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https://www.readbyqxmd.com/read/28920896/analysis-and-optimization-of-loss-functions-for-multiclass-top-k-and-multilabel-classification
#1
Maksim Lapin, Matthias Hein, Bernt Schiele
Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed light on the top-k error. First, we provide an in-depth analysis of established and recently proposed single-label multiclass methods along with a detailed account of efficient optimization algorithms for them...
September 13, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28918128/whole-network-temporal-and-parietal-lobe-contributions-to-the-earliest-phases-of-language-production
#2
Alessandro Principe, Marco Calabria, Adrià Tauste Campo, Josephine Cruzat, Gerardo Conesa, Albert Costa, Rodrigo Rocamora
We investigated whether it is possible to study the network dynamics and the anatomical regions involved in the earliest moments of picture naming by using invasive electroencephalogram (EEG) traces to predict naming errors. Four right-handed participants with focal epilepsy explored with extensive stereotactic implant montages that recorded temporal, parietal and occipital regions -in two patients of both hemispheres-named a total of 228 black and white pictures in three different sessions recorded in different days...
August 24, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28918098/identifying-novel-factor-xiia-inhibitors-with-pca-ga-svm-developed-vhts-models
#3
Jonathan Jun Feng Chen, Donald P Visco
There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening can identify potential drug candidates, but is inefficient. Virtual high-throughput screening can be used to raise efficiency by focusing experimental efforts on compounds predicted to be active and is applied here to identify new Factor XIIa inhibitors. We combine principal component analysis, genetic algorithm and support vector machine to create the models used in the virtual high-throughput screening...
September 1, 2017: European Journal of Medicinal Chemistry
https://www.readbyqxmd.com/read/28912803/optimal-parameter-selection-for-support-vector-machine-based-on-artificial-bee-colony-algorithm-a-case-study-of-grid-connected-pv-system-power-prediction
#4
Xiang-Ming Gao, Shi-Feng Yang, San-Bo Pan
Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912801/efficient-multiple-kernel-learning-algorithms-using-low-rank-representation
#5
Wenjia Niu, Kewen Xia, Baokai Zu, Jianchuan Bai
Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912043/abnormal-segments-of-right-uncinate-fasciculus-and-left-anterior-thalamic-radiation-in-major-and-bipolar-depression
#6
Feng Deng, Ying Wang, Huiyuan Huang, Meiqi Niu, Shuming Zhong, Ling Zhao, Zhangzhang Qi, Xiaoyan Wu, Yao Sun, Chen Niu, Yuan He, Li Huang, Ruiwang Huang
Differential brain structural abnormalities between bipolar disorder (BD) and major depressive disorder (MDD) may reflect different pathological mechanisms underlying these two brain disorders. However, few studies have directly compared the brain structural properties, especially in white matter (WM) tracts, between BD and MDD. Using automated fiber-tract quantification (AFQ), we utilized diffusion tensor images (DTI) from 67 unmedicated depressed patients, including 31 BD and 36 MDD, and 45 healthy controls (HC) to create fractional anisotropy (FA) tract profiles along 20 major WM tracts...
September 11, 2017: Progress in Neuro-psychopharmacology & Biological Psychiatry
https://www.readbyqxmd.com/read/28910748/local-functional-connectivity-density-is-closely-associated-with-the-response-of-electroconvulsive-therapy-in-major-depressive-disorder
#7
Jiaojian Wang, Qiang Wei, Xinru Yuan, Xiaoyan Jiang, Jinping Xu, Xiaoqin Zhou, Yanghua Tian, Kai Wang
BACKGROUND: Electroconvulsive therapy (ECT) has been demonstrated to be an effective treatment of major depressive disorder (MDD). However, the neuroanatomical basis of response to ECT is still largely unknown. METHODS: In present study, we used functional connectivity density (FCD) and resting-state functional connectivity (RSFC) to identify the relationship between the changes of resting-state activities and ECT responses in 23 MDD patients before and after ECT...
September 6, 2017: Journal of Affective Disorders
https://www.readbyqxmd.com/read/28901446/a-support-vector-machine-classifier-for-the-prediction-of-osteosarcoma-metastasis-with-high-accuracy
#8
Yunfei He, Jun Ma, Xiaojian Ye
In this study, gene expression profiles of osteosarcoma (OS) were analyzed to identify critical genes associated with metastasis. Five gene expression datasets were screened and downloaded from Gene Expression Omnibus (GEO). Following assessment by MetaQC, the dataset GSE9508 was excluded for poor quality. Subsequently, differentially expressed genes (DEGs) between metastatic and non-metastatic OS were identified using meta‑analysis. A protein-protein interaction (PPI) network was constructed with information from Human Protein Reference Database (HPRD) for the DEGs...
September 7, 2017: International Journal of Molecular Medicine
https://www.readbyqxmd.com/read/28895910/a-false-alarm-reduction-method-for-a-gas-sensor-based-electronic-nose
#9
Mohammad Mizanur Rahman, Chalie Charoenlarpnopparut, Prapun Suksompong, Pisanu Toochinda, Attaphongse Taparugssanagorn
Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used...
