Automated system function allocation and display format: Task information processing requirements

NASA Technical Reports Server (NTRS)

Czerwinski, Mary P.

1993-01-01

An important consideration when designing the interface to an intelligent system concerns function allocation between the system and the user. The display of information could be held constant, or 'fixed', leaving the user with the task of searching through all of the available information, integrating it, and classifying the data into a known system state. On the other hand, the system, based on its own intelligent diagnosis, could display only relevant information in order to reduce the user's search set. The user would still be left the task of perceiving and integrating the data and classifying it into the appropriate system state. Finally, the system could display the patterns of data. In this scenario, the task of integrating the data is carried out by the system, and the user's information processing load is reduced, leaving only the tasks of perception and classification of the patterns of data. Humans are especially adept at this form of display processing. Although others have examined the relative effectiveness of alphanumeric and graphical display formats, it is interesting to reexamine this issue together with the function allocation problem. Currently, Johnson Space Center is the test site for an intelligent Thermal Control System (TCS), TEXSYS, being tested for use with Space Station Freedom. Expert TCS engineers, as well as novices, were asked to classify several displays of TEXSYS data into various system states (including nominal and anomalous states). Three different display formats were used: fixed, subset, and graphical. The hypothesis tested was that the graphical displays would provide for fewer errors and faster classification times by both experts and novices, regardless of the kind of system state represented within the display. The subset displays were hypothesized to be the second most effective display format/function allocation condition, based on the fact that the search set is reduced in these displays. Both the subset and the graphic display conditions were hypothesized to be processed more efficiently than the fixed display conditions.

The application of artificial intelligence for the identification of the maceral groups and mineral components of coal

NASA Astrophysics Data System (ADS)

Mlynarczuk, Mariusz; Skiba, Marta

2017-06-01

The correct and consistent identification of the petrographic properties of coal is an important issue for researchers in the fields of mining and geology. As part of the study described in this paper, investigations concerning the application of artificial intelligence methods for the identification of the aforementioned characteristics were carried out. The methods in question were used to identify the maceral groups of coal, i.e. vitrinite, inertinite, and liptinite. Additionally, an attempt was made to identify some non-organic minerals. The analyses were performed using pattern recognition techniques (NN, kNN), as well as artificial neural network techniques (a multilayer perceptron - MLP). The classification process was carried out using microscopy images of polished sections of coals. A multidimensional feature space was defined, which made it possible to classify the discussed structures automatically, based on the methods of pattern recognition and algorithms of the artificial neural networks. Also, from the study we assessed the impact of the parameters for which the applied methods proved effective upon the final outcome of the classification procedure. The result of the analyses was a high percentage (over 97%) of correct classifications of maceral groups and mineral components. The paper discusses also an attempt to analyze particular macerals of the inertinite group. It was demonstrated that using artificial neural networks to this end makes it possible to classify the macerals properly in over 91% of cases. Thus, it was proved that artificial intelligence methods can be successfully applied for the identification of selected petrographic features of coal.

Assessing visual attention using eye tracking sensors in intelligent cognitive therapies based on serious games.

PubMed

Frutos-Pascual, Maite; Garcia-Zapirain, Begonya

2015-05-12

This study examines the use of eye tracking sensors as a means to identify children's behavior in attention-enhancement therapies. For this purpose, a set of data collected from 32 children with different attention skills is analyzed during their interaction with a set of puzzle games. The authors of this study hypothesize that participants with better performance may have quantifiably different eye-movement patterns from users with poorer results. The use of eye trackers outside the research community may help to extend their potential with available intelligent therapies, bringing state-of-the-art technologies to users. The use of gaze data constitutes a new information source in intelligent therapies that may help to build new approaches that are fully-customized to final users' needs. This may be achieved by implementing machine learning algorithms for classification. The initial study of the dataset has proven a 0.88 (±0.11) classification accuracy with a random forest classifier, using cross-validation and hierarchical tree-based feature selection. Further approaches need to be examined in order to establish more detailed attention behaviors and patterns among children with and without attention problems.

Assessing Visual Attention Using Eye Tracking Sensors in Intelligent Cognitive Therapies Based on Serious Games

PubMed Central

Frutos-Pascual, Maite; Garcia-Zapirain, Begonya

2015-01-01

This study examines the use of eye tracking sensors as a means to identify children's behavior in attention-enhancement therapies. For this purpose, a set of data collected from 32 children with different attention skills is analyzed during their interaction with a set of puzzle games. The authors of this study hypothesize that participants with better performance may have quantifiably different eye-movement patterns from users with poorer results. The use of eye trackers outside the research community may help to extend their potential with available intelligent therapies, bringing state-of-the-art technologies to users. The use of gaze data constitutes a new information source in intelligent therapies that may help to build new approaches that are fully-customized to final users' needs. This may be achieved by implementing machine learning algorithms for classification. The initial study of the dataset has proven a 0.88 (±0.11) classification accuracy with a random forest classifier, using cross-validation and hierarchical tree-based feature selection. Further approaches need to be examined in order to establish more detailed attention behaviors and patterns among children with and without attention problems. PMID:25985158

Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

Integrating forensic information in a crime intelligence database.

PubMed

Rossy, Quentin; Ioset, Sylvain; Dessimoz, Damien; Ribaux, Olivier

2013-07-10

Since 2008, intelligence units of six states of the western part of Switzerland have been sharing a common database for the analysis of high volume crimes. On a daily basis, events reported to the police are analysed, filtered and classified to detect crime repetitions and interpret the crime environment. Several forensic outcomes are integrated in the system such as matches of traces with persons, and links between scenes detected by the comparison of forensic case data. Systematic procedures have been settled to integrate links assumed mainly through DNA profiles, shoemarks patterns and images. A statistical outlook on a retrospective dataset of series from 2009 to 2011 of the database informs for instance on the number of repetition detected or confirmed and increased by forensic case data. Time needed to obtain forensic intelligence in regard with the type of marks treated, is seen as a critical issue. Furthermore, the underlying integration process of forensic intelligence into the crime intelligence database raised several difficulties in regards of the acquisition of data and the models used in the forensic databases. Solutions found and adopted operational procedures are described and discussed. This process form the basis to many other researches aimed at developing forensic intelligence models. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Heuristic pattern correction scheme using adaptively trained generalized regression neural networks.

PubMed

Hoya, T; Chambers, J A

2001-01-01

In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies.

36 CFR § 1260.26 - Who is responsible for issuing special procedures for declassification of records pertaining to...

Code of Federal Regulations, 2013 CFR

2013-07-01

... special procedures for declassification of records pertaining to intelligence activities and intelligence... procedures for declassification of records pertaining to intelligence activities and intelligence sources or... Intelligence is responsible for issuing special procedures for declassification of classified records...

Evolution of the speech intelligibility of prelinguistically deaf children who received a cochlear implant

NASA Astrophysics Data System (ADS)

Bouchard, Marie-Eve; Cohen, Henri; Lenormand, Marie-Therese

2005-04-01

The 2 main objectives of this investigation are (1) to assess the evolution of the speech intelligibility of 12 prelinguistically deaf children implanted between 25 and 78 months of age and (2) to clarify the influence of the age at implantation on the intelligibility. Speech productions videorecorded at 6, 18 and 36 months following surgery during a standardized free play session. Selected syllables were then presented to 40 adults listeners who were asked to identify the vowels or the consonants they heard and to judge the quality of the segments. Perceived vowels were then located in the vocalic space whereas consonants were classified according to voicing, manner and place of articulation. 3 (Groups) ×3 (Times) ANOVA with repeated measures revealed a clear influence of time as well as age at implantation on the acquisition patterns. Speech intelligibility of these implanted children tended to improve as their experience with the device increased. Based on these results, it is proposed that sensory restoration following cochlear implant served as a probe to develop articulatory strategies allowing them to reach the intended acoustico-perceptual target.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination.

PubMed

Jee Keen Raymond, Wong; Illias, Hazlee Azil; Abu Bakar, Ab Halim

2017-01-01

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

77 FR 62222 - Federal Advisory Committee; Defense Intelligence Agency (DIA) Advisory Board; Closed Meeting

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2012-10-12

... DEPARTMENT OF DEFENSE Office of the Secretary Federal Advisory Committee; Defense Intelligence.... The meeting necessarily includes discussions of classified information relating to DIA's intelligence... support of current intelligence operations. Agenda October 29, 2012: [[Page 62223

Enhancing Multiple Intelligences in Children Who Are Blind: A Guide to Improving Curricular Activities

ERIC Educational Resources Information Center

Al-Balushi, Sulaiman Mohammed

2006-01-01

Howard Gardner's Theory of Multiple Intelligences has provided educators with a new view of intelligence. It emphasizes that science, math and language are not the only ways to exhibit intelligence. People exhibit intelligence in many different ways. Each type of intelligence is as valuable as the others. Gardner classifies these intelligences…

77 FR 56192 - Federal Advisory Committee; Defense Intelligence Agency (DIA) Advisory Board; Closed Meeting

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2012-09-12

... DEPARTMENT OF DEFENSE Office of the Secretary Federal Advisory Committee; Defense Intelligence.... The meeting necessarily includes discussions of classified information relating to DIA's intelligence... capabilities in support of current intelligence operations. Agenda September 26, 2012: [[Page 56193

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

PubMed Central

Acampora, Giovanni; Brown, David; Rees, Robert C.

2016-01-01

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582). PMID:27258119

Dysfluencies in the speech of adults with intellectual disabilities and reported speech difficulties.

PubMed

Coppens-Hofman, Marjolein C; Terband, Hayo R; Maassen, Ben A M; van Schrojenstein Lantman-De Valk, Henny M J; van Zaalen-op't Hof, Yvonne; Snik, Ad F M

2013-01-01

In individuals with an intellectual disability, speech dysfluencies are more common than in the general population. In clinical practice, these fluency disorders are generally diagnosed and treated as stuttering rather than cluttering. To characterise the type of dysfluencies in adults with intellectual disabilities and reported speech difficulties with an emphasis on manifestations of stuttering and cluttering, which distinction is to help optimise treatment aimed at improving fluency and intelligibility. The dysfluencies in the spontaneous speech of 28 adults (18-40 years; 16 men) with mild and moderate intellectual disabilities (IQs 40-70), who were characterised as poorly intelligible by their caregivers, were analysed using the speech norms for typically developing adults and children. The speakers were subsequently assigned to different diagnostic categories by relating their resulting dysfluency profiles to mean articulatory rate and articulatory rate variability. Twenty-two (75%) of the participants showed clinically significant dysfluencies, of which 21% were classified as cluttering, 29% as cluttering-stuttering and 25% as clear cluttering at normal articulatory rate. The characteristic pattern of stuttering did not occur. The dysfluencies in the speech of adults with intellectual disabilities and poor intelligibility show patterns that are specific for this population. Together, the results suggest that in this specific group of dysfluent speakers interventions should be aimed at cluttering rather than stuttering. The reader will be able to (1) describe patterns of dysfluencies in the speech of adults with intellectual disabilities that are specific for this group of people, (2) explain that a high rate of dysfluencies in speech is potentially a major determiner of poor intelligibility in adults with ID and (3) describe suggestions for intervention focusing on cluttering rather than stuttering in dysfluent speakers with ID. Copyright © 2013 Elsevier Inc. All rights reserved.

78 FR 295 - Federal Advisory Committee; Defense Intelligence Agency (DIA) Advisory Board; Closed Meeting

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2013-01-03

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2012-06-26

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77 FR 18797 - Federal Advisory Committee; Defense Intelligence Agency (DIA) Advisory Board; Closed Meeting

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2012-03-28

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76 FR 70425 - Federal Advisory Committee; Defense Intelligence Agency (DIA) Advisory Board; Closed Meeting

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2011-11-14

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77 FR 2277 - Federal Advisory Committee; Defense Intelligence Agency (DIA) Advisory Board; Closed Meeting

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2012-01-17

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Technical support for creating an artificial intelligence system for feature extraction and experimental design

NASA Technical Reports Server (NTRS)

Glick, B. J.

1985-01-01

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

36 CFR 1260.26 - Who is responsible for issuing special procedures for declassification of records pertaining to...

Code of Federal Regulations, 2014 CFR

2014-07-01

... issuing special procedures for declassification of records pertaining to intelligence activities and intelligence sources or methods, or of classified cryptologic records in NARA's holdings? 1260.26 Section 1260... procedures for declassification of records pertaining to intelligence activities and intelligence sources or...

36 CFR 1260.26 - Who is responsible for issuing special procedures for declassification of records pertaining to...

Code of Federal Regulations, 2012 CFR

2012-07-01

... issuing special procedures for declassification of records pertaining to intelligence activities and intelligence sources or methods, or of classified cryptologic records in NARA's holdings? 1260.26 Section 1260... procedures for declassification of records pertaining to intelligence activities and intelligence sources or...

An intelligent classifier for prognosis of cardiac resynchronization therapy based on speckle-tracking echocardiograms.

PubMed

Chao, Pei-Kuang; Wang, Chun-Li; Chan, Hsiao-Lung

2012-03-01

Predicting response after cardiac resynchronization therapy (CRT) has been a challenge of cardiologists. About 30% of selected patients based on the standard selection criteria for CRT do not show response after receiving the treatment. This study is aimed to build an intelligent classifier to assist in identifying potential CRT responders by speckle-tracking radial strain based on echocardiograms. The echocardiograms analyzed were acquired before CRT from 26 patients who have received CRT. Sequential forward selection was performed on the parameters obtained by peak-strain timing and phase space reconstruction on speckle-tracking radial strain to find an optimal set of features for creating intelligent classifiers. Support vector machine (SVM) with a linear, quadratic, and polynominal kernel were tested to build classifiers to identify potential responders and non-responders for CRT by selected features. Based on random sub-sampling validation, the best classification performance is correct rate about 95% with 96-97% sensitivity and 93-94% specificity achieved by applying SVM with a quadratic kernel on a set of 3 parameters. The selected 3 parameters contain both indexes extracted by peak-strain timing and phase space reconstruction. An intelligent classifier with an averaged correct rate, sensitivity and specificity above 90% for assisting in identifying CRT responders is built by speckle-tracking radial strain. The classifier can be applied to provide objective suggestion for patient selection of CRT. Copyright © 2011 Elsevier B.V. All rights reserved.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

PubMed Central

2017-01-01

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination. PMID:28085953

32 CFR 1907.02 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Defense Other Regulations Relating to National Defense CENTRAL INTELLIGENCE AGENCY CHALLENGES TO... means the United States Central Intelligence Agency acting through the CIA Information and Privacy... specifically authorized by the Central Intelligence Agency to possess and use on official business classified...

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery.

PubMed

Tian, Shu; Yin, Xu-Cheng; Wang, Zhi-Bin; Zhou, Fang; Hao, Hong-Wei

2015-01-01

The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness.

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery

PubMed Central

Yin, Xu-Cheng; Wang, Zhi-Bin; Zhou, Fang; Hao, Hong-Wei

2015-01-01

The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness. PMID:26693249

Characterization of atypical language activation patterns in focal epilepsy.

PubMed

Berl, Madison M; Zimmaro, Lauren A; Khan, Omar I; Dustin, Irene; Ritzl, Eva; Duke, Elizabeth S; Sepeta, Leigh N; Sato, Susumu; Theodore, William H; Gaillard, William D

2014-01-01

Functional magnetic resonance imaging is sensitive to the variation in language network patterns. Large populations are needed to rigorously assess atypical patterns, which, even in neurological populations, are a minority. We studied 220 patients with focal epilepsy and 118 healthy volunteers who performed an auditory description decision task. We compared a data-driven hierarchical clustering approach to the commonly used a priori laterality index (LI) threshold (LI < 0.20 as atypical) to classify language patterns within frontal and temporal regions of interest. We explored (n = 128) whether IQ varied with different language activation patterns. The rate of atypical language among healthy volunteers (2.5%) and patients (24.5%) agreed with previous studies; however, we found 6 patterns of atypical language: a symmetrically bilateral, 2 unilaterally crossed, and 3 right dominant patterns. There was high agreement between classification methods, yet the cluster analysis revealed novel correlations with clinical features. Beyond the established association of left-handedness, early seizure onset, and vascular pathology with atypical language, cluster analysis identified an association of handedness with frontal lateralization, early seizure onset with temporal lateralization, and left hemisphere focus with a unilateral right pattern. Intelligence quotient was not significantly different among patterns. Language dominance is a continuum; however, our results demonstrate meaningful thresholds in classifying laterality. Atypical language patterns are less frequent but more variable than typical language patterns, posing challenges for accurate presurgical planning. Language dominance should be assessed on a regional rather than hemispheric basis, and clinical characteristics should inform evaluation of atypical language dominance. Reorganization of language is not uniformly detrimental to language functioning. © 2014 American Neurological Association.

32 CFR 319.13 - Specific exemptions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROGRAM DEFENSE INTELLIGENCE AGENCY PRIVACY PROGRAM § 319.13 Specific exemptions. (a) All systems of records maintained by the Director Intelligence Agency shall be exempt from the requirements of 5 U.S.C... has been properly classified. (b) The Director, Defense Intelligence Agency, designated the systems of...

32 CFR 319.13 - Specific exemptions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... PROGRAM DEFENSE INTELLIGENCE AGENCY PRIVACY PROGRAM § 319.13 Specific exemptions. (a) All systems of records maintained by the Director Intelligence Agency shall be exempt from the requirements of 5 U.S.C... has been properly classified. (b) The Director, Defense Intelligence Agency, designated the systems of...

36 CFR 1260.26 - Who is responsible for issuing special procedures for declassification of information pertaining...

Code of Federal Regulations, 2011 CFR

2011-07-01

... issuing special procedures for declassification of information pertaining to intelligence activities... procedures for declassification of information pertaining to intelligence activities, sources and methods, or of classified cryptologic information in NARA's holdings? (a) The Director of National Intelligence...

36 CFR 1260.26 - Who is responsible for issuing special procedures for declassification of information pertaining...

Code of Federal Regulations, 2010 CFR

2010-07-01

... issuing special procedures for declassification of information pertaining to intelligence activities... procedures for declassification of information pertaining to intelligence activities, sources and methods, or of classified cryptologic information in NARA's holdings? (a) The Director of National Intelligence...

32 CFR 319.13 - Specific exemptions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAM DEFENSE INTELLIGENCE AGENCY PRIVACY PROGRAM § 319.13 Specific exemptions. (a) All systems of records maintained by the Director Intelligence Agency shall be exempt from the requirements of 5 U.S.C... has been properly classified. (b) The Director, Defense Intelligence Agency, designated the systems of...

32 CFR 319.13 - Specific exemptions.

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2010-07-01

... PROGRAM DEFENSE INTELLIGENCE AGENCY PRIVACY PROGRAM § 319.13 Specific exemptions. (a) All systems of records maintained by the Director Intelligence Agency shall be exempt from the requirements of 5 U.S.C... has been properly classified. (b) The Director, Defense Intelligence Agency, designated the systems of...

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2010-06-28

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Airborne wildfire intelligence system: a decision support tool for wildland fire managers in Alberta

NASA Astrophysics Data System (ADS)

Campbell, Doug; Born, Wally G.; Beck, Judi; Bereska, Bill; Frederick, Kurt; Hua, Sun

2002-03-01

The Airborne Wildfire Intelligence System (AWIS) defines the state-of-the-art in remotely sensed wildfire intelligence. AWIS is a commercial, automated, intelligence service, delivering GIS integrated fire intelligence, classified interpretive and analysis layers, and higher level decision support products for wildfires in near real time via the Internet. The AWIS effort illustrates flexible and dynamic cooperation between industry and government to combine technology with field knowledge and experience into an effective, optimized end-user tool. In Alberta the Forest Protection Division of the department of Sustainable Resource Development uses AWIS for several applications: holdover and wildfire hotspot detection, fire front and burned area perimeter mapping, strategic and tactical support through 3D visualization, research into the effects of fire and its severity and to document burn patterns across the landscape. A discussion of all of the scientific themes behind the AWIS is outside the scope of this paper, however, the science of sub-element detection will be reviewed. An independent study has been conducted by the Forest Engineering Research Institute of Canada (FERIC) to investigate the capability of a variety of thermal infrared remote sensing systems to detect small and subtle hotspots in an effort to identify the strengths and weaknesses thereof. As a result of this work, method suitability guidelines have been established to match appropriate infrared technology with a given wildfire management objective.

Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

PubMed

Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

2017-05-01

Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

PubMed

Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

Study on a pattern classification method of soil quality based on simplified learning sample dataset

USGS Publications Warehouse

Zhang, Jiahua; Liu, S.; Hu, Y.; Tian, Y.

2011-01-01

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

Development of dog-like retrieving capability in a ground robot

NASA Astrophysics Data System (ADS)

MacKenzie, Douglas C.; Ashok, Rahul; Rehg, James M.; Witus, Gary

2013-01-01

This paper presents the Mobile Intelligence Team's approach to addressing the CANINE outdoor ground robot competition. The competition required developing a robot that provided retrieving capabilities similar to a dog, while operating fully autonomously in unstructured environments. The vision team consisted of Mobile Intelligence, the Georgia Institute of Technology, and Wayne State University. Important computer vision aspects of the project were the ability to quickly learn the distinguishing characteristics of novel objects, searching images for the object as the robot drove a search pattern, identifying people near the robot for safe operations, correctly identify the object among distractors, and localizing the object for retrieval. The classifier used to identify the objects will be discussed, including an analysis of its performance, and an overview of the entire system architecture presented. A discussion of the robot's performance in the competition will demonstrate the system's successes in real-world testing.

Intelligibility of 4-Year-Old Children with and without Cerebral Palsy

ERIC Educational Resources Information Center

Hustad, Katherine C.; Schueler, Brynn; Schultz, Laurel; DuHadway, Caitlin

2012-01-01

Purpose: The authors examined speech intelligibility in typically developing (TD) children and 3 groups of children with cerebral palsy (CP) who were classified into speech/language profile groups following Hustad, Gorton, and Lee (2010). Questions addressed differences in transcription intelligibility scores among groups, the effects of utterance…

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

The Physics of Intelligence

ERIC Educational Resources Information Center

Escultura, E. E.

2012-01-01

This paper explores the physics of intelligence and provides an overview of what happens in the brain when a person is engaged in mental activity that we classify under thought or intelligence. It traces the formation of a concept starting with reception of visible or detectable signals from the real world by and external to the sense organs,…

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.

PubMed

Alonso-Silverio, Gustavo A; Pérez-Escamirosa, Fernando; Bruno-Sanchez, Raúl; Ortiz-Simon, José L; Muñoz-Guerrero, Roberto; Minor-Martinez, Arturo; Alarcón-Paredes, Antonio

2018-05-01

A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language. Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting. Computer vision libraries were used to obtain the number of transferred points and simulated pattern cutting trace by means of tracking of the laparoscopic instrument. An artificial neural network (ANN) was trained to learn from experts and nonexperts' behavior for pattern cutting task, whereas the assessment of transferring task was performed using a preestablished threshold. Four expert surgeons in laparoscopic surgery, from hospital "Raymundo Abarca Alarcón," constituted the experienced class for the ANN. Sixteen trainees (10 medical students and 6 residents) without laparoscopic surgical skills and limited experience in minimal invasive techniques from School of Medicine at Universidad Autónoma de Guerrero constituted the nonexperienced class. Data from participants performing 5 daily repetitions for each task during 5 days were used to build the ANN. The participants tend to improve their learning curve and dexterity with this laparoscopic training system. The classifier shows mean accuracy and receiver operating characteristic curve of 90.98% and 0.93, respectively. Moreover, the ANN was able to evaluate the psychomotor skills of users into 2 classes: experienced or nonexperienced. We constructed and evaluated an affordable laparoscopic trainer system using computer vision, augmented reality, and an artificial intelligence algorithm. The proposed trainer has the potential to increase the self-confidence of trainees and to be applied to programs with limited resources.

WISC-R Verbal Performance IQ Discrepancies among Quay-Classified Adolescent Male Delinquents.

ERIC Educational Resources Information Center

Hubble, L.M.; Groff, M. G.

1982-01-01

This study examined the hypothesis that the Wechsler Verbal/Performance Intelligence quotient discrepancy would be larger or more frequent for persons classified as exhibiting a psychopathic delinquent adjustment than for persons classified as either neurotic or subculturally delinquent. (Author/PN)

Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos.

PubMed

Moghaddasi, Hanie; Nourian, Saeed

2016-06-01

Heart disease is the major cause of death as well as a leading cause of disability in the developed countries. Mitral Regurgitation (MR) is a common heart disease which does not cause symptoms until its end stage. Therefore, early diagnosis of the disease is of crucial importance in the treatment process. Echocardiography is a common method of diagnosis in the severity of MR. Hence, a method which is based on echocardiography videos, image processing techniques and artificial intelligence could be helpful for clinicians, especially in borderline cases. In this paper, we introduce novel features to detect micro-patterns of echocardiography images in order to determine the severity of MR. Extensive Local Binary Pattern (ELBP) and Extensive Volume Local Binary Pattern (EVLBP) are presented as image descriptors which include details from different viewpoints of the heart in feature vectors. Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Template Matching techniques are used as classifiers to determine the severity of MR based on textural descriptors. The SVM classifier with Extensive Uniform Local Binary Pattern (ELBPU) and Extensive Volume Local Binary Pattern (EVLBP) have the best accuracy with 99.52%, 99.38%, 99.31% and 99.59%, respectively, for the detection of Normal, Mild MR, Moderate MR and Severe MR subjects among echocardiography videos. The proposed method achieves 99.38% sensitivity and 99.63% specificity for the detection of the severity of MR and normal subjects. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Discovery of Feeblemindedness among Immigrant Children through Intelligence Tests in California in the 1910S

ERIC Educational Resources Information Center

Omori, Mariko

2018-01-01

This paper explores the way in which psychologists classified immigrant children as feebleminded through the use of intelligence testing and how state organisations consequently segregated them from public schools based on the scientific evidence. First, I show the way in which the psychologist Lewis Terman utilised intelligence testing to…

41 CFR 51-8.2 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... from disclosure by statute; (4) Trade secrets and commercial or financial information obtained from a... lawful national security intelligence investigation, information furnished by a confidential source, (v... intelligence or counterintelligence, or international terrorism, and the existence of the records is classified...

Rank Determination of Mental Functions by 1D Wavelets and Partial Correlation.

PubMed

Karaca, Y; Aslan, Z; Cattani, C; Galletta, D; Zhang, Y

2017-01-01

The main aim of this paper is to classify mental functions by the Wechsler Adult Intelligence Scale-Revised tests with a mixed method based on wavelets and partial correlation. The Wechsler Adult Intelligence Scale-Revised is a widely used test designed and applied for the classification of the adults cognitive skills in a comprehensive manner. In this paper, many different intellectual profiles have been taken into consideration to measure the relationship between the mental functioning and psychological disorder. We propose a method based on wavelets and correlation analysis for classifying mental functioning, by the analysis of some selected parameters measured by the Wechsler Adult Intelligence Scale-Revised tests. In particular, 1-D Continuous Wavelet Analysis, 1-D Wavelet Coefficient Method and Partial Correlation Method have been analyzed on some Wechsler Adult Intelligence Scale-Revised parameters such as School Education, Gender, Age, Performance Information Verbal and Full Scale Intelligence Quotient. In particular, we will show that gender variable has a negative but a significant role on age and Performance Information Verbal factors. The age parameters also has a significant relation in its role on Performance Information Verbal and Full Scale Intelligence Quotient change.

Sleep stages identification in patients with sleep disorder using k-means clustering

NASA Astrophysics Data System (ADS)

Fadhlullah, M. U.; Resahya, A.; Nugraha, D. F.; Yulita, I. N.

2018-05-01

Data mining is a computational intelligence discipline where a large dataset processed using a certain method to look for patterns within the large dataset. This pattern then used for real time application or to develop some certain knowledge. This is a valuable tool to solve a complex problem, discover new knowledge, data analysis and decision making. To be able to get the pattern that lies inside the large dataset, clustering method is used to get the pattern. Clustering is basically grouping data that looks similar so a certain pattern can be seen in the large data set. Clustering itself has several algorithms to group the data into the corresponding cluster. This research used data from patients who suffer sleep disorders and aims to help people in the medical world to reduce the time required to classify the sleep stages from a patient who suffers from sleep disorders. This study used K-Means algorithm and silhouette evaluation to find out that 3 clusters are the optimal cluster for this dataset which means can be divided to 3 sleep stages.

Where value lives in a networked world.

PubMed

Sawhney, M; Parikh, D

2001-01-01

While many management thinkers proclaim an era of radical uncertainty, authors Sawhney and Parikh assert that the seemingly endless upheavals of the digital age are more predictable than that: today's changes have a common root, and that root lies in the nature of intelligence in networks. Understanding the patterns of intelligence migration can help companies decipher and plan for the inevitable disruptions in today's business environment. Two patterns in network intelligence are reshaping industries and organizations. First, intelligence is decoupling--that is, modern high-speed networks are pushing back-end intelligence and front-end intelligence toward opposite ends of the network, making the ends the two major sources of potential profits. Second, intelligence is becoming more fluid and modular. Small units of intelligence now float freely like molecules in the ether, coalescing into temporary bundles whenever and wherever necessary to solve problems. The authors present four strategies that companies can use to profit from these patterns: arbitrage allows companies to move intelligence to new regions or countries where the cost of maintaining intelligence is lower; aggregation combines formerly isolated pieces of infrastructure intelligence into a large pool of shared infrastructure provided over a network; rewiring allows companies to connect islands of intelligence by creating common information backbones; and reassembly allows businesses to reorganize pieces of intelligence into coherent, personalized packages for customers. By being aware of patterns in network intelligence and by acting rather than reacting, companies can turn chaos into opportunity, say the authors.

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

NASA Technical Reports Server (NTRS)

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

RIT-CIA Case Study: Classified Research in a University Context.

ERIC Educational Resources Information Center

Carl, W. John, III

A controversy at the Rochester Institute of Technology (RIT) in New York State over that institution's involvement with classified research for the Central Intelligence Agency (CIA) raised issues regarding classified research and institutional leadership. In 1991 M. Richard Rose, then president of RIT, took a 4-month sabbatical to work for the…

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.

PubMed

El-Sayed, Hesham; Sankar, Sharmi; Daraghmi, Yousef-Awwad; Tiwari, Prayag; Rattagan, Ekarat; Mohanty, Manoranjan; Puthal, Deepak; Prasad, Mukesh

2018-05-24

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

An evaluation of an intelligent home monitoring system.

PubMed

Sixsmith, A J

2000-01-01

A trial was performed of an intelligent monitoring system which used sensors in the home to identify emergencies by detecting deviations from normal activity patterns. The field trial lasted three months. Twenty-two elderly people agreed to participate. Their ages ranged from early 60s to over 85, with two-thirds in the age range 75-84 years. They lived in four different localities within the UK--Ipswich, Northumberland, Merseyside and Nottingham. A total of 61 alerts was recorded, at a mean frequency about one alert per month per client. Of the 61 alerts generated, 46 were classified as false alerts and the other 15 as genuine, although no real emergencies occurred during the study. Many people in the field trial reported enhanced feelings of safety and security, which could help to stimulate independence and help them to remain living in their own homes. The monitoring system increased the care choices available to elderly people and supported and enhanced the carer's role.

Intelligence Is Associated With Voluntary Disclosure in Child Sexual Abuse Victims.

PubMed

Bae, Seung Min; Kang, Jae Myeong; Hwang, In Cheol; Cho, Hyeongrae; Cho, Seong-Jin

2017-09-01

The purpose of this study was (1) to determine whether intelligence level is associated with the pattern of the disclosure and (2) to elucidate which, between the verbal and performance intelligence, better reflect the pattern of disclosure in child and adolescent sexual abuse victims. Data were collected on 162 participants who visited a public center for sexually abused children and adolescents between January 2013 and December 2014. Demographic information, case characteristics, and disclosure pattern as well as intelligence quotients (IQs) of subjects were gathered. Intelligence was analyzed as level, full scale IQ, and the verbal and performance IQ. Eighty-one subjects (50.0%) voluntarily disclosed that they have been sexually abused. In regression analysis, intellectual level, age, and the number of perpetrators were associated with disclosure pattern. Full scale IQ was associated with the disclosure pattern (odds ratio = .983, 95% confidence interval = .968-.997, p = .017). When intelligence was divided into verbal and performance IQ, verbal IQ affected the pattern of disclosure (odds ratio = .973, 95% confidence interval = .956-.991, p = .003) with linear correlation (p = .001). We found that IQ was associated with the disclosure pattern. The intelligence, especially in verbal domain, is linearly correlated with the probability of voluntary disclosure. We suggest that special legal assistance and social concern are required for children and adolescent victims below normal intelligence to make them disclose the sexual abuse. Copyright © 2017 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Gesture recognition for smart home applications using portable radar sensors.

PubMed

Wan, Qian; Li, Yiran; Li, Changzhi; Pal, Ranadip

2014-01-01

In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.

Ability and Achievement Variables in Average, Low Average, and Borderline Students and the Roles of the School Psychologist

ERIC Educational Resources Information Center

Claypool, Tim; Marusiak, Christopher; Janzen, Henry L.

2008-01-01

This study contributes to ongoing research in the field of school psychology by examining some of the effects of using the Full Scale Intelligence Quotient (FSIQ) to classify students aged 6-16 years according to their results on an individual measure of intelligence, the Wechsler Intelligence Scale for Children, Third Edition (WISC-III, 1991).…

18 CFR 3a.31 - Classification markings and special notations.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Confidential will be stamped in red ink, printed, or written in letters considerably larger than those used in... disclosure subject to criminal sanctions. (4) Sensitive intelligence information. For classified information or material relating to sensitive intelligence sources and methods, the following warning notice...

A survey of mindset theories of intelligence and medical error self-reporting among pediatric housestaff and faculty.

