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https://www.readbyqxmd.com/read/28646852/the-facial-expression-of-schizophrenic-patients-applied-with-infrared-thermal-facial-image-sequence
#1
Bo-Lin Jian, Chieh-Li Chen, Wen-Lin Chu, Min-Wei Huang
BACKGROUND: Schizophrenia is a neurological disease characterized by alterations to patients' cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients...
June 24, 2017: BMC Psychiatry
https://www.readbyqxmd.com/read/28646763/deep-neural-mapping-support-vector-machines
#2
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28646177/hyperspectral-imaging-for-presymptomatic-detection-of-tobacco-disease-with-successive-projections-algorithm-and-machine-learning-classifiers
#3
Hongyan Zhu, Bingquan Chu, Chu Zhang, Fei Liu, Linjun Jiang, Yong He
We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380-1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM)...
June 23, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28644811/applying-pattern-recognition-to-high-resolution-images-to-determine-cellular-signaling-status
#4
Michael F Lohrer, Darrin M Hanna, Yang Liu, Kang-Hsin Wang, Fu-Tong Liu, Ted A Laurence, Gang-Yu Liu
Two frequently used tools to acquire high-resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and living cells. In current practice, these images are viewed by eye to determine cellular status, e.g. activated versus resting. Automatic and quantitative data analysis is lacking. This work develops an algorithm of pattern recognition that works very effectively for AFM and SEM images...
June 21, 2017: IEEE Transactions on Nanobioscience
https://www.readbyqxmd.com/read/28644809/structured-kernel-dictionary-learning-with-correlation-constraint-for-object-recognition
#5
Zhengjue Wang, Yinghua Wang, Hongwei Liu, Hao Zhang
In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes...
June 21, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28644140/a-pathways-based-prediction-model-for-classifying-breast-cancer-subtypes
#6
Tong Wu, Yunfeng Wang, Ronghui Jiang, Xinliang Lu, Jiawei Tian
Breast cancer is highly heterogeneous and is classified into four subtypes characterized by specific biological traits, treatment responses, and clinical prognoses. We performed a systemic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas project database. We identified 136 breast cancer genes differentially expressed among the four subtypes. Based on unsupervised clustering analysis, these 136 core genes efficiently categorized breast cancer patients into the appropriate subtypes. Functional enrichment based on Kyoto Encyclopedia of Genes and Genomes analysis identified six functional pathways regulated by these genes: JAK-STAT signaling, basal cell carcinoma, inflammatory mediator regulation of TRP channels, non-small cell lung cancer, glutamatergic synapse, and amyotrophic lateral sclerosis...
June 17, 2017: Oncotarget
https://www.readbyqxmd.com/read/28641264/a-single-channel-eog-based-speller
#7
Shenghong He, Yuanqing Li
Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface (GUI) are intensified in a random order...
June 15, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28640181/a-study-for-texture-feature-extraction-of-high-resolution-satellite-images-based-on-a-direction-measure-and-gray-level-co-occurrence-matrix-fusion-algorithm
#8
Xin Zhang, Jintian Cui, Weisheng Wang, Chao Lin
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image...
June 22, 2017: Sensors
https://www.readbyqxmd.com/read/28638405/a-combined-one-class-svm-and-template-matching-approach-for-user-aided-human-fall-detection-by-means-of-floor-acoustic-features
#9
Diego Droghini, Daniele Ferretti, Emanuele Principi, Stefano Squartini, Francesco Piazza
The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28635678/evaluation-of-classifier-performance-for-multiclass-phenotype-discrimination-in-untargeted-metabolomics
#10
Patrick J Trainor, Andrew P DeFilippis, Shesh N Rai
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination...
