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116 results for “Feature Selection”
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Research ArticleOpen accessIn the abstractEnhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNetSuhad A. Yousif, Venus W. Samawi, Islam Elkabani, Rached ZantoutArabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information (written in Arabic language) on the web. In most text classification processes, feature selection is crucial task since it highly affects the classification accuracy. Generally, two types of features could be used: Statistical based features and semantic and concept … …Journal of Computer Science, Vol. 11, Issue 3 · 15 Apr 2015 · 8,065 accessesView PDFCite
Research ArticleOpen accessIn the abstractFEATURE DIMENSION REDUCTION FOR EFFICIENT MEDICAL IMAGE RETRIEVAL SYSTEM USING UNIFIED FRAMEWORKYogapriya Jaganathan, Ila VennilaFeature dimensionality reduction problem is a major issue in Content Based Medical Image Retrieval (CBMIR) for the effective management of medical images with the support of visual features for the purpose of diagnosis and educational research field. The proposed CBMIR is used a unified approach based on extraction of visual features, optimized feature selection, classification of optimized features and similarity … …Journal of Computer Science, Vol. 9, Issue 11 · 24 Sep 2013 · 6,982 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn integrated Framework Based on Texture Features, Cuckoo Search and Relevance Vector Machine for Medical Image Retrieval SystemYogapriya Jaganathan, Ila VennilaAs medical images are widely used in healthcare applications, Content Based Medical Image Retrieval (CBMIR) system is needed for physicians to convey effective decisions to patients and for medical research students to learn imaging characteristics for their extensive research based on visual features. However the performance of the retrieval is restricted due to high feature dimensionality of visual features. To … …American Journal of Applied Sciences, Vol. 10, Issue 11 · 24 Sep 2013 · 6,878 accessesView PDFCite
Research ArticleOpen accessIn the abstractResearch Proposal: An Intrusion Detection System Alert Reduction and Assessment Framework Based on Data MiningKarim Al-Saedi, Selvakumar Manickam, Sureswaran Ramadass et al.The Intrusion Detection System (IDS) generates huge amounts of alerts that are mostly false positives. The abundance of false positive alerts makes it difficult for the security analyst to identify successful attacks and to take remedial actions. Such alerts to have not been classified in accordance with their degree of threats. They further need to be processed to ascertain the … …Journal of Computer Science, Vol. 9, Issue 4 · 8 May 2013 · 7,709 accessesView PDFCite
Research ArticleOpen accessIn the abstractContent Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid ApproachR. Suganya, S. RajaramProblem statement: In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from … …American Journal of Applied Sciences, Vol. 9, Issue 6 · 24 Mar 2012 · 7,643 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Improved Face Recognition Technique Based on Modular LPCA ApproachMathu Soothana S. Kumar, Retna Swami, Muneeswaran KaruppiahProblem statement: A face identification algorithm based on modular localized variation by Eigen Subspace technique, also called modular localized principal component analysis, is presented in this study. Approach: The face imagery was partitioned into smaller sub-divisions from a predefined neighborhood and they were ultimately fused to acquire many sets of features. Since a few of the normal facial features of … …Journal of Computer Science, Vol. 7, Issue 12 · 22 Oct 2011 · 6,950 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,873 accessesView PDFCite
Research ArticleOpen accessIn the abstractSwarm Negative Selection Algorithm for Electroencephalogram Signals ClassificationNasser Omer Sahel Ba-Karait, Siti Mariyam Shamsuddin, Rubita SudirmanProblem statement: The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust … …Journal of Computer Science, Vol. 5, Issue 12 · 31 Dec 2009 · 7,205 accessesView PDFCite
Research ArticleOpen accessIn the abstractBoosting Kernel Discriminative Common Vectors for Face RecognitionC. Lakshmi, M. PonnavaikkoProblem statement: Kernel discriminative common vector (KDCV) was one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. Approach: This study presented a new algorithm called Boosting Kernel Discriminative Common Vector (BKDCV) to further improve the overall performance of KDCV by integrating the boosting and KDCV techniques. Results: In BKDCV, the … …Journal of Computer Science, Vol. 5, Issue 11 · 30 Nov 2009 · 6,571 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