PubMed

Jegathesan, Mithila; Vitberg, Yaffa M; Pusic, Martin V

2016-02-11

Intelligence theory research has illustrated that people hold either "fixed" (intelligence is immutable) or "growth" (intelligence can be improved) mindsets and that these views may affect how people learn throughout their lifetime. Little is known about the mindsets of physicians, and how mindset may affect their lifetime learning and integration of feedback. Our objective was to determine if pediatric physicians are of the "fixed" or "growth" mindset and whether individual mindset affects perception of medical error reporting.  We sent an anonymous electronic survey to pediatric residents and attending pediatricians at a tertiary care pediatric hospital. Respondents completed the "Theories of Intelligence Inventory" which classifies individuals on a 6-point scale ranging from 1 (Fixed Mindset) to 6 (Growth Mindset). Subsequent questions collected data on respondents' recall of medical errors by self or others. We received 176/349 responses (50 %). Participants were equally distributed between mindsets with 84 (49 %) classified as "fixed" and 86 (51 %) as "growth". Residents, fellows and attendings did not differ in terms of mindset. Mindset did not correlate with the small number of reported medical errors. There is no dominant theory of intelligence (mindset) amongst pediatric physicians. The distribution is similar to that seen in the general population. Mindset did not correlate with error reports.

FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

PubMed Central

Seeja, K. R.; Zareapoor, Masoumeh

2014-01-01

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers. PMID:25302317

FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

PubMed

Seeja, K R; Zareapoor, Masoumeh

2014-01-01

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

Students’ thinking level based on intrapersonal intelligence

NASA Astrophysics Data System (ADS)

Sholikhati, Rahadian; Mardiyana; Retno Sari Saputro, Dewi

2017-12-01

This research aims to determine the students’ thinking level based on bloom taxonomy guidance and reviewed from students' Intrapersonal Intelligence. Taxonomy bloom is a taxonomy that classifies the students' thinking level into six, ie the remembering, understanding, applying, analyzing, creating, and evaluating levels. Students' Intrapersonal Intelligence is the intelligence associated with awareness and knowledge of oneself. The type of this research is descriptive research with qualitative approach. The research subject were taken by one student in each Intrapersonal Intelligence category (high, moderate, and low) which then given the problem solving test and the result was triangulated by interview. From this research, it is found that high Intrapersonal Intelligence students can achieve analyzing thinking level, subject with moderate Intrapersonal Intelligence being able to reach the level of applying thinking, and subject with low Intrapersonal Intelligence able to reach understanding level.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

Enhancing business intelligence by means of suggestive reviews.

PubMed

Qazi, Atika; Raj, Ram Gopal; Tahir, Muhammad; Cambria, Erik; Syed, Karim Bux Shah

2014-01-01

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

Enhancing Business Intelligence by Means of Suggestive Reviews

PubMed Central

Qazi, Atika

2014-01-01

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons. PMID:25054188

Static facial expression recognition with convolution neural networks

NASA Astrophysics Data System (ADS)

Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei

2018-03-01

Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

PubMed

Qazi, Emad-Ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

2016-01-01

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0-1.875 Hz (delta low) and 1.875-3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

Sequence-based protein superfamily classification using computational intelligence techniques: a review.

PubMed

Vipsita, Swati; Rath, Santanu Kumar

2015-01-01

Protein superfamily classification deals with the problem of predicting the family membership of newly discovered amino acid sequence. Although many trivial alignment methods are already developed by previous researchers, but the present trend demands the application of computational intelligent techniques. As there is an exponential growth in size of biological database, retrieval and inference of essential knowledge in the biological domain become a very cumbersome task. This problem can be easily handled using intelligent techniques due to their ability of tolerance for imprecision, uncertainty, approximate reasoning, and partial truth. This paper discusses the various global and local features extracted from full length protein sequence which are used for the approximation and generalisation of the classifier. The various parameters used for evaluating the performance of the classifiers are also discussed. Therefore, this review article can show right directions to the present researchers to make an improvement over the existing methods.

An intelligent identification algorithm for the monoclonal picking instrument

NASA Astrophysics Data System (ADS)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

Research of information classification and strategy intelligence extract algorithm based on military strategy hall

NASA Astrophysics Data System (ADS)

Chen, Lei; Li, Dehua; Yang, Jie

2007-12-01

Constructing virtual international strategy environment needs many kinds of information, such as economy, politic, military, diploma, culture, science, etc. So it is very important to build an information auto-extract, classification, recombination and analysis management system with high efficiency as the foundation and component of military strategy hall. This paper firstly use improved Boost algorithm to classify obtained initial information, then use a strategy intelligence extract algorithm to extract strategy intelligence from initial information to help strategist to analysis information.

Relations between Prejudice, Cultural Intelligence and Level of Entrepreneurship: A Study of School Principals

ERIC Educational Resources Information Center

Baltaci, Ali

2017-01-01

The aim of this study is to determine the mediating role of prejudice in the relationship between the cultural intelligence of school principals and the level of entrepreneurship. The design of this study was classified as correlational survey research. This study was designed by quantitative research method. The universe of this study constitutes…

Profile Analysis of Deaf Children Using the Universal Nonverbal Intelligence Test

ERIC Educational Resources Information Center

Krivitski, Erin C.; McIntosh, David E.; Rothlisberg, Barbara; Finch, Holmes

2004-01-01

This study was conducted to determine whether children who are deaf perform similarly to hearing children on the Universal Nonverbal Intelligence Test (UNIT; Bracken & McCallum, 1998). The children classified as deaf demonstrated a hearing loss of 60 dB or more, were prelingually deaf, and did not exhibit co-morbidity. They were matched on…

Correlations between the Stanford-Binet, 4th Edition, and the WISC-R with a Learning Disabled Population.

ERIC Educational Resources Information Center

Phelps, LeAdelle; And Others

1988-01-01

Compared Stanford-Binet (Fourth Edition) and the Wechsler Intelligence Scale for Children-Revised as instruments for assessing the intellectual strengths and weaknesses of students (N=35) classified as learning disabled in elementary and secondary grades. Results suggest the tests will yield similar intelligence quotients for the learning disabled…

Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties.

PubMed

Ung, Choong Yong; Li, Hu; Cao, Zhi Wei; Li, Yi Xue; Chen, Yu Zong

2007-05-04

Multi-herb prescriptions of traditional Chinese medicine (TCM) often include special herb-pairs for mutual enhancement, assistance, and restraint. These TCM herb-pairs have been assembled and interpreted based on traditionally defined herbal properties (TCM-HPs) without knowledge of mechanism of their assumed synergy. While these mechanisms are yet to be determined, properties of TCM herb-pairs can be investigated to determine if they exhibit features consistent with their claimed unique synergistic combinations. We analyzed distribution patterns of TCM-HPs of TCM herb-pairs to detect signs indicative of possible synergy and used artificial intelligence (AI) methods to examine whether combination of their TCM-HPs are distinguishable from those of non-TCM herb-pairs assembled by random combinations and by modification of known TCM herb-pairs. Patterns of the majority of 394 known TCM herb-pairs were found to exhibit signs of herb-pair correlation. Three AI systems, trained and tested by using 394 TCM herb-pairs and 2470 non-TCM herb-pairs, correctly classified 72.1-87.9% of TCM herb-pairs and 91.6-97.6% of the non-TCM herb-pairs. The best AI system predicted 96.3% of the 27 known non-TCM herb-pairs and 99.7% of the other 1,065,100 possible herb-pairs as non-TCM herb-pairs. Our studies suggest that TCM-HPs of known TCM herb-pairs contain features distinguishable from those of non-TCM herb-pairs consistent with their claimed synergistic or modulating combinations.

Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.

PubMed

Iakovidis, Dimitris K; Keramidas, Eystratios G; Maroulis, Dimitris

2010-09-01

This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Concurrent approach for evolving compact decision rule sets

NASA Astrophysics Data System (ADS)

Marmelstein, Robert E.; Hammack, Lonnie P.; Lamont, Gary B.

1999-02-01

The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.

Biclustering Learning of Trading Rules.

PubMed

Huang, Qinghua; Wang, Ting; Tao, Dacheng; Li, Xuelong

2015-10-01

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

Competitive intelligence information management and innovation in small technology-based companies

NASA Astrophysics Data System (ADS)

Tanev, Stoyan

2007-05-01

In this article we examine how (i) company type and (ii) the competitive intelligence information used by small technology-based companies affect their innovation performance. The focus is on the specific information types used and not on the information sources. Information topics are classified in four groups - customers (10), company (9), competitor (11) and industry (12). The sample consists of 45 small new technology-based companies, specialized suppliers, and service companies from a variety of sectors - software, photonics, telecommunications, biomedical engineering and biotech, traditional manufacturing etc. The results suggest that the total number of intelligence information topics companies use to make decisions about innovation is not associated with the number of their new products, processes, services and patents. Therefore the companies in our sample do not seem to have the resources, processes or value systems required to use different competitive intelligence information when making decisions on innovation or may rely more on their own internal logic than on external information. Companies are classified using a Pavitt-like taxonomy. Service companies are considered as a separate company type. This allows for explicitly studying both, the innovative role of new services in product driven companies, and the role of new product development in service companies.

Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.

PubMed

Cascianelli, Silvia; Scialpi, Michele; Amici, Serena; Forini, Nevio; Minestrini, Matteo; Fravolini, Mario Luca; Sinzinger, Helmut; Schillaci, Orazio; Palumbo, Barbara

2017-01-01

Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer's Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis - PCA - or Partial Least Squares - PLS - are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Compensatory neurofuzzy model for discrete data classification in biomedical

NASA Astrophysics Data System (ADS)

Ceylan, Rahime

2015-03-01

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

Computer interpretation of thallium SPECT studies based on neural network analysis

NASA Astrophysics Data System (ADS)

Wang, David C.; Karvelis, K. C.

1991-06-01

A class of artificial intelligence (Al) programs known as neural networks are well suited to pattern recognition. A neural network is trained rather than programmed to recognize patterns. This differs from "expert system" Al programs in that it is not following an extensive set of rules determined by the programmer, but rather bases its decision on a gestalt interpretation of the image. The "bullseye" images from cardiac stress thallium tests performed on 50 male patients, as well as several simulated images were used to train the network. The network was able to accurately classify all patients in the training set. The network was then tested against 50 unknown patients and was able to correctly categorize 77% of the areas of ischemia and 92% of the areas of infarction. While not yet matching the ability of a trained physician, the neural network shows great promise in this area and has potential application in other areas of medical imaging.

An Innovative Method for Testing Children's Achievement-Related Reactions: Recording Feelings of Helplessness by Means of an Intelligence Test-Battery

ERIC Educational Resources Information Center

Titscher, Anna; Kubinger, Klaus D.

2008-01-01

The present study, based on the work of Dweck (2000) and her description of helpless and mastery-orientated children, was designed to find a new, simple and economic way of assessing helplessness while testing a child's intelligence. Two hundred and thirty-two Austrian grammar-school children, previously classified as either helpless or…

Emotional intelligence as an aspect of general intelligence: what would David Wechsler say?

PubMed

Kaufman, A S; Kaufman, J C

2001-09-01

R. D. Roberts, M. Zeidner, and G. Matthews (2001) have carefully examined the controversial issue of whether emotional intelligence (EI) should be classified as an intelligence and whether EI's constructs meet the same psychometric standards as general intelligence's constructs. This article casts their efforts into the framework of both historical and modern IQ-testing theory and research. It details David Wechsler's attempts to integrate EI into his tests and how his conception of a good clinician would be that of an emotionally intelligent clinician. Current theories and research on IQ also have a role in EI beyond what Roberts et al. described, including J. L. Horn's (1989) expanded model and A. R. Luria's (1966) neuropsychological research, and better criteria than the Armed Services Vocational Aptitude Battery should be used in future EI studies. The authors look forward to more research being conducted on EI, particularly in future performance-based assessments.

Artificial intelligence and signal processing for infrastructure assessment

NASA Astrophysics Data System (ADS)

Assaleh, Khaled; Shanableh, Tamer; Yehia, Sherif

2015-04-01

The Ground Penetrating Radar (GPR) is being recognized as an effective nondestructive evaluation technique to improve the inspection process. However, data interpretation and complexity of the results impose some limitations on the practicality of using this technique. This is mainly due to the need of a trained experienced person to interpret images obtained by the GPR system. In this paper, an algorithm to classify and assess the condition of infrastructures utilizing image processing and pattern recognition techniques is discussed. Features extracted form a dataset of images of defected and healthy slabs are used to train a computer vision based system while another dataset is used to evaluate the proposed algorithm. Initial results show that the proposed algorithm is able to detect the existence of defects with about 77% success rate.

Analyzing Activity Behavior and Movement in a Naturalistic Environment using Smart Home Techniques

PubMed Central

Cook, Diane J.; Schmitter-Edgecombe, Maureen; Dawadi, Prafulla

2015-01-01

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study we use smart home and wearable sensors to collect data while (n=84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an AUC value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant. PMID:26259225

Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques.

PubMed

Cook, Diane J; Schmitter-Edgecombe, Maureen; Dawadi, Prafulla

2015-11-01

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study, we use smart home and wearable sensors to collect data, while ( n = 84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an area under the ROC curve value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.

Intelligent methods for the process parameter determination of plastic injection molding

NASA Astrophysics Data System (ADS)

Gao, Huang; Zhang, Yun; Zhou, Xundao; Li, Dequn

2018-03-01

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system- based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.

A decision support system using combined-classifier for high-speed data stream in smart grid

NASA Astrophysics Data System (ADS)

Yang, Hang; Li, Peng; He, Zhian; Guo, Xiaobin; Fong, Simon; Chen, Huajun

2016-11-01

Large volume of high-speed streaming data is generated by big power grids continuously. In order to detect and avoid power grid failure, decision support systems (DSSs) are commonly adopted in power grid enterprises. Among all the decision-making algorithms, incremental decision tree is the most widely used one. In this paper, we propose a combined classifier that is a composite of a cache-based classifier (CBC) and a main tree classifier (MTC). We integrate this classifier into a stream processing engine on top of the DSS such that high-speed steaming data can be transformed into operational intelligence efficiently. Experimental results show that our proposed classifier can return more accurate answers than other existing ones.

Bibliography of Reports and Journal Articles Approved for Public Release: FY1990

DTIC Science & Technology

1990-12-01

Holland G. D. Gottfredson This study found that classified vocational aspirations of H. G. Baker Navy recruits were superior to the Vocational...Testing Systems Journal Articles Intelligence , 14, 215-238 Novelty as "Representational Complexity": A Cognitive (1990). Interpretation of Sternberg and...Gastel. G. E. Larson Approved for public release; distribution is unlimited. A major principle of intelligence research is the ubiquitous relationship

Artificial intelligence in the diagnosis of low back pain.

PubMed

Mann, N H; Brown, M D

1991-04-01

Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

Environment-specific noise suppression for improved speech intelligibility by cochlear implant users.

PubMed

Hu, Yi; Loizou, Philipos C

2010-06-01

Attempts to develop noise-suppression algorithms that can significantly improve speech intelligibility in noise by cochlear implant (CI) users have met with limited success. This is partly because algorithms were sought that would work equally well in all listening situations. Accomplishing this has been quite challenging given the variability in the temporal/spectral characteristics of real-world maskers. A different approach is taken in the present study focused on the development of environment-specific noise suppression algorithms. The proposed algorithm selects a subset of the envelope amplitudes for stimulation based on the signal-to-noise ratio (SNR) of each channel. Binary classifiers, trained using data collected from a particular noisy environment, are first used to classify the mixture envelopes of each channel as either target-dominated (SNR>or=0 dB) or masker-dominated (SNR<0 dB). Only target-dominated channels are subsequently selected for stimulation. Results with CI listeners indicated substantial improvements (by nearly 44 percentage points at 5 dB SNR) in intelligibility with the proposed algorithm when tested with sentences embedded in three real-world maskers. The present study demonstrated that the environment-specific approach to noise reduction has the potential to restore speech intelligibility in noise to a level near to that attained in quiet.

Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.

PubMed

Obrzut, Bogdan; Kusy, Maciej; Semczuk, Andrzej; Obrzut, Marzanna; Kluska, Jacek

2017-12-12

Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5-year overall survival prediction in patients with cervical cancer treated by radical hysterectomy. The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model. The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse. The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy.

Intelligence Level and the Allocation of Resources for Creative Tasks: A Pupillometry Study

ERIC Educational Resources Information Center

Ojha, Amitash; Indurkhya, Bipin; Lee, Minho

2017-01-01

This pupillometry study examined the relationship between intelligence and creative cognition from the resource allocation perspective. It was hypothesized that, during a creative metaphor task, individuals with higher intelligence scores would have different resource allocation patterns than individuals with lower intelligence scores. The study…

Intelligence Score Profiles of Female Juvenile Offenders

ERIC Educational Resources Information Center

Werner, Shelby Spare; Hart, Kathleen J.; Ficke, Susan L.

2016-01-01

Previous studies have found that male juvenile offenders typically obtain low scores on measures of intelligence, often with a pattern of higher scores on measures of nonverbal relative to verbal tasks. The research on the intelligence performance of female juvenile offenders is limited. This study explored the Wechsler Intelligence Scale for…

Imaging structural covariance in the development of intelligence.

PubMed

Khundrakpam, Budhachandra S; Lewis, John D; Reid, Andrew; Karama, Sherif; Zhao, Lu; Chouinard-Decorte, Francois; Evans, Alan C

2017-01-01

Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6-18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence - not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Implications of State Dental Board Disciplinary Actions for Teaching Dental Students About Emotional Intelligence.

PubMed

Munk, Lyle Kris

2016-01-01

The primary emphasis in dental education is on developing students' cognitive intelligence (thinking) and technical intelligence (doing), while emotional intelligence (being) receives less emphasis. The aim of this study was to explore a potential consequence of the paucity of emotional intelligence education by determining the level of emotional intelligence-related (EI-R) infractions in state dental board disciplinary actions and characterizing the types of those infractions. For this study, 1,100 disciplinary action reports from 21 state dental boards were reviewed, and disciplinary infractions were classified as cognitive intelligence-related (CI-R) infractions, technical intelligence-related (TI-R) infractions, and EI-R infractions. EI-R infractions were then subcategorized into emotional intelligence clusters and competencies using the Emotional and Social Competency Inventory (ESCI). The results showed that 56.6% of the infractions were EI-R. When the EI-R infractions were subcategorized, the four competencies most frequently violated involved transparency, teamwork and collaboration, organizational awareness, and accurate self-assessment. Understanding the frequency and nature of EI-R infractions may promote awareness of the need for increased attention to principles of emotional intelligence in dental education and may encourage integration of those principles across dental curricula to help students understand the impact of emotional intelligence on their future well-being and livelihood.

Novel layered clustering-based approach for generating ensemble of classifiers.

PubMed

Rahman, Ashfaqur; Verma, Brijesh

2011-05-01

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification.

PubMed

AbuHassan, Kamal J; Bakhori, Noremylia M; Kusnin, Norzila; Azmi, Umi Z M; Tania, Marzia H; Evans, Benjamin A; Yusof, Nor A; Hossain, M A

2017-07-01

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.

Quantifying Emotional Intelligence: The Relationship between Thinking Patterns and Emotional Skills

ERIC Educational Resources Information Center

Cox, Judith E.; Nelson, Darwin B.

2008-01-01

This article explores the relationship between thinking patterns and emotional skills identified by 2 research-derived measures of emotional intelligence that reflect integrative and positive theories of human behavior. Findings suggest implications for planning educational and counseling interventions to facilitate positive growth and future…

Artificial Intelligence in ADA: Pattern-Directed Processing. Final Report.

ERIC Educational Resources Information Center

Reeker, Larry H.; And Others

To demonstrate to computer programmers that the programming language Ada provides superior facilities for use in artificial intelligence applications, the three papers included in this report investigate the capabilities that exist within Ada for "pattern-directed" programming. The first paper (Larry H. Reeker, Tulane University) is…

A Systems Engineering Survey of Artificial Intelligence and Smart Sensor Networks in a Network-Centric Environment

DTIC Science & Technology

2009-09-01

problems, to better model the problem solving of computer systems. This research brought about the intertwining of AI and cognitive psychology . Much of...where symbol sequences are sequential intelligent states of the network, and must be classified as normal, abnormal , or unknown. These symbols...is associated with abnormal behavior; and abcbc is associated with unknown behavior, as it fits no known behavior. Predicted outcomes from

An algorithm that improves speech intelligibility in noise for normal-hearing listeners.

PubMed

Kim, Gibak; Lu, Yang; Hu, Yi; Loizou, Philipos C

2009-09-01

Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target or the masker. Speech corrupted at low signal-to-noise ratio (SNR) levels (-5 and 0 dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility (over 60% points in -5 dB babble) over that attained by human listeners with unprocessed stimuli. The findings from this study suggest that algorithms that can estimate reliably the SNR in each T-F unit can improve speech intelligibility.

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

An Intelligent Pattern Recognition System Based on Neural Network and Wavelet Decomposition for Interpretation of Heart Sounds

DTIC Science & Technology

2001-10-25

wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a...Proceedings. pp. 415–418, 1990. [3] G. Ergun, “An intelligent diagnostic system for interpretation of arterpartum fetal heart rate tracings based on ANNs and...AN INTELLIGENT PATTERN RECOGNITION SYSTEM BASED ON NEURAL NETWORK AND WAVELET DECOMPOSITION FOR INTERPRETATION OF HEART SOUNDS I. TURKOGLU1, A

A novel expert system for objective masticatory efficiency assessment

PubMed Central

2018-01-01

Most of the tools and diagnosis models of Masticatory Efficiency (ME) are not well documented or severely limited to simple image processing approaches. This study presents a novel expert system for ME assessment based on automatic recognition of mixture patterns of masticated two-coloured chewing gums using a combination of computational intelligence and image processing techniques. The hypotheses tested were that the proposed system could accurately relate specimens to the number of chewing cycles, and that it could identify differences between the mixture patterns of edentulous individuals prior and after complete denture treatment. This study enrolled 80 fully-dentate adults (41 females and 39 males, 25 ± 5 years of age) as the reference population; and 40 edentulous adults (21 females and 19 males, 72 ± 8.9 years of age) for the testing group. The system was calibrated using the features extracted from 400 samples covering 0, 10, 15, and 20 chewing cycles. The calibrated system was used to automatically analyse and classify a set of 160 specimens retrieved from individuals in the testing group in two appointments. The ME was then computed as the predicted number of chewing strokes that a healthy reference individual would need to achieve a similar degree of mixture measured against the real number of cycles applied to the specimen. The trained classifier obtained a Mathews Correlation Coefficient score of 0.97. ME measurements showed almost perfect agreement considering pre- and post-treatment appointments separately (κ ≥ 0.95). Wilcoxon signed-rank test showed that a complete denture treatment for edentulous patients elicited a statistically significant increase in the ME measurements (Z = -2.31, p < 0.01). We conclude that the proposed expert system proved able and reliable to accurately identify patterns in mixture and provided useful ME measurements. PMID:29385165

THRESHOLD LOGIC IN ARTIFICIAL INTELLIGENCE

DTIC Science & Technology

COMPUTER LOGIC, ARTIFICIAL INTELLIGENCE , BIONICS, GEOMETRY, INPUT OUTPUT DEVICES, LINEAR PROGRAMMING, MATHEMATICAL LOGIC, MATHEMATICAL PREDICTION, NETWORKS, PATTERN RECOGNITION, PROBABILITY, SWITCHING CIRCUITS, SYNTHESIS

A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster

DTIC Science & Technology

2013-01-01

M. Ahmadi, and M. Shridhar, “ Handwritten Numeral Recognition with Multiple Features and Multistage Classifiers,” Proc. IEEE Int’l Symp. Circuits...ARTICLE (Post Print) 3. DATES COVERED (From - To) SEP 2011 – SEP 2013 4. TITLE AND SUBTITLE A PARALLEL NEUROMORPHIC TEXT RECOGNITION SYSTEM AND ITS...research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition

Intelligent vehicle initiative : business plan

DOT National Transportation Integrated Search

1996-06-01

This report identifies specific intermodal performance measures developed by 15 State departments of transportation. The performance measures are classified by goals and analyzed by frequency of use. The report discusses the role of performance measu...

32 CFR 2001.55 - Foreign disclosure of classified information.

Code of Federal Regulations, 2012 CFR

2012-07-01

... foreign government or international organization of governments, or any element thereof, in accordance... Intelligence may issue policy directives or guidelines pursuant to section 6.2(b) of the Order that modify such...

32 CFR 2001.55 - Foreign disclosure of classified information.

Code of Federal Regulations, 2011 CFR

2011-07-01

... foreign government or international organization of governments, or any element thereof, in accordance... Intelligence may issue policy directives or guidelines pursuant to section 6.2(b) of the Order that modify such...

32 CFR 2001.55 - Foreign disclosure of classified information.

Code of Federal Regulations, 2013 CFR

2013-07-01

... foreign government or international organization of governments, or any element thereof, in accordance... Intelligence may issue policy directives or guidelines pursuant to section 6.2(b) of the Order that modify such...

32 CFR 2001.55 - Foreign disclosure of classified information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... foreign government or international organization of governments, or any element thereof, in accordance... Intelligence may issue policy directives or guidelines pursuant to section 6.2(b) of the Order that modify such...

32 CFR 2001.55 - Foreign disclosure of classified information.

Code of Federal Regulations, 2014 CFR

2014-07-01

... foreign government or international organization of governments, or any element thereof, in accordance... Intelligence may issue policy directives or guidelines pursuant to section 6.2(b) of the Order that modify such...

32 CFR 1908.23 - Determination by originator or interested party.

Code of Federal Regulations, 2010 CFR

2010-07-01

... INTELLIGENCE AGENCY PUBLIC REQUESTS FOR MANDATORY DECLASSIFICATION REVIEW OF CLASSIFIED INFORMATION PURSUANT TO... provided expeditiously on a “first-in, first-out” basis taking into account the business requirements of...

Mediagraphy: Print and Nonprint Resources.

ERIC Educational Resources Information Center

Educational Media and Technology Yearbook, 1998

1998-01-01

Lists educational media-related journals, books, ERIC documents, journal articles, and nonprint resources classified by Artificial Intelligence, Robotics, Electronic Performance Support Systems; Computer-Assisted Instruction; Distance Education; Educational Research; Educational Technology; Electronic Publishing; Information Science and…

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation. [applied to Shuttle Mission Simulator

NASA Technical Reports Server (NTRS)

Nieten, Joseph; Burke, Roger

1993-01-01

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

A Split in the Verbal Comprehension Factor in WAIS and WISC-R Profiles.

ERIC Educational Resources Information Center

McGee, Shanna; Brown, Coke

1984-01-01

Examined the pattern of verbal subscale scores on the Wechsler Adult Intelligence Scale and Wechsler Intelligence Scale for Children-Revised given to college students (N=129) and elementary students (N=383). Results showed a triangle pattern (Comprehension scores higher than both Vocabulary and Information) that begins to appear at the…

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Artificial intelligence in cardiology.

PubMed

Bonderman, Diana

2017-12-01

Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiology are reviewed. The text also touches on the ethical issues and speculates on the future roles of automated algorithms versus clinicians in cardiology and medicine in general.

Writing and the Seven Intelligences.

ERIC Educational Resources Information Center

Grow, Gerald

In "Frames of Mind," Howard Gardner replaces the standard view of intelligence with the idea that human beings have several distinct intelligences. Using an elaborate set of criteria, including evidence from studies of brain damage, prodigies, developmental patterns, cross-cultural comparisons, and various kinds of tests, Gardner…

Property Specification Patterns for intelligence building software

NASA Astrophysics Data System (ADS)

Chun, Seungsu

2018-03-01

In this paper, through the property specification pattern research for Modal MU(μ) logical aspects present a single framework based on the pattern of intelligence building software. In this study, broken down by state property specification pattern classification of Dwyer (S) and action (A) and was subdivided into it again strong (A) and weaknesses (E). Through these means based on a hierarchical pattern classification of the property specification pattern analysis of logical aspects Mu(μ) was applied to the pattern classification of the examples used in the actual model checker. As a result, not only can a more accurate classification than the existing classification systems were easy to create and understand the attributes specified.

Prevalence scaling: applications to an intelligent workstation for the diagnosis of breast cancer.

PubMed

Horsch, Karla; Giger, Maryellen L; Metz, Charles E

2008-11-01

Our goal was to investigate the effects of changes that the prevalence of cancer in a population have on the probability of malignancy (PM) output and an optimal combination of a true-positive fraction (TPF) and a false-positive fraction (FPF) of a mammographic and sonographic automatic classifier for the diagnosis of breast cancer. We investigate how a prevalence-scaling transformation that is used to change the prevalence inherent in the computer estimates of the PM affects the numerical and histographic output of a previously developed multimodality intelligent workstation. Using Bayes' rule and the binormal model, we study how changes in the prevalence of cancer in the diagnostic breast population affect our computer classifiers' optimal operating points, as defined by maximizing the expected utility. Prevalence scaling affects the threshold at which a particular TPF and FPF pair is achieved. Tables giving the thresholds on the scaled PM estimates that result in particular pairs of TPF and FPF are presented. Histograms of PMs scaled to reflect clinically relevant prevalence values differ greatly from histograms of laboratory-designed PMs. The optimal pair (TPF, FPF) of our lower performing mammographic classifier is more sensitive to changes in clinical prevalence than that of our higher performing sonographic classifier. Prevalence scaling can be used to change computer PM output to reflect clinically more appropriate prevalence. Relatively small changes in clinical prevalence can have large effects on the computer classifier's optimal operating point.

Criminal Prohibitions on the Publication of Classified Defense Information

DTIC Science & Technology

2010-09-10

crimes , and we are aware of no case in which a publisher of information obtained through unauthorized disclosure by a government employee has been...904. 19 18 U.S.C. § 794(d). Proceeds go to the Crime Victims Fund. 20 § 795. Photographing and sketching defense installations (a) Whenever, in the...not authorized access to classified information, with reason to believe that such activities would impair U.S. foreign intelligence efforts. This crime

Acoustic Analyses and Intelligibility Assessments of Timing Patterns among Chinese English Learners with Different Dialect Backgrounds

ERIC Educational Resources Information Center

Chen, Hsueh Chu

2015-01-01

This paper includes two interrelated studies. The first production study investigates the timing patterns of English as spoken by Chinese learners with different dialect backgrounds. The second comprehension study explores native and non-native speakers' assessments of the intelligibility of Chinese-accented English, and examines the effects of…

Multiple Intelligences Patterns of Students at King Saud University and Its Relationship with Mathematics' Achievement

ERIC Educational Resources Information Center

Kandeel, Refat A. A.

2016-01-01

The purpose of this study was to determine the multiple intelligences patterns of students at King Saud University and its relationship with academic achievement for the courses of Mathematics. The study sample consisted of 917 students were selected a stratified random manner, the descriptive analysis method and Pearson correlation were used, the…

Creative-Dynamics Approach To Neural Intelligence

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Automatic system for radar echoes filtering based on textural features and artificial intelligence

NASA Astrophysics Data System (ADS)

Hedir, Mehdia; Haddad, Boualem

2017-10-01

Among the very popular Artificial Intelligence (AI) techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been retained to process Ground Echoes (GE) on meteorological radar images taken from Setif (Algeria) and Bordeaux (France) with different climates and topologies. To achieve this task, AI techniques were associated with textural approaches. We used Gray Level Co-occurrence Matrix (GLCM) and Completed Local Binary Pattern (CLBP); both methods were largely used in image analysis. The obtained results show the efficiency of texture to preserve precipitations forecast on both sites with the accuracy of 98% on Bordeaux and 95% on Setif despite the AI technique used. 98% of GE are suppressed with SVM, this rate is outperforming ANN skills. CLBP approach associated to SVM eliminates 98% of GE and preserves precipitations forecast on Bordeaux site better than on Setif's, while it exhibits lower accuracy with ANN. SVM classifier is well adapted to the proposed application since the average filtering rate is 95-98% with texture and 92-93% with CLBP. These approaches allow removing Anomalous Propagations (APs) too with a better accuracy of 97.15% with texture and SVM. In fact, textural features associated to AI techniques are an efficient tool for incoherent radars to surpass spurious echoes.

Mediagraphy: Print and Nonprint Resources.

ERIC Educational Resources Information Center

Educational Media and Technology Yearbook, 1996

1996-01-01

This annotated list includes media-related resources classified under the following headings: artificial intelligence and robotics, CD-ROM, computer-assisted instruction, databases and online searching, distance education, educational research, educational technology, electronic publishing, information science and technology, instructional design…

Mediagraphy: Print and Nonprint Resources.

ERIC Educational Resources Information Center

Educational Media and Technology Yearbook, 1997

1997-01-01

This annotated list includes media-related resources classified under the following headings: artificial intelligence and robotics, CD-ROM, computer-assisted instruction, databases and online searching, distance education, educational research, educational technology, electronic publishing, information science and technology, instructional design…

Intelligence is as intelligence does: can additional support needs replace disability?

PubMed

Arnold, Samuel R C; Riches, Vivienne C; Stancliffe, Roger J

2011-12-01

Abstract In many developed cultures there is an assumption that IQ is intelligence. However, emerging theories of multiple intelligences, of emotional intelligence, as well as the application of IQ testing to other cultural groups, and to people with disability, raises many questions as to what IQ actually measures. Despite recent research that shows IQ testing produces a floor effect when applied to people with lower IQ, as well as research that shows the Flynn effect also applies to people with lower IQ, in practice IQ scores below a certain cut-off are still being used to determine and classify a person's intellectual disability. However, a new paradigm is emerging, almost returning to the original intent of Binet, where measurement is made of the supports the person needs. In this paper, we argue that if one extends the notions of this supports paradigm that diagnosis of intellectual or physical disability could potentially be replaced by diagnosis of additional intellectual support needs, or additional physical support needs.

What Causes Birth Order-Intelligence Patterns? The Admixture Hypothesis, Revived.

