categories across AUSNUT 2007 and AUSNUT 2011-13 were calculated. Across the food composition databases, the overall proportions of foods classified as MP, PCI, P and ULP were 27%, 3%, 26% and 44% for AUSNUT 2007 and 38%, 2%, 24% and 36% for AUSNUT 2011-13. Although there was wide variation in the classifications of food processing within the food groups, approximately one-third of foodstuffs were classified as ULP food items across both the 2007 and 2011-13 AUSNUT databases. This Australian processed food classification system will allow researchers to easily quantify the contribution of processed foods within the Australian food supply to assist in assessing the nutritional quality of the dietary intake of population groups. © 2017 The British Dietetic Association Ltd.
Yu, Yingyan
2014-01-01
Histopathological classification is in a pivotal position in both basic research and clinical diagnosis and treatment of gastric cancer. Currently, there are different classification systems in basic science and clinical application. In medical literatures, different classifications are used including Lauren and WHO systems, which have confused many researchers. Lauren classification has been proposed for half a century, but is still used worldwide. It shows many advantages of simple, easy handling with prognostic significance. The WHO classification scheme is better than Lauren classification in that it is continuously being revised according to the progress of gastric cancer, and is always used in the clinical and pathological diagnosis of common scenarios. Along with the progression of genomics, transcriptomics, proteomics, metabolomics researches, molecular classification of gastric cancer becomes the current hot topics. The traditional therapeutic approach based on phenotypic characteristics of gastric cancer will most likely be replaced with a gene variation mode. The gene-targeted therapy against the same molecular variation seems more reasonable than traditional chemical treatment based on the same morphological change.
Influence of pansharpening techniques in obtaining accurate vegetation thematic maps
NASA Astrophysics Data System (ADS)
Ibarrola-Ulzurrun, Edurne; Gonzalo-Martin, Consuelo; Marcello-Ruiz, Javier
2016-10-01
In last decades, there have been a decline in natural resources, becoming important to develop reliable methodologies for their management. The appearance of very high resolution sensors has offered a practical and cost-effective means for a good environmental management. In this context, improvements are needed for obtaining higher quality of the information available in order to get reliable classified images. Thus, pansharpening enhances the spatial resolution of the multispectral band by incorporating information from the panchromatic image. The main goal in the study is to implement pixel and object-based classification techniques applied to the fused imagery using different pansharpening algorithms and the evaluation of thematic maps generated that serve to obtain accurate information for the conservation of natural resources. A vulnerable heterogenic ecosystem from Canary Islands (Spain) was chosen, Teide National Park, and Worldview-2 high resolution imagery was employed. The classes considered of interest were set by the National Park conservation managers. 7 pansharpening techniques (GS, FIHS, HCS, MTF based, Wavelet `à trous' and Weighted Wavelet `à trous' through Fractal Dimension Maps) were chosen in order to improve the data quality with the goal to analyze the vegetation classes. Next, different classification algorithms were applied at pixel-based and object-based approach, moreover, an accuracy assessment of the different thematic maps obtained were performed. The highest classification accuracy was obtained applying Support Vector Machine classifier at object-based approach in the Weighted Wavelet `à trous' through Fractal Dimension Maps fused image. Finally, highlight the difficulty of the classification in Teide ecosystem due to the heterogeneity and the small size of the species. Thus, it is important to obtain accurate thematic maps for further studies in the management and conservation of natural resources.
Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng
2013-08-01
Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.
Promoting consistent use of the communication function classification system (CFCS).
Cunningham, Barbara Jane; Rosenbaum, Peter; Hidecker, Mary Jo Cooley
2016-01-01
We developed a Knowledge Translation (KT) intervention to standardize the way speech-language pathologists working in Ontario Canada's Preschool Speech and Language Program (PSLP) used the Communication Function Classification System (CFCS). This tool was being used as part of a provincial program evaluation and standardizing its use was critical for establishing reliability and validity within the provincial dataset. Two theoretical foundations - Diffusion of Innovations and the Communication Persuasion Matrix - were used to develop and disseminate the intervention to standardize use of the CFCS among a cohort speech-language pathologists. A descriptive pre-test/post-test study was used to evaluate the intervention. Fifty-two participants completed an electronic pre-test survey, reviewed intervention materials online, and then immediately completed an electronic post-test survey. The intervention improved clinicians' understanding of how the CFCS should be used, their intentions to use the tool in the standardized way, and their abilities to make correct classifications using the tool. Findings from this work will be shared with representatives of the Ontario PSLP. The intervention may be disseminated to all speech-language pathologists working in the program. This study can be used as a model for developing and disseminating KT interventions for clinicians in paediatric rehabilitation. The Communication Function Classification System (CFCS) is a new tool that allows speech-language pathologists to classify children's skills into five meaningful levels of function. There is uncertainty and inconsistent practice in the field about the methods for using this tool. This study used combined two theoretical frameworks to develop an intervention to standardize use of the CFCS among a cohort of speech-language pathologists. The intervention effectively increased clinicians' understanding of the methods for using the CFCS, ability to make correct classifications, and
Grant, C C; Biggs, H C; Meissner, H H
1996-06-01
Mineral deficiencies that lead to production losses often occur concurrently with climatic and management changes. To diagnose these deficiencies in time to prevent production losses, long-term monitoring of mineral status is advisable. Different classification systems were examined to determine whether areas of possible mineral deficiencies could be identified, so that those which were promising could then be selected for further monitoring purposes. The classification systems addressed differences in soil, vegetation and geology, and were used to define the cattle-ranching areas in the central and northern districts of Namibia. Copper (Cu), Iron (Fe), zinc (Zn), manganese (Mn) and cobalt (Co) concentrations were determined in cattle livers collected at abattoirs. Pooled faecal grab samples and milk samples were collected by farmers, and used to determine phosphorus (P) and calcium (Ca), and iodine (I) status, respectively. Areas of low P concentrations could be identified by all classification systems. The lowest P concentrations were recorded in samples from the Kalahari-sand area, whereas faecal samples collected from cattle on farms in the more arid areas, where the harder soils are mostly found, rarely showed low P concentrations. In the north of the country, low iodine levels were found in milk samples collected from cows grazing on farms in the northern Kalahari broad-leaved woodland. Areas supporting animals with marginal Cu status, could be effectively identified by the detailed soil-classification system of irrigation potential. Copper concentrations were lowest in areas of arid soils, but no indication of Co, Fe, Zn, or Mn deficiencies were found. For most minerals, the geological classification was the best single indicator of areas of lower concentrations. Significant monthly variation for all minerals could also be detected within the classification system. It is concluded that specific classification systems can be useful as indicators of areas with
Classification and grading of muscle injuries: a narrative review
Hamilton, Bruce; Valle, Xavier; Rodas, Gil; Til, Luis; Grive, Ricard Pruna; Rincon, Josep Antoni Gutierrez; Tol, Johannes L
2015-01-01
A limitation to the accurate study of muscle injuries and their management has been the lack of a uniform approach to the categorisation and grading of muscle injuries. The goal of this narrative review was to provide a framework from which to understand the historical progression of the classification and grading of muscle injuries. We reviewed the classification and grading of muscle injuries in the literature to critically illustrate the strengths, weaknesses, contradictions or controversies. A retrospective, citation-based methodology was applied to search for English language literature which evaluated or utilised a novel muscle classification or grading system. While there is an abundance of literature classifying and grading muscle injuries, it is predominantly expert opinion, and there remains little evidence relating any of the clinical or radiological features to an established pathology or clinical outcome. While the categorical grading of injury severity may have been a reasonable solution to a clinical challenge identified in the middle of the 20th century, it is time to recognise the complexity of the injury, cease trying to oversimplify it and to develop appropriately powered research projects to answer important questions. PMID:25394420
Classification of earth terrain using polarimetric synthetic aperture radar images
NASA Technical Reports Server (NTRS)
Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.
1989-01-01
Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.
The Unified Classification System (UCS): improving our understanding of periprosthetic fractures.
Duncan, C P; Haddad, F S
2014-06-01
Periprosthetic fractures are an increasingly common complication following joint replacement. The principles which underpin their evaluation and treatment are common across the musculoskeletal system. The Unified Classification System proposes a rational approach to treatment, regardless of the bone that is broken or the joint involved. ©2014 The British Editorial Society of Bone & Joint Surgery.
Nutritional status in sick children and adolescents is not accurately reflected by BMI-SDS.
