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https://www.readbyqxmd.com/read/28445404/support-vector-data-description-model-to-map-specific-land-cover-with-optimal-parameters-determined-from-a-window-based-validation-set
#1
Jinshui Zhang, Zhoumiqi Yuan, Guanyuan Shuai, Yaozhong Pan, Xiufang Zhu
This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space...
April 26, 2017: Sensors
https://www.readbyqxmd.com/read/28445293/possible-pathways-used-to-predict-different-stages-of-lung-adenocarcinoma
#2
Xiaodong Chen, Qiongyu Duan, Ying Xuan, Yunan Sun, Rong Wu
We aimed to find some specific pathways that can be used to predict the stage of lung adenocarcinoma.RNA-Seq expression profile data and clinical data of lung adenocarcinoma (stage I [37], stage II 161], stage III [75], and stage IV [45]) were obtained from the TCGA dataset. The differentially expressed genes were merged, correlation coefficient matrix between genes was constructed with correlation analysis, and unsupervised clustering was carried out with hierarchical clustering method. The specific coexpression network in every stage was constructed with cytoscape software...
April 2017: Medicine (Baltimore)
https://www.readbyqxmd.com/read/28436898/extended-polynomial-growth-transforms-for-design-and-training-of-generalized-support-vector-machines
#3
Ahana Gangopadhyay, Oindrila Chatterjee, Shantanu Chakrabartty
Growth transformations constitute a class of fixed-point multiplicative update algorithms that were originally proposed for optimizing polynomial and rational functions over a domain of probability measures. In this paper, we extend this framework to the domain of bounded real variables which can be applied towards optimizing the dual cost function of a generic support vector machine (SVM). The approach can, therefore, not only be used to train traditional soft-margin binary SVMs, one-class SVMs, and probabilistic SVMs but can also be used to design novel variants of SVMs with different types of convex and quasi-convex loss functions...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436875/zero-shot-learning-with-transferred-samples
#4
Yuchen Guo, Guiguang Ding, Jungong Han, Yue Gao
By transferring knowledge from the abundant labeled samples of known source classes, zero-shot learning (ZSL) makes it possible to train recognition models for novel target classes that have no labeled samples. Conventional ZSL approaches usually adopt a two-step recognition strategy, in which the test sample is projected into an intermediary space in the first step, and then the recognition is carried out by considering the similarity between the sample and target classes in the intermediary space. Due to this redundant intermediate transformation, information loss is unavoidable, thus degrading the performance of overall system...
April 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28436405/a-comparison-of-methods-for-three-class-mammograms-classification
#5
Marina Milosevic, Zeljko Jovanovic, Dragan Jankovic
BACKGROUND: Mammography is considered the gold standard for early breast cancer detection but it is very difficult to interpret mammograms for many reason. Computer aided diagnosis (CAD) is an important development that may help to improve the performance in breast cancer detection. OBJECTIVE: We present a CAD system based on feature extraction techniques for detecting abnormal patterns in digital mammograms. METHODS: Computed features based on gray-level co-occurrence matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions...
April 14, 2017: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/28435046/a-multilevel-roi-features-based-machine-learning-method-for-detection-of-morphometric-biomarkers-in-parkinson-s-disease
#6
Bo Peng, Suhong Wang, Zhiyong Zhou, Yan Liu, Baotong Tong, Tao Zhang, Yakang Dai
Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisted diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to make the multilevel ROI features...
April 20, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/28434670/real-time-eutrophication-status-evaluation-of-coastal-waters-using-support-vector-machine-with-grid-search-algorithm
#7
Xianyu Kong, Yuyan Sun, Rongguo Su, Xiaoyong Shi
The development of techniques for real-time monitoring of the eutrophication status of coastal waters is of great importance for realizing potential cost savings in coastal monitoring programs and providing timely advice for marine health management. In this study, a GS optimized SVM was proposed to model relationships between 6 easily measured parameters (DO, Chl-a, C1, C2, C3 and C4) and the TRIX index for rapidly assessing marine eutrophication states of coastal waters. The good predictive performance of the developed method was indicated by the R(2) between the measured and predicted values (0...
April 20, 2017: Marine Pollution Bulletin
https://www.readbyqxmd.com/read/28433870/automated-detection-of-premature-delivery-using-empirical-mode-and-wavelet-packet-decomposition-techniques-with-uterine-electromyogram-signals
#8
U Rajendra Acharya, Vidya K Sudarshan, Soon Qing Rong, Zechariah Tan, Choo Min Lim, Joel Ew Koh, Sujatha Nayak, Sulatha V Bhandary
An accurate detection of preterm labor and the risk of preterm delivery before 37 weeks of gestational age is crucial to increase the chance of survival rate for both mother and the infant. Thus, the uterine contractions measured using uterine electromyogram (EMG) or electro hysterogram (EHG) need to have high sensitivity in the detection of true preterm labor signs. However, visual observation and manual interpretation of EHG signals at the time of emergency situation may lead to errors. Therefore, the employment of computer-based approaches can assist in fast and accurate detection during the emergency situation...