ERIC Educational Resources Information Center

Rodgers, Joseph Lee

2001-01-01

Describes why birth order interests both parents and researchers, discussing what really causes apparent birth order effects on intelligence, examining problems with using cross-sectional intelligence data, and noting how to move beyond cross-sectional inferences. Explains the admixture hypothesis, which finds that family size is much more…

An Assessment of Perceived Emotional Intelligence and Eating Attitudes among College Students

ERIC Educational Resources Information Center

Pettit, Michele L.; Jacobs, Sue C.; Page, Kyle S.; Porras, Claudia V.

2010-01-01

Background: Disordered eating patterns continue to surface on college campuses. Studies are needed to examine the potential influence of emotional intelligence on disordered eating behavior. Purpose: The purpose of this study was to assess relationships between perceived emotional intelligence factors and eating disorder symptoms among male and…

Strategies for Improved Interpretation of Computer-Aided Detections for CT Colonography Utilizing Distributed Human Intelligence

PubMed Central

McKenna, Matthew T.; Wang, Shijun; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Summers, Ronald M.

2012-01-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp • 6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for “easy” and “moderate” polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. PMID:22705287

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.

PubMed

McKenna, Matthew T; Wang, Shijun; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Summers, Ronald M

2012-08-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. Copyright © 2012. Published by Elsevier B.V.

Intelligent Systems For Aerospace Engineering: An Overview

NASA Technical Reports Server (NTRS)

KrishnaKumar, K.

2003-01-01

Intelligent systems are nature-inspired, mathematically sound, computationally intensive problem solving tools and methodologies that have become extremely important for advancing the current trends in information technology. Artificially intelligent systems currently utilize computers to emulate various faculties of human intelligence and biological metaphors. They use a combination of symbolic and sub-symbolic systems capable of evolving human cognitive skills and intelligence, not just systems capable of doing things humans do not do well. Intelligent systems are ideally suited for tasks such as search and optimization, pattern recognition and matching, planning, uncertainty management, control, and adaptation. In this paper, the intelligent system technologies and their application potential are highlighted via several examples.

Intelligent Systems for Aerospace Engineering: An Overview

NASA Technical Reports Server (NTRS)

Krishnakumar, Kalmanje

2002-01-01

Intelligent systems are nature-inspired, mathematically sound, computationally intensive problem solving tools and methodologies that have become extremely important for advancing the current trends in information technology. Artificially intelligent systems currently utilize computers to emulate various faculties of human intelligence and biological metaphors. They use a combination of symbolic and sub-symbolic systems capable of evolving human cognitive skills and intelligence, not just systems capable of doing things humans do not do well. Intelligent systems are ideally suited for tasks such as search and optimization, pattern recognition and matching, planning, uncertainty management, control, and adaptation. In this paper, the intelligent system technologies and their application potential are highlighted via several examples.

Parallel Algorithms for Computer Vision.

DTIC Science & Technology

1989-01-01

34 IEEE Tran. Pattern Ankyaij and Ma- Artifcial Intelligence , Tokyo, 1979. chine Intelligence , 6, 1984. Kirkpatrick, S., C.D. Gelatt, Jr. and M.P. Vecchi...MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB T P06010 JAN 89 ETL-0529 UNCLASSIFIED DACA76-85-C-0010 F.’G 12/1I N mommiimmmiiso...PoggioI Massachusetts Institute of Technology i Artificial Intelligence Laboratory 545 Technology Square Cambridge, Massachusetts 02139 DTIC January

Monitoring industrial facilities using principles of integration of fiber classifier and local sensor networks

NASA Astrophysics Data System (ADS)

Korotaev, Valery V.; Denisov, Victor M.; Rodrigues, Joel J. P. C.; Serikova, Mariya G.; Timofeev, Andrey V.

2015-05-01

The paper deals with the creation of integrated monitoring systems. They combine fiber-optic classifiers and local sensor networks. These systems allow for the monitoring of complex industrial objects. Together with adjacent natural objects, they form the so-called geotechnical systems. An integrated monitoring system may include one or more spatially continuous fiber-optic classifiers based on optic fiber and one or more arrays of discrete measurement sensors, which are usually combined in sensor networks. Fiber-optic classifiers are already widely used for the control of hazardous extended objects (oil and gas pipelines, railways, high-rise buildings, etc.). To monitor local objects, discrete measurement sensors are generally used (temperature, pressure, inclinometers, strain gauges, accelerometers, sensors measuring the composition of impurities in the air, and many others). However, monitoring complex geotechnical systems require a simultaneous use of continuous spatially distributed sensors based on fiber-optic cable and connected local discrete sensors networks. In fact, we are talking about integration of the two monitoring methods. This combination provides an additional way to create intelligent monitoring systems. Modes of operation of intelligent systems can automatically adapt to changing environmental conditions. For this purpose, context data received from one sensor (e.g., optical channel) may be used to change modes of work of other sensors within the same monitoring system. This work also presents experimental results of the prototype of the integrated monitoring system.

The role of cognitive versus emotional intelligence in Iowa Gambling Task performance: What's emotion got to do with it?

PubMed

Webb, Christian A; DelDonno, Sophie; Killgore, William D S

2014-01-01

Debate persists regarding the relative role of cognitive versus emotional processes in driving successful performance on the widely used Iowa Gambling Task (IGT). From the time of its initial development, patterns of IGT performance were commonly interpreted as primarily reflecting implicit, emotion-based processes. Surprisingly, little research has tried to directly compare the extent to which measures tapping relevant cognitive versus emotional competencies predict IGT performance in the same study. The current investigation attempts to address this question by comparing patterns of associations between IGT performance, cognitive intelligence (Wechsler Abbreviated Scale of Intelligence; WASI) and three commonly employed measures of emotional intelligence (EI; Mayer-Salovey-Caruso Emotional Intelligence Test, MSCEIT; Bar-On Emotional Quotient Inventory, EQ-i; Self-Rated Emotional Intelligence Scale, SREIS). Results indicated that IGT performance was more strongly associated with cognitive, than emotional, intelligence. To the extent that the IGT indeed mimics "real-world" decision-making, our findings, coupled with the results of existing research, may highlight the role of deliberate, cognitive capacities over implicit, emotional processes in contributing to at least some domains of decision-making relevant to everyday life.

The role of cognitive versus emotional intelligence in Iowa Gambling Task performance: What’s emotion got to do with it?

PubMed Central

Webb, Christian A.; DelDonno, Sophie; Killgore, William D.S.

2014-01-01

Debate persists regarding the relative role of cognitive versus emotional processes in driving successful performance on the widely used Iowa Gambling Task (IGT). From the time of its initial development, patterns of IGT performance were commonly interpreted as primarily reflecting implicit, emotion-based processes. Surprisingly, little research has tried to directly compare the extent to which measures tapping relevant cognitive versus emotional competencies predict IGT performance in the same study. The current investigation attempts to address this question by comparing patterns of associations between IGT performance, cognitive intelligence (Wechsler Abbreviated Scale of Intelligence; WASI) and three commonly employed measures of emotional intelligence (EI; Mayer–Salovey–Caruso Emotional Intelligence Test, MSCEIT; Bar-On Emotional Quotient Inventory, EQ-i; Self-Rated Emotional Intelligence Scale, SREIS). Results indicated that IGT performance was more strongly associated with cognitive, than emotional, intelligence. To the extent that the IGT indeed mimics “real-world” decision-making, our findings, coupled with the results of existing research, may highlight the role of deliberate, cognitive capacities over implicit, emotional processes in contributing to at least some domains of decision-making relevant to everyday life. PMID:25635149

14 CFR § 1203.400 - Specific classifying guidance.

Code of Federal Regulations, 2014 CFR

2014-01-01

... and operational information and material, and in some exceptional cases scientific information falling... activities), intelligence sources or methods, or cryptology; (d) Foreign relations or foreign activities of the United States, including confidential sources; (e) Scientific, technological, or economic matters...

14 CFR 1203.603 - Systematic review for declassification.

Code of Federal Regulations, 2011 CFR

2011-01-01

... National Security Agency. (3) Systematic review for declassification of classified information pertaining... coordinated through the Central Intelligence Agency. (4) The Chairperson, NASA Information Security Program... guidelines. The Chairperson, NASA Information Security Program Committee, shall develop, in coordination with...

Mediagraphy: Print and Nonprint Resources.

ERIC Educational Resources Information Center

Price, Brooke, Ed.

2001-01-01

Lists media-related journals, books, ERIC documents, journal articles, and nonprint resources published in 1999-2000. The annotated entries are classified under the following headings: artificial intelligence; computer assisted instruction; distance education; educational research; educational technology; information science and technology;…

Mediagraphy: Print and Nonprint Resources.

ERIC Educational Resources Information Center

Burdett, Anna E.

2003-01-01

Lists media-related journals, books, ERIC documents, journal articles, and nonprint resources published in 2001-2002. The annotated entries are classified under the following headings: artificial intelligence; computer assisted instruction; distance education; educational research; educational technology; information science and technology;…

Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease

PubMed Central

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2013-01-01

Background Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. Objective To develop a clinical decision–support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. Methods A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. Results The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision–support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k = 0.68 (p < 0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician’s diagnostic impression and the gold standard k = 0. 64 (p < 0.0001). There was moderate agreement between the physician’s diagnostic impression and CDSS k = 0.46 (p = 0.0008). Conclusions The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. PMID:21917512

Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

PubMed

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2011-11-01

Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008). The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.

PubMed

Parreco, Joshua; Hidalgo, Antonio; Parks, Jonathan J; Kozol, Robert; Rattan, Rishi

2018-08-01

Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement. The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all intensive care unit (ICU) stays with mechanical ventilation. PMV was defined as ventilation >7 d. Classifiers with a gradient-boosted decision trees algorithm were created for the outcomes of PMV and tracheostomy placement. The variables used were six different severity-of-illness scores calculated on the first day of ICU admission including their components and 30 comorbidities. Mean receiver operating characteristic curves were calculated for the outcomes, and variable importance was quantified. There were 20,262 ICU stays identified. PMV was required in 13.6%, and tracheostomy was performed in 6.6% of patients. The classifier for predicting PMV was able to achieve a mean area under the curve (AUC) of 0.820 ± 0.016, and tracheostomy was predicted with an AUC of 0.830 ± 0.011. There were 60.7% patients admitted to a surgical ICU, and the classifiers for these patients predicted PMV with an AUC of 0.852 ± 0.017 and tracheostomy with an AUC of 0.869 ± 0.015. The variable with the highest importance for predicting PMV was the logistic organ dysfunction score pulmonary component (13%), and the most important comorbidity in predicting tracheostomy was cardiac arrhythmia (12%). This study demonstrates the use of artificial intelligence through machine-learning classifiers for the early identification of patients at risk for PMV and tracheostomy. Application of these identification techniques could lead to improved outcomes by allowing for early intervention. Copyright © 2018 Elsevier Inc. All rights reserved.

SoM: a smart sensor for human activity monitoring and assisted healthy ageing.

PubMed

Naranjo-Hernández, David; Roa, Laura M; Reina-Tosina, Javier; Estudillo-Valderrama, Miguel Ángel

2012-11-01

This paper presents the hardware and software design and implementation of a low-cost, wearable, and unobstructive intelligent accelerometer sensor for the monitoring of human physical activities. In order to promote healthy lifestyles to elders for an active, independent, and healthy ageing, as well as for the early detection of psychomotor abnormalities, the activity monitoring is performed in a holistic manner in the same device through different approaches: 1) a classification of the level of activity that allows to establish patterns of behavior; 2) a daily activity living classifier that is able to distinguish activities such as climbing or descending stairs using a simple method to decouple the gravitational acceleration components of the motion components; and 3) an estimation of metabolic expenditure independent of the activity performed and the anthropometric characteristics of the user. Experimental results have demonstrated the feasibility of the prototype and the proposed algorithms.

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Artificial intelligence systems based on texture descriptors for vaccine development.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2011-02-01

The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.

PubMed

Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

2015-11-01

Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.

Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples

NASA Astrophysics Data System (ADS)

Muggleton, Stephen; Paes, Aline; Santos Costa, Vítor; Zaverucha, Gerson

The game of chess has been a major testbed for research in artificial intelligence, since it requires focus on intelligent reasoning. Particularly, several challenges arise to machine learning systems when inducing a model describing legal moves of the chess, including the collection of the examples, the learning of a model correctly representing the official rules of the game, covering all the branches and restrictions of the correct moves, and the comprehensibility of such a model. Besides, the game of chess has inspired the creation of numerous variants, ranging from faster to more challenging or to regional versions of the game. The question arises if it is possible to take advantage of an initial classifier of chess as a starting point to obtain classifiers for the different variants. We approach this problem as an instance of theory revision from examples. The initial classifier of chess is inspired by a FOL theory approved by a chess expert and the examples are defined as sequences of moves within a game. Starting from a standard revision system, we argue that abduction and negation are also required to best address this problem. Experimental results show the effectiveness of our approach.

Artificial intelligence techniques: predicting necessity for biopsy in renal transplant recipients suspected of acute cellular rejection or nephrotoxicity.

PubMed

Hummel, A D; Maciel, R F; Sousa, F S; Cohrs, F M; Falcão, A E J; Teixeira, F; Baptista, R; Mancini, F; da Costa, T M; Alves, D; Rodrigues, R G D S; Miranda, R; Pisa, I T

2011-05-01

The gold standard for nephrotoxicity and acute cellular rejection (ACR) is a biopsy, an invasive and expensive procedure. More efficient strategies to screen patients for biopsy are important from the clinical and financial points of view. The aim of this study was to evaluate various artificial intelligence techniques to screen for the need for a biopsy among patients suspected of nephrotoxicity or ACR during the first year after renal transplantation. We used classifiers like artificial neural networks (ANN), support vector machines (SVM), and Bayesian inference (BI) to indicate if the clinical course of the event suggestive of the need for a biopsy. Each classifier was evaluated by values of sensitivity and area under the ROC curve (AUC) for each of the classifiers. The technique that showed the best sensitivity value as an indicator for biopsy was SVM with an AUC of 0.79 and an accuracy rate of 79.86%. The results were better than those described in previous works. The accuracy for an indication of biopsy screening was efficient enough to become useful in clinical practice. Copyright © 2011 Elsevier Inc. All rights reserved.

Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

NASA Astrophysics Data System (ADS)

Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

PubMed

Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

2014-04-01

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Intelligence Surveillance And Reconnaissance Full Motion Video Automatic Anomaly Detection Of Crowd Movements: System Requirements For Airborne Application

DTIC Science & Technology

The collection of Intelligence , Surveillance, and Reconnaissance (ISR) Full Motion Video (FMV) is growing at an exponential rate, and the manual... intelligence for the warfighter. This paper will address the question of how can automatic pattern extraction, based on computer vision, extract anomalies in

Sex Differences in Brain Activity Related to General and Emotional Intelligence

ERIC Educational Resources Information Center

Jausovec, Norbert; Jausovec, Ksenija

2005-01-01

The study investigated gender differences in resting EEG (in three individually determined narrow [alpha] frequency bands) related to the level of general and emotional intelligence. Brain activity of males decreased with the level of general intelligence, whereas an opposite pattern of brain activity was observed in females. This difference was…

[The intelligence quotient and malnutrition. Iron deficiency and the lead concentration as confusing variables].

PubMed

Vega-Franco, L; Mejía, A M; Robles, B; Moreno, L; Pérez, Y

1991-11-01

This study gave us the opportunity to know the roles iron deficiency and the presence of lead in blood play, as confounding variables, in relation to the state of malnutrition and the intellect of those children. A sample of 169 school children were classified according to their state of nutrition, their condition in reference to serum iron and lead concentrations. In addition, their intelligence was evaluated. The results confirmed that those children with lower weights and heights registered lesser points of intelligence; in fact, iron deficiency cancels out the difference in favor of those taller and weighing more. Lead did not contribute as a confounding variable, but more than half of the children showed possible toxic levels of this metal.

Novel Topic Authorship Attribution

DTIC Science & Technology

2011-03-01

by fairness and public welfare concerns: plagiarism detection and identifying authors in a criminal investigation or intelligence setting. The...MEGAM format which makes this MaxEnt classifier a natural choice. MEGAM is publicly available for download [25] and has no restrictions for academic use

Graphical classification of DNA sequences of HLA alleles by deep learning.

PubMed

Miyake, Jun; Kaneshita, Yuhei; Asatani, Satoshi; Tagawa, Seiichi; Niioka, Hirohiko; Hirano, Takashi

2018-04-01

Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence "Deep Learning (Stacked autoencoder)". Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimensional representation and were plotted. Profiles of the two-dimensional plots indicate that the alleles can be classified as clusters are formed. The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles.

Common Criteria related security design patterns--validation on the intelligent sensor example designed for mine environment.

PubMed

Bialas, Andrzej

2010-01-01

The paper discusses the security issues of intelligent sensors that are able to measure and process data and communicate with other information technology (IT) devices or systems. Such sensors are often used in high risk applications. To improve their robustness, the sensor systems should be developed in a restricted way to provide them with assurance. One of assurance creation methodologies is Common Criteria (ISO/IEC 15408), used for IT products and systems. The contribution of the paper is a Common Criteria compliant and pattern-based method for the intelligent sensors security development. The paper concisely presents this method and its evaluation for the sensor detecting methane in a mine, focusing on the security problem of the intelligent sensor definition and solution. The aim of the validation is to evaluate and improve the introduced method.

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

PubMed

Dashtban, M; Balafar, Mohammadali

2017-03-01

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

Diversity of Emotional Intelligence among Nursing and Medical Students.

PubMed

Chun, Kyung Hee; Park, Euna

2016-08-01

The purpose of this study is to identify the types of perception of emotional intelligence among nursing and medical students and their characteristics using Q methodology, and to build the basic data for the development of a program for the would-be medical professionals to effectively adapt to various clinical settings in which their emotions are involved. Data were collected from 35 nursing and medical students by allowing them to classify 40 Q statements related to emotional intelligence and processed using the PC QUANL program. The perceptions of emotional intelligence by nursing and medical students were categorized into three types: "sensitivity-control type", "sympathy-motivation type", and "concern-sympathy type". The perceptions of emotional intelligence by nursing and medical students can represent an effective coping strategy in a situation where emotion is involved. In the medical profession, an occupation with a high level of emotional labor, it is important to identify the types of emotional intelligence for an effective coping strategy, which may have a positive effect on the performance of an organization. Based on the findings of this study, it is necessary to plan an education program for vocational adaptability for nursing and medical students by their types.

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

PubMed Central

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-01-01

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

PubMed

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-02-02

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Methods and means of diagnostics of oncological diseases on the basis of pattern recognition: intelligent morphological systems - problems and solutions

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.

2017-01-01

The development of methods of pattern recognition in modern intelligent systems of clinical cancer diagnosis are discussed. The histological (morphological) diagnosis - primary diagnosis for medical setting with cancer are investigated. There are proposed: interactive methods of recognition and structure of intellectual morphological complexes based on expert training-diagnostic and telemedicine systems. The proposed approach successfully implemented in clinical practice.

New approach for cognitive analysis and understanding of medical patterns and visualizations

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2003-11-01

This paper presents new opportunities for applying linguistic description of the picture merit content and AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted from the image using linguistic methods and expectations taken from the representaion of the medical knowledge, it is possible to understand the merit content of the image even if teh form of the image is very different from any known pattern. This article proves that structural techniques of artificial intelligence may be applied in the case of tasks related to automatic classification and machine perception based on semantic pattern content in order to determine the semantic meaning of the patterns. In the paper are described some examples presenting ways of applying such techniques in the creation of cognitive vision systems for selected classes of medical images. On the base of scientific research described in the paper we try to build some new systems for collecting, storing, retrieving and intelligent interpreting selected medical images especially obtained in radiological and MRI examinations.

Fetal growth, cognitive function, and brain volumes in childhood and adolescence.

PubMed

Rogne, Tormod; Engstrøm, Andreas Aass; Jacobsen, Geir Wenberg; Skranes, Jon; Østgård, Heidi Furre; Martinussen, Marit

2015-03-01

To evaluate the association between fetal growth pattern and cognitive function at 5 and 9 years and regional brain volumes at 15 years. Eighty-three term-born small-for-gestational-age (SGA) neonates and 105 non-SGA neonates in a control group were available for follow-up. Based on serial fetal ultrasound measurements from gestational weeks 25-37, SGA neonates were classified with fetal growth restriction (n=13) or non-fetal growth restriction (n=36). Cognitive function was assessed at 5 and 9 years, and brain volumes were estimated with cerebral magnetic resonance imaging at 15 years. Small-for-gestational-age children had lower performance intelligence quotient at 5 years compared with those in a control group (107.3 compared with 112.5, P<.05). Although there were no differences between the SGA non-fetal growth restriction and control groups, the SGA fetal growth restriction group had significantly lower performance intelligence quotient at 5 years (103.5 compared with 112.5, P<.05) and 9 years (96.2 compared with 107.5, P<.05) compared with those in the control group. There were some brain volume differences at 15 years between SGA children and those in the control group, but after adjustment for total intracranial volume, age at examination, and sex, there were only significant differences between the SGA fetal growth restriction and control groups for thalamic (17.4 compared with 18.6 cm, P<.01) and cerebellar white matter volumes (21.5 compared with 24.3 cm, P<.01). Small-for-gestational-age children had lower intelligence quotient scores at 5 and 9 years and smaller brain volumes at 15 years compared with those in the control group, but these findings were only found in those with fetal growth restriction, indicating a possible relationship to decelerated fetal growth. II.

48 CFR 252.239-7016 - Telecommunications security equipment, devices, techniques, and services.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., record, and data communications, including management information systems and local data networks that...) Sensitive information means any information the loss, misuse, or modification of which, or unauthorized... subcontractors to transmit— (i) Classified or sensitive information; (ii) Matters involving intelligence...

3 CFR - Classified Information and Controlled Unclassified Information

Code of Federal Regulations, 2010 CFR

2010-01-01

... declassification of information in the electronic environment, as recommended by the Commission on the Intelligence... need in recent years to enhance national security by establishing an information sharing environment... information within the information sharing environment. In the absence of a single, comprehensive framework...

Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

NASA Astrophysics Data System (ADS)

Khoury, Mehdi; Liu, Honghai

This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

PubMed

Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

2011-07-12

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

The Flynn Effect: A Quantitative Commentary on Modernity and Human Intelligence

ERIC Educational Resources Information Center

Clark, Cameron M.; Lawlor-Savage, Linette; Goghari, Vina M.

2016-01-01

Average intelligence quotient (IQ) scores have been rising throughout the 20th century and likely before--a pattern now known as the Flynn effect. The central thesis of this paper is that the Flynn effect does not represent genuine increases in general intelligence but rather an increasing aptitude for the types of modern thinking that modern life…

Theoretical Foundations of Software Technology.

DTIC Science & Technology

1983-02-14

major research interests are software testing, aritificial intelligence , pattern recogu- tion, and computer graphics. Dr. Chandranekaran is currently...produce PASCAL language code for the problems. Because of its relationship to many issues in Artificial Intelligence , we also investigated problems of...analysis to concurmt-prmcess software re- are not " intelligent " enough to discover these by themselves, ouirl more complex control flow models. The PAF

Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes.

PubMed

Ung, C Y; Li, H; Kong, C Y; Wang, J F; Chen, Y Z

2007-01-03

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an attractive to conventional medicine. Multi-herb recipes have been routinely used in TCM. These have been formulated by using TCM-defined herbal properties (TCM-HPs), the scientific basis of which is unclear. The usefulness of TCM-HPs was evaluated by analyzing the distribution pattern of TCM-HPs of the constituent herbs in 1161 classical TCM prescriptions, which shows patterns of multi-herb correlation. Two artificial intelligence (AI) methods were used to examine whether TCM-HPs are capable of distinguishing TCM prescriptions from non-TCM recipes. Two AI systems were trained and tested by using 1161 TCM prescriptions, 11,202 non-TCM recipes, and two separate evaluation methods. These systems correctly classified 83.1-97.3% of the TCM prescriptions, 90.8-92.3% of the non-TCM recipes. These results suggest that TCM-HPs are capable of separating TCM prescriptions from non-TCM recipes, which are useful for formulating TCM prescriptions and consistent with the expected correlation between TCM-HPs and the physicochemical properties of herbal ingredients responsible for producing the collective pharmacological and other effects of specific TCM prescriptions.

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Cellular-automata-based learning network for pattern recognition

NASA Astrophysics Data System (ADS)

Tzionas, Panagiotis G.; Tsalides, Phillippos G.; Thanailakis, Adonios

1991-11-01

Most classification techniques either adopt an approach based directly on the statistical characteristics of the pattern classes involved, or they transform the patterns in a feature space and try to separate the point clusters in this space. An alternative approach based on memory networks has been presented, its novelty being that it can be implemented in parallel and it utilizes direct features of the patterns rather than statistical characteristics. This study presents a new approach for pattern classification using pseudo 2-D binary cellular automata (CA). This approach resembles the memory network classifier in the sense that it is based on an adaptive knowledge based formed during a training phase, and also in the fact that both methods utilize pattern features that are directly available. The main advantage of this approach is that the sensitivity of the pattern classifier can be controlled. The proposed pattern classifier has been designed using 1.5 micrometers design rules for an N-well CMOS process. Layout has been achieved using SOLO 1400. Binary pseudo 2-D hybrid additive CA (HACA) is described in the second section of this paper. The third section describes the operation of the pattern classifier and the fourth section presents some possible applications. The VLSI implementation of the pattern classifier is presented in the fifth section and, finally, the sixth section draws conclusions from the results obtained.

Diagnosis of early gastric cancer using narrow band imaging and acetic acid

PubMed Central

Matsuo, Ken; Takedatsu, Hidetoshi; Mukasa, Michita; Sumie, Hiroaki; Yoshida, Hikaru; Watanabe, Yasutomo; Akiba, Jun; Nakahara, Keita; Tsuruta, Osamu; Torimura, Takuji

2015-01-01

AIM: To determine whether the endoscopic findings of depressed-type early gastric cancers (EGCs) could precisely predict the histological type. METHODS: Ninety depressed-type EGCs in 72 patients were macroscopically and histologically identified. We evaluated the microvascular (MV) and mucosal surface (MS) patterns of depressed-type EGCs using magnifying endoscopy (ME) with narrow-band imaging (NBI) (NBI-ME) and ME enhanced by 1.5% acetic acid, respectively. First, depressed-type EGCs were classified according to MV pattern by NBI-ME. Subsequently, EGCs unclassified by MV pattern were classified according to MS pattern by enhanced ME (EME) images obtained from the same angle. RESULTS: We classified the depressed-type EGCs into the following 2 MV patterns using NBI-ME: a fine-network pattern that indicated differentiated adenocarcinoma (25/25, 100%) and a corkscrew pattern that likely indicated undifferentiated adenocarcinoma (18/23, 78.3%). However, 42 of the 90 (46.7%) lesions could not be classified into MV patterns by NBI-ME. These unclassified lesions were then evaluated for MS patterns using EME, which classified 33 (81.0%) lesions as MS patterns, diagnosed as differentiated adenocarcinoma. As a result, 76 of the 90 (84.4%) lesions were matched with histological diagnoses using a combination of NBI-ME and EME. CONCLUSION: A combination of NBI-ME and EME was useful in predicting the histological type of depressed-type EGC. PMID:25632201

Learning with imperfectly labeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. The performance of the Bayes and nearest neighbor classifiers with imperfect labels is discussed using a probabilistic model for the mislabeling of the training patterns. Schemes for training the classifier using both parametric and non parametric techniques are presented. Methods for the correction of imperfect labels were developed. To gain an understanding of the learning process, expressions are derived for success probability as a function of training time for a one dimensional increment error correction classifier with imperfect labels. Feature selection with imperfectly labeled patterns is described.

Termination Criteria for Computerized Classification Testing

ERIC Educational Resources Information Center

Thompson, Nathan A.

2011-01-01

Computerized classification testing (CCT) is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as "pass" and "fail." Like adaptive testing for point estimation of ability, the key component is the…

32 CFR 293.6 - Procedures.

Code of Federal Regulations, 2011 CFR

2011-07-01

... public Internet website. (8) Coordinates with other DoD Components, other members of the Intelligence... currently and properly classified or falls within another FOIA exemption, GC prepares an analysis on the... by the agency AA. The AA reviews the initial FOIA request, GC's analysis, and the denial decision...

Common Criteria Related Security Design Patterns for Intelligent Sensors—Knowledge Engineering-Based Implementation

PubMed Central

Bialas, Andrzej

2011-01-01

Intelligent sensors experience security problems very similar to those inherent to other kinds of IT products or systems. The assurance for these products or systems creation methodologies, like Common Criteria (ISO/IEC 15408) can be used to improve the robustness of the sensor systems in high risk environments. The paper presents the background and results of the previous research on patterns-based security specifications and introduces a new ontological approach. The elaborated ontology and knowledge base were validated on the IT security development process dealing with the sensor example. The contribution of the paper concerns the application of the knowledge engineering methodology to the previously developed Common Criteria compliant and pattern-based method for intelligent sensor security development. The issue presented in the paper has a broader significance in terms that it can solve information security problems in many application domains. PMID:22164064

Common criteria related security design patterns for intelligent sensors--knowledge engineering-based implementation.

PubMed

Bialas, Andrzej

2011-01-01

Intelligent sensors experience security problems very similar to those inherent to other kinds of IT products or systems. The assurance for these products or systems creation methodologies, like Common Criteria (ISO/IEC 15408) can be used to improve the robustness of the sensor systems in high risk environments. The paper presents the background and results of the previous research on patterns-based security specifications and introduces a new ontological approach. The elaborated ontology and knowledge base were validated on the IT security development process dealing with the sensor example. The contribution of the paper concerns the application of the knowledge engineering methodology to the previously developed Common Criteria compliant and pattern-based method for intelligent sensor security development. The issue presented in the paper has a broader significance in terms that it can solve information security problems in many application domains.

Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

NASA Astrophysics Data System (ADS)

Patcharoen, Theerasak; Yoomak, Suntiti; Ngaopitakkul, Atthapol; Pothisarn, Chaichan

2018-04-01

This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

Domain-specific knowledge as the "dark matter" of adult intelligence: Gf/Gc, personality and interest correlates.

PubMed

Ackerman, P L

2000-03-01

An enduring controversy in intelligence theory and assessment, the argument that middle-aged adults are, on average, less intelligent than young adults, is addressed in this study. A sample of 228 educated adults between ages 21 and 62 years was given an array of tests that focused on a broad assessment of intelligence-as-knowledge, traditional estimates of fluid intelligence (Gf) and crystallized intelligence (Gc), personality, and interests. The results indicate that middle-aged adults are more knowledgeable in many domains, compared with younger adults. A coherent pattern of ability, personality, and interest relations is found. The results are consistent with a developmental perspective of intelligence that includes both traditional ability and non-ability determinants of intelligence during adulthood. A reassessment of the nature of intelligence in adulthood is provided, in the context of a lifelong learning and investment model, called PPIK, for intelligence-as-Process, Personality, Interests, and intelligence-as-Knowledge (Ackerman, 1996).

Appearing smart: the impression management of intelligence, person perception accuracy, and behavior in social interaction.

PubMed

Murphy, Nora A

2007-03-01

Intelligence is an important trait that affects everyday social interaction. The present research utilized the ecological perspective of social perception to investigate the impression management of intelligence and strangers' evaluations of targets' intelligence levels. The ability to effectively portray an impression of intelligence to outside judges as well as interaction partners was appraised and the effect of impression management on the accurate judgment of intelligence was assessed. In addition, targets' behavior was studied in relation to impression management, perceived intelligence, and actual measured intelligence. Impression-managing targets appeared more intelligent to video judges but not to their interaction partner as compared to controls. The intelligence quotient (IQ) of impression-managing targets was more accurately judged than controls' IQ. Impression-managing targets displayed distinct nonverbal behavioral patterns that differed from controls. Looking while speaking was a key behavior: It significantly correlated with IQ, was successfully manipulated by impression-managing targets, and contributed to higher perceived intelligence ratings.

A New Tool for Classifying Small Solar System Objects

NASA Astrophysics Data System (ADS)

Desfosses, Ryan; Arel, D.; Walker, M. E.; Ziffer, J.; Harvell, T.; Campins, H.; Fernandez, Y. R.

2011-05-01

An artificial intelligence program, AutoClass, which was developed by NASA's Artificial Intelligence Branch, uses Bayesian classification theory to automatically choose the most probable classification distribution to describe a dataset. To investigate its usefulness to the Planetary Science community, we tested its ability to reproduce the taxonomic classes as defined by Tholen and Barucci (1989). Of the 406 asteroids from the Eight Color Asteroid Survey (ECAS) we chose for our test, 346 were firmly classified and all but 3 (<1%) were classified by Autoclass as they had been in the previous classification system (Walker et al., 2011). We are now applying it to larger datasets to improve the taxonomy of currently unclassified objects. Having demonstrated AutoClass's ability to recreate existing classification effectively, we extended this work to investigations of albedo-based classification systems. To determine how predictive albedo can be, we used data from the Infrared Astronomical Satellite (IRAS) database in conjunction with the large Sloan Digital Sky Survey (SDSS), which contains color and position data for over 200,000 classified and unclassified asteroids (Ivesic et al., 2001). To judge our success we compared our results with a similar approach to classifying objects using IRAS albedo and asteroid color by Tedesco et al. (1989). Understanding the distribution of the taxonomic classes is important to understanding the history and evolution of our Solar System. AutoClass's success in categorizing ECAS, IRAS and SDSS asteroidal data highlights its potential to scan large domains for natural classes in small solar system objects. Based upon our AutoClass results, we intend to make testable predictions about asteroids observed with the Wide-field Infrared Survey Explorer (WISE).