Fusch, Gerhard; Raja, Preeya; Dung, Nguyen Quang; Karaolis-Danckert, Nadina; Barr, Ronald; Fusch, Christoph
2013-01-01
Nutritional status provides helpful information of disease severity and treatment effectiveness. Body mass index standard deviation scores (BMI-SDS) provide an approximation of body composition and thus are frequently used to classify nutritional status of sick children and adolescents. However, the accuracy of estimating body composition in this population using BMI-SDS has not been assessed. Thus, this study aims to evaluate the accuracy of nutritional status classification in sick infants and adolescents using BMI-SDS, upon comparison to classification using percentage body fat (%BF) reference charts. BMI-SDS was calculated from anthropometric measurements and %BF was measured using dual-energy x-ray absorptiometry (DXA) for 393 sick children and adolescents (5 months-18 years). Subjects were classified by nutritional status (underweight, normal weight, overweight, and obese), using 2 methods: (1) BMI-SDS, based on age- and gender-specific percentiles, and (2) %BF reference charts (standard). Linear regression and a correlation analysis were conducted to compare agreement between both methods of nutritional status classification. %BF reference value comparisons were also made between 3 independent sources based on German, Canadian, and American study populations. Correlation between nutritional status classification by BMI-SDS and %BF agreed moderately (r (2) = 0.75, 0.76 in boys and girls, respectively). The misclassification of nutritional status in sick children and adolescents using BMI-SDS was 27% when using German %BF references. Similar rates observed when using Canadian and American %BF references (24% and 23%, respectively). Using BMI-SDS to determine nutritional status in a sick population is not considered an appropriate clinical tool for identifying individual underweight or overweight children or adolescents. However, BMI-SDS may be appropriate for longitudinal measurements or for screening purposes in large field studies. When accurate nutritional
Multiclass classification of microarray data samples with a reduced number of genes
2011-01-01
Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. Results A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. Conclusions A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples. PMID:21342522
Lee, Yun Jin; Kim, Jung Yoon
2016-03-01
The objective of this study was to evaluate the effect of pressure ulcer classification system education on clinical nurses' knowledge and visual differential diagnostic ability of pressure ulcer (PU) classification and incontinence-associated dermatitis (IAD). One group pre and post-test was used. A convenience sample of 407 nurses, participating in PU classification education programme of continuing education, were enrolled. The education programme was composed of a 50-minute lecture on PU classification and case-studies. The PU Classification system and IAD knowledge test (PUCS-KT) and visual differential diagnostic ability tool (VDDAT), consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 20.0. The overall mean difference of PUCS-KT (t = -11·437, P<0·001) and VDDAT (t = -21·113, P<0·001) was significantly increased after PU classification education. Overall understanding of six PU classification and IAD after education programme was increased, but lacked visual differential diagnostic ability regarding Stage III PU, suspected deep tissue injury (SDTI), and Unstageable. Continuous differentiated education based on clinical practice is needed to improve knowledge and visual differential diagnostic ability for PU classification, and comparison experiment study is required to examine effects of education programmes. © 2016 Medicalhelplines.com Inc and John Wiley & Sons Ltd.
Diaz De Vivar, Andrea; Roma, Andres A; Park, Kay J; Alvarado-Cabrero, Isabel; Rasty, Golnar; Chanona-Vilchis, Jose G; Mikami, Yoshiki; Hong, Sung R; Arville, Brent; Teramoto, Norihiro; Ali-Fehmi, Rouba; Rutgers, Joanne K L; Tabassum, Farah; Barbuto, Denise; Aguilera-Barrantes, Irene; Shaye-Brown, Alexandra; Daya, Dean; Silva, Elvio G
2013-11-01
The management of endocervical adenocarcinoma is largely based on tumor size and depth of invasion (DOI); however, DOI is difficult to measure accurately. The surgical treatment includes resection of regional lymph nodes, even though most lymph nodes are negative and lymphadenectomies can cause significant morbidity. We have investigated alternative parameters to better identify patients at risk of node metastases. Cases of invasive endocervical adenocarcinoma from 12 institutions were reviewed, and clinical/pathologic features assessed: patients' age, tumor size, DOI, differentiation, lymph-vascular invasion, lymph node metastases, recurrences, and stage. Cases were classified according to a new pattern-based system into Pattern A (well-demarcated glands), B (early destructive stromal invasion arising from well-demarcated glands), and C (diffuse destructive invasion). In total, 352 cases (FIGO Stages I-IV) were identified. Patients' age ranged from 20 to 83 years (mean 45), DOI ranged from 0.2 to 27 mm (mean 6.73), and lymph-vascular invasion was present in 141 cases. Forty-nine (13.9%) demonstrated lymph node metastases. Using this new system, 73 patients (20.7%) with Pattern A tumors (all Stage I) were identified. None had lymph node metastases and/or recurrences. Ninety patients (25.6%) had Pattern B tumors, of which 4 (4.4%) had positive nodes; whereas 189 (53.7%) had Pattern C tumors, of which 45 (23.8%) had metastatic nodes. The proposed classification system can spare 20.7% of patients (Pattern A) of unnecessary lymphadenectomy. Patients with Pattern B rarely present with positive nodes. An aggressive approach is justified in patients with Pattern C. This classification system is simple, easy to apply, and clinically significant.
Branch classification: A new mechanism for improving branch predictor performance
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chang, P.Y.; Hao, E.; Patt, Y.
There is wide agreement that one of the most significant impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Speculative execution is one solution to the branch problem, but speculative work is discarded if a branch is mispredicted. For it to be effective, speculative work is discarded if a branch is mispredicted. For it to be effective, speculative execution requires a very accurate branch predictor; 95% accuracy is not good enough. This paper proposes branch classification, a methodology for building more accurate branch predictors. Branch classification allows anmore » individual branch instruction to be associated with the branch predictor best suited to predict its direction. Using this approach, a hybrid branch predictor can be constructed such that each component branch predictor predicts those branches for which it is best suited. To demonstrate the usefulness of branch classification, an example classification scheme is given and a new hybrid predictor is built based on this scheme which achieves a higher prediction accuracy than any branch predictor previously reported in the literature.« less
[Definition and classification of pulmonary arterial hypertension].
Nakanishi, Norifumi
2008-11-01
Pulmonary hypertension(PH) is a disorder that may occur either in the setting of a variety of underlying medical conditions or as a disease that uniquely affects the pulmonary vasculature. Because an accurate diagnosis of PH in a patient is essential to establish an effective treatment, a classification of PH has been helpful. The first classification, established at WHO Symposium in 1973, classified PH into groups based on the known cause and defined primary pulmonary hypertension (PPH) as a separate entity of unknown cause. In 1998, the second World Symposium on PPH was held in Evian. Evian classification introduced the concept of conditions that directly affected the pulmonary vasculature (i.e., PAH), which included PPH. In 2003, the third World Symposium on PAH convened in Venice. In Venice classification, the term 'PPH' was abandoned in favor of 'idiopathic' within the group of disease known as 'PAH'.
Pattern Classifications Using Grover's and Ventura's Algorithms in a Two-qubits System
NASA Astrophysics Data System (ADS)
Singh, Manu Pratap; Radhey, Kishori; Rajput, B. S.
2018-03-01
Carrying out the classification of patterns in a two-qubit system by separately using Grover's and Ventura's algorithms on different possible superposition, it has been shown that the exclusion superposition and the phase-invariance superposition are the most suitable search states obtained from two-pattern start-states and one-pattern start-states, respectively, for the simultaneous classifications of patterns. The higher effectiveness of Grover's algorithm for large search states has been verified but the higher effectiveness of Ventura's algorithm for smaller data base has been contradicted in two-qubit systems and it has been demonstrated that the unknown patterns (not present in the concerned data-base) are classified more efficiently than the known ones (present in the data-base) in both the algorithms. It has also been demonstrated that different states of Singh-Rajput MES obtained from the corresponding self-single- pattern start states are the most suitable search states for the classification of patterns |00>,|01 >, |10> and |11> respectively on the second iteration of Grover's method or the first operation of Ventura's algorithm.
Low-power wireless ECG acquisition and classification system for body sensor networks.
Lee, Shuenn-Yuh; Hong, Jia-Hua; Hsieh, Cheng-Han; Liang, Ming-Chun; Chang Chien, Shih-Yu; Lin, Kuang-Hao
2015-01-01
A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on-off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc-air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.
Railroad classification yard technology : computer system methodology : case study : Potomac Yard
DOT National Transportation Integrated Search
1981-08-01
This report documents the application of the railroad classification yard computer system methodology to Potomac Yard of the Richmond, Fredericksburg, and Potomac Railroad Company (RF&P). This case study entailed evaluation of the yard traffic capaci...
A classification system for characterization of physical and non-physical work factors.
