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Support vector machine

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https://www.readbyqxmd.com/read/28335480/weighted-kernel-entropy-component-analysis-for-fault-diagnosis-of-rolling-bearings
#1
Hongdi Zhou, Tielin Shi, Guanglan Liao, Jianping Xuan, Jie Duan, Lei Su, Zhenzhi He, Wuxing Lai
This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results...
March 18, 2017: Sensors
https://www.readbyqxmd.com/read/28335471/mobile-phonocardiogram-diagnosis-in-newborns-using-support-vector-machine
#2
Amir Mohammad Amiri, Mohammadreza Abtahi, Nick Constant, Kunal Mankodiya
Phonocardiogram (PCG) monitoring on newborns is one of the most important and challenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel approach for cardiac monitoring applied in PCG data. This basic system coupled with denoising, segmentation, cardiac cycle selection and classification of heart sound can be used widely for a large number of the data. This paper describes the problems and additional advantages of the PCG method including the possibility of recording heart sound at home, removing unwanted noises and data reduction on a mobile device, and an intelligent system to diagnose heart diseases on the cloud server...
March 18, 2017: Healthcare (Basel, Switzerland)
https://www.readbyqxmd.com/read/28335400/online-classification-of-contaminants-based-on-multi-classification-support-vector-machine-using-conventional-water-quality-sensors
#3
Pingjie Huang, Yu Jin, Dibo Hou, Jie Yu, Dezhan Tu, Yitong Cao, Guangxin Zhang
Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category...
March 13, 2017: Sensors
https://www.readbyqxmd.com/read/28333956/discriminating-between-hur-and-ttp-binding-sites-using-the-k-spectrum-kernel-method
#4
Shweta Bhandare, Debra S Goldberg, Robin Dowell
BACKGROUND: The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an interesting problem. Machine learning approaches, such as support vector machines (SVMs), may be useful in the identification of discriminative features. However, this method has yet to be applied to studies of RNA binding protein motifs. RESULTS: Applying the k-spectrum kernel to a support vector machine (SVM), we first verified the published binding sites of both HuR and TTP...
2017: PloS One
https://www.readbyqxmd.com/read/28333644/random-forest-classifier-for-zero-shot-learning-based-on-relative-attribute
#5
Yuhu Cheng, Xue Qiao, Xuesong Wang, Qiang Yu
For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes...
March 21, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28333592/dc-algorithm-for-extended-robust-support-vector-machine
#6
Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori
Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SVM), a robust variant of E[Formula: see text]-SVM. ER-SVM combines two types of nonconvexity from robust SVMs and E[Formula: see text]-SVM...
March 23, 2017: Neural Computation
https://www.readbyqxmd.com/read/28332439/use-of-biopartitioning-micellar-chromatography-and-rp-hplc-for-the-determination-of-blood-brain-barrier-penetration-of-%C3%AE-adrenergic-imidazoline-receptor-ligands-and-qspr-analysis
#7
J Vucicevic, M Popovic, K Nikolic, S Filipic, D Obradovic, D Agbaba
For this study, 31 compounds, including 16 imidazoline/α-adrenergic receptor (IRs/α-ARs) ligands and 15 central nervous system (CNS) drugs, were characterized in terms of the retention factors (k) obtained using biopartitioning micellar and classical reversed phase chromatography (log kBMC and log kwRP, respectively). Based on the retention factor (log kwRP) and slope of the linear curve (S) the isocratic parameter (φ0) was calculated. Obtained retention factors were correlated with experimental log BB values for the group of examined compounds...
March 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28328162/predictive-model-for-inflammation-grades-of-chronic-hepatitis-b-large-scale-analysis-of-clinical-parameters-and-gene-expressions
#8
Weichen Zhou, Yanyun Ma, Jun Zhang, Jingyi Hu, Menghan Zhang, Yi Wang, Yi Li, Lijun Wu, Yida Pan, Yitong Zhang, Xiaonan Zhang, Xinxin Zhang, Zhanqing Zhang, Jiming Zhang, Hai Li, Lungen Lu, Li Jin, Jiucun Wang, Zhenghong Yuan, Jie Liu
BACKGROUND: Liver biopsy is the gold standard to assess pathological features (e.g. inflammation grades) for hepatitis B virus infected patients, although it's invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small HBV-infected samples. We aimed to analyze correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase, and HBV-DNA) in large-scale CHB samples, and to predict inflammation grades by using clinical parameters and/or gene expressions...