Enhancement web proxy cache performance using Wrapper Feature Selection methods with NB and J48

NASA Astrophysics Data System (ADS)

Mahmoud Al-Qudah, Dua'a.; Funke Olanrewaju, Rashidah; Wong Azman, Amelia

2017-11-01

Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algorithm is used to determine evict page when the cache is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may discard important pages just before use. Furthermore, using conventional algorithm cannot be well optimized since it requires some decision to intelligently evict a page before replacement. Hence, most researchers propose an integration among intelligent classifiers and replacement algorithm to improves replacement algorithms performance. This research proposes using automated wrapper feature selection methods to choose the best subset of features that are relevant and influence classifiers prediction accuracy. The result present that using wrapper feature selection methods namely: Best First (BFS), Incremental Wrapper subset selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of features and have a good impact on reducing computation time. Using PSO enhance NB classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04% over using J48 classifier with all features, using IWSS-embedded NB and using BFS respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation time of J48 by 2.998.

Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation.

PubMed

Di Domenico, Stefano I; Rodrigo, Achala H; Ayaz, Hasan; Fournier, Marc A; Ruocco, Anthony C

2015-04-01

Research on the neural efficiency hypothesis of intelligence (NEH) has revealed that the brains of more intelligent individuals consume less energy when performing easy cognitive tasks but more energy when engaged in difficult mental operations. However, previous studies testing the NEH have relied on cognitive tasks that closely resemble psychometric tests of intelligence, potentially confounding efficiency during intelligence-test performance with neural efficiency per se. The present study sought to provide a novel test of the NEH by examining patterns of prefrontal activity while participants completed an experimental paradigm that is qualitatively distinct from the contents of psychometric tests of intelligence. Specifically, participants completed a personal decision-making task (e.g., which occupation would you prefer, dancer or chemist?) in which they made a series of forced choices according to their subjective preferences. The degree of decisional conflict (i.e., choice difficulty) between the available response options was manipulated on the basis of participants' unique preference ratings for the target stimuli, which were obtained prior to scanning. Evoked oxygenation of the prefrontal cortex was measured using 16-channel continuous-wave functional near-infrared spectroscopy. Consistent with the NEH, intelligence predicted decreased activation of the right inferior frontal gyrus (IFG) during low-conflict situations and increased activation of the right-IFG during high-conflict situations. This pattern of right-IFG activity among more intelligent individuals was complemented by faster reaction times in high-conflict situations. These results provide new support for the NEH and suggest that the neural efficiency of more intelligent individuals generalizes to the performance of cognitive tasks that are distinct from intelligence tests. Copyright © 2015 Elsevier Inc. All rights reserved.

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

PubMed

Das, Nilakash; Topalovic, Marko; Janssens, Wim

2018-03-01

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

32 CFR 1908.33 - Designation of authority to hear appeals.

Code of Federal Regulations, 2014 CFR

2014-07-01

... writing to the Director, Information Management Services (D/IMS). The appeal shall set forth clearly and... INTELLIGENCE AGENCY PUBLIC REQUESTS FOR MANDATORY DECLASSIFICATION REVIEW OF CLASSIFIED INFORMATION PURSUANT TO... Chief, Information Review and Release Group and composed of the Information Review Officers from the...

32 CFR 1908.33 - Designation of authority to hear appeals.

Code of Federal Regulations, 2013 CFR

2013-07-01

... writing to the Director, Information Management Services (D/IMS). The appeal shall set forth clearly and... INTELLIGENCE AGENCY PUBLIC REQUESTS FOR MANDATORY DECLASSIFICATION REVIEW OF CLASSIFIED INFORMATION PURSUANT TO... Chief, Information Review and Release Group and composed of the Information Review Officers from the...

32 CFR 1908.33 - Designation of authority to hear appeals.

Code of Federal Regulations, 2012 CFR

2012-07-01

... writing to the Director, Information Management Services (D/IMS). The appeal shall set forth clearly and... INTELLIGENCE AGENCY PUBLIC REQUESTS FOR MANDATORY DECLASSIFICATION REVIEW OF CLASSIFIED INFORMATION PURSUANT TO... Chief, Information Review and Release Group and composed of the Information Review Officers from the...

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

NASA Astrophysics Data System (ADS)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples produced by machines are typically more complete. These findings set the stage for further investigations, with the ultimate goal of efficiently and accurately labeling the wide range of data classes that will arise from the planned large astronomical surveys.

Relation of intelligence to ego functioning in an adult psychiatric population.

PubMed

Allen, J G; Coyne, L; David, E

1986-01-01

Wechsler Adult Intelligence Scale-Revised (WAIS-R) IQs and clinical ratings of 10 ego functions in a diagnostically heterogeneous sample of 60 adult psychiatric inpatients were correlated. With severity of pathology statistically controlled, higher intelligence was associated with more adequate ego functioning in several spheres: primary autonomous functions, thought processes, object relations, and mastery-competence. There were also some clinically meaningful differences between the Verbal and Performance IQs in the pattern of correlations. Extending Hartmann's original views, the authors employ an ethological framework to conceptualize intelligence in relation to the ego's role in adaptation, emphasizing that intelligence is an important-albeit neglected-aspect of ego functioning.

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Bloch, B. Nicholas; Chappelow, Jonathan; Patel, Pratik; Rofsky, Neil; Lenkinski, Robert; Genega, Elizabeth; Madabhushi, Anant

2011-03-01

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

Pedestrian detection based on redundant wavelet transform

NASA Astrophysics Data System (ADS)

Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

2016-10-01

Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation. Part two: an intelligent system based on multi-classifier technique.

PubMed

Elminir, Hamdy K; Own, Hala S; Azzam, Yosry A; Riad, A M

2008-03-28

The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt.

If Not I.Q. - What?

ERIC Educational Resources Information Center

Tyler, Ralph W.

The use of psychological and educational tests in World War I led to their adoption in schools for testing intelligence and achievement in order to classify students academically according to a national norm. After World War II, rapid changes in occupational and social structure demanded the education and identification of students for employment.…

A Graphical, Self-Organizing Approach to Classifying Electronic Meeting Output.

ERIC Educational Resources Information Center

Orwig, Richard E.; Chen, Hsinchun; Nunamaker, Jay F., Jr.

1997-01-01

Describes research using an artificial intelligence approach in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information…

Modal Profiles for the WISC-III.

ERIC Educational Resources Information Center

Pritchard, David A.; Livingston, Ronald B.; Reynolds, Cecil R.; Moses, James A., Jr.

2000-01-01

Presents a normative typology for classifying the Wechsler Intelligence Scale for Children-Third Edition (WISC-III) factor index profiles according to profile shape. Current analyses indicate that overall profile level accounted for a majority of the variance in WISC-III index scores, but a considerable proportion of the variance was because of…

10 CFR 95.5 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 2 2012-01-01 2012-01-01 false Definitions. 95.5 Section 95.5 Energy NUCLEAR REGULATORY... information. Act means the Atomic Energy Act of 1954 (68 Stat. 919), as amended. Classified mail address means.... These agencies are the Department of Defense, the department of Energy, the Central Intelligence Agency...

10 CFR 95.5 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 2 2014-01-01 2014-01-01 false Definitions. 95.5 Section 95.5 Energy NUCLEAR REGULATORY... information. Act means the Atomic Energy Act of 1954 (68 Stat. 919), as amended. Classified mail address means.... These agencies are the Department of Defense, the department of Energy, the Central Intelligence Agency...

10 CFR 95.5 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 2 2011-01-01 2011-01-01 false Definitions. 95.5 Section 95.5 Energy NUCLEAR REGULATORY... information. Act means the Atomic Energy Act of 1954 (68 Stat. 919), as amended. Classified mail address means.... These agencies are the Department of Defense, the department of Energy, the Central Intelligence Agency...

10 CFR 95.5 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 2 2013-01-01 2013-01-01 false Definitions. 95.5 Section 95.5 Energy NUCLEAR REGULATORY... information. Act means the Atomic Energy Act of 1954 (68 Stat. 919), as amended. Classified mail address means.... These agencies are the Department of Defense, the department of Energy, the Central Intelligence Agency...

78 FR 23785 - Agency Information Collection Activities: Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-22

...) whether small businesses are affected by this collection. In this notice, NARA is soliciting comments..., Business or other for- profit, Federal government. Estimated number of respondents: 1,500. Estimated time... Government intelligence agencies for storage of classified information and serves to comply with E.O. 12958...

An Historical Framework for Cohort Differences in Intelligence

PubMed Central

Schaie, K. Warner; Willis, Sherry L.; Pennak, Sara

2006-01-01

This article reviews key issues regarding the controversy on the direction and magnitude of cohort differences in intelligence. Data from the Seattle Longitudinal Study (SLS) illustrate why differences must be studied across multiple cohorts and multiple chronological ages. Differential cohort patterns for multiple dimensions of intelligence are described. A conceptual framework is suggested for the identification of historical influences important for developmental study of cohort differences. PMID:16858496

Three Billy Goats and Gardner.

ERIC Educational Resources Information Center

Merrefield, Gayle Emery

1997-01-01

Describes a Jewish Community Center's efforts to adapt Gardner's multiple-intelligences theory to a preschool special-education program. Since most students had moderate speech disorders, teachers decided to deemphasize linguistic expression in favor of the other seven intelligences. They created successful units exploring patterns and size…

Relationships between phenylalanine levels, intelligence and socioeconomic status of patients with phenylketonuria.

PubMed

Castro, Isabel Pimenta Spínola; Borges, Juliana Martins; Chagas, Heloísa Alves; Tibúrcio, Jacqueline; Starling, Ana Lúcia Pimenta; Aguiar, Marcos José Burle de

2012-07-01

To assess intelligence and its relationship with blood phenylalanine concentrations and socioeconomic status in patients with phenylketonuria after 6 to 12 years of treatment. Sixty-three children were classified according to phenylalanine levels and socioeconomic status and assessed using the Wechsler Intelligence Scale for Children. The Statistical Package for the Social Sciences (SPSS) was used to analyze phenylalanine; ANOVA was used to analyze intelligence quotients (IQ) and phenylalanine levels; and ordinal logistic regression was used to analyze the likelihood of higher IQ. The overall IQ scores of 90.5% of the children were within a range from borderline intellectual deficiency to very high intelligence; for verbal IQ this proportion was 96.8% and 92.1% had performance IQ scores within this band. The categories from low to upper-medium socioeconomic status contained 98.4% of patients' families. The likelihood of having medium to high IQ was 4.29 times greater for children with good phenylalanine control and 4.03 greater for those from higher socioeconomic strata. Treatment prevented mental retardation in 90.5% of the patients. Control of phenylalanine levels and higher socioeconomic status were associated with higher IQ scores.

Mapping spatial patterns with morphological image processing

Treesearch

Peter Vogt; Kurt H. Riitters; Christine Estreguil; Jacek Kozak; Timothy G. Wade; James D. Wickham

2006-01-01

We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as 'perforated,' 'edge,' 'patch,' and 'core' with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the...

[The relationship between cognitive intelligence, emotional intelligence, coping and stress symptoms in the context of type A personality pattern].

PubMed

Hisli Sahin, Nesrin; Güler, Murat; Basim, H Nejat

2009-01-01

This study aimed to determine the relationships between cognitive and emotional intelligence, coping and stress symptoms in the context of Type A personality pattern. The Raven Progressive Matrices, Emotional Intelligence Questionnaire, Ways of Coping Inventory, Stress Symptoms Scale, and Type A Personality Scale were administered to 271 university students. Two groups, Type As and Type Bs were created according to the Type A Personality Scale scores and were compared in terms of their scores on the other scales that were administered. Our analyses showed that stress symptoms were negatively correlated with effective coping, stress management, and general mood dimensions of the Emotional Intelligence Questionnaire. They were also positively correlated with ineffective coping and Type A behaviors. Being female also significantly predicted stress symptoms. When the participants were grouped according to Type A Personality Scale scores as Type As and Type Bs, the regression analysis showed that the stress symptoms of Type As were significantly predicted by the insufficient use of effective coping styles and deficiencies in the general mood component of emotional intelligence, whereas the stress symptoms of Type Bs were predicted by the insufficient use of effective coping styles, overuse of ineffective coping styles, and increase in the intrapersonal abilities component of emotional intelligence. Stress symptoms can be related to the variables associated with personality styles. It is suggested that stress management programs for Type As should include exercises that increase emotional intelligence, especially the components of drawing pleasure from their life situation, being more positive, hopeful and optimistic.

Use of a machine learning algorithm to classify expertise: analysis of hand motion patterns during a simulated surgical task.

PubMed

Watson, Robert A

2014-08-01

To test the hypothesis that machine learning algorithms increase the predictive power to classify surgical expertise using surgeons' hand motion patterns. In 2012 at the University of North Carolina at Chapel Hill, 14 surgical attendings and 10 first- and second-year surgical residents each performed two bench model venous anastomoses. During the simulated tasks, the participants wore an inertial measurement unit on the dorsum of their dominant (right) hand to capture their hand motion patterns. The pattern from each bench model task performed was preprocessed into a symbolic time series and labeled as expert (attending) or novice (resident). The labeled hand motion patterns were processed and used to train a Support Vector Machine (SVM) classification algorithm. The trained algorithm was then tested for discriminative/predictive power against unlabeled (blinded) hand motion patterns from tasks not used in the training. The Lempel-Ziv (LZ) complexity metric was also measured from each hand motion pattern, with an optimal threshold calculated to separately classify the patterns. The LZ metric classified unlabeled (blinded) hand motion patterns into expert and novice groups with an accuracy of 70% (sensitivity 64%, specificity 80%). The SVM algorithm had an accuracy of 83% (sensitivity 86%, specificity 80%). The results confirmed the hypothesis. The SVM algorithm increased the predictive power to classify blinded surgical hand motion patterns into expert versus novice groups. With further development, the system used in this study could become a viable tool for low-cost, objective assessment of procedural proficiency in a competency-based curriculum.

Local feature saliency classifier for real-time intrusion monitoring

NASA Astrophysics Data System (ADS)

Buch, Norbert; Velastin, Sergio A.

2014-07-01

We propose a texture saliency classifier to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real time. No temporal image information is used during the classification. The system is used for the task of detecting people entering a sterile zone, which is a common scenario for visual surveillance. Testing is performed on the Imagery Library for Intelligent Detection Systems sterile zone benchmark dataset of the United Kingdom's Home Office. The basic classifier is extended by fusing its output with simple motion information, which significantly outperforms standard motion tracking. A lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running at 10 fps gives the highest result of F1=0.92 for the 24-h test dataset. The paper concludes with a detailed analysis of the computation time required for the different parts of the algorithm.

Fall Down Detection Under Smart Home System.

PubMed

Juang, Li-Hong; Wu, Ming-Ni

2015-10-01

Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used.

Emotional intelligence, personality, and gender as factors in disordered eating patterns.

PubMed

Zysberg, Leehu

2014-08-01

We examined the hypotheses that proposing higher levels of emotional intelligence (ability test and self-report) and lower neuroticism, extraversion, and agreeableness associate with lower levels of disordered eating. In a correlational study, 126 Israeli college students completed two measures of emotional intelligence, a brief five-factor personality test, demographic data questionnaires, and questionnaires assessing food preoccupation, namely, the Body Weight, Image and Self-Esteem Scale and the Appearance Schema Inventory. Results suggested that ability emotional intelligence is associated with disordered eating beyond gender and personality. Self-reported emotional intelligence did not associate with any of the outcomes after controlling for personality. Implications and applications are briefly discussed. © The Author(s) 2013.

Performance evaluation of various classifiers for color prediction of rice paddy plant leaf

NASA Astrophysics Data System (ADS)

Singh, Amandeep; Singh, Maninder Lal

2016-11-01

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

Orthogonal Patterns In A Binary Neural Network

NASA Technical Reports Server (NTRS)

Baram, Yoram

1991-01-01

Report presents some recent developments in theory of binary neural networks. Subject matter relevant to associate (content-addressable) memories and to recognition of patterns - both of considerable importance in advancement of robotics and artificial intelligence. When probed by any pattern, network converges to one of stored patterns.

An Intelligent System for Document Retrieval in Distributed Office Environments.

ERIC Educational Resources Information Center

Mukhopadhyay, Uttam; And Others

1986-01-01

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns and user interests and preferences during system usage, it customizes document retrievals for…

Artificial Neural Networks and Instructional Technology.

ERIC Educational Resources Information Center

Carlson, Patricia A.

1991-01-01

Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

Robust through-the-wall radar image classification using a target-model alignment procedure.

PubMed

Smith, Graeme E; Mobasseri, Bijan G

2012-02-01

A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥ 97%. © 2011 IEEE

Intelligent query by humming system based on score level fusion of multiple classifiers

NASA Astrophysics Data System (ADS)

Pyo Nam, Gi; Thu Trang Luong, Thi; Ha Nam, Hyun; Ryoung Park, Kang; Park, Sung-Joo

2011-12-01

Recently, the necessity for content-based music retrieval that can return results even if a user does not know information such as the title or singer has increased. Query-by-humming (QBH) systems have been introduced to address this need, as they allow the user to simply hum snatches of the tune to find the right song. Even though there have been many studies on QBH, few have combined multiple classifiers based on various fusion methods. Here we propose a new QBH system based on the score level fusion of multiple classifiers. This research is novel in the following three respects: three local classifiers [quantized binary (QB) code-based linear scaling (LS), pitch-based dynamic time warping (DTW), and LS] are employed; local maximum and minimum point-based LS and pitch distribution feature-based LS are used as global classifiers; and the combination of local and global classifiers based on the score level fusion by the PRODUCT rule is used to achieve enhanced matching accuracy. Experimental results with the 2006 MIREX QBSH and 2009 MIR-QBSH corpus databases show that the performance of the proposed method is better than that of single classifier and other fusion methods.

Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

ERIC Educational Resources Information Center

García-Floriano, Andrés; Ferreira-Santiago, Angel; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Villuendas-Rey, Yenny

2017-01-01

Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present.…

Listening and Language at 4 Years of Age: Effects of Early Otitis Media.

ERIC Educational Resources Information Center

Gravel, Judith S.; Wallace, Ina F.

1992-01-01

Examination of 23 4-year-old children classified otitis media negative or positive during their first year of life indicated that otitis positive children required a more advantageous signal-to-competition ratio for sentence intelligibility, compared to otitis-negative peers. No intergroup differences were found in receptive or expressive language…

Setting National Priorities: The 1974 Budget.

ERIC Educational Resources Information Center

Fried, Edward R.; And Others

This book attempts to make the problem of budgetary choice at the federal level more intelligible by classifying, analyzing, and projecting into the future the components of the budget in a way which makes it possible to put together several comprehensive alternative budgets, each illustrating from a different view how the Federal Government could…

Satire, Surveillance, and the State: A Classified Primer

ERIC Educational Resources Information Center

Bogad, L. M.

2007-01-01

This article explores the use of ironic performance in education, particularly around issues of human rights. I examine my own efforts to engage audiences with the history of domestic espionage and sabotage by the intelligence agencies of the United States. This is a history well known to some marginalized counterpublics (see Fraser, 1997), but…

Soviet Espionage Effort Have Targeted U.S. Research Libraries and Staffs since 1962, FBI Charges in Report.

ERIC Educational Resources Information Center

Turner, Judith Axler

1988-01-01

Soviet intelligence agents have been collecting scientific and technical documents in research libraries to identify emerging technology before its components become classified or restricted. Librarians are also recruited as spies. However, asking librarians to identify suspicious library users would violate ethics and intellectual freedom. (MSE)

A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.

PubMed

Gunavathi, Chellamuthu; Premalatha, Kandasamy

2014-01-01

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.

Identification of COPD patients' health status using an intelligent system in the CHRONIOUS wearable platform.

PubMed

Bellos, Christos C; Papadopoulos, Athanasios; Rosso, Roberto; Fotiadis, Dimitrios I

2014-05-01

The CHRONIOUS system offers an integrated platform aiming at the effective management and real-time assessment of the health status of the patient suffering from chronic obstructive pulmonary disease (COPD). An intelligent system is developed for the analysis and the real-time evaluation of patient's condition. A hybrid classifier has been implemented on a personal digital assistant, combining a support vector machine, a random forest, and a rule-based system to provide a more advanced categorization scheme for the early and in real-time characterization of a COPD episode. This is followed by a severity estimation algorithm which classifies the identified pathological situation in different levels and triggers an alerting mechanism to provide an informative and instructive message/advice to the patient and the clinical supervisor. The system has been validated using data collected from 30 patients that have been annotated by experts indicating 1) the severity level of the current patient's health status, and 2) the COPD disease level of the recruited patients according to the GOLD guidelines. The achieved characterization accuracy has been found 94%.

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

NASA Astrophysics Data System (ADS)

Kao, Meng-Chun; Ting, Chien-Kun; Kuo, Wen-Chuan

2018-02-01

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Trends in telemedicine utilizing artificial intelligence

NASA Astrophysics Data System (ADS)

Pacis, Danica Mitch M.; Subido, Edwin D. C.; Bugtai, Nilo T.

2018-02-01

With the growth and popularity of the utilization of artificial intelligence (AI) in several fields and industries, studies in the field of medicine have begun to implement its capabilities in handling and analyzing data to telemedicine. With the challenges in the implementation of telemedicine, there has been a need to expand its capabilities and improve procedures to be specialized to solve specific problems. The versatility and flexibility of both AI and telemedicine gave the endless possibilities for development and these can be seen in the literature reviewed in this paper. The trends in the development of the utilization of this technology can be classified in to four: patient monitoring, healthcare information technology, intelligent assistance diagnosis, and information analysis collaboration. Each trend will be discussed and presented with examples of recent literature and the problems they aim to address. Related references will also be tabulated and categorized to see the future and potential of this current trend in telemedicine.

MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis.

PubMed

Salvatore, Christian; Cerasa, Antonio; Castiglioni, Isabella

2018-01-01

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests.

MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer’s Dementia 24 Months Before Probable Diagnosis

PubMed Central

Salvatore, Christian; Cerasa, Antonio; Castiglioni, Isabella

2018-01-01

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests. PMID:29881340

Can we make a carpet smart enough to detect falls?

PubMed

Muheidat, Fadi; Tyrer, Harry W

2016-08-01

In this paper, we have enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our hardware front end reads 128 sensors, with sensors output a voltage due to a person walking or falling on the carpet. The processor is Jetson TK1, which provides more computing power than before. We generated a dataset with volunteers who walked and fell to test our algorithms. Data obtained allowed examining data frames (a frame is a single scan of the carpet sensors) read from the data acquisition system. We used different algorithms and techniques, and varied the windows size of number of frames (WS ≥ 1) and threshold (TH) to build our data set, which later used machine learning to help decide a fall or no fall. We then used the dataset obtained from applying a set of fall detection algorithms and the video recorded for the fall pattern experiments to train a set of classifiers using multiple test options using the Weka framework. We measured the sensitivity and specificity of the system and other metrics for intelligent detection of falls. Results showed that Computational Intelligence techniques detect falls with 96.2% accuracy and 81% sensitivity and 97.8% specificity. In addition to fall detection, we developed a database system and web applications to retain these data for years. We can display this data in realtime and for all activities in the carpet for extensive data analysis any time in the future.

Resource-constrained Data Collection and Fusion for Identifying Weak Distributed Patterns in Networks

DTIC Science & Technology

2013-10-15

statistic,” in Artifical Intelligence and Statistics (AISTATS), 2013. [6] ——, “Detecting activity in graphs via the Graph Ellipsoid Scan Statistic... Artifical Intelligence and Statistics (AISTATS), 2013. [8] ——, “Near-optimal anomaly detection in graphs using Lovász Extended Scan Statistic,” in Neural...networks,” in Artificial Intelligence and Statistics (AISTATS), 2010. 11 [11] D. Aldous, “The random walk construction of uniform spanning trees and

Coupling artificial intelligence and numerical computation for engineering design (Invited paper)

NASA Astrophysics Data System (ADS)

Tong, S. S.

1986-01-01

The possibility of combining artificial intelligence (AI) systems and numerical computation methods for engineering designs is considered. Attention is given to three possible areas of application involving fan design, controlled vortex design of turbine stage blade angles, and preliminary design of turbine cascade profiles. Among the AI techniques discussed are: knowledge-based systems; intelligent search; and pattern recognition systems. The potential cost and performance advantages of an AI-based design-generation system are discussed in detail.

The Design and Performance Characteristics of a Cellular Logic 3-D Image Classification Processor.

DTIC Science & Technology

1981-04-01

34 AGARD Proc. No. 94 on Artificiel Intelligence , 217: 1-13 (1971) 7. Golay, Marcel J. E. "Hexagonal Parallel Pattern Transformations." IEEE Trans. on...nonrandom nature of the data and features must be understood in order to intelligently select a reasonable three-dimensional noise filter. This completes...tactical targets which are located hundreds of meters away and are controlled and disguised by equally intelligent human beings, the difficulty of the

Intelligibility of clear speech: effect of instruction.

PubMed

Lam, Jennifer; Tjaden, Kris

2013-10-01

The authors investigated how clear speech instructions influence sentence intelligibility. Twelve speakers produced sentences in habitual, clear, hearing impaired, and overenunciate conditions. Stimuli were amplitude normalized and mixed with multitalker babble for orthographic transcription by 40 listeners. The main analysis investigated percentage-correct intelligibility scores as a function of the 4 conditions and speaker sex. Additional analyses included listener response variability, individual speaker trends, and an alternate intelligibility measure: proportion of content words correct. Relative to the habitual condition, the overenunciate condition was associated with the greatest intelligibility benefit, followed by the hearing impaired and clear conditions. Ten speakers followed this trend. The results indicated different patterns of clear speech benefit for male and female speakers. Greater listener variability was observed for speakers with inherently low habitual intelligibility compared to speakers with inherently high habitual intelligibility. Stable proportions of content words were observed across conditions. Clear speech instructions affected the magnitude of the intelligibility benefit. The instruction to overenunciate may be most effective in clear speech training programs. The findings may help explain the range of clear speech intelligibility benefit previously reported. Listener variability analyses suggested the importance of obtaining multiple listener judgments of intelligibility, especially for speakers with inherently low habitual intelligibility.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

PubMed

Tian, Hao; Yan, Zhaoli; Yang, Jun

2018-04-09

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

Neural dissociation in the production of lexical versus classifier signs in ASL: distinct patterns of hemispheric asymmetry.

PubMed

Hickok, Gregory; Pickell, Herbert; Klima, Edward; Bellugi, Ursula

2009-01-01

We examine the hemispheric organization for the production of two classes of ASL signs, lexical signs and classifier signs. Previous work has found strong left hemisphere dominance for the production of lexical signs, but several authors have speculated that classifier signs may involve the right hemisphere to a greater degree because they can represent spatial information in a topographic, non-categorical manner. Twenty-one unilaterally brain damaged signers (13 left hemisphere damaged, 8 right hemisphere damaged) were presented with a story narration task designed to elicit both lexical and classifier signs. Relative frequencies of the two types of errors were tabulated. Left hemisphere damaged signers produced significantly more lexical errors than did right hemisphere damaged signers, whereas the reverse pattern held for classifier signs. Our findings argue for different patterns of hemispheric asymmetry for these two classes of ASL signs. We suggest that the requirement to encode analogue spatial information in the production of classifier signs results in the increased involvement of the right hemisphere systems.

Emotional Intelligence Levels and Counselling Skills of Prospective Psychological Counsellors

ERIC Educational Resources Information Center

Odaci, Hatice; Degerli, Fatma Irem; Bolat, Neslihan

2017-01-01

This research aimed to determine the correlation between emotional intelligence (EI) and counselling skills of Turkish prospective psychological counsellors and to investigate differences in both EI and counselling skills in terms of sex, previous experience of group studies, and class levels. Within a correlational pattern, the sample of the…

How Brain Research Has Changed Our Understanding of Giftedness

ERIC Educational Resources Information Center

Clark, Barbara

2009-01-01

Understanding brain development and its relationship to intelligence promotes a clearer understanding of giftedness. Children are born with unique patterns and pathways which provide potential for high levels of intelligence. Parents and teachers contribute to the development of giftedness with experiences that are appropriately stimulating. It is…

Working Memory Training: Improving Intelligence--Changing Brain Activity

ERIC Educational Resources Information Center

Jausovec, Norbert; Jausovec, Ksenija

2012-01-01

The main objectives of the study were: to investigate whether training on working memory (WM) could improve fluid intelligence, and to investigate the effects WM training had on neuroelectric (electroencephalography--EEG) and hemodynamic (near-infrared spectroscopy--NIRS) patterns of brain activity. In a parallel group experimental design,…

Career Indecision versus Indecisiveness: Associations with Personality Traits and Emotional Intelligence

ERIC Educational Resources Information Center

Di Fabio, Annamaria; Palazzeschi, Letizia; Asulin-Peretz, Lisa; Gati, Itamar

2013-01-01

The goal of the present study was to investigate the distinctions between career indecision and indecisiveness. The different patterns of the associations between career indecision and indecisiveness, on one hand, and personality traits, career decision-making self-efficacy, perceived social support, and emotional intelligence, on the other, were…

Deductive Error Diagnosis and Inductive Error Generalization for Intelligent Tutoring Systems.

ERIC Educational Resources Information Center

Hoppe, H. Ulrich

1994-01-01

Examines the deductive approach to error diagnosis for intelligent tutoring systems. Topics covered include the principles of the deductive approach to diagnosis; domain-specific heuristics to solve the problem of generalizing error patterns; and deductive diagnosis and the hypertext-based learning environment. (Contains 26 references.) (JLB)

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

NASA Technical Reports Server (NTRS)

Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

1995-01-01

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

Use of Pattern Classification Algorithms to Interpret Passive and Active Data Streams from a Walking-Speed Robotic Sensor Platform

NASA Astrophysics Data System (ADS)

Dieckman, Eric Allen

In order to perform useful tasks for us, robots must have the ability to notice, recognize, and respond to objects and events in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we investigate the performance of a number of sensor modalities in an unstructured outdoor environment, including the Microsoft Kinect, thermal infrared camera, and coffee can radar. Special attention is given to acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the Kinect microphone array records the reflected backscattered signal. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. A small-dimensional feature vector is created for each measurement using an intelligent feature selection process for use in statistical pattern classification routines. Using our experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results.

Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers.

PubMed

Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2010-07-15

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and relating neural activity to behavior. Copyright 2010 Elsevier Inc. All rights reserved.

Towards intelligent diagnostic system employing integration of mathematical and engineering model

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

Isa, Nor Ashidi Mat

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability ofmore » the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.« less

Towards intelligent diagnostic system employing integration of mathematical and engineering model

NASA Astrophysics Data System (ADS)

Isa, Nor Ashidi Mat

2015-05-01

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation

NASA Astrophysics Data System (ADS)

Li, Zheng; Jiang, Yi-han; Duan, Lian; Zhu, Chao-zhe

2017-08-01

Objective. Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). Approach. GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. Main results. Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus  <54% in two-choice classification accuracy. Significance. We believe GMMAC will be useful for clinical fNIRS-based brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.

An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

PubMed

Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

2015-04-01

In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Artificial Intelligence: Threat or Boon to Radiologists?

PubMed

Recht, Michael; Bryan, R Nick

2017-11-01

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

[Control of intelligent car based on electroencephalogram and neurofeedback].

PubMed

Li, Song; Xiong, Xin; Fu, Yunfa

2018-02-01

To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.

Demosaicking for full motion video 9-band SWIR sensor

NASA Astrophysics Data System (ADS)

Kanaev, Andrey V.; Rawhouser, Marjorie; Kutteruf, Mary R.; Yetzbacher, Michael K.; DePrenger, Michael J.; Novak, Kyle M.; Miller, Corey A.; Miller, Christopher W.

2014-05-01

Short wave infrared (SWIR) spectral imaging systems are vital for Intelligence, Surveillance, and Reconnaissance (ISR) applications because of their abilities to autonomously detect targets and classify materials. Typically the spectral imagers are incapable of providing Full Motion Video (FMV) because of their reliance on line scanning. We enable FMV capability for a SWIR multi-spectral camera by creating a repeating pattern of 3x3 spectral filters on a staring focal plane array (FPA). In this paper we present the imagery from an FMV SWIR camera with nine discrete bands and discuss image processing algorithms necessary for its operation. The main task of image processing in this case is demosaicking of the spectral bands i.e. reconstructing full spectral images with original FPA resolution from spatially subsampled and incomplete spectral data acquired with the choice of filter array pattern. To the best of author's knowledge, the demosaicking algorithms for nine or more equally sampled bands have not been reported before. Moreover all existing algorithms developed for demosaicking visible color filter arrays with less than nine colors assume either certain relationship between the visible colors, which are not valid for SWIR imaging, or presence of one color band with higher sampling rate compared to the rest of the bands, which does not conform to our spectral filter pattern. We will discuss and present results for two novel approaches to demosaicking: interpolation using multi-band edge information and application of multi-frame super-resolution to a single frame resolution enhancement of multi-spectral spatially multiplexed images.

Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods.

PubMed

Chesnokov, Yuriy V

2008-06-01

Paroxysmal atrial fibrillation (PAF) is a serious arrhythmia associated with morbidity and mortality. We explore the possibility of distant prediction of PAF by analyzing changes in heart rate variability (HRV) dynamics of non-PAF rhythms immediately before PAF event. We use that model for distant prognosis of PAF onset with artificial intelligence methods. We analyzed 30-min non-PAF HRV records from 51 subjects immediately before PAF onset and at least 45min distant from any PAF event. We used spectral and complexity analysis with sample (SmEn) and approximate (ApEn) entropies and their multiscale versions on extracted HRV data. We used that features to train the artificial neural networks (ANNs) and support vector machine (SVM) classifiers to differentiate the subjects. The trained classifiers were further tested for distant PAF event prognosis on 16 subjects from independent database on non-PAF rhythm lasting from 60 to 320 min before PAF onset classifying the 30-min segments as distant or leading to PAF. We found statistically significant increase in 30-min non-PAF HRV recordings from 51 subjects in the VLF, LF, HF bands and total power (p<0.0001) before PAF event compared to PAF distant ones. The SmEn and ApEn analysis provided significant decrease in complexity (p<0.0001 and p<0.001) before PAF onset. For training ANN and SVM classifiers the data from 51 subjects were randomly split to training, validation and testing. ANN provided better results in terms of sensitivity (Se), specificity (Sp) and positive predictivity (Pp) compared to SVM which became biased towards positive case. The validation results of the ANN classifier we achieved: Se 76%, Sp 93%, Pp 94%. Testing ANN and SVM classifiers on 16 subjects with non-PAF HRV data preceding PAF events we obtained distant prediction of PAF onset with SVM classifier in 10 subjects (58+/-18 min in advance). ANN classifier provided distant prediction of PAF event in 13 subjects (62+/-21 min in advance). From the results of distant PAF prediction we conclude that ANN and SVM classifiers learned the changes in the HRV dynamics immediately before PAF event and successfully identified them during distant PAF prognosis on independent database. This confirms the reported in the literature results that corresponding changes in the HRV data occur about 60 min before PAF onset and proves the possibility of distant PAF prediction with ANN and SVM methods.