Genaidy, A; Karwowski, W; Succop, P; Kwon, Y G; Alhemoud, A; Goyal, D
2000-01-01
A comprehensive evaluation of work-related performance factors is a prerequisite to developing integrated and long-term solutions to workplace performance improvement. This paper describes a work-factor classification system that categorizes the entire domain of workplace factors impacting performance. A questionnaire-based instrument was developed to implement this classification system in industry. Fifty jobs were evaluated in 4 different service and manufacturing companies using the proposed questionnaire-based instrument. The reliability coefficients obtained from the analyzed jobs were considered good (0.589 to 0.862). In general, the physical work factors resulted in higher reliability coefficients (0.847 to 0.862) than non-physical work factors (0.589 to 0.768).
Image interpretation for a multilevel land use classification system
NASA Technical Reports Server (NTRS)
1973-01-01
The potential use is discussed of three remote sensors for developing a four level land use classification system. Three types of imagery for photointerpretation are presented: ERTS-1 satellite imagery, high altitude photography, and medium altitude photography. Suggestions are given as to which remote sensors and imagery scales may be most effectively employed to provide data on specific types of land use.
NASA Astrophysics Data System (ADS)
Fink, Wolfgang
2009-05-01
Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.
5 CFR 9701.231 - Conversion of positions and employees to the DHS classification system.
Code of Federal Regulations, 2010 CFR
2010-01-01
... HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM (DEPARTMENT OF HOMELAND SECURITY-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Classification Transitional...
Dykes, Patricia C; Wantland, Dean; Whittenburg, Luann; Lipsitz, Stuart; Saba, Virginia K
2013-01-01
While nursing activities represent a significant proportion of inpatient care, there are no reliable methods for determining nursing costs based on the actual services provided by the nursing staff. Capture of data to support accurate measurement and reporting on the cost of nursing services is fundamental to effective resource utilization. Adopting standard terminologies that support tracking both the quality and the cost of care could reduce the data entry burden on direct care providers. This pilot study evaluated the feasibility of using a standardized nursing terminology, the Clinical Care Classification System (CCC), for developing a reliable costing method for nursing services. Two different approaches are explored; the Relative Value Unit RVU and the simple cost-to-time methods. We found that the simple cost-to-time method was more accurate and more transparent in its derivation than the RVU method and may support a more consistent and reliable approach for costing nursing services.
Colorectal Cancer Classification and Cell Heterogeneity: A Systems Oncology Approach
Blanco-Calvo, Moisés; Concha, Ángel; Figueroa, Angélica; Garrido, Federico; Valladares-Ayerbes, Manuel
2015-01-01
Colorectal cancer is a heterogeneous disease that manifests through diverse clinical scenarios. During many years, our knowledge about the variability of colorectal tumors was limited to the histopathological analysis from which generic classifications associated with different clinical expectations are derived. However, currently we are beginning to understand that under the intense pathological and clinical variability of these tumors there underlies strong genetic and biological heterogeneity. Thus, with the increasing available information of inter-tumor and intra-tumor heterogeneity, the classical pathological approach is being displaced in favor of novel molecular classifications. In the present article, we summarize the most relevant proposals of molecular classifications obtained from the analysis of colorectal tumors using powerful high throughput techniques and devices. We also discuss the role that cancer systems biology may play in the integration and interpretation of the high amount of data generated and the challenges to be addressed in the future development of precision oncology. In addition, we review the current state of implementation of these novel tools in the pathological laboratory and in clinical practice. PMID:26084042
Classification of "multipole" superconductivity in multiorbital systems and its implications
NASA Astrophysics Data System (ADS)
Nomoto, T.; Hattori, K.; Ikeda, H.
2016-11-01
Motivated by a growing interest in multiorbital superconductors with spin-orbit interactions, we perform the group-theoretical classification of various unconventional superconductivity emerging in symmorphic O , D4, and D6 space groups. The generalized Cooper pairs, which we here call "multipole" superconductivity, possess spin-orbital coupled (multipole) degrees of freedom, instead of the conventional spin singlet/triplet in single-orbital systems. From the classification, we obtain the following key consequences, which have never been focused in the long history of research in this field: (1) A superconducting gap function with Γ9⊗Γ9 in D6 possesses nontrivial momentum dependence different from the usual spin-1/2 classification. (2) Unconventional gap structure can be realized in the BCS approximation of purely local (onsite) interactions irrespective of attraction/repulsion. It implies the emergence of an electron-phonon (e-ph) driven unconventional superconductivity. (3) Reflecting symmetry of orbital basis functions there appear not symmetry protected but inevitable line nodes/gap minima, and thus, anisotropic s -wave superconductivity can be naturally explained even in the absence of competing fluctuations.
5 CFR 9701.221 - Classification requirements.
Code of Federal Regulations, 2013 CFR
2013-01-01
... Section 9701.221 Administrative Personnel DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM (DEPARTMENT OF HOMELAND SECURITY-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Classification Classification Process § 9701.221 Classification...
5 CFR 9701.221 - Classification requirements.
Code of Federal Regulations, 2012 CFR
2012-01-01
... Section 9701.221 Administrative Personnel DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM (DEPARTMENT OF HOMELAND SECURITY-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Classification Classification Process § 9701.221 Classification...
5 CFR 9701.221 - Classification requirements.
Code of Federal Regulations, 2014 CFR
2014-01-01
... Section 9701.221 Administrative Personnel DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM (DEPARTMENT OF HOMELAND SECURITY-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Classification Classification Process § 9701.221 Classification...
5 CFR 9701.221 - Classification requirements.
Code of Federal Regulations, 2011 CFR
2011-01-01
... Section 9701.221 Administrative Personnel DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM (DEPARTMENT OF HOMELAND SECURITY-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Classification Classification Process § 9701.221 Classification...
NASA Astrophysics Data System (ADS)
Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude
2010-02-01
Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.
Classification of hyperbolic singularities of rank zero of integrable Hamiltonian systems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Oshemkov, Andrey A
2010-10-06
A complete invariant is constructed that is a solution of the problem of semilocal classification of saddle singularities of integrable Hamiltonian systems. Namely, a certain combinatorial object (an f{sub n}-graph) is associated with every nondegenerate saddle singularity of rank zero; as a result, the problem of semilocal classification of saddle singularities of rank zero is reduced to the problem of enumeration of the f{sub n}-graphs. This enables us to describe a simple algorithm for obtaining the lists of saddle singularities of rank zero for a given number of degrees of freedom and a given complexity. Bibliography: 24 titles.
Assessment and Develop the Saudi's Contractors Classification System
ERIC Educational Resources Information Center
Almutairi, Saud
2017-01-01
Research has shown that construction projects in Saudi Arabia have had a perceived poor performance for the past three decades, from 1970-2016. The Saudi construction industry relies on a Contractor Classification System (CCS) to determine contractors' capabilities, and prevent underperformance. Through this study, a survey was conducted among…
5 CFR 9901.221 - Classification requirements.
Code of Federal Regulations, 2011 CFR
2011-01-01
... Section 9901.221 Administrative Personnel DEPARTMENT OF DEFENSE HUMAN RESOURCES MANAGEMENT AND LABOR RELATIONS SYSTEMS (DEPARTMENT OF DEFENSE-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF DEFENSE NATIONAL SECURITY PERSONNEL SYSTEM (NSPS) Classification Classification Process § 9901.221 Classification...
A model for anomaly classification in intrusion detection systems
NASA Astrophysics Data System (ADS)
Ferreira, V. O.; Galhardi, V. V.; Gonçalves, L. B. L.; Silva, R. C.; Cansian, A. M.
2015-09-01
Intrusion Detection Systems (IDS) are traditionally divided into two types according to the detection methods they employ, namely (i) misuse detection and (ii) anomaly detection. Anomaly detection has been widely used and its main advantage is the ability to detect new attacks. However, the analysis of anomalies generated can become expensive, since they often have no clear information about the malicious events they represent. In this context, this paper presents a model for automated classification of alerts generated by an anomaly based IDS. The main goal is either the classification of the detected anomalies in well-defined taxonomies of attacks or to identify whether it is a false positive misclassified by the IDS. Some common attacks to computer networks were considered and we achieved important results that can equip security analysts with best resources for their analyses.
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
Spatial-spectral blood cell classification with microscopic hyperspectral imagery
NASA Astrophysics Data System (ADS)
Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng
2017-10-01
Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.