June 21, 2017: Metabolites
https://www.readbyqxmd.com/read/28635541/association-between-abnormal-brain-functional-connectivity-in-children-and-psychopathology-a-study-based-on-graph-theory-and-machine-learning
#11
João Ricardo Sato, Claudinei Eduardo Biazoli, Giovanni Abrahão Salum, Ary Gadelha, Nicolas Crossley, Gilson Vieira, André Zugman, Felipe Almeida Picon, Pedro Mario Pan, Marcelo Queiroz Hoexter, Edson Amaro, Mauricio Anés, Luciana Monteiro Moura, Marco Antonio Gomes Del'Aquilla, Philip Mcguire, Luis Augusto Rohde, Euripedes Constantino Miguel, Andrea Parolin Jackowski, Rodrigo Affonseca Bressan
OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). METHODS: We applied this approach to resting-state fMRI data from 622 children and adolescents...
February 8, 2017: World Journal of Biological Psychiatry
https://www.readbyqxmd.com/read/28630883/detection-of-prostate-cancer-in-multiparametric-mri-using-random-forest-with-instance-weighting
#12
Nathan Lay, Yohannes Tsehay, Matthew D Greer, Baris Turkbey, Jin Tae Kwak, Peter L Choyke, Peter Pinto, Bradford J Wood, Ronald M Summers
A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28630865/2d-qsar-and-3d-qsar-analyses-for-egfr-inhibitors
#13
Manman Zhao, Lin Wang, Linfeng Zheng, Mengying Zhang, Chun Qiu, Yuhui Zhang, Dongshu Du, Bing Niu
Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28629202/a-novel-extreme-learning-machine-classification-model-for-e-nose-application-based-on-the-multiple-kernel-approach
#14
Yulin Jian, Daoyu Huang, Jia Yan, Kun Lu, Ying Huang, Tailai Wen, Tanyue Zeng, Shijie Zhong, Qilong Xie
A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs)...
June 19, 2017: Sensors
https://www.readbyqxmd.com/read/28628630/bird-sound-spectrogram-decomposition-through-non-negative-matrix-factorization-for-the-acoustic-classification-of-bird-species
#15
Jimmy Ludeña-Choez, Raisa Quispe-Soncco, Ascensión Gallardo-Antolín
Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms...
2017: PloS One
https://www.readbyqxmd.com/read/28628163/-quantitative-structure-activity-relationship-model-for-prediction-of-cardiotoxicity-of-chemical-components-in-traditional-chinese-medicines
#16
(no author information available yet)
OBJECTIVE: Some quantitative structure-activity relationship (QSAR) models have been developed to predict cardiac toxicity of drugs, which have limited predictive power due to based on hERG channel inhibition. The objective of this study was try to develop a QSAR model based on all kinds of cardiac adverse effects, and to predict the potential cardiotoxicity of chemical components in traditional Chinese medicines (TCM). METHODS: In this study, the compounds data of all kinds of cardiac adverse reactions were selected as the training set...
June 18, 2017: Beijing da Xue Xue Bao. Yi Xue Ban, Journal of Peking University. Health Sciences
https://www.readbyqxmd.com/read/28624712/classification-of-ecg-heartbeats-using-nonlinear-decomposition-methods-and-support-vector-machine
#17
Kandala N V P S Rajesh, Ravindra Dhuli
Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a challenging task due to minute variations in the amplitude, duration and morphology of the ECG signal. In this paper, we propose two feature extraction approaches to classify five types of heartbeats: normal, premature ventricular contraction, atrial premature contraction, left bundle branch block and right bundle branch block. In the first approach, ECG beats are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD)...
June 15, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28624024/differentiation-between-acute-and-chronic-myocardial-infarction-by-means-of-texture-analysis-of-late-gadolinium-enhancement-and-cine-cardiac-magnetic-resonance-imaging
#18
Andrés Larroza, Andrzej Materka, María P López-Lereu, José V Monmeneu, Vicente Bodí, David Moratal
The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI...
July 2017: European Journal of Radiology
https://www.readbyqxmd.com/read/28623478/feature-fusion-for-lung-nodule-classification
#19
Amal A Farag, Asem Ali, Salwa Elshazly, Aly A Farag
PURPOSE: This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning. METHODS: Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest...
June 16, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28623317/prediction-of-lncrna-protein-interactions-using-hetesim-scores-based-on-heterogeneous-networks
#20
Yun Xiao, Jingpu Zhang, Lei Deng
Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores...
June 16, 2017: Scientific Reports
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