Style consistent classification of isogenous patterns.

PubMed

Sarkar, Prateek; Nagy, George

2005-01-01

In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.

Genes and personality characteristics: Possible association of the genetic background with intelligence and decision making in 830 Caucasian Greek subjects

PubMed Central

Marinos, Georgios; Naziris, Nikolaos; Limnaios, Stefanos A.; Drakoulis, Nikolaos

2014-01-01

It is well known that intelligence consists of a variety of interactional and cognitive skills and abilities (e.g. tradecraft; critical and divergent thinking; perception of foreign information). Decision making is defined as the conscious choice between given options, relating to a problem. Both genetic background and environment comprise key elements for personality characteristics of the human being. The aim of this study is to determine the frequency distribution of rs324420, rs1800497, rs363050, rs6265, rs1328674 polymorphisms known to be involved in individual personality characteristics, in 830 Greek Subjects. The study is independent from direct clinical measurements (e.g. IQ measurements; physiological tests). The population of the volunteers is described, based on genotype, sex, with the respective gene frequencies, including the Minor Allele Frequency (MAF). A potential influence of the volunteer gender with the above characteristics (based on genotypes and alleles) is examined and finally, volunteers are classified as follows: A volunteer receives + 1, for each genotype/allele, which enhances his intelligence or his decision-making. In contrast, he receives − 1, for each genotype/allele, which relegates the individual characteristic. No statistically significant gender-characteristics correlation is observed. According to their genetic profile, a rate of 92.5%, of the volunteers may be characterized by prudence and temperance of thought, with only a small proportion of them (7.5%) may be classified as genetically spontaneous and adventurous. Regarding intelligence, the study population may lay around average and a little above it, at a rate of 96.3%, while the edges of the scale suggest only a 0.5% of the volunteers, who, although the “smartest”, somehow seem to lack prudence. In conclusion, individuals with low cognitive ability may be more prudent than others and vice versa, while the “smartest” ones tend to be more risky, in decision-making. Therefore, intelligence and decision-making may, after all, be less linked to each other than expected. PMID:25606466

Classifying free-text triage chief complaints into syndromic categories with natural language processing.

PubMed

Chapman, Wendy W; Christensen, Lee M; Wagner, Michael M; Haug, Peter J; Ivanov, Oleg; Dowling, John N; Olszewski, Robert T

2005-01-01

Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form. We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah. The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively. Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.

3 CFR 13526 - Executive Order 13526 of December 29, 2009. Classified National Security Information

Code of Federal Regulations, 2010 CFR

2010-01-01

... of weapons of mass destruction. Sec. 1.5. Duration of Classification. (a) At the time of original... intelligence source or key design concepts of weapons of mass destruction, the date or event shall not exceed the time frame established in paragraph (b) of this section. (b) If the original classification...

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

NASA Technical Reports Server (NTRS)

Swain, P. H. (Principal Investigator)

1981-01-01

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented.

Futures Thinking, Learning, and Leading: Applying Multiple Intelligences to Success and Innovation

ERIC Educational Resources Information Center

Buchen, Irving H.

2006-01-01

The focus of this book is to explore the extent to which our thinking, learning, and leading is influenced and shaped by the future. In the process, professionals and organizations are classified into three basic types: future-oriented, future-poised, and future-driven. The last typically employs divergent and convergent thinking and planning; and…

Passively Classifying Student Mood and Performance within Intelligent Tutors

ERIC Educational Resources Information Center

Sottilare, Robert A.; Proctor, Michael

2012-01-01

It has been long recognized that successful human tutors are capable of adapting instruction to mitigate barriers (e.g., withdrawal or frustration) to learning during the one-to-one tutoring process. A significant part of the success of human tutors is based on their perception of student affect (e.g., mood or emotions). To at least match the…

An Operational Definition of Learning Disabilities (Cognitive Domain) Using WISC Full Scale IQ and Peabody Individual Achievement Test Scores

ERIC Educational Resources Information Center

Brenton, Beatrice White; Gilmore, Doug

1976-01-01

An operational index of discrepancy to assist in identifying learning disabilities was derived using the Full Scale IQ, Wechsler Intelligence Scale for Children, and relevant subtest scores on the Peabody Individual Achievement Test. Considerable caution should be exercised when classifying children, especially females, as learning disabled.…

General intelligence does not help us understand cognitive evolution.

PubMed

Shuker, David M; Barrett, Louise; Dickins, Thomas E; Scott-Phillips, Thom C; Barton, Robert A

2017-01-01

Burkart et al. conflate the domain-specificity of cognitive processes with the statistical pattern of variance in behavioural measures that partly reflect those processes. General intelligence is a statistical abstraction, not a cognitive trait, and we argue that the former does not warrant inferences about the nature or evolution of the latter.

Study of Emotional Intelligence Patterns with Teachers Working in Public Education

ERIC Educational Resources Information Center

Balázs, László

2015-01-01

The data necessary for the empirical research presented it this study were provided by 572 people, from altogether 26 schools. The schools included 18 primary schools, 7 secondary training institutions and 1 primary and secondary school. The major question of the study related to the pedagogues' emotional intelligence, more precisely if the…

Adapting Collaboration Dialogue in Response to Intelligent Tutoring System Feedback

ERIC Educational Resources Information Center

Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

To be able to provide better support for collaborative learning in Intelligent Tutoring Systems, it is important to understand how collaboration patterns change. Prior work has looked at the interdependencies between utterances and the change of dialogue over time, but it has not addressed how dialogue changes during a lesson, an analysis that…

Automated Detection of Essay Revising Patterns: Applications for Intelligent Feedback in a Writing Tutor

ERIC Educational Resources Information Center

Roscoe, Rod D.; Snow, Erica L.; Allen, Laura K.; McNamara, Danielle S.

2015-01-01

The Writing Pal is an intelligent tutoring system designed to support writing proficiency and strategy acquisition for adolescent writers. A fundamental aspect of the instructional model is automated formative feedback that provides concrete information and strategies oriented toward student improvement. In this paper, the authors explore…

Dietary Patterns and Intelligence in Early and Middle Childhood

ERIC Educational Resources Information Center

Theodore, Reremoana F.; Thompson, John M. D.; Waldie, Karen E.; Wall, Clare; Becroft, David M. O.; Robinson, Elizabeth; Wild, Chris J.; Clark, Philippa M.; Mitchell, Ed A.

2009-01-01

The association between intelligence and diet at 3.5 and 7 years was examined in 591 children of European descent. Approximately half of the children were born small-for-gestational age (birth weight @?10th percentile). The relationship between IQ and diet (measured by food frequency) was investigated using multiple regression analyses. Eating…

Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning

ERIC Educational Resources Information Center

Bouchet, Francois; Azevedo, Roger; Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…

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

PubMed Central

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

2016-01-01

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

Family Background Buys an Education in Minnesota but Not in Sweden

PubMed Central

Johnson, Wendy; Deary, Ian J.; Silventoinen, Karri; Tynelius, Per; Rasmussen, Finn

2010-01-01

Educational attainment, the highest degree or level of schooling obtained, is associated with important life outcomes, at both the individual level and the group level. Because of this, and because education is expensive, the allocation of education across society is an important social issue. A dynamic quantitative environmental-genetic model can help document the effects of social allocation patterns. We used this model to compare the moderating effect of general intelligence on the environmental and genetic factors that influence educational attainment in Sweden and the U.S. state of Minnesota. Patterns of genetic influence on educational outcomes were similar in these two regions, but patterns of shared environmental influence differed markedly. In Sweden, shared environmental influence on educational attainment was particularly important for people of high intelligence, whereas in Minnesota, shared environmental influences on educational attainment were particularly important for people of low intelligence. This difference may be the result of differing access to education: state-supported access (on the basis of ability) to a uniform higher-education system in Sweden, versus family-supported access to a more diverse higher-education system in the United States. PMID:20679521

Family background buys an education in Minnesota but not in Sweden.

PubMed

Johnson, Wendy; Deary, Ian J; Silventoinen, Karri; Tynelius, Per; Rasmussen, Finn

2010-09-01

Educational attainment, the highest degree or level of schooling obtained, is associated with important life outcomes, at both the individual level and the group level. Because of this, and because education is expensive, the allocation of education across society is an important social issue. A dynamic quantitative environmental-genetic model can help document the effects of social allocation patterns. We used this model to compare the moderating effect of general intelligence on the environmental and genetic factors that influence educational attainment in Sweden and the U.S. state of Minnesota. Patterns of genetic influence on educational outcomes were similar in these two regions, but patterns of shared environmental influence differed markedly. In Sweden, shared environmental influence on educational attainment was particularly important for people of high intelligence, whereas in Minnesota, shared environmental influences on educational attainment were particularly important for people of low intelligence. This difference may be the result of differing access to education: state-supported access (on the basis of ability) to a uniform higher-education system in Sweden versus family-supported access to a more diverse higher-education system in the United States.

The role of soft computing in intelligent machines.

PubMed

de Silva, Clarence W

2003-08-15

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Distribution of dilemma zone after intelligent transportation system established

NASA Astrophysics Data System (ADS)

Deng, Yuanchang; Yang, Huiqin; Wu, Linying

2017-03-01

Dilemma zone refers to an area where vehicles can neither clear the intersection during the yellow interval nor stop safely before the stop line. The purpose of this paper is to analyzing the distribution of two types of dilemma zone after intelligent transportation system (ITS) established at Outer Ring Roads signalized intersections in Guangzhou Higher Education Mega Center. To collect field data a drone aircraft was used. When calculating the type II dilemma zone's distribution, we considered the information of drivers' aggressiveness, which was classified by driving speed and type I dilemma zone as well. We also compared the two types dilemma zone's distribution before and after ITS established and analyzed the changes, which was brought by ITS.

An intelligent interactive visual database management system for Space Shuttle closeout image management

NASA Technical Reports Server (NTRS)

Ragusa, James M.; Orwig, Gary; Gilliam, Michael; Blacklock, David; Shaykhian, Ali

1994-01-01

Status is given of an applications investigation on the potential for using an expert system shell for classification and retrieval of high resolution, digital, color space shuttle closeout photography. This NASA funded activity has focused on the use of integrated information technologies to intelligently classify and retrieve still imagery from a large, electronically stored collection. A space shuttle processing problem is identified, a working prototype system is described, and commercial applications are identified. A conclusion reached is that the developed system has distinct advantages over the present manual system and cost efficiencies will result as the system is implemented. Further, commercial potential exists for this integrated technology.

Consistency of hand preference: predictions to intelligence and school achievement.

PubMed

Kee, D W; Gottfried, A; Bathurst, K

1991-05-01

Gottfried and Bathurst (1983) reported that hand preference consistency measured over time during infancy and early childhood predicts intellectual precocity for females, but not for males. In the present study longitudinal assessments of children previously classified by Gottfried and Bathurst as consistent or nonconsistent in cross-time hand preference were conducted during middle childhood (ages 5 to 9). Findings show that (a) early measurement of hand preference consistency for females predicts school-age intellectual precocity, (b) the locus of the difference between consistent vs. nonconsistent females is in verbal intelligence, and (c) the precocity of the consistent females was also revealed on tests of school achievement, particularly tests of reading and mathematics.

Artificial Intelligence In Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Vogel, Alison Andrews

1991-01-01

Paper compares four first-generation artificial-intelligence (Al) software systems for computational fluid dynamics. Includes: Expert Cooling Fan Design System (EXFAN), PAN AIR Knowledge System (PAKS), grid-adaptation program MITOSIS, and Expert Zonal Grid Generation (EZGrid). Focuses on knowledge-based ("expert") software systems. Analyzes intended tasks, kinds of knowledge possessed, magnitude of effort required to codify knowledge, how quickly constructed, performances, and return on investment. On basis of comparison, concludes Al most successful when applied to well-formulated problems solved by classifying or selecting preenumerated solutions. In contrast, application of Al to poorly understood or poorly formulated problems generally results in long development time and large investment of effort, with no guarantee of success.

Intelligence May Moderate the Cognitive Profile of Patients with ASD.

PubMed

Rommelse, Nanda; Langerak, Ilse; van der Meer, Jolanda; de Bruijn, Yvette; Staal, Wouter; Oerlemans, Anoek; Buitelaar, Jan

2015-01-01

The intelligence of individuals with Autism Spectrum Disorder (ASD) varies considerably. The pattern of cognitive deficits associated with ASD may differ depending on intelligence. We aimed to study the absolute and relative severity of cognitive deficits in participants with ASD in relation to IQ. A total of 274 children (M age = 12.1, 68.6% boys) participated: 30 ASD and 22 controls in the below average Intelligence Quotient (IQ) group (IQ<85), 57 ASD and 54 controls in the average IQ group (85115). Matching for age, sex, Full Scale IQ (FSIQ), Verbal IQ (VIQ), Performance IQ (PIQ) and VIQ-PIQ difference was performed. Speed and accuracy of social cognition, executive functioning, visual pattern recognition and basic processing speed were examined per domain and as a composite score. The composite score revealed a trend significant IQ by ASD interaction (significant when excluding the average IQ group). In absolute terms, participants with below average IQs performed poorest (regardless of diagnosis). However, in relative terms, above average intelligent participants with ASD showed the most substantial cognitive problems (particularly for social cognition, visual pattern recognition and verbal working memory) since this group differed significantly from the IQ-matched control group (p < .001), whereas this was not the case for below-average intelligence participants with ASD (p = .57). In relative terms, cognitive deficits appear somewhat more severe in individuals with ASD and above average IQs compared to the below average IQ patients with ASD. Even though high IQ ASD individuals enjoy a certain protection from their higher IQ, they clearly demonstrate cognitive impairments that may be targeted in clinical assessment and treatment. Conversely, even though in absolute terms ASD patients with below average IQs were clearly more impaired than ASD patients with average to above average IQs, the differences in cognitive functioning between participants with and without ASD on the lower end of the IQ spectrum were less pronounced. Clinically this may imply that cognitive assessment and training of cognitive skills in below average intelligent children with ASD may be a less fruitful endeavour. These findings tentatively suggest that intelligence may act as a moderator in the cognitive presentation of ASD, with qualitatively different cognitive processes affected in patients at the high and low end of the IQ spectrum.

Intelligence May Moderate the Cognitive Profile of Patients with ASD

PubMed Central

Rommelse, Nanda; Langerak, Ilse; van der Meer, Jolanda; de Bruijn, Yvette; Staal, Wouter; Oerlemans, Anoek; Buitelaar, Jan

2015-01-01

Background The intelligence of individuals with Autism Spectrum Disorder (ASD) varies considerably. The pattern of cognitive deficits associated with ASD may differ depending on intelligence. We aimed to study the absolute and relative severity of cognitive deficits in participants with ASD in relation to IQ. Methods A total of 274 children (M age = 12.1, 68.6% boys) participated: 30 ASD and 22 controls in the below average Intelligence Quotient (IQ) group (IQ<85), 57 ASD and 54 controls in the average IQ group (85115). Matching for age, sex, Full Scale IQ (FSIQ), Verbal IQ (VIQ), Performance IQ (PIQ) and VIQ-PIQ difference was performed. Speed and accuracy of social cognition, executive functioning, visual pattern recognition and basic processing speed were examined per domain and as a composite score. Results The composite score revealed a trend significant IQ by ASD interaction (significant when excluding the average IQ group). In absolute terms, participants with below average IQs performed poorest (regardless of diagnosis). However, in relative terms, above average intelligent participants with ASD showed the most substantial cognitive problems (particularly for social cognition, visual pattern recognition and verbal working memory) since this group differed significantly from the IQ-matched control group (p < .001), whereas this was not the case for below-average intelligence participants with ASD (p = .57). Conclusions In relative terms, cognitive deficits appear somewhat more severe in individuals with ASD and above average IQs compared to the below average IQ patients with ASD. Even though high IQ ASD individuals enjoy a certain protection from their higher IQ, they clearly demonstrate cognitive impairments that may be targeted in clinical assessment and treatment. Conversely, even though in absolute terms ASD patients with below average IQs were clearly more impaired than ASD patients with average to above average IQs, the differences in cognitive functioning between participants with and without ASD on the lower end of the IQ spectrum were less pronounced. Clinically this may imply that cognitive assessment and training of cognitive skills in below average intelligent children with ASD may be a less fruitful endeavour. These findings tentatively suggest that intelligence may act as a moderator in the cognitive presentation of ASD, with qualitatively different cognitive processes affected in patients at the high and low end of the IQ spectrum. PMID:26444877

Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

PubMed

Cui, Zhihua; Zhang, Yi

2014-02-01

As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.

HClass: Automatic classification tool for health pathologies using artificial intelligence techniques.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya

2015-01-01

The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.

Opponent Classification in Poker

NASA Astrophysics Data System (ADS)

Ahmad, Muhammad Aurangzeb; Elidrisi, Mohamed

Modeling games has a long history in the Artificial Intelligence community. Most of the games that have been considered solved in AI are perfect information games. Imperfect information games like Poker and Bridge represent a domain where there is a great deal of uncertainty involved and additional challenges with respect to modeling the behavior of the opponent etc. Techniques developed for playing imperfect games also have many real world applications like repeated online auctions, human computer interaction, opponent modeling for military applications etc. In this paper we explore different techniques for playing poker, the core of these techniques is opponent modeling via classifying the behavior of opponent according to classes provided by domain experts. We utilize windows of full observation in the game to classify the opponent. In Poker, the behavior of an opponent is classified into four standard poker-playing styles based on a subjective function.

Diagnostic Utility of the Bannatyne WISC-III Pattern. Learning Disabilities Practice

ERIC Educational Resources Information Center

Smith, Courtney B.; Watkins, Marley W.

2004-01-01

Regrouping Wechsler Intelligence Scale for Children-Third Edition (WISC-III) subtests into Bannatyne's spatial, conceptual, and sequential patterns has been thought by many to identify children with learning disabilities (LD). This study investigated the prevalence and diagnostic utility of WISC-III Bannatyne patterns by comparing 1,302 children…

Preschooler Sleep Patterns Related to Cognitive and Adaptive Functioning

ERIC Educational Resources Information Center

Keefe-Cooperman, Kathleen; Brady-Amoon, Peggy

2014-01-01

Research Findings: Preschoolers' sleep patterns were examined related to cognitive and adaptive functioning. The sample consisted of 874 typically developing preschool children with a mean age of 40.01 months. Parent/caregiver reports of children's sleep pattern factors, Stanford-Binet 5 intelligence scale scores, and Behavior Assessment System…

The Myth of the L.D. WISC-R Profile.

ERIC Educational Resources Information Center

Miller, Maurice; Walker, Kenneth P.

1981-01-01

The review cites methodological and statistical flaws in studies attempting to identify subtest patterns on the Wechsler Intelligence Scale for Children-Revised indicative of learning disabilities (LD) and concludes that no LD pattern has been found and the search for such a pattern is not justified. (Author/CL)

Comparison of artificial intelligence classifiers for SIP attack data

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Slachta, Jiri

2016-05-01

Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

The effectiveness of cognitive-behavioral group therapy training on improving emotional intelligence and general health of adolescents.

PubMed

Aghel Masjedi, M; Taghavizadeh, M; Azadi, N; Hosseinzadeh, F; Koushkestani, A

2015-01-01

Introduction: The aim of the current research was to examine the effectiveness of cognitive-behavioral group therapy (CBT) training on the general health and improve the emotional intelligence of male adolescents in Tehran city. Methodology: The current research is a semi-trial research with pretest-posttest experimental design and two test and control groups, which were carried out in the 2014-2015 academic year. 40 high school male students were chosen via proper sampling approach and they were stochastically classified into test and control team (each team, n = 20). The students were protested via Baron emotional intelligence and GHQ-28 general health questionnaire. Subsequently, the test group was trained in the cognitive-behavioral group therapy for eight sessions and the control group received no interventions. In the end, both groups were post-tested, and the data were investigated by using a multivariate investigation of covariance method and SPSS-20. Findings: The outcomes demonstrated that there were notable distinctions between the experiment and the checking teams after the implementation of the CBT training (P < 0.001) so that the average score of emotional intelligence and general health increased in test group rather than in the check team. Conclusion: The findings indicated that the CBT practice is useful in improving emotional intelligence and general health in adolescent male students. Thus, one can recommend that appropriate therapy training could be designed to improve their emotional intelligence and general health.

The effectiveness of cognitive-behavioral group therapy training on improving emotional intelligence and general health of adolescents

PubMed Central

Aghel Masjedi, M; Taghavizadeh, M; Azadi, N; Hosseinzadeh, F; Koushkestani, A

2015-01-01

Introduction: The aim of the current research was to examine the effectiveness of cognitive-behavioral group therapy (CBT) training on the general health and improve the emotional intelligence of male adolescents in Tehran city. Methodology: The current research is a semi-trial research with pretest-posttest experimental design and two test and control groups, which were carried out in the 2014-2015 academic year. 40 high school male students were chosen via proper sampling approach and they were stochastically classified into test and control team (each team, n = 20). The students were protested via Baron emotional intelligence and GHQ-28 general health questionnaire. Subsequently, the test group was trained in the cognitive-behavioral group therapy for eight sessions and the control group received no interventions. In the end, both groups were post-tested, and the data were investigated by using a multivariate investigation of covariance method and SPSS-20. Findings: The outcomes demonstrated that there were notable distinctions between the experiment and the checking teams after the implementation of the CBT training (P < 0.001) so that the average score of emotional intelligence and general health increased in test group rather than in the check team. Conclusion: The findings indicated that the CBT practice is useful in improving emotional intelligence and general health in adolescent male students. Thus, one can recommend that appropriate therapy training could be designed to improve their emotional intelligence and general health. PMID:28316719

Autism-specific covariation in perceptual performances: "g" or "p" factor?

PubMed

Meilleur, Andrée-Anne S; Berthiaume, Claude; Bertone, Armando; Mottron, Laurent

2014-01-01

Autistic perception is characterized by atypical and sometimes exceptional performance in several low- (e.g., discrimination) and mid-level (e.g., pattern matching) tasks in both visual and auditory domains. A factor that specifically affects perceptive abilities in autistic individuals should manifest as an autism-specific association between perceptual tasks. The first purpose of this study was to explore how perceptual performances are associated within or across processing levels and/or modalities. The second purpose was to determine if general intelligence, the major factor that accounts for covariation in task performances in non-autistic individuals, equally controls perceptual abilities in autistic individuals. We asked 46 autistic individuals and 46 typically developing controls to perform four tasks measuring low- or mid-level visual or auditory processing. Intelligence was measured with the Wechsler's Intelligence Scale (FSIQ) and Raven Progressive Matrices (RPM). We conducted linear regression models to compare task performances between groups and patterns of covariation between tasks. The addition of either Wechsler's FSIQ or RPM in the regression models controlled for the effects of intelligence. In typically developing individuals, most perceptual tasks were associated with intelligence measured either by RPM or Wechsler FSIQ. The residual covariation between unimodal tasks, i.e. covariation not explained by intelligence, could be explained by a modality-specific factor. In the autistic group, residual covariation revealed the presence of a plurimodal factor specific to autism. Autistic individuals show exceptional performance in some perceptual tasks. Here, we demonstrate the existence of specific, plurimodal covariation that does not dependent on general intelligence (or "g" factor). Instead, this residual covariation is accounted for by a common perceptual process (or "p" factor), which may drive perceptual abilities differently in autistic and non-autistic individuals.

Autism-Specific Covariation in Perceptual Performances: “g” or “p” Factor?

PubMed Central

Meilleur, Andrée-Anne S.; Berthiaume, Claude; Bertone, Armando; Mottron, Laurent

2014-01-01

Background Autistic perception is characterized by atypical and sometimes exceptional performance in several low- (e.g., discrimination) and mid-level (e.g., pattern matching) tasks in both visual and auditory domains. A factor that specifically affects perceptive abilities in autistic individuals should manifest as an autism-specific association between perceptual tasks. The first purpose of this study was to explore how perceptual performances are associated within or across processing levels and/or modalities. The second purpose was to determine if general intelligence, the major factor that accounts for covariation in task performances in non-autistic individuals, equally controls perceptual abilities in autistic individuals. Methods We asked 46 autistic individuals and 46 typically developing controls to perform four tasks measuring low- or mid-level visual or auditory processing. Intelligence was measured with the Wechsler's Intelligence Scale (FSIQ) and Raven Progressive Matrices (RPM). We conducted linear regression models to compare task performances between groups and patterns of covariation between tasks. The addition of either Wechsler's FSIQ or RPM in the regression models controlled for the effects of intelligence. Results In typically developing individuals, most perceptual tasks were associated with intelligence measured either by RPM or Wechsler FSIQ. The residual covariation between unimodal tasks, i.e. covariation not explained by intelligence, could be explained by a modality-specific factor. In the autistic group, residual covariation revealed the presence of a plurimodal factor specific to autism. Conclusions Autistic individuals show exceptional performance in some perceptual tasks. Here, we demonstrate the existence of specific, plurimodal covariation that does not dependent on general intelligence (or “g” factor). Instead, this residual covariation is accounted for by a common perceptual process (or “p” factor), which may drive perceptual abilities differently in autistic and non-autistic individuals. PMID:25117450

Data mining: sophisticated forms of managed care modeling through artificial intelligence.

PubMed

Borok, L S

1997-01-01

Data mining is a recent development in computer science that combines artificial intelligence algorithms and relational databases to discover patterns automatically, without the use of traditional statistical methods. Work with data mining tools in health care is in a developmental stage that holds great promise, given the combination of demographic and diagnostic information.

Evaluating an Intelligent Tutoring System for Design Patterns: The DEPTHS Experience

ERIC Educational Resources Information Center

Jeremic, Zoran; Jovanovic, Jelena; Gasevic, Dragan

2009-01-01

The evaluation of intelligent tutoring systems (ITSs) is an important though often neglected stage of ITS development. There are many evaluation methods available but literature does not provide clear guidelines for the selection of evaluation method(s) to be used in a particular context. This paper describes the evaluation study of DEPTHS, an…

Analyzing User Interaction to Design an Intelligent e-Learning Environment

ERIC Educational Resources Information Center

Sharma, Richa

2011-01-01

Building intelligent course designing systems adaptable to the learners' needs is one of the key goals of research in e-learning. This goal is all the more crucial as gaining knowledge in an e-learning environment depends solely on computer mediated interaction within the learner group and among the learners and instructors. The patterns generated…

Applications of artificial intelligence systems in the analysis of epidemiological data.

PubMed

Flouris, Andreas D; Duffy, Jack

2006-01-01

A brief review of the germane literature suggests that the use of artificial intelligence (AI) statistical algorithms in epidemiology has been limited. We discuss the advantages and disadvantages of using AI systems in large-scale sets of epidemiological data to extract inherent, formerly unidentified, and potentially valuable patterns that human-driven deductive models may miss.

Production and Perception of Temporal Patterns in Native and Non-Native Speech

PubMed Central

Bent, Tessa; Bradlow, Ann R.; Smith, Bruce L.

2012-01-01

Two experiments examined production and perception of English temporal patterns by native and non-native participants. Experiment 1 indicated that native and non-native (L1 = Chinese) talkers differed significantly in their production of one English duration pattern (i.e., vowel lengthening before voiced versus voiceless consonants) but not another (i.e., tense versus lax vowels). Experiment 2 tested native and non-native listener identification of words that differed in voicing of the final consonant by the native and non-native talkers whose productions were substantially different in experiment 1. Results indicated that differences in native and non-native intelligibility may be partially explained by temporal pattern differences in vowel duration although other cues such as presence of stop releases and burst duration may also contribute. Additionally, speech intelligibility depends on shared phonetic knowledge between talkers and listeners rather than only on accuracy relative to idealized production norms. PMID:18679042

A SOFTWARE PACKAGE FOR UNSUPERVISED PATTERN RECOGNITION AND SYNOPTIC REPRESENTATION OF RESULTS: APPLICATION TO VOLCANIC TREMOR DATA OF MT ETNA

NASA Astrophysics Data System (ADS)

Langer, H. K.; Falsaperla, S. M.; Behncke, B.; Messina, A.; Spampinato, S.

2009-12-01

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form. References: Falsaperla, S., S. Graziani, G. Nunnari, and S. Spampinato (1996). Automatic classification of volcanic earthquakes by using multy-layered neural networks. Natural Hazard, 13, 205-228. Langer, H., S. Falsaperla, M. Masotti, R. Campanini, S. Spampinato, and A. Messina (2008). Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy. Geophys. J. Int., doi:10.1111/j.1365-246X.2009.04179.x.

Artificial intelligence, neural network, and Internet tool integration in a pathology workstation to improve information access

NASA Astrophysics Data System (ADS)

Sargis, J. C.; Gray, W. A.

1999-03-01

The APWS allows user friendly access to several legacy systems which would normally each demand domain expertise for proper utilization. The generalized model, including objects, classes, strategies and patterns is presented. The core components of the APWS are the Microsoft Windows 95 Operating System, Oracle, Oracle Power Objects, Artificial Intelligence tools, a medical hyperlibrary and a web site. The paper includes a discussion of how could be automated by taking advantage of the expert system, object oriented programming and intelligent relational database tools within the APWS.

A new modelling approach for zooplankton behaviour

NASA Astrophysics Data System (ADS)

Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.

We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.

Classifier utility modeling and analysis of hypersonic inlet start/unstart considering training data costs

NASA Astrophysics Data System (ADS)

Chang, Juntao; Hu, Qinghua; Yu, Daren; Bao, Wen

2011-11-01

Start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection control of scramjet. The inlet start/unstart detection can be attributed to a standard pattern classification problem, and the training sample costs have to be considered for the classifier modeling as the CFD numerical simulations and wind tunnel experiments of hypersonic inlets both cost time and money. To solve this problem, the CFD simulation of inlet is studied at first step, and the simulation results could provide the training data for pattern classification of hypersonic inlet start/unstart. Then the classifier modeling technology and maximum classifier utility theories are introduced to analyze the effect of training data cost on classifier utility. In conclusion, it is useful to introduce support vector machine algorithms to acquire the classifier model of hypersonic inlet start/unstart, and the minimum total cost of hypersonic inlet start/unstart classifier can be obtained by the maximum classifier utility theories.

Bridging Social and Semantic Computing - Design and Evaluation of User Interfaces for Hybrid Systems

ERIC Educational Resources Information Center

Bostandjiev, Svetlin Alex I.

2012-01-01

The evolution of the Web brought new interesting problems to computer scientists that we loosely classify in the fields of social and semantic computing. Social computing is related to two major paradigms: computations carried out by a large amount of people in a collective intelligence fashion (i.e. wikis), and performing computations on social…

An Intelligent Tutoring System for Classifying Students into Instructional Treatments with Mastery Scores. Research Report 94-15.

ERIC Educational Resources Information Center

Vos, Hans J.

As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…

Swarm intelligence applied to the risk evaluation for congenital heart surgery.

PubMed

Zapata-Impata, Brayan S; Ruiz-Fernandez, Daniel; Monsalve-Torra, Ana

2015-01-01

Particle Swarm Optimization is an optimization technique based on the positions of several particles created to find the best solution to a problem. In this work we analyze the accuracy of a modification of this algorithm to classify the levels of risk for a surgery, used as a treatment to correct children malformations that imply congenital heart diseases.

Vocational Guidance for the Deaf; A Classified Guide to the Basic Requirements for Occupations Open to the Profoundly Deaf.

ERIC Educational Resources Information Center

Montgomery, G.W.G.

Published in Britain for use by counselors and placement officials, the book offers a systematic attack on occupational placement problems of the prelingually deaf. The system is based on a vocational guidance profile, which is developed from intelligence and achievement test scores. The vocational guidance profile is explained, and occupational…

Attachment-based classifications of children's family drawings: psychometric properties and relations with children's adjustment in kindergarten.

PubMed

Pianta, R C; Longmaid, K; Ferguson, J E

1999-06-01

Investigated an attachment-based theoretical framework and classification system, introduced by Kaplan and Main (1986), for interpreting children's family drawings. This study concentrated on the psychometric properties of the system and the relation between drawings classified using this system and teacher ratings of classroom social-emotional and behavioral functioning, controlling for child age, ethnic status, intelligence, and fine motor skills. This nonclinical sample consisted of 200 kindergarten children of diverse racial and socioeconomic status (SES). Limited support for reliability of this classification system was obtained. Kappas for overall classifications of drawings (e.g., secure) exceeded .80 and mean kappa for discrete drawing features (e.g., figures with smiles) was .82. Coders' endorsement of the presence of certain discrete drawing features predicted their overall classification at 82.5% accuracy. Drawing classification was related to teacher ratings of classroom functioning independent of child age, sex, race, SES, intelligence, and fine motor skills (with p values for the multivariate effects ranging from .043-.001). Results are discussed in terms of the psychometric properties of this system for classifying children's representations of family and the limitations of family drawing techniques for young children.