Behavior Based Social Dimensions Extraction for Multi-Label Classification
Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin
2016-01-01
Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849
Method and System for Controlling a Dexterous Robot Execution Sequence Using State Classification
NASA Technical Reports Server (NTRS)
Sanders, Adam M. (Inventor); Quillin, Nathaniel (Inventor); Platt, Robert J., Jr. (Inventor); Pfeiffer, Joseph (Inventor); Permenter, Frank Noble (Inventor)
2014-01-01
A robotic system includes a dexterous robot and a controller. The robot includes a plurality of robotic joints, actuators for moving the joints, and sensors for measuring a characteristic of the joints, and for transmitting the characteristics as sensor signals. The controller receives the sensor signals, and is configured for executing instructions from memory, classifying the sensor signals into distinct classes via the state classification module, monitoring a system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the system state. A method for controlling the robot in the above system includes receiving the signals via the controller, classifying the signals using the state classification module, monitoring the present system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the present system state.
Code of Federal Regulations, 2012 CFR
2012-10-01
... Industry Classification System (NAICS) codes and size standards. 19.303 Section 19.303 Federal Acquisition... of Small Business Status for Small Business Programs 19.303 Determining North American Industry... user, the added text is set forth as follows: 19.303 Determining North American Industry Classification...
Sahm, Felix; Schrimpf, Daniel; Stichel, Damian; Jones, David T W; Hielscher, Thomas; Schefzyk, Sebastian; Okonechnikov, Konstantin; Koelsche, Christian; Reuss, David E; Capper, David; Sturm, Dominik; Wirsching, Hans-Georg; Berghoff, Anna Sophie; Baumgarten, Peter; Kratz, Annekathrin; Huang, Kristin; Wefers, Annika K; Hovestadt, Volker; Sill, Martin; Ellis, Hayley P; Kurian, Kathreena M; Okuducu, Ali Fuat; Jungk, Christine; Drueschler, Katharina; Schick, Matthias; Bewerunge-Hudler, Melanie; Mawrin, Christian; Seiz-Rosenhagen, Marcel; Ketter, Ralf; Simon, Matthias; Westphal, Manfred; Lamszus, Katrin; Becker, Albert; Koch, Arend; Schittenhelm, Jens; Rushing, Elisabeth J; Collins, V Peter; Brehmer, Stefanie; Chavez, Lukas; Platten, Michael; Hänggi, Daniel; Unterberg, Andreas; Paulus, Werner; Wick, Wolfgang; Pfister, Stefan M; Mittelbronn, Michel; Preusser, Matthias; Herold-Mende, Christel; Weller, Michael; von Deimling, Andreas
2017-05-01
individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. German Cancer Aid, Else Kröner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program. Copyright © 2017 Elsevier Ltd. All rights reserved.
Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data.
Guo, Guodong; Guffey, Keegan; Chen, Wenbin; Pergami, Paola
2017-01-01
Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children's development, we also investigated the possibility of age estimation based on this data. Our results demonstrate that the data collected by the GAITRite system can be used for normal/pathological gait classification. Combining age information and normal/pathological gait classification increases the accuracy of the classifier. This novel approach could support the development of an accurate, real-time, and economic measure of gait abnormalities in children, able to provide important feedback to clinicians regarding the effect of rehabilitation interventions, and to support targeted treatment modifications.
ERIC Educational Resources Information Center
Hidecker, Mary Jo Cooley; Ho, Nhan Thi; Dodge, Nancy; Hurvitz, Edward A.; Slaughter, Jaime; Workinger, Marilyn Seif; Kent, Ray D.; Rosenbaum, Peter; Lenski, Madeleine; Messaros, Bridget M.; Vanderbeek, Suzette B.; Deroos, Steven; Paneth, Nigel
2012-01-01
Aim: To investigate the relationships among the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and Communication Function Classification System (CFCS) in children with cerebral palsy (CP). Method: Using questionnaires describing each scale, mothers reported GMFCS, MACS, and CFCS levels in 222…
Classification of US hydropower dams by their modes of operation
McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh; ...
2016-02-19
A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less
Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery.
Schols, Rutger M; Alic, Lejla; Wieringa, Fokko P; Bouvy, Nicole D; Stassen, Laurents P S
2017-03-01
In (para-)thyroid surgery iatrogenic parathyroid injury should be prevented. To aid the surgeons' eye, a camera system enabling parathyroid-specific image enhancement would be useful. Hyperspectral camera technology might work, provided that the spectral signature of parathyroid tissue offers enough specific features to be reliably and automatically distinguished from surrounding tissues. As a first step to investigate this, we examined the feasibility of wide band diffuse reflectance spectroscopy (DRS) for automated spectroscopic tissue classification, using silicon (Si) and indium-gallium-arsenide (InGaAs) sensors. DRS (350-1830 nm) was performed during (para-)thyroid resections. From the acquired spectra 36 features at predefined wavelengths were extracted. The best features for classification of parathyroid from adipose or thyroid were assessed by binary logistic regression for Si- and InGaAs-sensor ranges. Classification performance was evaluated by leave-one-out cross-validation. In 19 patients 299 spectra were recorded (62 tissue sites: thyroid = 23, parathyroid = 21, adipose = 18). Classification accuracy of parathyroid-adipose was, respectively, 79% (Si), 82% (InGaAs) and 97% (Si/InGaAs combined). Parathyroid-thyroid classification accuracies were 80% (Si), 75% (InGaAs), 82% (Si/InGaAs combined). Si and InGaAs sensors are fairly accurate for automated spectroscopic classification of parathyroid, adipose and thyroid tissues. Combination of both sensor technologies improves accuracy. Follow-up research, aimed towards hyperspectral imaging seems justified. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Classification of US hydropower dams by their modes of operation
DOE Office of Scientific and Technical Information (OSTI.GOV)
McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh
A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less
Consensus Classification Using Non-Optimized Classifiers.
Brownfield, Brett; Lemos, Tony; Kalivas, John H
2018-04-03
Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.
A data-stream classification system for investigating terrorist threats
NASA Astrophysics Data System (ADS)
Schulz, Alexia; Dettman, Joshua; Gottschalk, Jeffrey; Kotson, Michael; Vuksani, Era; Yu, Tamara
2016-05-01
The role of cyber forensics in criminal investigations has greatly increased in recent years due to the wealth of data that is collected and available to investigators. Physical forensics has also experienced a data volume and fidelity revolution due to advances in methods for DNA and trace evidence analysis. Key to extracting insight is the ability to correlate across multi-modal data, which depends critically on identifying a touch-point connecting the separate data streams. Separate data sources may be connected because they refer to the same individual, entity or event. In this paper we present a data source classification system tailored to facilitate the investigation of potential terrorist activity. This taxonomy is structured to illuminate the defining characteristics of a particular terrorist effort and designed to guide reporting to decision makers that is complete, concise, and evidence-based. The classification system has been validated and empirically utilized in the forensic analysis of a simulated terrorist activity. Next-generation analysts can use this schema to label and correlate across existing data streams, assess which critical information may be missing from the data, and identify options for collecting additional data streams to fill information gaps.
McDermott, P A; Hale, R L
1982-07-01
Tested diagnostic classifications of child psychopathology produced by a computerized technique known as multidimensional actuarial classification (MAC) against the criterion of expert psychological opinion. The MAC program applies series of statistical decision rules to assess the importance of and relationships among several dimensions of classification, i.e., intellectual functioning, academic achievement, adaptive behavior, and social and behavioral adjustment, to perform differential diagnosis of children's mental retardation, specific learning disabilities, behavioral and emotional disturbance, possible communication or perceptual-motor impairment, and academic under- and overachievement in reading and mathematics. Classifications rendered by MAC are compared to those offered by two expert child psychologists for cases of 73 children referred for psychological services. Experts' agreement with MAC was significant for all classification areas, as was MAC's agreement with the experts held as a conjoint reference standard. Whereas the experts' agreement with MAC averaged 86.0% above chance, their agreement with one another averaged 76.5% above chance. Implications of the findings are explored and potential advantages of the systems-actuarial approach are discussed.
A survey to identify the clinical coding and classification systems currently in use across Europe.
de Lusignan, S; Minmagh, C; Kennedy, J; Zeimet, M; Bommezijn, H; Bryant, J
2001-01-01
This is a survey to identify what clinical coding systems are currently in use across the European Union, and the states seeking membership to it. We sought to identify what systems are currently used and to what extent they were subject to local adaptation. Clinical coding should facilitate identifying key medical events in a computerised medical record, and aggregating information across groups of records. The emerging new driver is as the enabler of the life-long computerised medical record. A prerequisite for this level of functionality is the transfer of information between different computer systems. This transfer can be facilitated either by working on the interoperability problems between disparate systems or by harmonising the underlying data. This paper examines the extent to which the latter has occurred across Europe. Literature and Internet search. Requests for information via electronic mail to pan-European mailing lists of health informatics professionals. Coding systems are now a de facto part of health information systems across Europe. There are relatively few coding systems in existence across Europe. ICD9 and ICD 10, ICPC and Read were the most established. However the local adaptation of these classification systems either on a by country or by computer software manufacturer basis; significantly reduces the ability for the meaning coded with patients computer records to be easily transferred from one medical record system to another. There is no longer any debate as to whether a coding or classification system should be used. Convergence of different classifications systems should be encouraged. Countries and computer manufacturers within the EU should be encouraged to stop making local modifications to coding and classification systems, as this practice risks significantly slowing progress towards easy transfer of records between computer systems.