March 22, 2017: Liver International: Official Journal of the International Association for the Study of the Liver
https://www.readbyqxmd.com/read/28326008/disease-specific-regions-outperform-whole-brain-approaches-in-identifying-progressive-supranuclear-palsy-a-multicentric-mri-study
#9
Karsten Mueller, Robert Jech, Cecilia Bonnet, Jaroslav Tintěra, Jaromir Hanuška, Harald E Möller, Klaus Fassbender, Albert Ludolph, Jan Kassubek, Markus Otto, Evžen Růžička, Matthias L Schroeter
To identify progressive supranuclear palsy (PSP), we combined voxel-based morphometry (VBM) and support vector machine (SVM) classification using disease-specific features in multicentric magnetic resonance imaging (MRI) data. Structural brain differences were investigated at four centers between 20 patients with PSP and 20 age-matched healthy controls with T1-weighted MRI at 3T. To pave the way for future application in personalized medicine, we applied SVM classification to identify PSP on an individual level besides group analyses based on VBM...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28325450/classification-of-nervous-system-withdrawn-and-approved-drugs-with-toxprint-features-via-machine-learning-strategies
#10
Aytun Onay, Melih Onay, Osman Abul
BACKGROUND AND OBJECTIVES: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. METHODS: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325017/diffuse-reflectance-spectroscopy-can-differentiate-high-grade-and-low-grade-prostatic-carcinoma
#11
Priya N Werahera, Edward A Jasion, E David Crawford, M Scott Lucia, Adrie van Bokhoven, Holly T Sullivan, Fernando J Kim, Paul D Maroni, J David Port, John W Daily, Francisco G La Rosa
Prostate tumors are graded by the revised Gleason Score (GS) which is the sum of the two predominant Gleason grades present ranging from 6-10. GS 6 cancer exclusively with Gleason grade 3 is designated as low grade (LG) and correlates with better clinical prognosis for patients. GS >7 cancer with at least one of the Gleason grades 4 and 5 is designated as HG indicate worse prognosis for patients. Current transrectal ultrasound guided prostate biopsies often fail to correctly diagnose HG prostate cancer due to sampling errors...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324991/features-of-cerebral-oxygenation-detects-brain-injury-in-premature-infants
#12
John M O'Toole, Mmoloki Kenosi, Daragh Finn, Geraldine B Boylan, Eugene M Dempsey
Babies born prematurely can develop brain injury within days after birth. Early identification of high-risk infants enables appropriate clinical care to mitigate potential lifelong disabilities. Near infra-red spectroscopy is an established technology that can provide continuous measurements of cerebral oxygen saturation (rcSO2) over this critical period. We develop a feature set of the rcSO2 signal for the purpose of detecting brain injury. Our feature set contains amplitude, spectral, and fractal dimension features within 5 frequency bands...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324984/prediction-method-for-intronic-alternative-polyadenylation-sites
#13
Shanxin Zhang
Alternative Polyadenylation (APA) of mRNAs has been proven as a considerable mechanism for post-transcriptional gene regulation. The interplay between Intronic APA and splicing may affect the isoforms of mRNAs. In this paper, we have found four prevalent motifs, i.e. AATAAA, TTTTTTTT, CCAGSCTGG and RGYRYRGTGG surrounding the polyadenylation sites; then we proposed a new computational method to identify the Intronic APA sites in the human genome, which is based on a Support Vector Machine (SVM) with weighted degree string kernel...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324952/the-influence-of-the-pre-stimulation-neural-state-on-the-post-stimulation-neural-dynamics-via-distributed-microstimulation-of-the-hippocampus
#14
Mark J Connolly, Robert E Gross, Babak Mahmoudi
In this study we investigated how the neural state influences how the brain responds to electrical stimulation using a 16-channel microelectrode array with 8 stimulation and recording channels implanted in the rat hippocampus. In two experiments we identified the stimulation threshold at which the brain changes to an afterdischarge state. In one experiment a range of suprathreshold stimulations were applied, and in another the stimulation was not changed. The neural state was measured by the power spectral density prior to stimulation...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324694/screening-of-autism-based-on-task-free-fmri-using-graph-theoretical-approach
#15
Masoumeh Sadeghi, Reza Khosrowabadi, Fatemeh Bakouie, Hoda Mahdavi, Changiz Eslahchi, Hamidreza Pouretemad
Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, findings have not been established as a general diagnostic tool yet. In this regard, current study proposed an automatic screening method for recognition of ASDs from healthy controls (HCs) based on their brain functional abnormalities. In this paradigm, brain functional networks of 60 adolescent and young adult males (29 ASDs and 31 HCs) were estimated from subjects' task-free fMRI data...