April 18, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28433431/application-of-structured-support-vector-machine-backpropagation-to-a-convolutional-neural-network-for-human-pose-estimation
#9
Peerajak Witoonchart, Prabhas Chongstitvatana
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28431389/prediction-of-size-fractionated-airborne-particle-bound-metals-using-mlr-bp-ann-and-svm-analyses
#10
Xiang'zi Leng, Jinhua Wang, Haibo Ji, Qin'geng Wang, Huiming Li, Xin Qian, Fengying Li, Meng Yang
Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters...
April 6, 2017: Chemosphere
https://www.readbyqxmd.com/read/28429665/the-2016-aha-acc-guideline-on-the-management-of-patients-with-lower-extremity-peripheral-artery-disease-an-interview-with-svm-members-of-the-writing-committee
#11
Ehrin J Armstrong, Heather L Gornik, Naomi M Hamburg, Scott Kinlay
No abstract text is available yet for this article.
April 2017: Vascular Medicine
https://www.readbyqxmd.com/read/28428140/automated-annotation-and-classification-of-bi-rads-assessment-from-radiology-reports
#12
Sergio M Castro, Eugene Tseytlin, Olga Medvedeva, Kevin Mitchell, Shyam Visweswaran, Tanja Bekhuis, Rebecca S Jacobson
The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach...
April 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28428048/multi-center-machine-learning-in-imaging-psychiatry-a-meta-model-approach
#13
Petr Dluhoš, Daniel Schwarz, Wiepke Cahn, Neeltje van Haren, René Kahn, Filip Španiel, Jiří Horáček, Tomáš Kašpárek, Hugo Schnack
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues...
April 17, 2017: NeuroImage
https://www.readbyqxmd.com/read/28424959/non-essential-element-concentrations-in-brown-grain-rice-assessment-by-advanced-data-mining-techniques
#14
Roxana Villafañe, Melisa Hidalgo, Analía Piccoli, Eduardo Marchevsky, Roberto Pellerano
The concentrations of 17 non-essential elements (Al, As, Ba, Be, Cd, Ce, Cr, Hg, La, Li, Pb, Sb, Sn, Sr, Th, Ti, and Tl) were determined in brown grain rice samples of two varieties: Fortuna and Largo Fino. The samples were collected from the four main producing regions of Corrientes province (Argentina). Quantitative determinations were performed by inductively coupled plasma mass spectrometry (ICP-MS), using a validated method. The contents of As, Be, Cd, Ce, Cr, Hg, Pb, Sb, Sn, Th, and Tl were very low or not detected in most samples...
April 20, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28424788/fully-automated-robust-system-to-detect-retinal-edema-central-serous-chorioretinopathy-and-age-related-macular-degeneration-from-optical-coherence-tomography-images
#15
Samina Khalid, M Usman Akram, Taimur Hassan, Ammara Nasim, Amina Jameel
Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28423793/automated-classification-of-semi-structured-pathology-reports-into-icd-o-using-svm-in-portuguese
#16
Michel Oleynik, Diogo F C Patrão, Marcelo Finger
Pathology reports are a main source of information regarding cancer diagnosis and are commonly written following semi-structured templates that include tumour localisation and behaviour. In this work, we evaluated the efficiency of support vector machines (SVMs) to classify pathology reports written in Portuguese into the International Classification of Diseases for Oncology (ICD-O), a biaxial classification of cancer topography and morphology. A partnership program with the Brazilian hospital A.C. Camargo Cancer Center provided anonymised pathology reports and structured data from 94,980 patients used for training and validation...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423781/learning-differentially-expressed-gene-pairs-in-microarray-data
#17
Xiao-Lei Xia, Sinead Brophy, Shang-Ming Zhou
To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers of discrimination in their combinations. This paper proposed an impurity metric in which the number of split intervals for each feature is considered as a parameter to be optimized for gaining maximal discrimination. The proposed method was first evaluated by applying to a synthesized noisy rectangular grid dataset, in which the significant feature pair which forms a rectangular grid pattern was successfully recognized...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423655/sequence-based-predictive-modeling-to-identify-cancerlectins
#18
Hong-Yan Lai, Xin-Xin Chen, Wei Chen, Hua Tang, Hao Lin
Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer...
March 7, 2017: Oncotarget
https://www.readbyqxmd.com/read/28423569/accurate-prediction-of-protein-protein-interactions-by-integrating-potential-evolutionary-information-embedded-in-pssm-profile-and-discriminative-vector-machine-classifier
#19
Zheng-Wei Li, Zhu-Hong You, Xing Chen, Li-Ping Li, De-Shuang Huang, Gui-Ying Yan, Ru Nie, Yu-An Huang
Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era...
April 4, 2017: Oncotarget
https://www.readbyqxmd.com/read/28422080/study-on-temperature-and-synthetic-compensation-of-piezo-resistive-differential-pressure-sensors-by-coupled-simulated-annealing-and-simplex-optimized-kernel-extreme-learning-machine
#20
Ji Li, Guoqing Hu, Yonghong Zhou, Chong Zou, Wei Peng, Jahangir Alam Sm
As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task...
April 19, 2017: Sensors
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