A new classification system for shoulder instability.
Kuhn, John E
2010-04-01
Glenohumeral joint instability is extremely common yet the definition and classification of instability remains unclear. In order to find the best ways to treat instability, the condition must be clearly defined and classified. This is particularly important so that treatment studies can be compared or combined, which can only be done if the patient population under study is the same. The purpose of this paper was to review the problems with historical methods of defining and classifying instability and to introduce the FEDS system of classifying instability, which was developed to have content validity and found to have high interobserver and intraobserver agreement.
Aksungur, N; Korkut, E
2018-05-24
We read Atamanalp classification, treatment algorithm and prognosis-estimating systems for sigmoid volvulus (SV) and ileosigmoid knotting (ISK) in Colorectal Disease [1,2]. Our comments relate to necessity and utility of these new classification systems. Classification or staging systems are generally used in malignant or premalignant pathologies such as colorectal cancers [3] or polyps [4]. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
43 CFR 2461.2 - Classifications.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Classifications. 2461.2 Section 2461.2..., DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.2 Classifications. Not less than 60 days after publication of the...
43 CFR 2461.2 - Classifications.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Classifications. 2461.2 Section 2461.2..., DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.2 Classifications. Not less than 60 days after publication of the...
43 CFR 2461.2 - Classifications.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Classifications. 2461.2 Section 2461.2..., DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.2 Classifications. Not less than 60 days after publication of the...
43 CFR 2461.2 - Classifications.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Classifications. 2461.2 Section 2461.2..., DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.2 Classifications. Not less than 60 days after publication of the...
Accurate Vehicle Location System Using RFID, an Internet of Things Approach.
Prinsloo, Jaco; Malekian, Reza
2016-06-04
Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.
Accurate Vehicle Location System Using RFID, an Internet of Things Approach
Prinsloo, Jaco; Malekian, Reza
2016-01-01
Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved. PMID:27271638
Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.
2016-01-01
Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171
Di Spiezio Sardo, A.; Campo, R.; Gordts, S.; Spinelli, M.; Cosimato, C.; Tanos, V.; Brucker, S.; Li, T. C.; Gergolet, M.; De Angelis, C.; Gianaroli, L.; Grimbizis, G.
2015-01-01
STUDY QUESTION How comprehensive is the recently published European Society of Human Reproduction and Embryology (ESHRE)/European Society for Gynaecological Endoscopy (ESGE) classification system of female genital anomalies? SUMMARY ANSWER The ESHRE/ESGE classification provides a comprehensive description and categorization of almost all of the currently known anomalies that could not be classified properly with the American Fertility Society (AFS) system. WHAT IS KNOWN ALREADY Until now, the more accepted classification system, namely that of the AFS, is associated with serious limitations in effective categorization of female genital anomalies. Many cases published in the literature could not be properly classified using the AFS system, yet a clear and accurate classification is a prerequisite for treatment. STUDY DESIGN, SIZE AND DURATION The CONUTA (CONgenital UTerine Anomalies) ESHRE/ESGE group conducted a systematic review of the literature to examine if those types of anomalies that could not be properly classified with the AFS system could be effectively classified with the use of the new ESHRE/ESGE system. An electronic literature search through Medline, Embase and Cochrane library was carried out from January 1988 to January 2014. Three participants independently screened, selected articles of potential interest and finally extracted data from all the included studies. Any disagreement was discussed and resolved after consultation with a fourth reviewer and the results were assessed independently and approved by all members of the CONUTA group. PARTICIPANTS/MATERIALS, SETTING, METHODS Among the 143 articles assessed in detail, 120 were finally selected reporting 140 cases that could not properly fit into a specific class of the AFS system. Those 140 cases were clustered in 39 different types of anomalies. MAIN RESULTS AND THE ROLE OF CHANCE The congenital anomaly involved a single organ in 12 (30.8%) out of the 39 types of anomalies, while multiple organs
Thompson, R.S.; Shafer, S.L.; Anderson, K.H.; Strickland, L.E.; Pelltier, R.T.; Bartlein, P.J.; Kerwin, M.W.
2005-01-01
Ecoregion classification systems are increasingly used for policy and management decisions, particularly among conservation and natural resource managers. A number of ecoregion classification systems are currently available, with each system defining ecoregions using different classification methods and different types of data. As a result, each classification system describes a unique set of ecoregions. To help potential users choose the most appropriate ecoregion system for their particular application, we used three latitudinal transects across North America to compare the boundaries and environmental characteristics of three ecoregion classification systems [Ku??chler, World Wildlife Fund (WWF), and Bailey]. A variety of variables were used to evaluate the three systems, including woody plant species richness, normalized difference in vegetation index (NDVI), and bioclimatic variables (e.g., mean temperature of the coldest month) along each transect. Our results are dominated by geographic patterns in temperature, which are generally aligned north-south, and in moisture, which are generally aligned east-west. In the west, the dramatic changes in physiography, climate, and vegetation impose stronger controls on ecoregion boundaries than in the east. The Ku??chler system has the greatest number of ecoregions on all three transects, but does not necessarily have the highest degree of internal consistency within its ecoregions with regard to the bioclimatic and species richness data. In general, the WWF system appears to track climatic and floristic variables the best of the three systems, but not in all regions on all transects. ?? 2005 Springer Science+Business Media, Inc.
Robert E. Keane; Jason M. Herynk; Chris Toney; Shawn P. Urbanski; Duncan C. Lutes; Roger D. Ottmar
2015-01-01
Fuel classifications are integral tools in fire management and planning because they are used as inputs to fire behavior and effects simulation models. Fuel Loading Models (FLMs) and Fuel Characteristic Classification System (FCCSs) fuelbeds are the most popular classifications used throughout wildland fire science and management, but they have yet to be thoroughly...
Hanif, Madiha; Hafeez, Abdul; Suleman, Yusuf; Mustafa Rafique, M; Butt, Ali R; Iqbal, Samir M
2016-10-01
Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Accurate Classification of RNA Structures Using Topological Fingerprints
Li, Kejie; Gribskov, Michael
2016-01-01
While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC > 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint. PMID:27755571
43 CFR 2461.1 - Proposed classifications.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Proposed classifications. 2461.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.1 Proposed classifications. (a) Proposed classifications will...
43 CFR 2461.4 - Changing classifications.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...
43 CFR 2461.1 - Proposed classifications.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Proposed classifications. 2461.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.1 Proposed classifications. (a) Proposed classifications will...
43 CFR 2461.4 - Changing classifications.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...
43 CFR 2461.1 - Proposed classifications.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Proposed classifications. 2461.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.1 Proposed classifications. (a) Proposed classifications will...
43 CFR 2461.4 - Changing classifications.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...
43 CFR 2461.1 - Proposed classifications.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Proposed classifications. 2461.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.1 Proposed classifications. (a) Proposed classifications will...
43 CFR 2461.4 - Changing classifications.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...
Government Classification: An Overview.
ERIC Educational Resources Information Center
Brown, Karen M.
Classification of government documents (confidential, secret, top secret) is a system used by the executive branch to, in part, protect national security and foreign policy interests. The systematic use of classification markings with precise definitions was established during World War I, and since 1936 major changes in classification have…
Movement imagery classification in EMOTIV cap based system by Naïve Bayes.
Stock, Vinicius N; Balbinot, Alexandre
2016-08-01
Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters. The EPOC electrodes cap is used for EEG acquisition, in two test subjects, for two distinct trial formats. The channels picked are FC5, FC6, P7 and P8 of the 10-20 system, and a discussion about the differences of using C3, C4, P3 and P4 positions is proposed. Dataset 3 of the BCI Competition II is also analyzed using the implemented algorithms. The maximum classification results for the proposed experiment and for the BCI Competition dataset were, respectively, 79% and 85% The conclusion of this study is that the picked positions for electrodes may be applied for BCI systems with satisfactory classification rates.
