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https://www.readbyqxmd.com/read/29224063/an-efficient-scheme-for-mental-task-classification-utilizing-reflection-coefficients-obtained-from-autocorrelation-function-of-eeg-signal
#1
M M Rahman, M A Chowdhury, S A Fattah
Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI...
December 9, 2017: Brain Informatics
https://www.readbyqxmd.com/read/29223792/prediction-of-persistent-hemodynamic-depression-after-carotid-angioplasty-and-stenting-using-artificial-neural-network-model
#2
Jin Pyeong Jeon, Chulho Kim, Byoung-Doo Oh, Sun Jeong Kim, Yu-Seop Kim
OBJECTIVES: To assess and compare predictive factors for persistent hemodynamic depression (PHD) after carotid artery angioplasty and stenting (CAS) using artificial neural network (ANN) and multiple logistic regression (MLR) or support vector machines (SVM) models. PATIENTS AND METHODS: A retrospective data set of patients (n=76) who underwent CAS from 2007 to 2014 was used as input (training cohort) to a back-propagation ANN using TensorFlow platform. PHD was defined when systolic blood pressure was less than 90mmHg or heart rate was less 50 beats/min that lasted for more than one hour...
December 5, 2017: Clinical Neurology and Neurosurgery
https://www.readbyqxmd.com/read/29223012/support-vector-machine-with-dirichlet-feature-mapping
#3
Ali Nedaie, Amir Abbas Najafi
The Support Vector Machine (SVM) is a supervised learning algorithm to analyze data and recognize patterns. The standard SVM suffers from some limitations in nonlinear classification problems. To tackle these limitations, the nonlinear form of the SVM poses a modified machine based on the kernel functions or other nonlinear feature mappings obviating the mentioned imperfection. However, choosing an efficient kernel or feature mapping function is strongly dependent on data structure. Thus, a flexible feature mapping can be confidently applied in different types of data structures without challenging a kernel selection and its tuning...
November 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29220476/egenpub-a-text-mining-system-for-extending-computationally-mapped-bibliography-for-uniprot-knowledgebase-by-capturing-centrality
#4
Ruoyao Ding, Emmanuel Boutet, Damien Lieberherr, Michel Schneider, Michael Tognolli, Cathy H Wu, K Vijay-Shanker, Cecilia N Arighi
UniProt Knowledgebase (UniProtKB) is a publicly available database with access to a vast amount of protein sequence and functional information. To widen the scope of the publications associated with a protein entry, UniProt has introduced the computationally mapped additional bibliography section, which includes literature collected from external sources. In this article, we describe a text mining system, eGenPub, which selects articles that are 'about' specific proteins and allows automatic identification of additional bibliography for given UniProt protein entries...
January 1, 2017: Database: the Journal of Biological Databases and Curation
https://www.readbyqxmd.com/read/29220393/process-service-quality-evaluation-based-on-dempster-shafer-theory-and-support-vector-machine
#5
Feng-Que Pei, Dong-Bo Li, Yi-Fei Tong, Fei He
Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors...
2017: PloS One
https://www.readbyqxmd.com/read/29220305/spiking-neural-classifier-with-lumped-dendritic-nonlinearity-and-binary-synapses-a-current-mode-vlsi-implementation-and-analysis
#6
Aritra Bhaduri, Amitava Banerjee, Subhrajit Roy, Sougata Kar, Arindam Basu
We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources...
December 8, 2017: Neural Computation
https://www.readbyqxmd.com/read/29219069/a-boosting-approach-for-prediction-of-protein-rna-binding-residues
#7
Yongjun Tang, Diwei Liu, Zixiang Wang, Ting Wen, Lei Deng
BACKGROUND: RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex. RESULTS: We propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties...
December 1, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29213188/a-novel-framework-for-intelligent-surveillance-system-based-on-abnormal-human-activity-detection-in-academic-environments
#8
Malek Al-Nawashi, Obaida M Al-Hazaimeh, Mohamad Saraee
Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase...
2017: Neural Computing & Applications
https://www.readbyqxmd.com/read/29212468/a-comparison-of-graph-and-kernel-based-omics-data-integration-algorithms-for-classifying-complex-traits
#9
Kang K Yan, Hongyu Zhao, Herbert Pang
BACKGROUND: High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking...
December 6, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29209363/a-new-approach-for-mobile-advertising-click-through-rate-estimation-based-on-deep-belief-nets
#10
Jie-Hao Chen, Zi-Qian Zhao, Ji-Yun Shi, Chong Zhao
In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29209190/classification-of-eeg-signals-based-on-pattern-recognition-approach
#11
Hafeez Ullah Amin, Wajid Mumtaz, Ahmad Rauf Subhani, Mohamad Naufal Mohamad Saad, Aamir Saeed Malik
Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29208422/support-vector-machine-based-classification-of-first-episode-drug-na%C3%A3-ve-schizophrenia-patients-and-healthy-controls-using-structural-mri
#12
Yuan Xiao, Zhihan Yan, Youjin Zhao, Bo Tao, Huaiqiang Sun, Fei Li, Li Yao, Wenjing Zhang, Shah Chandan, Jieke Liu, Qiyong Gong, John A Sweeney, Su Lui
Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective biomarkers to discriminate patients from healthy controls individually. To address this question, a total of 326 right-handed participants were recruited, including 163 drug-naïve first-episode schizophrenia (FES) patients and 163 demographically matched healthy controls...
December 2, 2017: Schizophrenia Research
https://www.readbyqxmd.com/read/29208345/multi-class-parkinsonian-disorders-classification-with-quantitative-mr-markers-and-graph-based-features-using-support-vector-machines
#13
Rita Morisi, David Neil Manners, Giorgio Gnecco, Nico Lanconelli, Claudia Testa, Stefania Evangelisti, Lia Talozzi, Laura Ludovica Gramegna, Claudio Bianchini, Giovanna Calandra-Buonaura, Luisa Sambati, Giulia Giannini, Pietro Cortelli, Caterina Tonon, Raffaele Lodi
BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. METHODS: We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders...