A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

PubMed Central

Mourão-Miranda, Janaina; Hardoon, David R.; Hahn, Tim; Marquand, Andre F.; Williams, Steve C.R.; Shawe-Taylor, John; Brammer, Michael

2011-01-01

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers. PMID:21723950

Open ended intelligence: the individuation of intelligent agents

NASA Astrophysics Data System (ADS)

Weinbaum Weaver, David; Veitas, Viktoras

2017-03-01

Artificial general intelligence is a field of research aiming to distil the principles of intelligence that operate independently of a specific problem domain and utilise these principles in order to synthesise systems capable of performing any intellectual task a human being is capable of and beyond. While "narrow" artificial intelligence which focuses on solving specific problems such as speech recognition, text comprehension, visual pattern recognition and robotic motion has shown impressive breakthroughs lately, understanding general intelligence remains elusive. We propose a paradigm shift from intelligence perceived as a competence of individual agents defined in relation to an a priori given problem domain or a goal, to intelligence perceived as a formative process of self-organisation. We call this process open-ended intelligence. Starting with a brief introduction of the current conceptual approach, we expose a number of serious limitations that are traced back to the ontological roots of the concept of intelligence. Open-ended intelligence is then developed as an abstraction of the process of human cognitive development, so its application can be extended to general agents and systems. We introduce and discuss three facets of the idea: the philosophical concept of individuation, sense-making and the individuation of general cognitive agents. We further show how open-ended intelligence can be framed in terms of a distributed, self-organising network of interacting elements and how such process is scalable. The framework highlights an important relation between coordination and intelligence and a new understanding of values.

Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

PubMed

Sudha, M

2017-09-27

As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

Exploiting Sequential Patterns Found in Users' Solutions and Virtual Tutor Behavior to Improve Assistance in ITS

ERIC Educational Resources Information Center

Fournier-Viger, Philippe; Faghihi, Usef; Nkambou, Roger; Nguifo, Engelbert Mephu

2010-01-01

We propose to mine temporal patterns in Intelligent Tutoring Systems (ITSs) to uncover useful knowledge that can enhance their ability to provide assistance. To discover patterns, we suggest using a custom, sequential pattern-mining algorithm. Two ways of applying the algorithm to enhance an ITS's capabilities are addressed. The first is to…

Predicting asthma exacerbations using artificial intelligence.

PubMed

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval.

PubMed

ElAdel, Asma; Zaied, Mourad; Amar, Chokri Ben

2017-11-01

Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification and retrieval. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization is still a challenging subject. In this paper, we propose a fast DCNN based on Fast Wavelet Transform (FWT), intelligent dropout and layer skipping. The proposed approach led to improve the image retrieval accuracy as well as the searching time. This was possible thanks to three key advantages: First, the rapid way to compute the features using FWT. Second, the proposed intelligent dropout method is based on whether or not a unit is efficiently and not randomly selected. Third, it is possible to classify the image using efficient units of earlier layer(s) and skipping all the subsequent hidden layers directly to the output layer. Our experiments were performed on CIFAR-10 and MNIST datasets and the obtained results are very promising. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of Pattern Matching, Pattern Discrimination, and Experience in the Development of Diagnostic Expertise.

ERIC Educational Resources Information Center

Papa, Frank; And Others

1990-01-01

In this study an artificial intelligence assessment tool used disease-by-feature frequency estimates to create disease prototypes for nine common causes of acute chest pain. The tool then used each subject's prototypes and a pattern-recognition-based decision-making mechanism to diagnose 18 myocardial infarction cases. (MLW)

An Ensemble Method for Classifying Regional Disease Patterns of Diffuse Interstitial Lung Disease Using HRCT Images from Different Vendors.

PubMed

Jun, Sanghoon; Kim, Namkug; Seo, Joon Beom; Lee, Young Kyung; Lynch, David A

2017-12-01

We propose the use of ensemble classifiers to overcome inter-scanner variations in the differentiation of regional disease patterns in high-resolution computed tomography (HRCT) images of diffuse interstitial lung disease patients obtained from different scanners. A total of 600 rectangular 20 × 20-pixel regions of interest (ROIs) on HRCT images obtained from two different scanners (GE and Siemens) and the whole lung area of 92 HRCT images were classified as one of six regional pulmonary disease patterns by two expert radiologists. Textual and shape features were extracted from each ROI and the whole lung parenchyma. For automatic classification, individual and ensemble classifiers were trained and tested with the ROI dataset. We designed the following three experimental sets: an intra-scanner study in which the training and test sets were from the same scanner, an integrated scanner study in which the data from the two scanners were merged, and an inter-scanner study in which the training and test sets were acquired from different scanners. In the ROI-based classification, the ensemble classifiers showed better (p < 0.001) accuracy (89.73%, SD = 0.43) than the individual classifiers (88.38%, SD = 0.31) in the integrated scanner test. The ensemble classifiers also showed partial improvements in the intra- and inter-scanner tests. In the whole lung classification experiment, the quantification accuracies of the ensemble classifiers with integrated training (49.57%) were higher (p < 0.001) than the individual classifiers (48.19%). Furthermore, the ensemble classifiers also showed better performance in both the intra- and inter-scanner experiments. We concluded that the ensemble classifiers provide better performance when using integrated scanner images.

The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility.

PubMed

Bentsen, Thomas; May, Tobias; Kressner, Abigail A; Dau, Torsten

2018-01-01

Computational speech segregation attempts to automatically separate speech from noise. This is challenging in conditions with interfering talkers and low signal-to-noise ratios. Recent approaches have adopted deep neural networks and successfully demonstrated speech intelligibility improvements. A selection of components may be responsible for the success with these state-of-the-art approaches: the system architecture, a time frame concatenation technique and the learning objective. The aim of this study was to explore the roles and the relative contributions of these components by measuring speech intelligibility in normal-hearing listeners. A substantial improvement of 25.4 percentage points in speech intelligibility scores was found going from a subband-based architecture, in which a Gaussian Mixture Model-based classifier predicts the distributions of speech and noise for each frequency channel, to a state-of-the-art deep neural network-based architecture. Another improvement of 13.9 percentage points was obtained by changing the learning objective from the ideal binary mask, in which individual time-frequency units are labeled as either speech- or noise-dominated, to the ideal ratio mask, where the units are assigned a continuous value between zero and one. Therefore, both components play significant roles and by combining them, speech intelligibility improvements were obtained in a six-talker condition at a low signal-to-noise ratio.

Integrating medical and research information: a big data approach.

PubMed

Tilve Álvarez, Carlos M; Ayora Pais, Alberto; Ruíz Romero, Cristina; Llamas Gómez, Daniel; Carrajo García, Lino; Blanco García, Francisco J; Vázquez González, Guillermo

2015-01-01

Most of the information collected in different fields by Instituto de Investigación Biomédica de A Coruña (INIBIC) is classified as unstructured due to its high volume and heterogeneity. This situation, linked to the recent requirement of integrating it to the medical information, makes it necessary to implant specific architectures to collect and organize it before it can be analysed. The purpose of this article is to present the Hadoop framework as a solution to the problem of integrating research information in the Business Intelligence field. This framework can collect, explore, process and structure the aforementioned information, which allow us to develop an equivalent function to a data mart in an Intelligence Business system.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

Strategies for Assessing Intellectual Patterns in Black, Anglo, and Mexican-American Boys--or Any Other Children--and Implications for Education.

ERIC Educational Resources Information Center

Meeker, Mary; Meeker, Robert

In this analysis of intelligence testing of minority group children, the implications of inadequate testing practices are discussed. Several aspects of test design are examined: deficiencies in intelligence testing, cultural bias, construct validity, and diagnostic utility. A sample set of results derived from a Stanford-Binet test administered to…

Some Factors Underlying Mathematical Performance: The Role of Visuospatial Working Memory and Non-Verbal Intelligence

ERIC Educational Resources Information Center

Kyttala, Minna; Lehto, Juhani E.

2008-01-01

Passive and active visuospatial working memory (VSWM) were investigated in relation to maths performance. The mental rotation task was employed as a measure of active VSWM whereas passive VSWM was investigated using a modified Corsi Blocks task and a matrix pattern task. The Raven Progressive Matrices Test measured fluid intelligence. A total of…

How Achievement Error Patterns of Students with Mild Intellectual Disability Differ from Low IQ and Low Achievement Students without Diagnoses

ERIC Educational Resources Information Center

Root, Melissa M.; Marchis, Lavinia; White, Erica; Courville, Troy; Choi, Dowon; Bray, Melissa A.; Pan, Xingyu; Wayte, Jessica

2017-01-01

This study investigated the differences in error factor scores on the Kaufman Test of Educational Achievement-Third Edition between individuals with mild intellectual disabilities (Mild IDs), those with low achievement scores but average intelligence, and those with low intelligence but without a Mild ID diagnosis. The two control groups were…

[Multicenter paragliding accident study 1990].

PubMed

Lautenschlager, S; Karli, U; Matter, P

1992-01-01

During the period from 1.1.90 until 31.12.90, 86 injuries associated with paragliding were analyzed in a prospective study in 12 different Swiss hospitals with reference to causes, patterns, and frequencies. The injuries showed a mean score of over 2 and were classified as severe. Most frequent spine injuries (36%) and lesions of the lower extremity (35%) with a high risk of the ankles were diagnosed. One accident was fatal. 60% of the accidents happened during landing, 26% during launching and 14% during flight. Half of the pilots were affected during their primary training course. Most accidents were caused by inflight error of judgement--especially incorrect estimation of wind conditions--and further the choice of unfavourable landing sites. In contrast to previous injury-reports, only one equipment failure could be noted, but often the equipment was not corresponding with the experience and the weight of the pilot. To reduce the frequency of paragliding-injuries an accurate choice of equipment and an increased attention to environmental factors is mandatory. Furthermore an education-program regarding the attitude and intelligence of the pilot should be included in training courses.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

PubMed

Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

2013-02-01

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

Father's occupational group and daily smoking during adolescence: patterns and predictors.

PubMed

Droomers, Mariël; Schrijvers, Carola T M; Casswell, Sally; Mackenbach, Johan P

2005-04-01

We investigated the relationship among father's occupational group, daily smoking, and smoking determinants in a cohort of New Zealand adolescents. The longitudinal Multidisciplinary Health and Development Study provided information on adolescents' self-reported smoking behavior and potential predictors of smoking, such as social and material factors, personality characteristics, educational achievement, and individual attitudes and beliefs regarding smoking. Longitudinal logistic generalized estimating equation analyses were used. Adolescents whose fathers were classified in the lowest-status occupational group were twice as likely as those whose fathers occupied the highest-status occupational group to be daily smokers. This high risk of daily smoking among the adolescents from the lowest occupational group was largely predicted by their lower intelligence scores and by the higher prevalence of smoking among fathers and friends. To prevent socioeconomic differences in smoking, school-based interventions should seek to prevent smoking uptake among adolescents, particularly those of lower socioeconomic status. Programs need to provide positive, nonsmoking role models consonant with the culture and norms of lower-socioeconomic-status groups. Adolescents need to acquire resistance skills and protective behaviors against social pressure and influences.

Automatic classification of canine PRG neuronal discharge patterns using K-means clustering.

PubMed

Zuperku, Edward J; Prkic, Ivana; Stucke, Astrid G; Miller, Justin R; Hopp, Francis A; Stuth, Eckehard A

2015-02-01

Respiratory-related neurons in the parabrachial-Kölliker-Fuse (PB-KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non-respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K-means clustering technique and it is implemented via SigmaPlot User-Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into seven distinct subgroups. Additional methods for choosing the optimal number of clusters are described. Analysis of the results suggests that the K-means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons. Published by Elsevier B.V.

Multiple degree of freedom optical pattern recognition

NASA Technical Reports Server (NTRS)

Casasent, D.

1987-01-01

Three general optical approaches to multiple degree of freedom object pattern recognition (where no stable object rest position exists) are advanced. These techniques include: feature extraction, correlation, and artificial intelligence. The details of the various processors are advanced together with initial results.

Intelligent Automatic Classification of True and Counterfeit Notes Based on Spectrum Analysis

NASA Astrophysics Data System (ADS)

Matsunaga, Shohei; Omatu, Sigeru; Kosaka, Toshohisa

The purpose of this paper is to classify bank notes into “true” or “counterfeit” ones faster and more precisely compared with a conventional method. We note that thin lines are represented by direct lines in the images of true notes while they are represented in the counterfeit notes by dotted lines. This is due to properties of dot printers or scanner levels. To use the properties, we propose two method to classify a note into true or counterfeited one by checking whether there exist thin lines or dotted lines of the note. First, we use Fourier transform of the note to find quantity of features for classification and we classify a note into true or counterfeit one by using the features by Fourier transform. Then we propose a classification method by using wavelet transform in place of Fourier transform. Finally, some classification results are illustrated to show the effectiveness of the proposed methods.

Intelligent judgements over health risks in a spatial agent-based model.

PubMed

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

The Scientific Search for Extraterrestrial Intelligence: a Sociological Analysis.

NASA Astrophysics Data System (ADS)

Romesberg, Daniel Ray

1992-01-01

This study examines the search for extraterrestrial intelligence, as it has been conducted by scientists over the past century. The following questions are explored: (1) What are the historical patterns of American scientific interest in extraterrestrial intelligence? From a sociology of science perspective, how can these patterns of interest be explained? (2) Who are the most prominent scientists involved in SETI? What are their academic backgrounds? (3) How has the rather exotic idea of extraterrestrial intelligence managed to penetrate the realm of respectable science?. In order to measure the historical fluctuations of scientific interest in extraterrestrial intelligence, a frequency distribution of relevant articles published in American scientific journals over the past century has been constructed. The core scholars of the "extraterrestrial" field have been determined via citation analysis, in a selected portion of the scientific literature. An analysis of recent scientific literature on the Search for Extraterrestrial Intelligence (SETI) has revealed a number of tactics of legitimation and de-legitimation used by SETI proponents, as well as opponents. This study has generated the following findings: (1) Historically, there are three factors which tend to stimulate general scientific interest in extraterrestrial intelligence: First, the strong demonstration of the plausibility of extraterrestrial intelligence, or life, especially in a tangible, and therefore studiable location. Scientific laboratories are primary agents of plausibility here. Second, the organized political activity of SETI scientists. Third, the availability of government funding for searches for extraterrestrial intelligence, or life. (2) Statistically, the leading scholars of modern SETI are Sagan, Drake and Morrison. The field itself tends to be dominated by astronomers and physicists. (3) Because SETI has no concrete data, and is easily stigmatized as an illegitimate scientific activity, it must engage in an intense campaign of scientific legitimation. Most importantly, SETI scientists must try to resemble scientists who are engaged in "normal," respectable scientific activities. (4) The sociological study of SETI's history demonstrates the strengths and limits of the constructivist and realist approaches to the sociology of science. It suggests that sociological analyses of science should attempt to incorporate both analytical perspectives.

The fuzzy system classifier using an intelligent mattress

NASA Astrophysics Data System (ADS)

Hnatiuc, Mihaela; Caruntu, George; Sarbu, Vasile

2009-01-01

Quality sleep enables your body and mind to be efficient during the day. The good rest of subject on bed depends by many factors; one of them can be the physical discomfort. In this paper one presents the medical system to prevent the discomfort and to identify the subject behavior during the sleep. The epochs of the sleep are classified using the fuzzy system with many inputs. The aim of this analysis is to realize an expert system to diagnose the sleep. A good diagnostic is obtained if the patient is supervised along all day. The system has a microsystem to control, command the pressure sensors and the relay for tuning the airbags pressure.

Learning time series for intelligent monitoring

NASA Technical Reports Server (NTRS)

Manganaris, Stefanos; Fisher, Doug

1994-01-01

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features using Bayesian model induction and the minimum message length approach to assign priors. In the performance task, we classify a time series in one of the learned classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. We report results from experiments in a monitoring domain of interest to NASA.

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence.

PubMed

Hill, W D; Marioni, R E; Maghzian, O; Ritchie, S J; Hagenaars, S P; McIntosh, A M; Gale, C R; Davies, G; Deary, I J

2018-01-11

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r g  = 0.70). We used these findings as foundations for our use of a novel approach-multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)-to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination-as well as genes expressed in the synapse, and those involved in the regulation of the nervous system-may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.

Psychokinesis and Its Possible Implication to Warfare Strategy

DTIC Science & Technology

1985-01-01

E. 4Rhine began to study psychic phenomena; their research "initially focused on telepathy and clairvoyance. In 1934, J. B. Rhine instituted PK... dream recall (1983 PRL Annual Report). The efforts of PRL to standardize or at least to establish uniformity within the subject populations being...64 -77). Soviet Parapsychology Research. Medical Intelligence and Information Agency. (CLASSIFIED). (S18841.80). Ix, Stanescu, S. Telepathy in

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention are Coordinated Using Surface-Based Attentional Shrouds

ERIC Educational Resources Information Center

Fazl, Arash; Grossberg, Stephen; Mingolla, Ennio

2009-01-01

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified…

Non-Metric Similarity Measures

DTIC Science & Technology

2015-03-26

Sunil Aryal and Kai Ming Ting. (2015) A generic ensemble approach to estimate multi-dimensional likelihood in Bayesian classifier learning...Computational Intelligence. http://onlinelibrary.wiley.com/doi/10.1111/coin.12063/abstract 5.2 List of peer-reviewed conference publications [3] Sunil Aryal...International Conference on Data Mining. 707-711. [4] Sunil Aryal, Kai Ming Ting, Jonathan R. Wells and Takashi Washio. (2014) Improv- ing iForest with

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

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

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K., E-mail: s.farrell@physics.usyd.edu.au

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of amore » random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.« less

Intelligence's likelihood and evolutionary time frame

NASA Astrophysics Data System (ADS)

Bogonovich, Marc

2011-04-01

This paper outlines hypotheses relevant to the evolution of intelligent life and encephalization in the Phanerozoic. If general principles are inferable from patterns of Earth life, implications could be drawn for astrobiology. Many of the outlined hypotheses, relevant data, and associated evolutionary and ecological theory are not frequently cited in astrobiological journals. Thus opportunity exists to evaluate reviewed hypotheses with an astrobiological perspective. A quantitative method is presented for testing one of the reviewed hypotheses (hypothesis i; the diffusion hypothesis). Questions are presented throughout, which illustrate that the question of intelligent life's likelihood can be expressed as multiple, broadly ranging, more tractable questions.

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

PubMed

Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

2015-06-01

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

Bannatyne-Recategorized WISC-R Patterns of Mentally Retarded, Learning Disabled, Normal, and Intellectually Superior Children: A Meta-Analysis.

ERIC Educational Resources Information Center

Mueller, Horst H.; And Others

1983-01-01

Metaanalytical procedures examined the Wechsler Intelligence Scale-Revised subtest performance patterns of 36 samples of below average, normal average, learning disabled average, and above average IQ children from research. Relative patterning of WISC-R subtests as reflected in children's Bannatyne-recategorized performance profiles appeared to be…

Decimated Input Ensembles for Improved Generalization

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Norvig, Peter (Technical Monitor)

1999-01-01

Recently, many researchers have demonstrated that using classifier ensembles (e.g., averaging the outputs of multiple classifiers before reaching a classification decision) leads to improved performance for many difficult generalization problems. However, in many domains there are serious impediments to such "turnkey" classification accuracy improvements. Most notable among these is the deleterious effect of highly correlated classifiers on the ensemble performance. One particular solution to this problem is generating "new" training sets by sampling the original one. However, with finite number of patterns, this causes a reduction in the training patterns each classifier sees, often resulting in considerably worsened generalization performance (particularly for high dimensional data domains) for each individual classifier. Generally, this drop in the accuracy of the individual classifier performance more than offsets any potential gains due to combining, unless diversity among classifiers is actively promoted. In this work, we introduce a method that: (1) reduces the correlation among the classifiers; (2) reduces the dimensionality of the data, thus lessening the impact of the 'curse of dimensionality'; and (3) improves the classification performance of the ensemble.

Mass classification in mammography with multi-agent based fusion of human and machine intelligence

NASA Astrophysics Data System (ADS)

Xi, Dongdong; Fan, Ming; Li, Lihua; Zhang, Juan; Shan, Yanna; Dai, Gang; Zheng, Bin

2016-03-01

Although the computer-aided diagnosis (CAD) system can be applied for classifying the breast masses, the effects of this method on improvement of the radiologist' accuracy for distinguishing malignant from benign lesions still remain unclear. This study provided a novel method to classify breast masses by integrating the intelligence of human and machine. In this research, 224 breast masses were selected in mammography from database of DDSM with Breast Imaging Reporting and Data System (BI-RADS) categories. Three observers (a senior and a junior radiologist, as well as a radiology resident) were employed to independently read and classify these masses utilizing the Positive Predictive Values (PPV) for each BI-RADS category. Meanwhile, a CAD system was also implemented for classification of these breast masses between malignant and benign. To combine the decisions from the radiologists and CAD, the fusion method of the Multi-Agent was provided. Significant improvements are observed for the fusion system over solely radiologist or CAD. The area under the receiver operating characteristic curve (AUC) of the fusion system increased by 9.6%, 10.3% and 21% compared to that of radiologists with senior, junior and resident level, respectively. In addition, the AUC of this method based on the fusion of each radiologist and CAD are 3.5%, 3.6% and 3.3% higher than that of CAD alone. Finally, the fusion of the three radiologists with CAD achieved AUC value of 0.957, which was 5.6% larger compared to CAD. Our results indicated that the proposed fusion method has better performance than radiologist or CAD alone.

Season of Birth and Childhood Intelligence: Findings from the Aberdeen Children of the 1950s Cohort Study

ERIC Educational Resources Information Center

Lawlor, Debbie A.; Clark, Heather; Ronalds, Georgina; Leon, David A.

2006-01-01

Background: In this study, 2 main hypotheses have been put forward to explain the variation in childhood intelligence or school performance by season of birth. In the first hypothesis, it is suggested that it is due to school policy concerning school entry, whereas the second suggests that a seasonally patterned exposure such as temperature,…

Does Emotional Intelligence Depend on Gender? The Socialization of Emotional Competencies in Men and Women and Its Implications

ERIC Educational Resources Information Center

Sanchez-Nunez, M. Trinidad; Fernandez-Berrocal, Pablo; Montanes, Juan; Latorre, Jose Miguel

2008-01-01

This article attempts to justify gender differences found for the main factors that comprise emotional intelligence from the standpoint of the Mayer and Salovey Skill Model (1997). In order to do so, we carry out a review of the different emotional socialization patterns used by parents on the basis of their children's gender and look into their…

Relationships of Personality, Affect, Emotional Intelligence and Coping with Student Stress and Academic Success: Different Patterns of Association for Stress and Success

ERIC Educational Resources Information Center

Saklofske, Donald H.; Austin, Elizabeth J.; Mastoras, Sarah M.; Beaton, Laura; Osborne, Shona E.

2012-01-01

The associations of personality, affect, trait emotional intelligence (EI) and coping style measured at the start of the academic year with later academic performance were examined in a group of undergraduate students at the University of Edinburgh. The associations of the dispositional and affect measures with concurrent stress and life…

Emotional Intelligence and Its Relation with the Social Skills and Religious Behaviour of Female Students at Dammam University in the Light of Some Variables

ERIC Educational Resources Information Center

Al-Tamimi, Eman Mohammad Reda Ali; Al-Khawaldeh, Naseer Ahmad

2016-01-01

The study has examined the correlation between emotional intelligence, social skills, and religious behavior among university female students, since it had been noticed that there was escalation in the frequency of some behavioral and emotional problems such as vandalism, aggression, social withdrawal, weakness of social relations, patterns of…

Machine learning techniques to predict sensitive patterns to fault attack in the Java Card application

NASA Astrophysics Data System (ADS)

Chahrazed, Yahiaoui; Jean-Louis, Lanet; Mohamed, Mezghiche; Karim, Tamine

2018-01-01

Fault attack represents one of the serious threats against Java Card security. It consists of physical perturbation of chip components to introduce faults in the code execution. A fault may be induced using a laser beam to impact opcodes and operands of instructions. This could lead to a mutation of the application code in such a way that it becomes hostile. Any successful attack may reveal a secret information stored in the card or grant an undesired authorisation. We propose a methodology to recognise, during the development step, the sensitive patterns to the fault attack in the Java Card applications. It is based on the concepts from text categorisation and machine learning. In fact, in this method, we represented the patterns using opcodes n-grams as features, and we evaluated different machine learning classifiers. The results show that the classifiers performed poorly when classifying dangerous sensitive patterns, due to the imbalance of our data-set. The number of dangerous sensitive patterns is much lower than the number of not dangerous patterns. We used resampling techniques to balance the class distribution in our data-set. The experimental results indicated that the resampling techniques improved the accuracy of the classifiers. In addition, our proposed method reduces the execution time of sensitive patterns classification in comparison to the SmartCM tool. This tool is used in our study to evaluate the effect of faults on Java Card applications.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

On the Application of Pattern Recognition and AI Technique to the Cytoscreening of Vaginal Smears by Computer

NASA Astrophysics Data System (ADS)

Bow, Sing T.; Wang, Xia-Fang

1989-05-01

In this paper the concepts of pattern recognition, image processing and artificial intelligence are applied to the development of an intelligent cytoscreening system to differentiate the abnormal cytological objects from the normal ones in vaginal smears. To achieve this goal,work listed below are involved: 1. Enhancement of the microscopic images of the smears; 2. Elevation of the qualitative differentiation under the microscope by cytologists to a quantitative differentiation plateau on the epithelial cells, ciliated cells, vacuolated cells, foreign-body-giant cells, plasma cells, lymph cells, white blood cells, red blood cells, etc. These knowledges are to be inputted into our intelligent cyto-screening system to ameliorate machine differentiation; 3. Selection of a set of effective features to characterize the cytological objects onto various regions of the multiclustered by computer algorithms; and 4. Systematical summarization of the knowledge that a gynecologist has and the way he/she follows when dealing with a case.

Classroom Behavior Patterns of EMH, LD, and EH Students.

ERIC Educational Resources Information Center

McKinney, James D.; Forman, Susan G.

1982-01-01

Investigated whether classroom teachers could differentiate among educable mentally handicapped (EMH), learning disabled (LD), and emotionally handicapped (EH) students based on perceptions of classroom behavior patterns. Ratings from classroom behavior inventory scales revealed that EMH students were distinguished by low intelligence, creativity,…

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

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.

THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

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

Sikora, R.; Chady, T.; Baniukiewicz, P.

2010-02-22

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Twomore » weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.« less

The Choice of Optimal Structure of Artificial Neural Network Classifier Intended for Classification of Welding Flaws

NASA Astrophysics Data System (ADS)

Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.

2010-02-01

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

Development and evaluation of a hand tracker using depth images captured from an overhead perspective.

PubMed

Czarnuch, Stephen; Mihailidis, Alex

2015-03-27

We present the development and evaluation of a robust hand tracker based on single overhead depth images for use in the COACH, an assistive technology for people with dementia. The new hand tracker was designed to overcome limitations experienced by the COACH in previous clinical trials. We train a random decision forest classifier using ∼5000 manually labeled, unbalanced, training images. Hand positions from the classifier are translated into task actions based on proximity to environmental objects. Tracker performance is evaluated using a large set of ∼24 000 manually labeled images captured from 41 participants in a fully-functional washroom, and compared to the system's previous colour-based hand tracker. Precision and recall were 0.994 and 0.938 for the depth tracker compared to 0.981 and 0.822 for the colour tracker with the current data, and 0.989 and 0.466 in the previous study. The improved tracking performance supports integration of the depth-based tracker into the COACH toward unsupervised, real-world trials. Implications for Rehabilitation The COACH is an intelligent assistive technology that can enable people with cognitive disabilities to stay at home longer, supporting the concept of aging-in-place. Automated prompting systems, a type of intelligent assistive technology, can help to support the independent completion of activities of daily living, increasing the independence of people with cognitive disabilities while reducing the burden of care experienced by caregivers. Robust motion tracking using depth imaging supports the development of intelligent assistive technologies like the COACH. Robust motion tracking also has application to other forms of assistive technologies including gaming, human-computer interaction and automated assessments.

Artificial intelligence approaches for rational drug design and discovery.

PubMed

Duch, Włodzisław; Swaminathan, Karthikeyan; Meller, Jarosław

2007-01-01

Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

Masticatory path pattern during mastication of chewing gum with regard to gender difference.

PubMed

Kobayashi, Yoshinori; Shiga, Hiroshi; Arakawa, Ichiro; Yokoyama, Masaoki; Nakajima, Kunihisa

2009-01-01

To clarify the masticatory path patterns of the mandibular incisal point during mastication of softened chewing gum with regard to gender difference. One hundred healthy subjects (50 males and 50 females) were asked to chew softened chewing gum on one side at a time (right side and left side) and the movement of the mandibular incisal point was recorded using MKG K6I. After a catalog of path patterns was made, the movement path was classified into one of the pattern groups, and then the frequency of each pattern was investigated. A catalog of path patterns consisting of the three types of opening path (op1, linear or concave path; op2, path toward the chewing side after toward the non-working side; op3, convex path) and two types of closing path (cl1, convex path; cl2, concave path) was made. The movement path was classified into one of seven patterns, with six patterns being from the catalog and a final extra pattern in which the opening and closing paths crossed. The most common pattern among the subjects was Pattern I, followed by Patterns III, II, IV, V, VII, and VI, in that order. The majority of cases, 149 (74.5%) of 200 cases, showed either Pattern I (op1 and cl1) or Pattern III (op2 and cl1). There was no significant difference between the two genders in the frequency of each pattern. The movement path could be classified into seven patterns and no gender-related difference was found in the frequency of each pattern.

Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2016-01-01

Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

The quality of preterm infants' spontaneous movements: an early indicator of intelligence and behaviour at school age.

PubMed

Butcher, Phillipa R; van Braeckel, Koen; Bouma, Anke; Einspieler, Christa; Stremmelaar, Elisabeth F; Bos, Arend F

2009-08-01

The quality of very preterm infants' spontaneous movements at 11 to 16 weeks post-term age is a powerful predictor of their later neurological status. This study investigated whether early spontaneous movements also have predictive value for the intellectual and behavioural problems that children born very preterm often experience. Spontaneous movement quality was assessed, using Prechtl's method, at 11 to 16 weeks post-term in 65 infants born at

Brief Report: Performance Pattern Differences between Children with Autism Spectrum Disorders and Attention Deficit-Hyperactivity Disorder on Measures of Verbal Intelligence

ERIC Educational Resources Information Center

Zayat, Maya; Kalb, Luther; Wodka, Ericka L.

2011-01-01

Performance patterns on verbal subtests from the WISC-IV were compared between a clinically-referred sample of children with either autism spectrum disorders (ASD) or attention deficit/hyperactivity disorder (ADHD). Children with ASD demonstrated a statistically significant stepwise pattern where performance on Similarities was best, followed by…

Development of intelligent model to determine favorable wheelchair tilt and recline angles for people with spinal cord injury.

PubMed

Fu, Jicheng; Jan, Yih-Kuen; Jones, Maria

2011-01-01

Machine-learning techniques have found widespread applications in bioinformatics. Such techniques provide invaluable insight on understanding the complex biomedical mechanisms and predicting the optimal individualized intervention for patients. In our case, we are particularly interested in developing an individualized clinical guideline on wheelchair tilt and recline usage for people with spinal cord injury (SCI). The current clinical practice suggests uniform settings to all patients. However, our previous study revealed that the response of skin blood flow to wheelchair tilt and recline settings varied largely among patients. Our finding suggests that an individualized setting is needed for people with SCI to maximally utilize the residual neurological function to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop an intelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk for wheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct an intelligent model that can predict whether a given tilt and recline setting will be favorable to people with SCI based on neurological functions and SCI injury history. Our results indicate that the intelligent model significantly outperforms the traditional statistical approach in accurately classifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is the first study using intelligent models to predict the favorable wheelchair tilt and recline angles. Our methods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt and recline guidance for people with SCI.

Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke.

PubMed

Jiménez-Xarrié, Elena; Davila, Myriam; Candiota, Ana Paula; Delgado-Mederos, Raquel; Ortega-Martorell, Sandra; Julià-Sapé, Margarida; Arús, Carles; Martí-Fàbregas, Joan

2017-01-13

Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier. A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier ( http://gabrmn.uab.es/?q=sc ). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity). SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content).

MDA DS COI Spiral 3 - NOA, SILO and ABAC

DTIC Science & Technology

2009-06-01

agencies. The National Plan to Achieve MDA, a by-product of the Maritime Security Policy, established the national maritime common operating picture...information about vessels determined to be of interest by intelligence and operational organizations and is normally classified or highly sensitive. Exposing...makes it available to its users. For Spiral 3, the Coast Guard team, consisting of CG-26, the Operations Systems Center (OSC), and the Coast Guard

Intelligence and Electronic Warfare (IEW) System Fact Sheets

DTIC Science & Technology

1994-04-06

unattended ground sensor system that detects, classifies, and determines direction of movement of intruding personnel and vehicles . It uses remotely...fixed and moving target locations, speed and direction of movement, and classification of tracked/wheeled vehicles . The GSM is equipped with standard... Vehicle The Pointer is a Hand-Launched Unmanned Aerial Vehicle (HL-UAV) to be employed by battalion scouts for t"over-the-hillll reconnaissance and

Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques

NASA Astrophysics Data System (ADS)

Altıparmak, Hamit; Al Shahadat, Mohamad; Kiani, Ehsan; Dimililer, Kamil

2018-04-01

Robotic agriculture requires smart and doable techniques to substitute the human intelligence with machine intelligence. Strawberry is one of the important Mediterranean product and its productivity enhancement requires modern and machine-based methods. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical strawberry production area at a summer mid-day in Cyprus produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not.

Beyond the Floor Effect on the Wechsler Intelligence Scale for Children-4th Ed. (WISC-IV): Calculating IQ and Indexes of Subjects Presenting a Floored Pattern of Results

ERIC Educational Resources Information Center

Orsini, A.; Pezzuti, L.; Hulbert, S.

2015-01-01

Background: It is now widely known that children with severe intellectual disability show a 'floor effect' on the Wechsler scales. This effect emerges because the practice of transforming raw scores into scaled scores eliminates any variability present in participants with low intellectual ability and because intelligence quotient (IQ) scores are…

Genie: An Inference Engine with Applications to Vulnerability Analysis.