Emami Riedmaier, Arian; Lindley, David J; Hall, Jeffrey A; Castleberry, Steven; Slade, Russell T; Stuart, Patricia; Carr, Robert A; Borchardt, Thomas B; Bow, Daniel A J; Nijsen, Marjoleen
2018-01-01
Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
Application of LANDSAT system for improving methodology for inventory and classification of wetlands
NASA Technical Reports Server (NTRS)
Gilmer, D. S. (Principal Investigator)
1976-01-01
The author has identified the following significant results. A newly developed software system for generating statistics on surface water features was tested using LANDSAT data acquired previous to 1975. This software test provided a satisfactory evaluation of the system and also allowed expansion of data base on prairie water features. The software system recognizes water on the basis of a classification algorithm. This classification is accomplished by level thresholding a single near infrared data channel. After each pixel is classified as water or nonwater, the software system then recognizes ponds or lakes as sets of contiguous pixels or as single isolated pixels in the case of very small ponds. Pixels are considered to be contiguous if they are adjacent between successive scan lines. After delineating each water feature, the software system then assigns the feature a position based upon a geographic grid system and calculates the feature's planimetric area, its perimeter, and a parameter known as the shape factor.
Inferior turbinate classification system, grades 1 to 4: development and validation study.
Camacho, Macario; Zaghi, Soroush; Certal, Victor; Abdullatif, Jose; Means, Casey; Acevedo, Jason; Liu, Stanley; Brietzke, Scott E; Kushida, Clete A; Capasso, Robson
2015-02-01
To develop a validated inferior turbinate grading scale. Development and validation study. Phase 1 development (alpha test) consisted of a proposal of 10 different inferior turbinate grading scales (>1,000 clinic patients). Phase 2 validation (beta test) utilized 10 providers grading 27 standardized endoscopic photos of inferior turbinates using two different classification systems. Phase 3 validation (pilot study) consisted of 100 live consecutive clinic patients (n = 200 inferior turbinates) who were each prospectively graded by 18 different combinations of two independent raters, and grading was repeated by each of the same two raters, two separate times for each patient. In the development phase, 25% (grades 1-4) and 33% (grades 1-4) were the most useful systems. In the validation phase, the 25% classification system was found to be the best balance between potential clinical utility and ability to grade; the photo grading demonstrated a Cohen's kappa (κ) = 0.4671 ± 0.0082 (moderate inter-rater agreement). Live-patient grading with the 25% classification system demonstrated an overall inter-rater reliability of 71.5% (95% confidence interval [CI]: 64.8-77.3), with overall substantial agreement (κ = 0.704 ± 0.028). Intrarater reliability was 91.5% (95% CI: 88.7-94.3). Distribution for the 200 inferior turbinates was as follows: 25% quartile = grade 1, 50% quartile (median) = grade 2, 75% quartile = grade 3, and 90% quartile = grade 4. Mean turbinate size was 2.22 (95% CI: 2.07-2.34; standard deviation 1.02). Categorical κ was as follows: grade 1, 0.8541 ± 0.0289; grade 2, 0.7310 ± 0.0289; grade 3, 0.6997 ± 0.0289, and grade 4, 0.7760 ± 0.0289. The 25% (grades 1-4) inferior turbinate classification system is a validated grading scale with high intrarater and inter-rater reliability. This system can facilitate future research by tracking the effect of interventions on inferior turbinates. 2c. © 2014 The
Burchett, John; Shankar, Mohan; Hamza, A Ben; Guenther, Bob D; Pitsianis, Nikos; Brady, David J
2006-05-01
We use pyroelectric detectors that are differential in nature to detect motion in humans by their heat emissions. Coded Fresnel lens arrays create boundaries that help to localize humans in space as well as to classify the nature of their motion. We design and implement a low-cost biometric tracking system by using off-the-shelf components. We demonstrate two classification methods by using data gathered from sensor clusters of dual-element pyroelectric detectors with coded Fresnel lens arrays. We propose two algorithms for person identification, a more generalized spectral clustering method and a more rigorous example that uses principal component regression to perform a blind classification.
A dental vision system for accurate 3D tooth modeling.
Zhang, Li; Alemzadeh, K
2006-01-01
This paper describes an active vision system based reverse engineering approach to extract the three-dimensional (3D) geometric information from dental teeth and transfer this information into Computer-Aided Design/Computer-Aided Manufacture (CAD/CAM) systems to improve the accuracy of 3D teeth models and at the same time improve the quality of the construction units to help patient care. The vision system involves the development of a dental vision rig, edge detection, boundary tracing and fast & accurate 3D modeling from a sequence of sliced silhouettes of physical models. The rig is designed using engineering design methods such as a concept selection matrix and weighted objectives evaluation chart. Reconstruction results and accuracy evaluation are presented on digitizing different teeth models.
5 CFR 9901.231 - Conversion of positions and employees to NSPS classification system.
Code of Federal Regulations, 2010 CFR
2010-01-01
... HUMAN RESOURCES MANAGEMENT AND LABOR RELATIONS SYSTEMS (DEPARTMENT OF DEFENSE-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF DEFENSE NATIONAL SECURITY PERSONNEL SYSTEM (NSPS) Classification Transitional... employee's career group, pay schedule, and band upon conversion. (d) Grade retention prior to conversion...
ERIC Educational Resources Information Center
Wu, C. K.; Wu, K. S.
Since the 17th century, Chinese lexicography has been dominated by a character classification system divided into 214 radical groups. The proposed initial three-stroke system would eliminate the need to select (or guess) the proper radical and count strokes. The aim of the system is to facilitate the use of dictionaries and provide the student…
A new classification system for lesbians: the Dyke Diagnostic Manual.
Eliason, Michele J
2010-01-01
There has been a long-standing need for a diagnostic manual that documents the unique pathological behaviors of lesbians. The Dyke Diagnostic Manual (DDM) is meant to supplement mainstream classification systems used to identify problematic behaviors in heterosexuals. This article presents thirteen uniquely lesbian conditions that are nowhere to be found in heterosexist diagnostic systems. The DDM may help to reduce the pain and suffering found in many lesbian relationships where one or both partners are afflicted.
NASA Astrophysics Data System (ADS)
Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.
2016-03-01
The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care
NASA Astrophysics Data System (ADS)
Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng
2016-09-01
It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image
Soleymani, Zahra; Joveini, Ghodsiye; Baghestani, Ahmad Reza
2015-03-01
This study developed a Farsi language Communication Function Classification System and then tested its reliability and validity. Communication Function Classification System is designed to classify the communication functions of individuals with cerebral palsy. Up until now, there has been no instrument for assessment of this communication function in Iran. The English Communication Function Classification System was translated into Farsi and cross-culturally modified by a panel of experts. Professionals and parents then assessed the content validity of the modified version. A backtranslation of the Farsi version was confirmed by the developer of the English Communication Function Classification System. Face validity was assessed by therapists and parents of 10 patients. The Farsi Communication Function Classification System was administered to 152 individuals with cerebral palsy (age, 2 to 18 years; median age, 10 years; mean age, 9.9 years; standard deviation, 4.3 years). Inter-rater reliability was analyzed between parents, occupational therapists, and speech and language pathologists. The test-retest reliability was assessed for 75 patients with a 14 day interval between tests. The inter-rater reliability of the Communication Function Classification System was 0.81 between speech and language pathologists and occupational therapists, 0.74 between parents and occupational therapists, and 0.88 between parents and speech and language pathologists. The test-retest reliability was 0.96 for occupational therapists, 0.98 for speech and language pathologists, and 0.94 for parents. The findings suggest that the Farsi version of Communication Function Classification System is a reliable and valid measure that can be used in clinical settings to assess communication function in patients with cerebral palsy. Copyright © 2015 Elsevier Inc. All rights reserved.
Automated classification of Acid Rock Drainage potential from Corescan drill core imagery
NASA Astrophysics Data System (ADS)
Cracknell, M. J.; Jackson, L.; Parbhakar-Fox, A.; Savinova, K.
