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30 articles for “irrelevant data”
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Research ArticleOpen accessIn the abstractHYBRID SEARCH AND DELIVERY OF LEARNING OBJECTS SYSTEMAnthony N. Ilukwe, Yevgen BiletskyRetrieving learning material from the internet is a tedious process that has begged for a solution to filter out of the cluster of data and irrelevant material on the internet and deliver material that is relevant to a specific user. The Hybrid Search and Delivery of Learning Objects (HSDLO) system, put forward in this study, facilitates the personalized search and … …Journal of Computer Science, Vol. 10, Issue 6 · 25 Jan 2014 · 6,600 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 abstractCombined Heuristic Technique for Optimization of Bloom Filter in Spam FilteringArulanand Natarajan, S. Subramanian, K. PremalathaProblem statement: Spam is an irrelevant or inappropriate message sent on the internet to a large number of newsgroups or users. A spam word is a list of well-known words that often appear in spam mails. Bloom Filter (BF) is used for identification of spam word. Approach: BF is a simple but powerful data structure that can check membership to … …Journal of Computer Science, Vol. 7, Issue 9 · 27 Jul 2011 · 7,226 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 abstractBio-Potential Signal Extraction from Multi-Channel Paper Recorded ChartsAli S.A. Al-MejradProblem Statement: Almost all of the modern biomedical equipments that record the biopotential actions have digital output but the paper chart records are a must. The volume of these records is significant and increasing rapidly. Keeping a bio-signal chart of a patient make it easy for quick assessment but still create problems in the essence of data storage, archiving , … …American Journal of Applied Sciences, Vol. 8, Issue 6 · 13 Jun 2011 · 7,106 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Enhancement of Bayesian Inference Network for Ligand-Based Virtual Screening using Features SelectionAli Ahmed, Ammar Abdo, Naomie SalimProblem statement: Similarity based Virtual Screening (VS) deals with a large amount of data containing irrelevant and/or redundant fragments or features. Recent use of Bayesian network as an alternative for existing tools for similarity based VS has received noticeable attention of the researchers in the field of chemoinformatics. Approach: To this end, different models of Bayesian network have been developed. … …American Journal of Applied Sciences, Vol. 8, Issue 4 · 18 Apr 2011 · 7,721 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegrated Clustering and Feature Selection Scheme for Text Documents.M. Thangamani, P. ThangarajProblem statement: Text documents are the unstructured databases that contain raw data collection. The clustering techniques are used group up the text documents with reference to its similarity. Approach: The feature selection techniques were used to improve the efficiency and accuracy of clustering process. The feature selection was done by eliminate the redundant and irrelevant items from the text document … …Journal of Computer Science, Vol. 6, Issue 5 · 31 May 2010 · 7,163 accessesView PDFCite
Research ArticleOpen accessIn the abstractModified Center Interpolation Net for Classification of Composite Structures Tested Using Low Frequency Electronic TappingMahmoud Z. IskandaraniProblem statement: Most exhaustive NDT techniques require large capital equipment, are difficult to apply to complex geometric structures and, above all, are time-consuming to use and some take a considerable time to complete. As a solution to the problems associated with NDT applications, there is a need to establish an intelligent analysis system that supports a portable testing environment, which … …American Journal of Applied Sciences, Vol. 7, Issue 2 · 28 Feb 2010 · 7,108 accessesView PDFCite
Research ArticleOpen accessIn the abstractFixed Representative Colors Feature Extraction Algorithm for Moving Picture Experts Group-7 Dominant Color DescriptorAhmed Ibrahim, Ala'a Al-Zou'bi, Raed Sahawneh, Maria MakhadmehProblem statement: MPEG-7 has defined a set of color descriptors for the multimedia contents; one of these descriptors is the Dominant Color Descriptor (DCD). The DCD adopted the Generalized Lloyd Algorithm (GLA) as a feature extraction technique. However, using this algorithm in image retrieval systems may produce perceptually irrelevant images in the ranked results. Approach: In this study, we proposed … …Journal of Computer Science, Vol. 5, Issue 11 · 30 Nov 2009 · 7,542 accessesView PDFCite
Research ArticleOpen accessIn the abstractUnbalance Quantitative Structure Activity Relationship Problem Reduction in Drug DesignD. Pugazhenthi, S. P. RajagopalanProblem statement: Activities of drug molecules can be predicted by Quantitative Structure Activity Relationship (QSAR) models, which overcome the disadvantage of high cost and long cycle by employing traditional experimental methods. With the fact that number of drug molecules with positive activity is rather fewer than that with negatives, it is important to predict molecular activities considering such an unbalanced … …Journal of Computer Science, Vol. 5, Issue 10 · 31 Oct 2009 · 6,704 accessesView PDFCite