DTIC Science & Technology

1986-06-01

Stanford Artifcial intelligence Laboratory, 1976. 15 D. A. Waterman and F. Hayes-Roth, eds. Pattern-Directed Inference Systems. Academic Press, Inc...Continue an reverse aide It nlecessary mid Identify by block rnmbor) ; f Expert Systems Artificial Intelligence % Vulnerability Analysis Knowledge...deduction it is used wherever possible in data -driven mode (forward chaining). Production rules - JIM 0 g79OOFMV55@S I INCLASSTpnF SECURITY CLASSIFICATION OF

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Brain-computer interface using wavelet transformation and naïve bayes classifier.

PubMed

Bassani, Thiago; Nievola, Julio Cesar

2010-01-01

The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.

An overview of Space Communication Artificial Intelligence for Link Evaluation Terminal (SCAILET) Project

NASA Technical Reports Server (NTRS)

Shahidi, Anoosh K.; Schlegelmilch, Richard F.; Petrik, Edward J.; Walters, Jerry L.

1991-01-01

A software application to assist end-users of the link evaluation terminal (LET) for satellite communications is being developed. This software application incorporates artificial intelligence (AI) techniques and will be deployed as an interface to LET. The high burst rate (HBR) LET provides 30 GHz transmitting/20 GHz receiving (220/110 Mbps) capability for wideband communications technology experiments with the Advanced Communications Technology Satellite (ACTS). The HBR LET can monitor and evaluate the integrity of the HBR communications uplink and downlink to the ACTS satellite. The uplink HBR transmission is performed by bursting the bit-pattern as a modulated signal to the satellite. The HBR LET can determine the bit error rate (BER) under various atmospheric conditions by comparing the transmitted bit pattern with the received bit pattern. An algorithm for power augmentation will be applied to enhance the system's BER performance at reduced signal strength caused by adverse conditions.

An Analysis of Document Category Prediction Responses to Classifier Model Parameter Treatment Permutations within the Software Design Patterns Subject Domain

ERIC Educational Resources Information Center

Pankau, Brian L.

2009-01-01

This empirical study evaluates the document category prediction effectiveness of Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier treatments built from different feature selection and machine learning settings and trained and tested against textual corpora of 2300 Gang-Of-Four (GOF) design pattern documents. Analysis of the experiment's…

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

How feasible is the rapid development of artificial superintelligence?

NASA Astrophysics Data System (ADS)

Sotala, Kaj

2017-11-01

What kinds of fundamental limits are there in how capable artificial intelligence (AI) systems might become? Two questions in particular are of interest: (1) How much more capable could AI become relative to humans, and (2) how easily could superhuman capability be acquired? To answer these questions, we will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how AI could improve on humans in two major aspects of thought and expertise, namely simulation and pattern recognition. We find that although there are very real limits to prediction, it seems like AI could still substantially improve on human intelligence.

System diagnostic builder

NASA Technical Reports Server (NTRS)

Nieten, Joseph L.; Burke, Roger

1992-01-01

The System Diagnostic Builder (SDB) is an automated software verification and validation tool using state-of-the-art Artificial Intelligence (AI) technologies. The SDB is used extensively by project BURKE at NASA-JSC as one component of a software re-engineering toolkit. The SDB is applicable to any government or commercial organization which performs verification and validation tasks. The SDB has an X-window interface, which allows the user to 'train' a set of rules for use in a rule-based evaluator. The interface has a window that allows the user to plot up to five data parameters (attributes) at a time. Using these plots and a mouse, the user can identify and classify a particular behavior of the subject software. Once the user has identified the general behavior patterns of the software, he can train a set of rules to represent his knowledge of that behavior. The training process builds rules and fuzzy sets to use in the evaluator. The fuzzy sets classify those data points not clearly identified as a particular classification. Once an initial set of rules is trained, each additional data set given to the SDB will be used by a machine learning mechanism to refine the rules and fuzzy sets. This is a passive process and, therefore, it does not require any additional operator time. The evaluation component of the SDB can be used to validate a single software system using some number of different data sets, such as a simulator. Moreover, it can be used to validate software systems which have been re-engineered from one language and design methodology to a totally new implementation.

A system for tracking and recognizing pedestrian faces using a network of loosely coupled cameras

NASA Astrophysics Data System (ADS)

Gagnon, L.; Laliberté, F.; Foucher, S.; Branzan Albu, A.; Laurendeau, D.

2006-05-01

A face recognition module has been developed for an intelligent multi-camera video surveillance system. The module can recognize a pedestrian face in terms of six basic emotions and the neutral state. Face and facial features detection (eyes, nasal root, nose and mouth) are first performed using cascades of boosted classifiers. These features are used to normalize the pose and dimension of the face image. Gabor filters are then sampled on a regular grid covering the face image to build a facial feature vector that feeds a nearest neighbor classifier with a cosine distance similarity measure for facial expression interpretation and face model construction. A graphical user interface allows the user to adjust the module parameters.

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

PubMed

Ha, S R; Park, S Y; Park, D H

2003-01-01

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

Student Responses Toward Student Worksheets Based on Discovery Learning for Students with Intrapersonal and Interpersonal Intelligence

NASA Astrophysics Data System (ADS)

Yerizon, Y.; Putra, A. A.; Subhan, M.

2018-04-01

Students have a low mathematical ability because they are used to learning to hear the teacher's explanation. For that students are given activities to sharpen his ability in math. One way to do that is to create discovery learning based work sheet. The development of this worksheet took into account specific student learning styles including in schools that have classified students based on multiple intelligences. The dominant learning styles in the classroom were intrapersonal and interpersonal. The purpose of this study was to discover students’ responses to the mathematics work sheets of the junior high school with a discovery learning approach suitable for students with Intrapersonal and Interpersonal Intelligence. This tool was developed using a development model adapted from the Plomp model. The development process of this tools consists of 3 phases: front-end analysis/preliminary research, development/prototype phase and assessment phase. From the results of the research, it is found that students have good response to the resulting work sheet. The worksheet was understood well by students and its helps student in understanding the concept learned.

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1996-01-01

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

Motion Control of Drives for Prosthetic Hand Using Continuous Myoelectric Signals

NASA Astrophysics Data System (ADS)

Purushothaman, Geethanjali; Ray, Kalyan Kumar

2016-03-01

In this paper the authors present motion control of a prosthetic hand, through continuous myoelectric signal acquisition, classification and actuation of the prosthetic drive. A four channel continuous electromyogram (EMG) signal also known as myoelectric signals (MES) are acquired from the abled-body to classify the six unique movements of hand and wrist, viz, hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), ulnar deviation (UD) and radial deviation (RD). The classification technique involves in extracting the features/pattern through statistical time domain (TD) parameter/autoregressive coefficients (AR), which are reduced using principal component analysis (PCA). The reduced statistical TD features and or AR coefficients are used to classify the signal patterns through k nearest neighbour (kNN) as well as neural network (NN) classifier and the performance of the classifiers are compared. Performance comparison of the above two classifiers clearly shows that kNN classifier in identifying the hidden intended motion in the myoelectric signals is better than that of NN classifier. Once the classifier identifies the intended motion, the signal is amplified to actuate the three low power DC motor to perform the above mentioned movements.

Corticonic models of brain mechanisms underlying cognition and intelligence

NASA Astrophysics Data System (ADS)

Farhat, Nabil H.

The concern of this review is brain theory or more specifically, in its first part, a model of the cerebral cortex and the way it: (a) interacts with subcortical regions like the thalamus and the hippocampus to provide higher-level-brain functions that underlie cognition and intelligence, (b) handles and represents dynamical sensory patterns imposed by a constantly changing environment, (c) copes with the enormous number of such patterns encountered in a lifetime by means of dynamic memory that offers an immense number of stimulus-specific attractors for input patterns (stimuli) to select from, (d) selects an attractor through a process of “conjugation” of the input pattern with the dynamics of the thalamo-cortical loop, (e) distinguishes between redundant (structured) and non-redundant (random) inputs that are void of information, (f) can do categorical perception when there is access to vast associative memory laid out in the association cortex with the help of the hippocampus, and (g) makes use of “computation” at the edge of chaos and information driven annealing to achieve all this. Other features and implications of the concepts presented for the design of computational algorithms and machines with brain-like intelligence are also discussed. The material and results presented suggest, that a Parametrically Coupled Logistic Map network (PCLMN) is a minimal model of the thalamo-cortical complex and that marrying such a network to a suitable associative memory with re-entry or feedback forms a useful, albeit, abstract model of a cortical module of the brain that could facilitate building a simple artificial brain. In the second part of the review, the results of numerical simulations and drawn conclusions in the first part are linked to the most directly relevant works and views of other workers. What emerges is a picture of brain dynamics on the mesoscopic and macroscopic scales that gives a glimpse of the nature of the long sought after brain code underlying intelligence and other higher level brain functions.

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

NASA Astrophysics Data System (ADS)

Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

2016-05-01

Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

Artificial Intelligence Methods: Choice of algorithms, their complexity, and appropriateness within the context of hydrology and water resources. (Invited)

NASA Astrophysics Data System (ADS)

Bastidas, L. A.; Pande, S.

2009-12-01

Pattern analysis deals with the automatic detection of patterns in the data and there are a variety of algorithms available for the purpose. These algorithms are commonly called Artificial Intelligence (AI) or data driven algorithms, and have been applied lately to a variety of problems in hydrology and are becoming extremely popular. When confronting such a range of algorithms, the question of which one is the “best” arises. Some algorithms may be preferred because of the lower computational complexity; others take into account prior knowledge of the form and the amount of the data; others are chosen based on a version of the Occam’s razor principle that a simple classifier performs better. Popper has argued, however, that Occam’s razor is without operational value because there is no clear measure or criterion for simplicity. An example of measures that can be used for this purpose are: the so called algorithmic complexity - also known as Kolmogorov complexity or Kolmogorov (algorithmic) entropy; the Bayesian information criterion; or the Vapnik-Chervonenkis dimension. On the other hand, the No Free Lunch Theorem states that there is no best general algorithm, and that specific algorithms are superior only for specific problems. It should be noted also that the appropriate algorithm and the appropriate complexity are constrained by the finiteness of the available data and the uncertainties associated with it. Thus, there is compromise between the complexity of the algorithm, the data properties, and the robustness of the predictions. We discuss the above topics; briefly review the historical development of applications with particular emphasis on statistical learning theory (SLT), also known as machine learning (ML) of which support vector machines and relevant vector machines are the most commonly known algorithms. We present some applications of such algorithms for distributed hydrologic modeling; and introduce an example of how the complexity measure can be applied for appropriate model choice within the context of applications in hydrologic modeling intended for use in studies about water resources and water resources management and their direct relation to extreme conditions or natural hazards.

An Analysis of Personality Patterns of Women in Selected Professions. Final Report.

ERIC Educational Resources Information Center

Martin, Dorothy R.; Saunders, David R.

The need for adequate knowledge of the personality patterns associated with professional competence, especially that of women professionals, spurred the authors to study this relationship, using 221 professional woman as subjects. The subjects, professionals from nine different occupations, were administered the Wechsler Adult Intelligence Scale…

Some Ethnic Cognitive Patterns.

ERIC Educational Resources Information Center

Curtis, Patricia Gelber

It was hypothesized that there are significant differences in intellectual patterns between black and white populations which can be demonstrated on the Wechsler Adult Intelligence Scale (WAIS). A one-way analysis of variance was performed on the subjects' scores on the WAIS subtests and the Verbal, Peformance and Full Scale IQ using the ethnic…

Laboratory Instrumentation Design Research for Scalable Next Generation Epitaxy: Non-Equilibrium Wide Application Epitaxial Patterning by Intelligent Control (NEW-EPIC). Volume 1. 3D Composition/Doping Control via Micromiror Patterned Deep UV Photodesorption: Revolutionary in situ Characterization/Control

DTIC Science & Technology

2009-02-19

magnesium dopant concentration. A digital micromirror device is introduced to pattern incident UV radiation during InGaN growth, demonstrating that the...magnesium dopant concentration. A digital micromirror device is introduced to pattern incident UV radiation during InGaN growth, demonstrating that the...successful compositional patterning of InGaN using in situ digital micromirror device (DMD) patterning of ultraviolet (UV

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

PubMed

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 recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

PubMed Central

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 recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior. PMID:29723994

Automated time activity classification based on global positioning system (GPS) tracking data

PubMed Central

2011-01-01

Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Conclusions Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns. PMID:22082316

Automated time activity classification based on global positioning system (GPS) tracking data.

PubMed

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

Arrogance analysis of several typical pattern recognition classifiers

NASA Astrophysics Data System (ADS)

Jing, Chen; Xia, Shengping; Hu, Weidong

2007-04-01

Various kinds of classification methods have been developed. However, most of these classical methods, such as Back-Propagation (BP), Bayesian method, Support Vector Machine(SVM), Self-Organizing Map (SOM) are arrogant. A so-called arrogance, for a human, means that his decision, which even is a mistake, overstates his actual experience. Accordingly, we say that he is a arrogant if he frequently makes arrogant decisions. Likewise, some classical pattern classifiers represent the similar characteristic of arrogance. Given an input feature vector, we say a classifier is arrogant in its classification if its veracity is high yet its experience is low. Typically, for a new sample which is distinguishable from original training samples, traditional classifiers recognize it as one of the known targets. Clearly, arrogance in classification is an undesirable attribute. Conversely, a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its experience. Inquisitiveness is, in many ways, the opposite of arrogance. In nature, inquisitiveness is an eagerness for knowledge characterized by the drive to question, to seek a deeper understanding. The human capacity to doubt present beliefs allows us to acquire new experiences and to learn from our mistakes. Within the discrete world of computers, inquisitive pattern recognition is the constructive investigation and exploitation of conflict in information. Thus, we quantify this balance and discuss new techniques that will detect arrogance in a classifier.

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

PubMed Central

Vildjiounaite, Elena; Gimel'farb, Georgy; Kyllönen, Vesa; Peltola, Johannes

2015-01-01

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design. PMID:26473165

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

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

PubMed Central

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

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

ERIC Educational Resources Information Center

Ye, Qiang

2010-01-01

Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

Morphological Diversity of the Colony Produced by Bacteria Proteus mirabilis

NASA Astrophysics Data System (ADS)

Nakahara, Akio; Shimada, Yuji; Wakita, Jun-ichi; Matsushita, Mitsugu; Matsuyama, Tohey

1996-08-01

Morphological changes of colonies have been investigatedfor a bacterial strain of Proteus mirabilis, which is a famous speciesfor producing concentric-ring-like colonies. It was found that colony patterns can be classified into three types,i.e., cyclic spreading, diffusion-limited growth (DLA-like)and three-dimensional growth (inside the agar medium) patterns. Cyclic spreading patterns can further be classifiedinto three subgroups, i.e., concentric-ring, homogeneous and spatiotemporal patterns. These subgroups were classified by examining the development of colony structure after colonies spread all over petri-dishes. Comparison of the results with thoseof another bacterial species Bacillus subtilis is also discussed.

Distributed Patterns of Reactivation Predict Vividness of Recollection.

PubMed

St-Laurent, Marie; Abdi, Hervé; Buchsbaum, Bradley R

2015-10-01

According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.

Can personality traits and intelligence compensate for background disadvantage? Predicting status attainment in adulthood.

PubMed

Damian, Rodica Ioana; Su, Rong; Shanahan, Michael; Trautwein, Ulrich; Roberts, Brent W

2015-09-01

This study investigated the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large U.S. representative sample of high school students; N = 81,000) in predicting educational attainment, annual income, and occupational prestige 11 years later. Specifically, we tested whether individual differences followed 1 of 3 patterns in relation to parental socioeconomic status (SES) when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., "the rich get richer"; individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, the interaction models being more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a "full catch-up" effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors. (c) 2015 APA, all rights reserved).

Can Personality Traits and Intelligence Compensate for Background Disadvantage? Predicting Status Attainment in Adulthood

PubMed Central

Damian, Rodica Ioana; Su, Rong; Shanahan, Michael; Trautwein, Ulrich; Roberts, Brent W.

2014-01-01

This paper investigates the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large US representative sample of high school students; N = 81,000) in predicting educational attainment, annual income, and occupational prestige eleven years later. Specifically, we tested whether individual differences followed one of three patterns in relation to parental SES when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer,” individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, where interaction models were more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a “full catch up” effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors. PMID:25402679

Using artificial intelligence to improve identification of nanofluid gas-liquid two-phase flow pattern in mini-channel

NASA Astrophysics Data System (ADS)

Xiao, Jian; Luo, Xiaoping; Feng, Zhenfei; Zhang, Jinxin

2018-01-01

This work combines fuzzy logic and a support vector machine (SVM) with a principal component analysis (PCA) to create an artificial-intelligence system that identifies nanofluid gas-liquid two-phase flow states in a vertical mini-channel. Flow-pattern recognition requires finding the operational details of the process and doing computer simulations and image processing can be used to automate the description of flow patterns in nanofluid gas-liquid two-phase flow. This work uses fuzzy logic and a SVM with PCA to improve the accuracy with which the flow pattern of a nanofluid gas-liquid two-phase flow is identified. To acquire images of nanofluid gas-liquid two-phase flow patterns of flow boiling, a high-speed digital camera was used to record four different types of flow-pattern images, namely annular flow, bubbly flow, churn flow, and slug flow. The textural features extracted by processing the images of nanofluid gas-liquid two-phase flow patterns are used as inputs to various identification schemes such as fuzzy logic, SVM, and SVM with PCA to identify the type of flow pattern. The results indicate that the SVM with reduced characteristics of PCA provides the best identification accuracy and requires less calculation time than the other two schemes. The data reported herein should be very useful for the design and operation of industrial applications.

Enhancements for a Dynamic Data Warehousing and Mining System for Large-Scale Human Social Cultural Behavioral (HSBC) Data

DTIC Science & Technology

2016-09-26

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Enhancements for a Dynamic Data Warehousing and Mining System for N00014-16-P-3014 Large-Scale Human Social Cultural Behavioral (HSBC) Data 5b. GRANT NUMBER...Representative Media Gallery View. We perform Scraawl’s NER algorithm to the text associated with YouTube post, which classifies the named entities into

Space Communications Artificial Intelligence for Link Evaluation Terminal (SCAILET)

NASA Technical Reports Server (NTRS)

Shahidi, Anoosh

1991-01-01

A software application to assis end-users of the Link Evaluation Terminal (LET) for satellite communication is being developed. This software application incorporates artificial intelligence (AI) techniques and will be deployed as an interface to LET. The high burst rate (HBR) LET provides 30 GHz transmitting/20 GHz receiving, 220/110 Mbps capability for wideband communications technology experiments with the Advanced Communications Technology Satellite (ACTS). The HBR LET and ACTS are being developed at the NASA Lewis Research Center. The HBR LET can monitor and evaluate the integrity of the HBR communications uplink and downlink to the ACTS satellite. The uplink HBR transmission is performed by bursting the bit-pattern as a modulated signal to the satellite. By comparing the transmitted bit pattern with the received bit pattern, HBR LET can determine the bit error rate BER) under various atmospheric conditions. An algorithm for power augmentation is applied to enhance the system's BER performance at reduced signal strength caused by adverse conditions. Programming scripts, defined by the design engineer, set up the HBR LET terminal by programming subsystem devices through IEEE488 interfaces. However, the scripts are difficult to use, require a steep learning curve, are cryptic, and are hard to maintain. The combination of the learning curve and the complexities involved with editing the script files may discourage end-users from utilizing the full capabilities of the HBR LET system. An intelligent assistant component of SCAILET that addresses critical end-user needs in the programming of the HBR LET system as anticipated by its developers is described. A close look is taken at the various steps involved in writing ECM software for a C&P, computer and at how the intelligent assistant improves the HBR LET system and enhances the end-user's ability to perform the experiments.

Brain entropy and human intelligence: A resting-state fMRI study

PubMed Central

Calderone, Daniel; Morales, Leah J.

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns. PMID:29432427

Brain entropy and human intelligence: A resting-state fMRI study.

PubMed

Saxe, Glenn N; Calderone, Daniel; Morales, Leah J

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

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

Koch, Mark William; Steinbach, Ryan Matthew; Moya, Mary M

2015-10-01

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithmsmore » using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.« less

Classifying Urban Space Types of Seoul using Time-series Heat Island map

NASA Astrophysics Data System (ADS)

Jung, S.; KIM, H.; JE, M.

2017-12-01

In August 2016, the hottest heat occurred in Korea since the weather observation started in Korea. Due to climate changes, this heat phenomenon is expected to be severe more in the future. Thus, this study analyzed the heatwave occurred in 2016 with regard to Seoul from various angles to identify the characteristics of urban regions where the heat island phenomenon occurred. To do this, first, temperature data for two days on August 6 and 12 in 2016 when the hottest heatwave occurred were collected from 287 places of automatic weather stations (AWS) installed in Seoul and adjacent suburbs. The temperature distribution of Seoul was mapped using interpolation in every hour using the collected temperature data. Second, regions in Seoul were classified using statistical methods based on spatial characteristics such as land coverage, density, use type, and traffic volume in Seoul. Third, a daily pattern of change in temperature in the classified regions was depicted with a graph, and regions were re-classified based on the daily pattern of change in temperature. Finally, the characteristics of the classified regions were re-reviewed and then, heat island occurrence, continuation, and reduction measure by region type were discussed. The analysis results showed that a pattern of heatwave occurrence was exhibited differently by the classified region type. The results also showed that not only physical characteristics such as land coverage but also socioeconomic index such as population density and floating population that induced a traffic volume influenced the pattern of heatwave occurrence despite of the same land usage regions. This study not only classified urban climate regions by existing mean temperature and specific time-point temperature but also proposed a methodology that analyzed heat island phenomenon inside cities by using time-series temperature data in a day. Furthermore, this study enabled regional classification based on heat island characteristics to contribute to establishment of measure for each regional classification.

FICE in Predicting Colorectal Flat Lesion Histology.

PubMed

Akarsu, Cevher; Sahbaz, Nuri A; Dural, Ahmet C; Kones, Osman; Binboga, Sinan; Kabuli, Hamit A; Gumusoglu, Alpen Y; Alis, Halil

2017-01-01

Colonoscopy is the gold standard for detection of polyps and is preventive against colorectal cancers. Flat adenomas are small, superficial lesions and have a high rate of going undetected during conventional white-light endoscopy. This article adds to the scant body of literature in English regarding in vivo detection and diagnosis of flat adenomas using Fujinon intelligent color enhancement (FICE) system. In this study, we investigated the diagnosis of flat lesions via the FICE endoscopy system and in vivo histologic diagnostic estimations of flat lesions. This prospective study was conducted in patients who underwent colonoscopy that found flat adenomas. Lesions were classified morphologically with regard to the Paris Classification and sent for histopathologic examination after in vivo histologic diagnostic estimations were made according to Kudo's pit pattern classification. The positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, and accuracy of in vivo endoscopic diagnostic estimations of flat lesions with the FICE system were analyzed. A total of 217 flat lesions were identified in 137 patients. Of the lesions, 85.7% were Paris type 0-IIa, and 59.4% were Kudo pit pattern type III. When the FICE diagnostic estimations of flat lesions and final pathology results were considered, PPV was 68.5%, NPV value was 89.6%, sensitivity was 94.7%, specificity was 50.9%, and accuracy was 74.2%. Biologic importance of flat lesions is obscure, as they are usually missed during colonoscopy. The use of novel endoscopic techniques may improve their detection and diagnosis rates.

The Spatial Pattern of Intelligence in a Small Town.

ERIC Educational Resources Information Center

Bailey, William H.

The document measures the spatial patterns of mental abilities of 94 seventh-grade students within a small town by correlating and mapping four variables--IQ test scores, achievement test scores, neighborhood quality as seen by town officials, and creativity test scores from the Torrance Tests of Creative Thinking. Objectives were to ascertain the…

Neural Networks for the Beginner.

ERIC Educational Resources Information Center

Snyder, Robin M.

Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

Between-Word Simplification Patterns in the Continuous Speech of Children with Speech Sound Disorders

ERIC Educational Resources Information Center

Klein, Harriet B.; Liu-Shea, May

2009-01-01

Purpose: This study was designed to identify and describe between-word simplification patterns in the continuous speech of children with speech sound disorders. It was hypothesized that word combinations would reveal phonological changes that were unobserved with single words, possibly accounting for discrepancies between the intelligibility of…

WISC-R Subtest Pattern Stability and Learning Disabilities: A Profile Analysis.

ERIC Educational Resources Information Center

Mealor, David J.; Abrams, Pamela F.

Profile analysis was performed on Wechsler Intelligence Scale for Children-Revised (WISC-R) scores of 29 learning disabled students (6-10 years old) in a Specific Learning Disabilities (SLD) program, to determine whether subtest patterns for initial and re-evaluation WISC-R administrations would differ significantly. Profile analysis was applied…

WISC-R Profile Analysis in Differentiating Learning Disabled from Emotionally Disabled Children.

ERIC Educational Resources Information Center

Vance, Booney

The paper examines the usefulness of the Wechsler Intelligence Scale for Children-Revised (WISC-R) subtest score pattern for distinguishing between groups of handicapped children, specifically learning disabled and emotionally disturbed students. The author asserts that no single clear cut pattern characteristic of either population is likely to…

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

PubMed

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

PubMed Central

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups. PMID:24905072

[The design and applications of a non-invasive intelligent detector for cardiovascular functions].

PubMed

Li, Feng; Xing, Wu; Chen, Ming-zhi; Shang, Huai

2006-05-01

An apparatus based on a high sensitive sensor which detects cardiovascular functions is introduced in this paper. Some intelligent detecting technologies, such as syntactic pattern recognition and a medical expert system are used in this detector. Its embedded single-chip microcomputer processes and analyzes pulse signals for gaining automatically the parameters about heart, blood vessel and blood etc., so as to get the health evaluation, correct medical diagnosis and prediction of cardiovascular diseases.

The Quest for Contact: NASA's Search for Extraterrestrial Intelligence

NASA Technical Reports Server (NTRS)

1992-01-01

This video details the history and current efforts of NASA's Search for Extraterrestrial Intelligence program. The video explains the use of radiotelescopes to monitor electromagnetic frequencies reaching the Earth, and the analysis of this data for patterns or signals that have no natural origin. The video presents an overview of Frank Drake's 1960 'Ozma' experiment, the current META experiment, and planned efforts incorporating an international Deep Space Network of radiotelescopes that will be trained on over 800 stars.

Future View: Web Navigation based on Learning User's Browsing Strategy

NASA Astrophysics Data System (ADS)

Nagino, Norikatsu; Yamada, Seiji

In this paper, we propose a Future View system that assists user's usual Web browsing. The Future View will prefetch Web pages based on user's browsing strategies and present them to a user in order to assist Web browsing. To learn user's browsing strategy, the Future View uses two types of learning classifier systems: a content-based classifier system for contents change patterns and an action-based classifier system for user's action patterns. The results of learning is applied to crawling by Web robots, and the gathered Web pages are presented to a user through a Web browser interface. We experimentally show effectiveness of navigation using the Future View.

Discovering the intelligence in molecular biology.

PubMed

Uberbacher, E

1995-12-01

The Third International Conference on Intelligent Systems in Molecular Biology was truly an outstanding event. Computational methods in molecular biology have reached a new level of maturity and utility, resulting in many high-impact applications. The success of this meeting bodes well for the rapid and continuing development of computational methods, intelligent systems and information-based approaches for the biosciences. The basic technology, originally most often applied to 'feasibility' problems, is now dealing effectively with the most difficult real-world problems. Significant progress has been made in understanding protein-structure information, structural classification, and how functional information and the relevant features of active-site geometry can be gleaned from structures by automated computational approaches. The value and limits of homology-based methods, and the ability to classify proteins by structure in the absence of homology, have reached a new level of sophistication. New methods for covariation analysis in the folding of large structures such as RNAs have shown remarkably good results, indicating the long-term potential to understand very complicated molecules and multimolecular complexes using computational means. Novel methods, such as HMMs, context-free grammars and the uses of mutual information theory, have taken center stage as highly valuable tools in our quest to represent and characterize biological information. A focus on creative uses of intelligent systems technologies and the trend toward biological application will undoubtedly continue and grow at the 1996 ISMB meeting in St Louis.

Evolving rule-based systems in two medical domains using genetic programming.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

2004-11-01

To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

A semi-automated method for bone age assessment using cervical vertebral maturation.

PubMed

Baptista, Roberto S; Quaglio, Camila L; Mourad, Laila M E H; Hummel, Anderson D; Caetano, Cesar Augusto C; Ortolani, Cristina Lúcia F; Pisa, Ivan T

2012-07-01

To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.

An intelligent space for mobile robot localization using a multi-camera system.

PubMed

Rampinelli, Mariana; Covre, Vitor Buback; de Queiroz, Felippe Mendonça; Vassallo, Raquel Frizera; Bastos-Filho, Teodiano Freire; Mazo, Manuel

2014-08-15

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously. The system was programmed as a client/server with TCP/IP connections, and a communication protocol was defined. The client coordinates the activities inside the intelligent space, and the servers provide the information needed for that. Once the cameras are used for localization, they have to be properly calibrated. Therefore, a calibration method for a multi-camera network is also proposed in this paper. A robot is used to move a calibration pattern throughout the field of view of the cameras. Then, the captured images and the robot odometry are used for calibration. As a result, the proposed algorithm provides a solution for multi-camera calibration and robot localization at the same time. The intelligent space and the calibration method were evaluated under different scenarios using computer simulations and real experiments. The results demonstrate the proper functioning of the intelligent space and validate the multi-camera calibration method, which also improves robot localization.

An Intelligent Space for Mobile Robot Localization Using a Multi-Camera System

PubMed Central

Rampinelli, Mariana.; Covre, Vitor Buback.; de Queiroz, Felippe Mendonça.; Vassallo, Raquel Frizera.; Bastos-Filho, Teodiano Freire.; Mazo, Manuel.

2014-01-01

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously. The system was programmed as a client/server with TCP/IP connections, and a communication protocol was defined. The client coordinates the activities inside the intelligent space, and the servers provide the information needed for that. Once the cameras are used for localization, they have to be properly calibrated. Therefore, a calibration method for a multi-camera network is also proposed in this paper. A robot is used to move a calibration pattern throughout the field of view of the cameras. Then, the captured images and the robot odometry are used for calibration. As a result, the proposed algorithm provides a solution for multi-camera calibration and robot localization at the same time. The intelligent space and the calibration method were evaluated under different scenarios using computer simulations and real experiments. The results demonstrate the proper functioning of the intelligent space and validate the multi-camera calibration method, which also improves robot localization. PMID:25196009

Information and redundancy: key concepts in understanding the genetic control of health and intelligence.

PubMed

Morris, J A

1999-08-01

A model is proposed in which information from the environment is analysed by complex biological decision-making systems which are highly redundant. A correct response is intelligent behaviour which preserves health; incorrect responses lead to disease. Mutations in genes which code for the redundant systems will accumulate in the genome and impair decision-making. The number of mutant genes will depend upon a balance between the new mutation rate per generation and systems of elimination based on synergistic interaction in redundant systems. This leads to a polygenic pattern of inheritance for intelligence and the common diseases. The model also gives a simple explanation for some of the hitherto puzzling aspects of work on the genetic basis of intelligence including the recorded rise in IQ this century. There is a prediction that health, intelligence and socio-economic position will be correlated generating a health differential in the social hierarchy. Furthermore, highly competitive societies will place those least able to cope in the harshest environment and this will impair health overall. The model points to a need for population monitoring of somatic mutation in order to preserve the health and intelligence of future generations.

Emotional intelligence, anxiety, and emotional eating: A deeper insight into a recently reported association?

PubMed

Zysberg, Leehu

2018-04-01

Recent studies reported a negative association between emotional intelligence (EI: defined here as individual predispositions associated with effective identification and regulation of emotions) and emotional eating. Although theory provides some insights into how the concept represents mechanisms that may serve as protective factors, empirical evidence of the mechanism behind the association has yet to be presented. This study tested a proposed model in which anxiety levels mediate the association between emotional intelligence and emotional-eating patterns in a normative sample of women in Israel. A cross-sectional/correlational design was used to gather data from 208 generally healthy female participants who completed measures of trait emotional intelligence, anxiety, and tendency toward emotional eating, as well as demographics. Anxiety levels mediated the negative association between emotional intelligence and emotional eating. Background variables had only marginal involvement in this model. The results shed light on the mechanisms underlying the association between emotional intelligence and emotional eating. Should future studies corroborate the findings, they may serve as a basis for future screening protocols, prevention and interventions with individuals and groups at risk of EE and eating disorders. Copyright © 2018 Elsevier Ltd. All rights reserved.

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum.

PubMed

Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

2015-01-01

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently 'intelligent' behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton-a ubiquitous cellular protein scaffold whose functions are manifold and essential to life-and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

NASA Astrophysics Data System (ADS)

Wang, Liming; Zhang, Kai; Liu, Xiyang; Long, Erping; Jiang, Jiewei; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Li, Wangting; Lin, Haotian

2017-01-01

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Deep learning application: rubbish classification with aid of an android device

NASA Astrophysics Data System (ADS)

Liu, Sijiang; Jiang, Bo; Zhan, Jie

2017-06-01

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

A Big-Data-based platform of workers' behavior: Observations from the field.

PubMed

Guo, S Y; Ding, L Y; Luo, H B; Jiang, X Y

2016-08-01

Behavior-Based Safety (BBS) has been used in construction to observe, analyze and modify workers' behavior. However, studies have identified that BBS has several limitations, which have hindered its effective implementation. To mitigate the negative impact of BBS, this paper uses a case study approach to develop a Big-Data-based platform to classify, collect and store data about workers' unsafe behavior that is derived from a metro construction project. In developing the platform, three processes were undertaken: (1) a behavioral risk knowledge base was established; (2) images reflecting workers' unsafe behavior were collected from intelligent video surveillance and mobile application; and (3) images with semantic information were stored via a Hadoop Distributed File System (HDFS). The platform was implemented during the construction of the metro-system and it is demonstrated that it can effectively analyze semantic information contained in images, automatically extract workers' unsafe behavior and quickly retrieve on HDFS as well. The research presented in this paper can enable construction organizations with the ability to visualize unsafe acts in real-time and further identify patterns of behavior that can jeopardize safety outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Glossary of terms used by radiologists in image processing].