2017-12-01
Classification of the acid forming potential of waste rock is important for managing environmental hazards associated with mining operations. Current methods for the classification of acid rock drainage (ARD) potential usually involve labour intensive and subjective assessment of drill core and/or hand specimens. Manual methods are subject to operator bias, human error and the amount of material that can be assessed within a given time frame is limited. The automated classification of ARD potential documented here is based on the ARD Index developed by Parbhakar-Fox et al. (2011). This ARD Index involves the combination of five indicators: A - sulphide content; B - sulphide alteration; C - sulphide morphology; D - primary neutraliser content; and E - sulphide mineral association. Several components of the ARD Index require accurate identification of sulphide minerals. This is achieved by classifying Corescan Red-Green-Blue true colour images into the presence or absence of sulphide minerals using supervised classification. Subsequently, sulphide classification images are processed and combined with Corescan SWIR-based mineral classifications to obtain information on sulphide content, indices representing sulphide textures (disseminated versus massive and degree of veining), and spatially associated minerals. This information is combined to calculate ARD Index indicator values that feed into the classification of ARD potential. Automated ARD potential classifications of drill core samples associated with a porphyry Cu-Au deposit are compared to manually derived classifications and those obtained by standard static geochemical testing and X-ray diffractometry analyses. Results indicate a high degree of similarity between automated and manual ARD potential classifications. Major differences between approaches are observed in sulphide and neutraliser mineral percentages, likely due to the subjective nature of manual estimates of mineral content. The automated approach
Toward an endovascular internal carotid artery classification system.
Shapiro, M; Becske, T; Riina, H A; Raz, E; Zumofen, D; Jafar, J J; Huang, P P; Nelson, P K
2014-02-01
Does the world need another ICA classification scheme? We believe so. The purpose of proposed angiography-driven classification is to optimize description of the carotid artery from the endovascular perspective. A review of existing, predominantly surgically-driven classifications is performed, and a new scheme, based on the study of NYU aneurysm angiographic and cross-sectional databases is proposed. Seven segments - cervical, petrous, cavernous, paraophthlamic, posterior communicating, choroidal, and terminus - are named. This nomenclature recognizes intrinsic uncertainty in precise angiographic and cross-sectional localization of aneurysms adjacent to the dural rings, regarding all lesions distal to the cavernous segment as potentially intradural. Rather than subdividing various transitional, ophthalmic, and hypophyseal aneurysm subtypes, as necessitated by their varied surgical approaches and risks, the proposed classification emphasizes their common endovascular treatment features, while recognizing that many complex, trans-segmental, and fusiform aneurysms not readily classifiable into presently available, saccular aneurysm-driven schemes, are being increasingly addressed by endovascular means. We believe this classification may find utility in standardizing nomenclature for outcome tracking, treatment trials and physician communication.
Cho, Chul-Hyun; Oh, Joo Han; Jung, Gu-Hee; Moon, Gi-Hyuk; Rhyou, In Hyeok; Yoon, Jong Pil; Lee, Ho Min
2015-10-01
As there is substantial variation in the classification and diagnosis of lateral clavicle fractures, proper management can be challenging. Although the Neer classification system modified by Craig has been widely used, no study has assessed its validity through inter- and intrarater agreement. To determine the inter- and intrarater agreement of the modified Neer classification system and associated treatment choice for lateral clavicle fractures and to assess whether 3-dimensional computed tomography (3D CT) improves the level of agreement. Cohort study (diagnosis); Level of evidence, 3. Nine experienced shoulder specialists and 9 orthopaedic fellows evaluated 52 patients with lateral clavicle fractures, completing fracture typing according to the modified Neer classification system and selecting a treatment choice for each case. Web-based assessment was performed using plain radiographs only, followed by the addition of 3D CT images 2 weeks later. This procedure was repeated 4 weeks later. Fleiss κ values were calculated to estimate the inter- and intrarater agreement. Based on plain radiographs only, the inter- and intrarater agreement of the modified Neer classification system was regarded as fair (κ = 0.344) and moderate (κ = 0.496), respectively; the inter- and intrarater agreement of treatment choice was both regarded as moderate (κ = 0.465 and 0.555, respectively). Based on the plain radiographs and 3D CT images, the inter- and intrarater agreement of the classification system was regarded as fair (κ = 0.317) and moderate (κ = 0.508), respectively; the inter- and intrarater agreement of treatment choice was regarded as moderate (κ = 0.463) and substantial (κ = 0.623), respectively. There were no significant differences in the level of agreement between the plain radiographs only and plain radiographs plus 3D CT images for any κ values (all P > .05). The level of interrater agreement of the modified Neer classification system for lateral clavicle
Audigé, Laurent; Cornelius, Carl-Peter; Ieva, Antonio Di; Prein, Joachim
2014-01-01
Validated trauma classification systems are the sole means to provide the basis for reliable documentation and evaluation of patient care, which will open the gateway to evidence-based procedures and healthcare in the coming years. With the support of AO Investigation and Documentation, a classification group was established to develop and evaluate a comprehensive classification system for craniomaxillofacial (CMF) fractures. Blueprints for fracture classification in the major constituents of the human skull were drafted and then evaluated by a multispecialty group of experienced CMF surgeons and a radiologist in a structured process during iterative agreement sessions. At each session, surgeons independently classified the radiological imaging of up to 150 consecutive cases with CMF fractures. During subsequent review meetings, all discrepancies in the classification outcome were critically appraised for clarification and improvement until consensus was reached. The resulting CMF classification system is structured in a hierarchical fashion with three levels of increasing complexity. The most elementary level 1 simply distinguishes four fracture locations within the skull: mandible (code 91), midface (code 92), skull base (code 93), and cranial vault (code 94). Levels 2 and 3 focus on further defining the fracture locations and for fracture morphology, achieving an almost individual mapping of the fracture pattern. This introductory article describes the rationale for the comprehensive AO CMF classification system, discusses the methodological framework, and provides insight into the experiences and interactions during the evaluation process within the core groups. The details of this system in terms of anatomy and levels are presented in a series of focused tutorials illustrated with case examples in this special issue of the Journal. PMID:25489387
Audigé, Laurent; Cornelius, Carl-Peter; Di Ieva, Antonio; Prein, Joachim
2014-12-01
Validated trauma classification systems are the sole means to provide the basis for reliable documentation and evaluation of patient care, which will open the gateway to evidence-based procedures and healthcare in the coming years. With the support of AO Investigation and Documentation, a classification group was established to develop and evaluate a comprehensive classification system for craniomaxillofacial (CMF) fractures. Blueprints for fracture classification in the major constituents of the human skull were drafted and then evaluated by a multispecialty group of experienced CMF surgeons and a radiologist in a structured process during iterative agreement sessions. At each session, surgeons independently classified the radiological imaging of up to 150 consecutive cases with CMF fractures. During subsequent review meetings, all discrepancies in the classification outcome were critically appraised for clarification and improvement until consensus was reached. The resulting CMF classification system is structured in a hierarchical fashion with three levels of increasing complexity. The most elementary level 1 simply distinguishes four fracture locations within the skull: mandible (code 91), midface (code 92), skull base (code 93), and cranial vault (code 94). Levels 2 and 3 focus on further defining the fracture locations and for fracture morphology, achieving an almost individual mapping of the fracture pattern. This introductory article describes the rationale for the comprehensive AO CMF classification system, discusses the methodological framework, and provides insight into the experiences and interactions during the evaluation process within the core groups. The details of this system in terms of anatomy and levels are presented in a series of focused tutorials illustrated with case examples in this special issue of the Journal.
Design and update of a classification system: the UCSD map of science.
Börner, Katy; Klavans, Richard; Patek, Michael; Zoss, Angela M; Biberstine, Joseph R; Light, Robert P; Larivière, Vincent; Boyack, Kevin W
2012-01-01
Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier's Scopus (about 15,000 source titles, 2001-2005) and Thomson Reuters' Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001-2004)-about 16,000 unique source titles. The updated map and classification adds six years (2005-2010) of WoS data and three years (2006-2008) from Scopus to the existing category structure-increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others.
Design and Update of a Classification System: The UCSD Map of Science
Börner, Katy; Klavans, Richard; Patek, Michael; Zoss, Angela M.; Biberstine, Joseph R.; Light, Robert P.; Larivière, Vincent; Boyack, Kevin W.
2012-01-01
Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier’s Scopus (about 15,000 source titles, 2001–2005) and Thomson Reuters’ Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001–2004)–about 16,000 unique source titles. The updated map and classification adds six years (2005–2010) of WoS data and three years (2006–2008) from Scopus to the existing category structure–increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others. PMID:22808037
Goshvarpour, Ateke; Goshvarpour, Atefeh
2018-04-30
Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.
Classifying environmental pollutants: Part 3. External validation of the classification system.
Verhaar, H J; Solbé, J; Speksnijder, J; van Leeuwen, C J; Hermens, J L
2000-04-01
In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.
5 CFR 9701.221 - Classification requirements.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Classification requirements. 9701.221 Section 9701.221 Administrative Personnel DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT... HUMAN RESOURCES MANAGEMENT SYSTEM Classification Classification Process § 9701.221 Classification...
Successional stage of biological soil crusts: an accurate indicator of ecohydrological condition
Belnap, Jayne; Wilcox, Bradford P.; Van Scoyoc, Matthew V.; Phillips, Susan L.