November 28, 2017: Parkinsonism & related Disorders
https://www.readbyqxmd.com/read/29205933/a-highly-selective-3d-spiked-ultraflexible-neural-sun-interface-for-decoding-peripheral-nerve-sensory-information
#14
Jiahui Wang, Xin Yuan Thow, Hao Wang, Sanghoon Lee, Kai Voges, Nitish V Thakor, Shih-Cheng Yen, Chengkuo Lee
Artificial sensors on the skin are proposed as a way to capture information that can be used in intracortical microstimulation or peripheral intraneural stimulation to restore sensory feedback to persons with tetraplegia. However, the ability of these artificial sensors to replicate the density and complexity of the natural mechanoreceptors is limited. One relatively unexplored approach is to make use of the signals from surviving tactile and proprioceptive receptors in existing limbs by recording from their transmitting axons within the primary sensory nerves...
December 4, 2017: Advanced Healthcare Materials
https://www.readbyqxmd.com/read/29204763/automated-segmentation-and-quantification-of-drusen-in-fundus-and-optical-coherence-tomography-images-for-detection-of-armd
#15
Samina Khalid, M Usman Akram, Taimur Hassan, Amina Jameel, Tehmina Khalil
Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology...
December 4, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29203775/glandular-morphometrics-for-objective-grading-of-colorectal-adenocarcinoma-histology-images
#16
Ruqayya Awan, Korsuk Sirinukunwattana, David Epstein, Samuel Jefferyes, Uvais Qidwai, Zia Aftab, Imaad Mujeeb, David Snead, Nasir Rajpoot
Determining the grade of colon cancer from tissue slides is a routine part of the pathological analysis. In the case of colorectal adenocarcinoma (CRA), grading is partly determined by morphology and degree of formation of glandular structures. Achieving consistency between pathologists is difficult due to the subjective nature of grading assessment. An objective grading using computer algorithms will be more consistent, and will be able to analyse images in more detail. In this paper, we measure the shape of glands with a novel metric that we call the Best Alignment Metric (BAM)...
December 4, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29202286/pm10-concentration-forecasting-in-the-metropolitan-area-of-oviedo-northern-spain-using-models-based-on-svm-mlp-varma-and-arima-a-case-study
#17
P J García Nieto, F Sánchez Lasheras, E García-Gonzalo, F J de Cos Juez
Atmospheric particulate matter (PM) is one of the pollutants that may have a significant impact on human health. Data collected over seven years in a city of the north of Spain is analyzed using four different mathematical models: vector autoregressive moving-average (VARMA), autoregressive integrated moving-average (ARIMA), multilayer perceptron (MLP) neural networks and support vector machines (SVMs) with regression. Measured monthly average pollutants and PM10 (particles with a diameter less than 10μm) concentration are used as input to forecast the monthly averaged concentration of PM10 from one to seven months ahead...
December 1, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/29202177/distinctive-neuroanatomical-substrates-for-depression-in-bipolar-disorder-versus-major-depressive-disorder
#18
Koji Matsuo, Kenichiro Harada, Yusuke Fujita, Yasumasa Okamoto, Miho Ota, Hisashi Narita, Benson Mwangi, Carlos A Gutierrez, Go Okada, Masahiro Takamura, Hirotaka Yamagata, Ichiro Kusumi, Hiroshi Kunugi, Takeshi Inoue, Jair C Soares, Shigeto Yamawaki, Yoshifumi Watanabe
No neuroanatomical substrates for distinguishing between depression of bipolar disorder (dBD) and major depressive disorder (dMDD) are currently known. The aim of the current multicenter study was to identify neuroanatomical patterns distinct to depressed patients with the two disorders. Further analysis was conducted on an independent sample to enable generalization of results. We directly compared MR images of these subjects using voxel-based morphometry (VBM) and a support vector machine (SVM) algorithm using 1531 participants...
November 30, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/29202017/molecular-motor-dnm1-synergistically-induces-membrane-curvature-to-facilitate-mitochondrial-fission
#19
Michelle W Lee, Ernest Y Lee, Ghee Hwee Lai, Nolan W Kennedy, Ammon E Posey, Wujing Xian, Andrew L Ferguson, R Blake Hill, Gerard C L Wong
Dnm1 and Fis1 are prototypical proteins that regulate yeast mitochondrial morphology by controlling fission, the dysregulation of which can result in developmental disorders and neurodegenerative diseases in humans. Loss of Dnm1 blocks the formation of fission complexes and leads to elongated mitochondria in the form of interconnected networks, while overproduction of Dnm1 results in excessive mitochondrial fragmentation. In the current model, Dnm1 is essentially a GTP hydrolysis-driven molecular motor that self-assembles into ring-like oligomeric structures that encircle and pinch the outer mitochondrial membrane at sites of fission...
November 22, 2017: ACS Central Science
https://www.readbyqxmd.com/read/29201640/connectome-analysis-with-diffusion-mri-in-idiopathic-parkinson-s-disease-evaluation-using-multi-shell-multi-tissue-constrained-spherical-deconvolution
#20
Koji Kamagata, Andrew Zalesky, Taku Hatano, Maria Angelique Di Biase, Omar El Samad, Shinji Saiki, Keigo Shimoji, Kanako K Kumamaru, Kouhei Kamiya, Masaaki Hori, Nobutaka Hattori, Shigeki Aoki, Christos Pantelis
Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls...
2018: NeuroImage: Clinical
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