September 12, 2017: Sensors
https://www.readbyqxmd.com/read/28895908/a-robust-sparse-representation-model-for-hyperspectral-image-classification
#10
Shaoguang Huang, Hongyan Zhang, Aleksandra Pižurica
Sparse representation has been extensively investigated for hyperspectral image (HSI) classification and led to substantial improvements in the performance over the traditional methods, such as support vector machine (SVM). However, the existing sparsity-based classification methods typically assume Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust classification model that admits realistic mixed noise, which includes Gaussian noise and sparse noise...
September 12, 2017: Sensors
https://www.readbyqxmd.com/read/28894589/automated-detection-of-heart-ailments-from-12-lead-ecg-using-complex-wavelet-sub-band-bi-spectrum-features
#11
Rajesh Kumar Tripathy, Samarendra Dandapat
The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated...
April 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28894545/comparing-performances-of-intelligent-classifier-algorithms-for-predicting-type-of-pain-in-patients-with-spinal-cord-injury
#12
Nasrolah Nasr HeidarAbadi, Laleh Hakemi, Pirhossein Kolivand, Reza Safdari, Marjan Ghazi Saeidi
BACKGROUND AND AIM: In this study, performances of classification techniques were compared in order to predict type of pain in patients with spinal cord injury. Pain is one of the main problems in people with spinal cord injury. Identifying the optimal classification technique will help improve decision support systems in clinical settings. METHODS: A descriptive retrospective analysis was performed in 253 patients. We compared performances of "Bayesian Networks", "Decision Tree", neural networks: "Multi-Layer Perceptron" (MLP), and "Support Vector Machines" (SVM)...
July 2017: Electronic Physician
https://www.readbyqxmd.com/read/28894460/an-automatic-gastrointestinal-polyp-detection-system-in-video-endoscopy-using-fusion-of-color-wavelet-and-convolutional-neural-network-features
#13
Mustain Billah, Sajjad Waheed, Mohammad Motiur Rahman
Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to...
2017: International Journal of Biomedical Imaging
https://www.readbyqxmd.com/read/28894115/in-silico-prediction-of-drug-target-interaction-networks-based-on-drug-chemical-structure-and-protein-sequences
#14
Zhengwei Li, Pengyong Han, Zhu-Hong You, Xiao Li, Yusen Zhang, Haiquan Yu, Ru Nie, Xing Chen
Analysis of drug-target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor...
September 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28894088/a-de-novo-substructure-generation-algorithm-for-identifying-the-privileged-chemical-fragments-of-liver-x-receptor%C3%AE-agonists
#15
He Peng, Zhihong Liu, Xin Yan, Jian Ren, Jun Xu
Liver X receptorβ (LXRβ) is a promising therapeutic target for lipid disorders, atherosclerosis, chronic inflammation, autoimmunity, cancer and neurodegenerative diseases. Druggable LXRβ agonists have been explored over the past decades. However, the pocket of LXRβ ligand-binding domain (LBD) is too large to predict LXRβ agonists with novel scaffolds based on either receptor or agonist structures. In this paper, we report a de novo algorithm which drives privileged LXRβ agonist fragments by starting with individual chemical bonds (de novo) from every molecule in a LXRβ agonist library, growing the bonds into substructures based on the agonist structures with isomorphic and homomorphic restrictions, and electing the privileged fragments from the substructures with a popularity threshold and background chemical and biological knowledge...
September 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28891942/automatic-classification-of-tremor-severity-in-parkinson-s-disease-using-a-wearable-device
#16
Hyoseon Jeon, Woongwoo Lee, Hyeyoung Park, Hong Ji Lee, Sang Kyong Kim, Han Byul Kim, Beomseok Jeon, Kwang Suk Park
Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson's Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson's disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope...
September 9, 2017: Sensors
https://www.readbyqxmd.com/read/28891683/six-global-and-local-qspr-models-of-aqueous-solubility-at-ph%C3%A2-%C3%A2-7-4-based-on-structural-similarity-and-physicochemical-descriptors
#17
O A Raevsky, V Y Grigorev, D E Polianczyk, O E Raevskaja, J C Dearden
Aqueous solubility at pH = 7.4 is a very important property for medicinal chemists because this is the pH value of physiological media. The present work describes the application of three different methods (support vector machine (SVM), random forest (RF) and multiple linear regression (MLR)) and three local quantitative structure-property relationship (QSPR) models (regression corrected by nearest neighbours (RCNN), arithmetic mean property (AMP) and local regression property (LoReP)) to construct stable QSPRs with clear mechanistic interpretation...
September 11, 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28891512/decoding-of-visual-activity-patterns-from-fmri-responses-using-multivariate-pattern-analyses-and-convolutional-neural-network
#18
Raheel Zafar, Nidal Kamel, Mohamad Naufal, Aamir Saeed Malik, Sarat C Dass, Rana Fayyaz Ahmad, Jafri M Abdullah, Faruque Reza
Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set...
2017: Journal of Integrative Neuroscience
https://www.readbyqxmd.com/read/28890728/ambient-air-quality-classification-by-grey-wolf-optimizer-based-support-vector-machine
#19
Akash Saxena, Shalini Shekhawat
With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2...
2017: Journal of Environmental and Public Health
https://www.readbyqxmd.com/read/28887746/diagnosis-of-multiple-sclerosis-from-eeg-signals-using-nonlinear-methods
#20
Ali Torabi, Mohammad Reza Daliri, Seyyed Hojjat Sabzposhan
EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion...
September 8, 2017: Australasian Physical & Engineering Sciences in Medicine
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