2013-01-01
Biological soil crusts are a key component of many dryland ecosystems. Following disturbance, biological soil crusts will recover in stages. Recently, a simple classification of these stages has been developed, largely on the basis of external features of the crusts, which reflects their level of development (LOD). The classification system has six LOD classes, from low (1) to high (6). To determine whether the LOD of a crust is related to its ecohydrological function, we used rainfall simulation to evaluate differences in infiltration, runoff, and erosion among crusts in the various LODs, across a range of soil depths and with different wetting pre-treatments. We found large differences between the lowest and highest LODs, with runoff and erosion being greatest from the lowest LOD. Under dry antecedent conditions, about 50% of the water applied ran off the lowest LOD plots, whereas less than 10% ran off the plots of the two highest LODs. Similarly, sediment loss was 400 g m-2 from the lowest LOD and almost zero from the higher LODs. We scaled up the results from these simulations using the Rangeland Hydrology and Erosion Model. Modelling results indicate that erosion increases dramatically as slope length and gradient increase, especially beyond the threshold values of 10 m for slope length and 10% for slope gradient. Our findings confirm that the LOD classification is a quick, easy, nondestructive, and accurate index of hydrological condition and should be incorporated in field and modelling assessments of ecosystem health.
The Alaska vegetation classification.
L.A. Viereck; C.T. Dyrness; A.R. Batten; K.J. Wenzlick
1992-01-01
The Alaska vegetation classification presented here is a comprehensive, statewide system that has been under development since 1976. The classification is based, as much as possible, on the characteristics of the vegetation itself and is designed to categorize existing vegetation, not potential vegetation. A hierarchical system with five levels of resolution is used...
Highly accurate and fast optical penetration-based silkworm gender separation system
NASA Astrophysics Data System (ADS)
Kamtongdee, Chakkrit; Sumriddetchkajorn, Sarun; Chanhorm, Sataporn
2015-07-01
Based on our research work in the last five years, this paper highlights our innovative optical sensing system that can identify and separate silkworm gender highly suitable for sericulture industry. The key idea relies on our proposed optical penetration concepts and once combined with simple image processing operations leads to high accuracy in identifying of silkworm gender. Inside the system, there are electronic and mechanical parts that assist in controlling the overall system operation, processing the optical signal, and separating the female from male silkworm pupae. With current system performance, we achieve a very highly accurate more than 95% in identifying gender of silkworm pupae with an average system operational speed of 30 silkworm pupae/minute. Three of our systems are already in operation at Thailand's Queen Sirikit Sericulture Centers.
Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps
NASA Astrophysics Data System (ADS)
Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.
2017-07-01
Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed
Tissue classification and segmentation of pressure injuries using convolutional neural networks.
Zahia, Sofia; Sierra-Sosa, Daniel; Garcia-Zapirain, Begonya; Elmaghraby, Adel
2018-06-01
This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. Our system has been proven to make recognition of complicated structures in biomedical images feasible. Copyright © 2018 Elsevier B.V. All rights reserved.
Vehicle classification system : FHWA perspective
DOT National Transportation Integrated Search
2000-08-01
Issues related to vehicle classification in light of the Federal Highway Administration (FHWA) Traffic Monitoring Guide (TMG) revision will be discussed. The discussion will look at the expressed needs of the data users and the associated impacts on ...
Identification of an Efficient Gene Expression Panel for Glioblastoma Classification
Zelaya, Ivette; Laks, Dan R.; Zhao, Yining; Kawaguchi, Riki; Gao, Fuying; Kornblum, Harley I.; Coppola, Giovanni
2016-01-01
We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu. PMID:27855170
HAMAP in 2013, new developments in the protein family classification and annotation system
Pedruzzi, Ivo; Rivoire, Catherine; Auchincloss, Andrea H.; Coudert, Elisabeth; Keller, Guillaume; de Castro, Edouard; Baratin, Delphine; Cuche, Béatrice A.; Bougueleret, Lydie; Poux, Sylvain; Redaschi, Nicole; Xenarios, Ioannis; Bridge, Alan
2013-01-01
HAMAP (High-quality Automated and Manual Annotation of Proteins—available at http://hamap.expasy.org/) is a system for the classification and annotation of protein sequences. It consists of a collection of manually curated family profiles for protein classification, and associated annotation rules that specify annotations that apply to family members. HAMAP was originally developed to support the manual curation of UniProtKB/Swiss-Prot records describing microbial proteins. Here we describe new developments in HAMAP, including the extension of HAMAP to eukaryotic proteins, the use of HAMAP in the automated annotation of UniProtKB/TrEMBL, providing high-quality annotation for millions of protein sequences, and the future integration of HAMAP into a unified system for UniProtKB annotation, UniRule. HAMAP is continuously updated by expert curators with new family profiles and annotation rules as new protein families are characterized. The collection of HAMAP family classification profiles and annotation rules can be browsed and viewed on the HAMAP website, which also provides an interface to scan user sequences against HAMAP profiles. PMID:23193261
Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System
NASA Technical Reports Server (NTRS)
Penaloza, Mauel A.; Welch, Ronald M.
1996-01-01
Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.
Study of USGS/NASA land use classification system. [computer analysis from LANDSAT data
NASA Technical Reports Server (NTRS)
Spann, G. W.
1975-01-01
The results of a computer mapping project using LANDSAT data and the USGS/NASA land use classification system are summarized. During the computer mapping portion of the project, accuracies of 67 percent to 79 percent were achieved using Level II of the classification system and a 4,000 acre test site centered on Douglasville, Georgia. Analysis of response to a questionaire circulated to actual and potential LANDSAT data users reveals several important findings: (1) there is a substantial desire for additional information related to LANDSAT capabilities; (2) a majority of the respondents feel computer mapping from LANDSAT data could aid present or future projects; and (3) the costs of computer mapping are substantially less than those of other methods.
Crowdsourced validation of a machine-learning classification system for autism and ADHD
Duda, M; Haber, N; Daniels, J; Wall, D P
2017-01-01
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. Currently, there is no screening test designed to differentiate between the two disorders, and with waiting times from initial suspicion to diagnosis upwards of a year, methods to quickly and accurately assess risk for these and other developmental disorders are desperately needed. In a previous study, we found that four machine-learning algorithms were able to accurately (area under the curve (AUC)>0.96) distinguish ASD from ADHD using only a small subset of items from the Social Responsiveness Scale (SRS). Here, we expand upon our prior work by including a novel crowdsourced data set of responses to our predefined top 15 SRS-derived questions from parents of children with ASD (n=248) or ADHD (n=174) to improve our model’s capability to generalize to new, ‘real-world’ data. By mixing these novel survey data with our initial archival sample (n=3417) and performing repeated cross-validation with subsampling, we created a classification algorithm that performs with AUC=0.89±0.01 using only 15 questions. PMID:28509905
Crowdsourced validation of a machine-learning classification system for autism and ADHD.
Duda, M; Haber, N; Daniels, J; Wall, D P
2017-05-16
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. Currently, there is no screening test designed to differentiate between the two disorders, and with waiting times from initial suspicion to diagnosis upwards of a year, methods to quickly and accurately assess risk for these and other developmental disorders are desperately needed. In a previous study, we found that four machine-learning algorithms were able to accurately (area under the curve (AUC)>0.96) distinguish ASD from ADHD using only a small subset of items from the Social Responsiveness Scale (SRS). Here, we expand upon our prior work by including a novel crowdsourced data set of responses to our predefined top 15 SRS-derived questions from parents of children with ASD (n=248) or ADHD (n=174) to improve our model's capability to generalize to new, 'real-world' data. By mixing these novel survey data with our initial archival sample (n=3417) and performing repeated cross-validation with subsampling, we created a classification algorithm that performs with AUC=0.89±0.01 using only 15 questions.
Maxillectomy defects: a suggested classification scheme.
Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F
2013-06-01
The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.
On-board multispectral classification study
NASA Technical Reports Server (NTRS)
Ewalt, D.
1979-01-01
The factors relating to onboard multispectral classification were investigated. The functions implemented in ground-based processing systems for current Earth observation sensors were reviewed. The Multispectral Scanner, Thematic Mapper, Return Beam Vidicon, and Heat Capacity Mapper were studied. The concept of classification was reviewed and extended from the ground-based image processing functions to an onboard system capable of multispectral classification. Eight different onboard configurations, each with varying amounts of ground-spacecraft interaction, were evaluated. Each configuration was evaluated in terms of turnaround time, onboard processing and storage requirements, geometric and classification accuracy, onboard complexity, and ancillary data required from the ground.