March 9, 2017: Psychiatry Research
https://www.readbyqxmd.com/read/28323285/utilization-of-machine-learning-for-prediction-of-post-traumatic-stress-a-re-examination-of-cortisol-in-the-prediction-and-pathways-to-non-remitting-ptsd
#16
I R Galatzer-Levy, S Ma, A Statnikov, R Yehuda, A Y Shalev
To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate...
March 21, 2017: Translational Psychiatry
https://www.readbyqxmd.com/read/28322997/a-new-hybrid-coding-for-protein-secondary-structure-prediction-based-on-primary-structure-similarity
#17
Zhong Li, Jing Wang, Shunpu Zhang, Qifeng Zhang, Wuming Wu
The coding pattern of protein can greatly affect the prediction accuracy of protein secondary structure. In this paper, a novel hybrid coding method based on the physicochemical properties of amino acids and tendency factors is proposed for the prediction of protein secondary structure. The principal component analysis (PCA) is first applied to the physicochemical properties of amino acids to construct a 3-bit-code, and then the 3 tendency factors of amino acids are calculated to generate another 3-bit-code...
March 16, 2017: Gene
https://www.readbyqxmd.com/read/28321182/sparse-and-specific-coding-during-information-transmission-between-co-cultured-dentate-gyrus-and-ca3-hippocampal-networks
#18
Daniele Poli, Srikanth Thiagarajan, Thomas B DeMarse, Bruce C Wheeler, Gregory J Brewer
To better understand encoding and decoding of stimulus information in two specific hippocampal sub-regions, we isolated and co-cultured rat primary dentate gyrus (DG) and CA3 neurons within a two-chamber device with axonal connectivity via micro-tunnels. We tested the hypothesis that, in these engineered networks, decoding performance of stimulus site information would be more accurate when stimuli and information flow occur in anatomically correct feed-forward DG to CA3 vs. CA3 back to DG. In particular, we characterized the neural code of these sub-regions by measuring sparseness and uniqueness of the responses evoked by specific paired-pulse stimuli...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/28316652/a-novel-descriptor-based-on-atom-pair-properties
#19
Masataka Kuroda
BACKGROUND: Molecular descriptors have been widely used to predict biological activities and physicochemical properties or to analyze chemical libraries on the basis of similarity. Although fingerprints and properties are generally used as descriptors, neither is perfect for these purposes. A fingerprint can distinguish between molecules, whereas a property may not do the same in certain cases, and vice versa. When the number of the training set is especially small, the construction of good predictive models is difficult...
2017: Journal of Cheminformatics
https://www.readbyqxmd.com/read/28306716/localization-and-diagnosis-framework-for-pediatric-cataracts-based-on-slit-lamp-images-using-deep-features-of-a-convolutional-neural-network
#20
Xiyang Liu, Jiewei Jiang, Kai Zhang, Erping Long, Jiangtao Cui, Mingmin Zhu, Yingying An, Jia Zhang, Zhenzhen Liu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Qianzhong Cao, Jing Li, Xiaohang Wu, Dongni Wang, Haotian Lin
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets...
2017: PloS One
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