PubMed

Rolland, Y; Collorec, R; Bruno, A; Ramée, A; Morcet, N; Haigron, P

1995-01-01

We give the definition of 166 words used in image processing. Adaptivity, aliazing, analog-digital converter, analysis, approximation, arc, artifact, artificial intelligence, attribute, autocorrelation, bandwidth, boundary, brightness, calibration, class, classification, classify, centre, cluster, coding, color, compression, contrast, connectivity, convolution, correlation, data base, decision, decomposition, deconvolution, deduction, descriptor, detection, digitization, dilation, discontinuity, discretization, discrimination, disparity, display, distance, distorsion, distribution dynamic, edge, energy, enhancement, entropy, erosion, estimation, event, extrapolation, feature, file, filter, filter floaters, fitting, Fourier transform, frequency, fusion, fuzzy, Gaussian, gradient, graph, gray level, group, growing, histogram, Hough transform, Houndsfield, image, impulse response, inertia, intensity, interpolation, interpretation, invariance, isotropy, iterative, JPEG, knowledge base, label, laplacian, learning, least squares, likelihood, matching, Markov field, mask, matching, mathematical morphology, merge (to), MIP, median, minimization, model, moiré, moment, MPEG, neural network, neuron, node, noise, norm, normal, operator, optical system, optimization, orthogonal, parametric, pattern recognition, periodicity, photometry, pixel, polygon, polynomial, prediction, pulsation, pyramidal, quantization, raster, reconstruction, recursive, region, rendering, representation space, resolution, restoration, robustness, ROC, thinning, transform, sampling, saturation, scene analysis, segmentation, separable function, sequential, smoothing, spline, split (to), shape, threshold, tree, signal, speckle, spectrum, spline, stationarity, statistical, stochastic, structuring element, support, syntaxic, synthesis, texture, truncation, variance, vision, voxel, windowing.

Improved image classification with neural networks by fusing multispectral signatures with topological data

NASA Technical Reports Server (NTRS)

Harston, Craig; Schumacher, Chris

1992-01-01

Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent.

Tuberculosis control, and the where and why of artificial intelligence

PubMed Central

Falzon, Dennis; Thomas, Bruce V.; Temesgen, Zelalem; Sadasivan, Lal; Raviglione, Mario

2017-01-01

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB. PMID:28656130

Common Criteria Related Security Design Patterns—Validation on the Intelligent Sensor Example Designed for Mine Environment

PubMed Central

Bialas, Andrzej

2010-01-01

The paper discusses the security issues of intelligent sensors that are able to measure and process data and communicate with other information technology (IT) devices or systems. Such sensors are often used in high risk applications. To improve their robustness, the sensor systems should be developed in a restricted way to provide them with assurance. One of assurance creation methodologies is Common Criteria (ISO/IEC 15408), used for IT products and systems. The contribution of the paper is a Common Criteria compliant and pattern-based method for the intelligent sensors security development. The paper concisely presents this method and its evaluation for the sensor detecting methane in a mine, focusing on the security problem of the intelligent sensor definition and solution. The aim of the validation is to evaluate and improve the introduced method. PMID:22399888

Cognitive differences between orang-utan species: a test of the cultural intelligence hypothesis

PubMed Central

Forss, Sofia I. F.; Willems, Erik; Call, Josep; van Schaik, Carel P.

2016-01-01

Cultural species can - or even prefer to - learn their skills from conspecifics. According to the cultural intelligence hypothesis, selection on underlying mechanisms not only improves this social learning ability but also the asocial (individual) learning ability. Thus, species with systematically richer opportunities to socially acquire knowledge and skills should over time evolve to become more intelligent. We experimentally compared the problem-solving ability of Sumatran orang-utans (Pongo abelii), which are sociable in the wild, with that of the closely related, but more solitary Bornean orang-utans (P. pygmaeus), under the homogeneous environmental conditions provided by zoos. Our results revealed that Sumatrans showed superior innate problem-solving skills to Borneans, and also showed greater inhibition and a more cautious and less rough exploration style. This pattern is consistent with the cultural intelligence hypothesis, which predicts that the more sociable of two sister species experienced stronger selection on cognitive mechanisms underlying learning. PMID:27466052

Cognitive differences between orang-utan species: a test of the cultural intelligence hypothesis.

PubMed

Forss, Sofia I F; Willems, Erik; Call, Josep; van Schaik, Carel P

2016-07-28

Cultural species can - or even prefer to - learn their skills from conspecifics. According to the cultural intelligence hypothesis, selection on underlying mechanisms not only improves this social learning ability but also the asocial (individual) learning ability. Thus, species with systematically richer opportunities to socially acquire knowledge and skills should over time evolve to become more intelligent. We experimentally compared the problem-solving ability of Sumatran orang-utans (Pongo abelii), which are sociable in the wild, with that of the closely related, but more solitary Bornean orang-utans (P. pygmaeus), under the homogeneous environmental conditions provided by zoos. Our results revealed that Sumatrans showed superior innate problem-solving skills to Borneans, and also showed greater inhibition and a more cautious and less rough exploration style. This pattern is consistent with the cultural intelligence hypothesis, which predicts that the more sociable of two sister species experienced stronger selection on cognitive mechanisms underlying learning.

Tuberculosis control, and the where and why of artificial intelligence.

PubMed

Doshi, Riddhi; Falzon, Dennis; Thomas, Bruce V; Temesgen, Zelalem; Sadasivan, Lal; Migliori, Giovanni Battista; Raviglione, Mario

2017-04-01

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB.

Autocorrelation factors and intelligibility of Japanese monosyllables in individuals with sensorineural hearing loss.

PubMed

Shimokura, Ryota; Akasaka, Sakie; Nishimura, Tadashi; Hosoi, Hiroshi; Matsui, Toshie

2017-02-01

Some Japanese monosyllables contain consonants that are not easily discernible for individuals with sensorineural hearing loss. However, the acoustic features that make these monosyllables difficult to discern have not been clearly identified. Here, this study used the autocorrelation function (ACF), which can capture temporal features of signals, to clarify the factors influencing speech intelligibility. For each monosyllable, five factors extracted from the ACF [Φ(0): total energy; τ 1 and ϕ 1 : delay time and amplitude of the maximum peak; τ e : effective duration; W ϕ (0) : spectral centroid], voice onset time, speech intelligibility index, and loudness level were compared with the percentage of correctly perceived articulations (144 ears) obtained by 50 Japanese vowel and consonant-vowel monosyllables produced by one female speaker. Results showed that median effective duration [(τ e ) med ] was strongly correlated with the percentage of correctly perceived articulations of the consonants (r = 0.87, p < 0.01). (τ e ) med values were computed by running ACFs with the time lag at which the magnitude of the logarithmic-ACF envelope had decayed to -10 dB. Effective duration is a measure of temporal pattern persistence, i.e., the duration over which the waveform maintains a stable pattern. The authors postulate that low recognition ability is related to degraded perception of temporal fluctuation patterns.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Two-stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems.

PubMed

Neocleous, A C; Syngelaki, A; Nicolaides, K H; Schizas, C N

2018-04-01

To estimate the risk of fetal trisomy 21 (T21) and other chromosomal abnormalities (OCA) at 11-13 weeks' gestation using computational intelligence classification methods. As a first step, a training dataset consisting of 72 054 euploid pregnancies, 295 cases of T21 and 305 cases of OCA was used to train an artificial neural network. Then, a two-stage approach was used for stratification of risk and diagnosis of cases of aneuploidy in the blind set. In Stage 1, using four markers, pregnancies in the blind set were classified into no risk and risk. No-risk pregnancies were not examined further, whereas the risk pregnancies were forwarded to Stage 2 for further examination. In Stage 2, using seven markers, pregnancies were classified into three types of risk, namely no risk, moderate risk and high risk. Of 36 328 unknown to the system pregnancies (blind set), 17 512 euploid, two T21 and 18 OCA were classified as no risk in Stage 1. The remaining 18 796 cases were forwarded to Stage 2, of which 7895 euploid, two T21 and two OCA cases were classified as no risk, 10 464 euploid, 83 T21 and 61 OCA as moderate risk and 187 euploid, 50 T21 and 52 OCA as high risk. The sensitivity and the specificity for T21 in Stage 2 were 97.1% and 99.5%, respectively, and the false-positive rate from Stage 1 to Stage 2 was reduced from 51.4% to ∼1%, assuming that the cell-free DNA test could identify all euploid and aneuploid cases. We propose a method for early diagnosis of chromosomal abnormalities that ensures that most T21 cases are classified as high risk at any stage. At the same time, the number of euploid cases subjected to invasive or cell-free DNA examinations was minimized through a routine procedure offered in two stages. Our method is minimally invasive and of relatively low cost, highly effective at T21 identification and it performs better than do other existing statistical methods. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications

NASA Astrophysics Data System (ADS)

Roverso, Davide

2003-08-01

Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications to large full-scale applications. The Autonomous Recursive Task Decomposition (ARTD) algorithm is here proposed as a solution to the problem of many-class learning. Example applications of ARTD to neural classifier training are also presented. In these examples, improvements in training time are shown to range from 4-fold to more than 30-fold in pattern classification tasks of both static and dynamic character.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

NASA Astrophysics Data System (ADS)

Assaleh, Khaled; Al-Rousan, M.

2005-12-01

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Variability and Intelligibility of Clarified Speech to Different Listener Groups

NASA Astrophysics Data System (ADS)

Silber, Ronnie F.

Two studies examined the modifications that adult speakers make in speech to disadvantaged listeners. Previous research that has focused on speech to the deaf individuals and to young children has shown that adults clarify speech when addressing these two populations. Acoustic measurements suggest that the signal undergoes similar changes for both populations. Perceptual tests corroborate these results for the deaf population, but are nonsystematic in developmental studies. The differences in the findings for these populations and the nonsystematic results in the developmental literature may be due to methodological factors. The present experiments addressed these methodological questions. Studies of speech to hearing impaired listeners have used read, nonsense, sentences, for which speakers received explicit clarification instructions and feedback, while in the child literature, excerpts of real-time conversations were used. Therefore, linguistic samples were not precisely matched. In this study, experiments used various linguistic materials. Experiment 1 used a children's story; experiment 2, nonsense sentences. Four mothers read both types of material in four ways: (1) in "normal" adult speech, (2) in "babytalk," (3) under the clarification instructions used in the "hearing impaired studies" (instructed clear speech) and (4) in (spontaneous) clear speech without instruction. No extra practice or feedback was given. Sentences were presented to 40 normal hearing college students with and without simultaneous masking noise. Results were separately tabulated for content and function words, and analyzed using standard statistical tests. The major finding in the study was individual variation in speaker intelligibility. "Real world" speakers vary in their baseline intelligibility. The four speakers also showed unique patterns of intelligibility as a function of each independent variable. Results were as follows. Nonsense sentences were less intelligible than story sentences. Function words were equal to, or more intelligible than, content words. Babytalk functioned as a clear speech style in story sentences but not nonsense sentences. One of the two clear speech styles was clearer than normal speech in adult-directed clarification. However, which style was clearer depended on interactions among the variables. The individual patterns seemed to result from interactions among demand characteristics, baseline intelligibility, materials, and differences in articulatory flexibility.

Using artificial intelligence strategies for process-related automated inspection in the production environment

NASA Astrophysics Data System (ADS)

Anding, K.; Kuritcyn, P.; Garten, D.

2016-11-01

In this paper a new method for the automatic visual inspection of metallic surfaces is proposed by using Convolutional Neural Networks (CNN). The different combinations of network parameters were developed and tested. The obtained results of CNN were analysed and compared with the results of our previous investigations with color and texture features as input parameters for a Support Vector Machine. Advantages and disadvantages of the different classifying methods are explained.

Criminal Prohibitions on the Publication of Classified Defense Information

DTIC Science & Technology

2010-12-06

information to the press have only rarely been punished as crimes , and we are aware of no case in which a publisher of information obtained through...Proceeds go to the Crime Victims Fund. 34 § 795. Photographing and sketching defense installations (a) Whenever, in the interests of national...intelligence efforts. This crime is subject to a fine or imprisonment for a term of not more than three years. To be convicted, a violator must have

Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques.

PubMed

Acharya, Rajendra Udyavara; Yu, Wenwei; Zhu, Kuanyi; Nayak, Jagadish; Lim, Teik-Cheng; Chan, Joey Yiptong

2010-08-01

Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.

Protein classification using sequential pattern mining.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2006-01-01

Protein classification in terms of fold recognition can be employed to determine the structural and functional properties of a newly discovered protein. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. One of the most efficient SPM algorithms, cSPADE, is employed for protein primary structure analysis. Then a classifier uses the extracted sequential patterns for classifying proteins of unknown structure in the appropriate fold category. The proposed methodology exhibited an overall accuracy of 36% in a multi-class problem of 17 candidate categories. The classification performance reaches up to 65% when the three most probable protein folds are considered.

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Season of birth and childhood intelligence: findings from the Aberdeen Children of the 1950s cohort study.

PubMed

Lawlor, Debbie A; Clark, Heather; Ronalds, Georgina; Leon, David A

2006-09-01

In this study, 2 main hypotheses have been put forward to explain the variation in childhood intelligence or school performance by season of birth. In the first hypothesis, it is suggested that it is due to school policy concerning school entry, whereas the second suggests that a seasonally patterned exposure such as temperature, maternal nutrition, or infection during critical periods of brain development have a lasting effect on intelligence. To determine whether childhood performance on tests of different domains of intelligence is patterned by season of birth and to examine possible mechanisms for any associations. 12,150 individuals born in Aberdeen, Scotland between 1950 and 1956. Birth cohort study in which the variation in different domains of childhood intelligence measured at ages 7, 9, and 11 by season of birth were examined. Reading ability at age 9 and arithmetic ability at age 11 varied by season of birth, with lowest scores among those born in autumn or early winter (September-December) and highest scores among those born in later winter or spring (February-April); p=.002 for joint sine-cosine functions for reading ability at age 9 and p=.05 for sine-cosine function for arithmetic ability at age 11. The child's perception and understanding of pictorial differences at age 7, verbal reasoning at 11, and English language ability at 11 did not vary by season of birth. Age at starting primary school and age relative to class peers were both associated with the different measurements of childhood intelligence and both attenuated the association between month of birth and reading ability at age 9 and arithmetic ability at age 11 towards the null. Both adjusted and unadjusted differences in reading ability at age 9 and arithmetic ability at age 11 between those born from September to December compared with other times of the year were less than 0.1 of a standard deviation of the test scores. Ambient temperature around the time of conception, during gestation, and around the time of birth did not affect intelligence. Any variation in mean childhood intelligence by season of birth is weak and largely explained by age at school entry and age relative to class peers.

Electropalatography in home training of retracted articulation in a Swedish child with cleft palate: effect on articulation pattern and speech.

PubMed

Lohmander, Anette; Henriksson, Cecilia; Havstam, Christina

2010-12-01

The aim was to evaluate the effectiveness of electropalatography (EPG) in home training of persistent articulation errors in an 11-year-old Swedish girl born with isolated cleft palate. The /t/ and /s/ sounds were trained in a single subject design across behaviours during an eight month period using a portable training unit (PTU). Both EPG analysis and perceptual analysis showed an improvement in the production of /t/ and /s/ in words and sentences after therapy. Analysis of tongue-contact patterns showed that the participant had more normal articulatory patterns of /t/ and /s/ after just 2 months (after approximately 8 hours of training) respectively. No statistically significant transfer by means of intelligibility in connected speech was found. The present results show that EPG home training can be a sufficient method for treating persistent speech disorders associated with cleft palate. Methods for transfer from function (articulation) to activity (intelligibility) need to be explored.

Fisher classifier and its probability of error estimation

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Conformal Predictions in Multimedia Pattern Recognition

ERIC Educational Resources Information Center

Nallure Balasubramanian, Vineeth

2010-01-01

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Sex Differences in Mental Rotation and Cortical Activation Patterns: Can Training Change Them?

ERIC Educational Resources Information Center

Jausovec, Norbert; Jausovec, Ksenija

2012-01-01

In two experiments the neuronal mechanisms of sex differences in mental rotation were investigated. In Experiment 1 cortical activation was studied in women and men with similar levels of mental rotation ability (high, and average to low), who were equalized with respect to general intelligence. Sex difference in neuroelectric patterns of brain…

Travelling Careers: Overseas Migration Patterns in the Professional Lives of Women Attending Girton and Newnham before 1939

ERIC Educational Resources Information Center

Goodman, Joyce; Jacobs, Andrea; Kisby, Fiona; Loader, Helen

2011-01-01

This paper explores the migration patterns of women who studied at Girton and Newnham prior to 1939 through whom dissemination of knowledge and values flowed from Cambridge overseas. It also considers organisations that fostered women's mobility in empire, particularly the Colonial Intelligence League for Educated Women and the International…

Breast Feeding Pattern and the Health of Children in Ado-Ekiti Local Government Area of Ekiti State, Nigeria

ERIC Educational Resources Information Center

Odu, Bimbola Kemi; Dotun, Owoeye Olajumoke

2008-01-01

This study investigated the different patterns of feeding infants and their corresponding effects on children's health. There are anti-effective properties present in human milk which help children to fight against many childhood diseases. The long-term effect of breast milk like intelligence, socialization and personality development of children…

Metaphorically speaking: cognitive abilities and the production of figurative language.

PubMed

Beaty, Roger E; Silvia, Paul J

2013-02-01

Figurative language is one of the most common expressions of creative behavior in everyday life. However, the cognitive mechanisms behind figures of speech such as metaphors remain largely unexplained. Recent evidence suggests that fluid and executive abilities are important to the generation of conventional and creative metaphors. The present study investigated whether several factors of the Cattell-Horn-Carroll model of intelligence contribute to generating these different types of metaphors. Specifically, the roles of fluid intelligence (Gf), crystallized intelligence (Gc), and broad retrieval ability (Gr) were explored. Participants completed a series of intelligence tests and were asked to produce conventional and creative metaphors. Structural equation modeling was used to assess the contribution of the different factors of intelligence to metaphor production. For creative metaphor, there were large effects of Gf (β = .45) and Gr (β = .52); for conventional metaphor, there was a moderate effect of Gc (β = .30). Creative and conventional metaphors thus appear to be anchored in different patterns of abilities: Creative metaphors rely more on executive processes, whereas conventional metaphors primarily draw from acquired vocabulary knowledge.

Intelligence and cortical thickness in children with complex partial seizures.

PubMed

Tosun, Duygu; Caplan, Rochelle; Siddarth, Prabha; Seidenberg, Michael; Gurbani, Suresh; Toga, Arthur W; Hermann, Bruce

2011-07-15

Prior studies on healthy children have demonstrated regional variations and a complex and dynamic relationship between intelligence and cerebral tissue. Yet, there is little information regarding the neuroanatomical correlates of general intelligence in children with epilepsy compared to healthy controls. In vivo imaging techniques, combined with methods for advanced image processing and analysis, offer the potential to examine quantitative mapping of brain development and its abnormalities in childhood epilepsy. A surface-based, computational high resolution 3-D magnetic resonance image analytic technique was used to compare the relationship of cortical thickness with age and intelligence quotient (IQ) in 65 children and adolescents with complex partial seizures (CPS) and 58 healthy controls, aged 6-18 years. Children were grouped according to health status (epilepsy; controls) and IQ level (average and above; below average) and compared on age-related patterns of cortical thickness. Our cross-sectional findings suggest that disruption in normal age-related cortical thickness expression is associated with intelligence in pediatric CPS patients both with average and below average IQ scores. Copyright © 2011 Elsevier Inc. All rights reserved.

Proceedings of the 1986 IEEE international conference on systems, man and cybernetics

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

Not Available

1986-01-01

This book presents the papers given at a conference on man-machine systems. Topics considered at the conference included neural model-based cognitive theory and engineering, user interfaces, adaptive and learning systems, human interaction with robotics, decision making, the testing and evaluation of expert systems, software development, international conflict resolution, intelligent interfaces, automation in man-machine system design aiding, knowledge acquisition in expert systems, advanced architectures for artificial intelligence, pattern recognition, knowledge bases, and machine vision.

Autonomous operations through onboard artificial intelligence

NASA Technical Reports Server (NTRS)

Sherwood, R. L.; Chien, S.; Castano, R.; Rabideau, G.

2002-01-01

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Patterns of 11C-PIB cerebral retention in mild cognitive impairment patients.

PubMed

Banzo, I; Jiménez-Bonilla, J F; Martínez-Rodríguez, I; Quirce, R; de Arcocha-Torres, M; Bravo-Ferrer, Z; Lavado-Pérez, C; Sánchez-Juan, P; Rodríguez, E; Jiménez-Alonso, M; López-Defilló, J; Carril, J M

2016-01-01

To evaluate the patterns of cerebral cortical distribution of (11)C-PIB in patients with mild cognitive impairment (MCI). The study included 69 patients (37 male, age range 42-79 years) with MCI, sub-classified as 53 with amnestic-MCI (A-MCI), and 16 with non-amnestic-MCI (NA-MCI). Patients underwent (11)C-PIB PET/CT scan 60min after intravenous injection of the radiotracer. A visual analysis of the images was performed by 2 experienced physicians. (11)C-PIB-positive studies were considered when gray matter uptake was equal to or greater than white matter. According to the regions involved, (11)C-PIB-positive studies were classified into A-pattern (predominant retention in frontal, anterior cingulate, lateral temporal, and basal ganglia) and B-pattern (generalized retention). Thirty-nine of the 69 (56%) patients with MCI showed (11)C-PIB retention. Of the 53 A-MCI patients, 36 (68%) showed (11)C-PIB retention. Eleven out of 36 (30%) positive scans in A-MCI patients showed A-pattern, and 25 out of 36 (70%) patients had a B-pattern. Positive (11)C-PIB was observed in 3 out of 16 (19%) patients with NA-MCI. Regional distribution in these 3 patients showed A-pattern in 1, and B-pattern in 2 patients. Cortical retention of (11)C-PIB was more frequent in A-MCI than in NA-MCI patients, and also B-pattern than A-pattern in the (11)C-PIB positive group. The recognition of (11)C-PIB distribution patterns allows MCI patients to be classified, and the A-pattern may offer a therapeutic window for potential future treatments. Copyright © 2015 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

Laser Fluence Recognition Using Computationally Intelligent Pulsed Photoacoustics Within the Trace Gases Analysis

NASA Astrophysics Data System (ADS)

Lukić, M.; Ćojbašić, Ž.; Rabasović, M. D.; Markushev, D. D.; Todorović, D. M.

2017-11-01

In this paper, the possibilities of computational intelligence applications for trace gas monitoring are discussed. For this, pulsed infrared photoacoustics is used to investigate SF6-Ar mixtures in a multiphoton regime, assisted by artificial neural networks. Feedforward multilayer perceptron networks are applied in order to recognize both the spatial characteristics of the laser beam and the values of laser fluence Φ from the given photoacoustic signal and prevent changes. Neural networks are trained in an offline batch training regime to simultaneously estimate four parameters from theoretical or experimental photoacoustic signals: the laser beam spatial profile R(r), vibrational-to-translational relaxation time τ _{V-T} , distance from the laser beam to the absorption molecules in the photoacoustic cell r* and laser fluence Φ . The results presented in this paper show that neural networks can estimate an unknown laser beam spatial profile and the parameters of photoacoustic signals in real time and with high precision. Real-time operation, high accuracy and the possibility of application for higher intensities of radiation for a wide range of laser fluencies are factors that classify the computational intelligence approach as efficient and powerful for the in situ measurement of atmospheric pollutants.

Intelligent image processing for vegetation classification using multispectral LANDSAT data

NASA Astrophysics Data System (ADS)

Santos, Stewart R.; Flores, Jorge L.; Garcia-Torales, G.

2015-09-01

We propose an intelligent computational technique for analysis of vegetation imaging, which are acquired with multispectral scanner (MSS) sensor. This work focuses on intelligent and adaptive artificial neural network (ANN) methodologies that allow segmentation and classification of spectral remote sensing (RS) signatures, in order to obtain a high resolution map, in which we can delimit the wooded areas and quantify the amount of combustible materials present into these areas. This could provide important information to prevent fires and deforestation of wooded areas. The spectral RS input data, acquired by the MSS sensor, are considered in a random propagation remotely sensed scene with unknown statistics for each Thematic Mapper (TM) band. Performing high-resolution reconstruction and adding these spectral values with neighbor pixels information from each TM band, we can include contextual information into an ANN. The biggest challenge in conventional classifiers is how to reduce the number of components in the feature vector, while preserving the major information contained in the data, especially when the dimensionality of the feature space is high. Preliminary results show that the Adaptive Modified Neural Network method is a promising and effective spectral method for segmentation and classification in RS images acquired with MSS sensor.

Profiling nonhuman intelligence: An exercise in developing unbiased tools for describing other "types" of intelligence on earth

NASA Astrophysics Data System (ADS)

Herzing, Denise L.

2014-02-01

Intelligence has historically been studied by comparing nonhuman cognitive and language abilities with human abilities. Primate-like species, which show human-like anatomy and share evolutionary lineage, have been the most studied. However, when comparing animals of non-primate origins our abilities to profile the potential for intelligence remains inadequate. Historically our measures for nonhuman intelligence have included a variety of tools: (1) physical measurements - brain to body ratio, brain structure/convolution/neural density, presence of artifacts and physical tools, (2) observational and sensory measurements - sensory signals, complexity of signals, cross-modal abilities, social complexity, (3) data mining - information theory, signal/noise, pattern recognition, (4) experimentation - memory, cognition, language comprehension/use, theory of mind, (5) direct interfaces - one way and two way interfaces with primates, dolphins, birds and (6) accidental interactions - human/animal symbiosis, cross-species enculturation. Because humans tend to focus on "human-like" attributes and measures and scientists are often unwilling to consider other "types" of intelligence that may not be human equated, our abilities to profile "types" of intelligence that differ on a variety of scales is weak. Just as biologists stretch their definitions of life to look at extremophiles in unusual conditions, so must we stretch our descriptions of types of minds and begin profiling, rather than equating, other life forms we may encounter.

Intelligent systems in the context of surrounding environment.

PubMed

Wakeling, J; Bak, P

2001-11-01

We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the minority model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique "rogue" agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons. In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment).

Decision making and problem solving with computer assistance

NASA Technical Reports Server (NTRS)

Kraiss, F.

1980-01-01

In modern guidance and control systems, the human as manager, supervisor, decision maker, problem solver and trouble shooter, often has to cope with a marginal mental workload. To improve this situation, computers should be used to reduce the operator from mental stress. This should not solely be done by increased automation, but by a reasonable sharing of tasks in a human-computer team, where the computer supports the human intelligence. Recent developments in this area are summarized. It is shown that interactive support of operator by intelligent computer is feasible during information evaluation, decision making and problem solving. The applied artificial intelligence algorithms comprehend pattern recognition and classification, adaptation and machine learning as well as dynamic and heuristic programming. Elementary examples are presented to explain basic principles.

A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

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

Bramer, Lisa M.; Chatterjee, Samrat; Holmes, Aimee E.

Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used tomore » develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.« less

Functional outcome after total and subtotal glossectomy with free flap reconstruction.

PubMed

Yanai, Chie; Kikutani, Takesi; Adachi, Masatosi; Thoren, Hanna; Suzuki, Munekazu; Iizuka, Tateyuki

2008-07-01

The aim of this study was to evaluate postoperative oral functions of patients who had undergone total or subtotal (75%) glossectomy with preservation of the larynx for oral squamous cell carcinomas. Speech intelligibility and swallowing capacity of 17 patients who had been treated between 1992 and 2002 were scored and classified using standard protocols 6 to 36 months postoperatively. The outcomes were finally rated as good, acceptable, or poor. The 4-year disease-specific survival rate was 64%. Speech intelligibility and swallowing capacity were satisfactory (acceptable or good) in 82.3%. Only 3 patients were still dependent on tube feeding. Good speech perceptibility did not always go together with normal diet tolerance, however. Our satisfactory results are attributable to the use of large, voluminous soft tissue flaps for reconstruction, and to the instigation of postoperative swallowing and speech therapy on a routine basis and at an early juncture.

A review of intelligent systems for heart sound signal analysis.

PubMed

Nabih-Ali, Mohammed; El-Dahshan, El-Sayed A; Yahia, Ashraf S

2017-10-01

Intelligent computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. CAD systems could provide physicians with a suggestion about the diagnostic of heart diseases. The objective of this paper is to review the recent published preprocessing, feature extraction and classification techniques and their state of the art of phonocardiogram (PCG) signal analysis. Published literature reviewed in this paper shows the potential of machine learning techniques as a design tool in PCG CAD systems and reveals that the CAD systems for PCG signal analysis are still an open problem. Related studies are compared to their datasets, feature extraction techniques and the classifiers they used. Current achievements and limitations in developing CAD systems for PCG signal analysis using machine learning techniques are presented and discussed. In the light of this review, a number of future research directions for PCG signal analysis are provided.

iBEST: intelligent Balance assessment and Stability Training system using smartphone.

PubMed

Wai, Aung Aung Phyo; Duc, Pham Duy; Syin, Chan; Zhang, Haihong

2014-01-01

Patients with postural instability could lead to falls and injuries while walking due to balance disorders. So those patients need regular balance training and evaluation to improve and examine balance deficiencies. But many do not notice such balance issues; resulting lack of timely preventive measures. This shows the needs of affordable and accessible solution for balance training and assessment. So iBEST (intelligent Balance assessment and Stability Training) is proposed enabling to train and assess balance conveniently anywhere anytime. Moreover, therapists can remotely evaluate and manage their recovery progress. These benefits can be realized leveraging sensors from smartphone, cloud-based data analytics and web applications. iBEST employs sensorised automated balance assessment in digitizing Berg Balance Scale (BBS) clinical risk assessment tool. The initial feasibility study showed average accuracy of 90.22% using smartphone in classifying the specified BBS test items.

Criterion validity of the Wechsler Intelligence Scale for Children-Fourth Edition after pediatric traumatic brain injury.

PubMed

Donders, Jacobus; Janke, Kelly

2008-07-01

The performance of 40 children with complicated mild to severe traumatic brain injury on the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV; Wechsler, 2003) was compared with that of 40 demographically matched healthy controls. Of the four WISC-IV factor index scores, only Processing Speed yielded a statistically significant group difference (p < .001) as well as a statistically significant negative correlation with length of coma (p < .01). Logistic regression, using Processing Speed to classify individual children, yielded a sensitivity of 72.50% and a specificity of 62.50%, with false positive and false negative rates both exceeding 30%. We conclude that Processing Speed has acceptable criterion validity in the evaluation of children with complicated mild to severe traumatic brain injury but that the WISC-IV should be supplemented with other measures to assure sufficient accuracy in the diagnostic process.

Toward detecting deception in intelligent systems

NASA Astrophysics Data System (ADS)

Santos, Eugene, Jr.; Johnson, Gregory, Jr.

2004-08-01

Contemporary decision makers often must choose a course of action using knowledge from several sources. Knowledge may be provided from many diverse sources including electronic sources such as knowledge-based diagnostic or decision support systems or through data mining techniques. As the decision maker becomes more dependent on these electronic information sources, detecting deceptive information from these sources becomes vital to making a correct, or at least more informed, decision. This applies to unintentional disinformation as well as intentional misinformation. Our ongoing research focuses on employing models of deception and deception detection from the fields of psychology and cognitive science to these systems as well as implementing deception detection algorithms for probabilistic intelligent systems. The deception detection algorithms are used to detect, classify and correct attempts at deception. Algorithms for detecting unexpected information rely upon a prediction algorithm from the collaborative filtering domain to predict agent responses in a multi-agent system.

Iranians' contribution to world literature on neuroscience.

PubMed

Ashrafi, Farzad; Mohammadhassanzadeh, Hafez; Shokraneh, Farhad; Valinejadi, Ali; Johari, Karim; Saemi, Nazanin; Zali, Alireza; Mohaghegh, Niloofar; Ashayeri, Hassan

2012-12-01

The purpose of this study is to analyse Iranian scientific publications in the neuroscience subfields by librarians and neuroscientists, using Science Citation Index Expanded (SCIE) via Web of Science data over the period, 2002-2008. Data were retrieved from the SCIE. Data were collected from the 'subject area' of the database and classified by neuroscience experts into 14 subfields. To identify the citation patterns, we applied the 'impact factor' and the 'number of publication'. Data were also analysed using HISTCITE, Excel 2007 and SPSS. Seven hundred and thirty-four papers have been published by Iranian between 2002 and 2008. Findings showed a growing trend of neuroscience papers in the last 3 years with most papers (264) classified in the neuropharmacology subfield. There were fewer papers in neurohistory, psychopharmacology and artificial intelligence. International contributions of authors were mostly in the neurology subfield, and 'Collaboration Coefficient' for the neuroscience subfields in Iran was 0.686 which is acceptable. Most international collaboration between Iranians and developed countries was from USA. Eighty-seven percent of the published papers were in journals with the impact factor between 0 and 4; 25% of papers were published by the researchers affiliated to Tehran University of Medical Sciences. Progress of neuroscience in Iran is mostly seen in the neuropharmacology and the neurology subfields. Other subfields should also be considered as a research priority by health policymakers. As this study was carried out by the collaboration of librarians and neuroscientists, it has been proved valuable for both librarians and policymakers. This study may be encouraging for librarians from other developing countries. © 2012 The authors. Health Information and Libraries Journal © 2012 Health Libraries Group.

Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

PubMed

Lai, Daniel T H; Begg, Rezaul K; Taylor, Simon; Palaniswami, Marimuthu

2008-01-01

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.