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62 results for “SVM Classifier”
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Research ArticleOpen accessIn the abstractAn Improved Approach for Topic Ontology Based Categorization of Blogs Using Support Vector MachineV. Subramaniyaswamy, S. Chenthur PandianProblem statement: Information search, collection and categorization from the blogosphere are still one of the important issues to be resolved. Mainly, the blogs assist the variety of interesting and useful information. Because of its increasing growth, blogs can not be categorized effectively. Therefore it is difficult to find relevant topics from the blogs. Hence blogs need to be categorized topically … …Journal of Computer Science, Vol. 8, Issue 2 · 29 Nov 2011 · 7,963 accessesView PDFCite
Research ArticleOpen accessIn the abstractEfficient Classification of Cancer using Support Vector Machines and Modified Extreme Learning Machine based on Analysis of Variance FeaturesA. Bharathi, A.M. NatarajanProblem statement: The primary objective is to propose efficient cancer classification techniques which provide reliable and significant classification accuracy. To achieve this primary research goal is to find the smallest set of genes that can ensure high accuracy in classification using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: (a) The computational burden and … …American Journal of Applied Sciences, Vol. 8, Issue 12 · 22 Oct 2011 · 7,441 accessesView PDFCite
Research ArticleOpen accessIn the abstractSurface Electromyography-Based Facial Expression Recognition in Bi-Polar ConfigurationMahyar Hamedi, Sh-Hussain Salleh, Tan Tian Swee, KamarulafizamProblem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some … …Journal of Computer Science, Vol. 7, Issue 9 · 25 Jul 2011 · 9,943 accessesView PDFCite
Research ArticleOpen accessIn the abstractCancer Classification using Support Vector Machines and Relevance Vector Machine based on Analysis of Variance FeaturesA. Bharathi, A.M. NatarajanProblem statement: The objective of this study is, to find the smallest set of genes that can ensure highly accurate classification of cancer from micro array data by using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: The computational burden and noise arising from irrelevant genes are much reduced; the cost for cancer testing … …Journal of Computer Science, Vol. 7, Issue 9 · 25 Jul 2011 · 7,593 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnalysis of Resistance to Human Immunodeficiency Virus Protease Inhibitors Using Molecular Mechanics and Machine Learning StrategiesA. Harishchander, D. Alex Anand, Sanjib SenapatiProblem statement: Drug resistance is the most important factor, which influences the failure of current HIV therapies. So, the ability to predict the drug resistance of HIV-1 protease mutants will be useful in developing more effective and longer lasting treatment regimens. Approach: Drug resistance of HIV-1 protease is predicted with two current protease inhibitors (Indinavir and Saquinavir). The problem was … …Current Research in Medicine, Vol. 1, Issue 2 · 31 Oct 2010 · 7,204 accessesView PDFCite
Research ArticleOpen accessIn the abstractMyocardial-Infraction Based on Intelligent TechniquesIbrahim Abdulrab AhmedProblem statement: Heart disease is the leading cause of death in the world. One of the most common types of heart diseases is Myocardial-Infraction. The ability to automatically identify Myocardial-Infraction from cardiac profiles test is important for clinical diagnosis and treatment. Approach: The aim of this study is to see whether the heart is healthy or not, in another words … …American Journal of Applied Sciences, Vol. 7, Issue 3 · 31 Mar 2010 · 7,607 accessesView PDFCite
Research ArticleOpen accessIn the abstractNovel Moment Features Extraction for Recognizing Handwritten Arabic LettersGheith Abandah, Nasser AnssariProblem statement: Offline recognition of handwritten Arabic text awaits accurate recognition solutions. Most of the Arabic letters have secondary components that are important in recognizing these letters. However these components have large writing variations. We targeted enhancing the feature extraction stage in recognizing handwritten Arabic text. Approach: In this study, we proposed a novel feature extraction approach of handwritten Arabic … …Journal of Computer Science, Vol. 5, Issue 3 · 31 Mar 2009 · 7,495 accessesView PDFCite
Research ArticleOpen accessIn the abstractSupport Vector Machine for Discrimination Between Fault and Magnetizing Inrush Current in Power TransformerV. Malathi, N. S. MarimuthuThis study presents a novel technique based on Support Vector Machine (SVM) for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena in power transformer. …Journal of Computer Science, Vol. 3, Issue 11 · 30 Nov 2007 · 6,737 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdverse Drug Reaction Detection Using Latent Semantic AnalysisAhmed Adil Nafea, Nazlia Omar, Mohammed M. AL-AniDetecting Adverse Drug Reactions (ADRs) is one of the important information for determining the view of the patient on one drug. Most studies have investigated the extraction of ADRs from social networks, in which users share their opinion on a particular medication. Some studies have used trigger terms to detect ADRs. Such studies showed remarkable performance in terms of extracting … …Journal of Computer Science, Vol. 17, Issue 10 · 28 Oct 2021 · 7,808 accessesView PDFCite
Research ArticleOpen accessIn the abstractPhonocardiogram Classification Based on Machine Learning with Multiple Sound FeaturesKhalid M.O. Nahar, Obaida M. Al-Hazaimeh, Ashraf Abu-Ein, Nasr GharaibehIn this study the heartbeat sound signals were tackled by classifying them into heart disease categories such as normal, artifact, murmur and extrahals in an attempt for early detection of heart defects. Phonocardiogram (i.e., PCG) is used to obtain the digital recording dataset of the heart sounds using an electronic stethoscope or mobile device. Multiple features are extracted from the … …Journal of Computer Science, Vol. 16, Issue 11 · 26 Nov 2020 · 8,259 accessesView PDFCite