A Literature Survey on Computer-Aided Diagnosis in Detection and Classification of Polyp in Colon Cancer Using CT Colonography
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 8, AUGUST 2013 1863 Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach Yuichi Motai, Senior Member, IEEE, and Hiroyuki Yoshida, Member, IEEE Abstract—Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace -
Data Mining Based Soft Computing Methods for Web Intelligence
International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected] Volume 3, Issue 3, March 2014 ISSN 2319 - 4847 DATA MINING BASED SOFT COMPUTING METHODS FOR WEB INTELLIGENCE Mr. Ankit R. Deshmukh1, Prof. Sunil R. Gupta2 1ME (CSE) ,First Year,Department of CSE,Prof. Ram Meghe Institute Of Technology and Research, Badnera,Amravati. Sant Gadgebaba Amravati University, Amarvati, Maharashtra, India - 444701 2Assitantant Professor, Department of CSE, Prof. Ram Meghe Institute Of Technology and Research, Badnera,Amravati. Sant Gadgebaba Amravati University, Amarvati, Maharashtra, India - 444701 Abstract Web has become the primary means for information distribution. It is being used for commercial, entertainment or educational purposes and thus its popularity resulted in heavy traffic in the Internet. Web Intelligence (WI) deals with the scientific exploration of the new territories of the Web. As a new field of computer science, it combines artificial intelligence and advanced information technology in the context of the Web, and goes beyond each of them. Data mining has a lot of scope in e- Applications. The key problem is how to find useful hidden patterns for better application. Problem to address soft computing techniques like Neural networks, Fuzzy Logic, Support Vector Machines, Genetic Algorithms in Evolutionary Computation. In this paper, we explore soft computing techniques use to achieve web intelligences. Keywords: Web, Web intelligence, Data mining, Soft computing, Neural networks, Support Vector Machines, Fuzzy Logic, Genetic Algorithm. 1. INTRODUCTION Data mining has useful business applications such as finding useful hidden information from databases, predicting future trends, and making good business decisions [1,6,7]. -
VCU School of Engineering Research Magazine, Vol. 5
TOUCHING LIVES Volume 5 Dear Colleagues and Friends, ur School of Engineering is committed to creativity and discovery O in all aspects of our experience here at Virginia Commonwealth University and in the community at large. We engage in respectful collaborations, we foster problem solving and we are driven by the need to improve human life at home and around the world. I proudly share this issue of the VCU Research Report in which you will learn of remarkable work devoted to improving the human condition. In the pages to follow, you will learn about nanoparticle-based drug- delivery technology to help treat brain cancer in a less invasive way, a new type of smart material to reduce invasive surgical procedures, and privacy protection in cloud computing environments. You will read about promising work to create resorbable vascular grafts, battery-free computer processors, and improved pulmonary drug delivery through aerosol dynamics—as well as machine-based early cancer detection advancements, biomedical signal and image processing to detect hemorrhagic shock, and a breakthrough in soft-surface antimicrobial coatings. It is this work that exemplifies our devotion to all things possible. As you read these research summaries, I invite you to contact the individual faculty members and students—join in the meaningful discovery to solve problems and improve human life. Please join me in celebrating all of our scholars and students. Your School of Engineering is truly amazing and has advanced more in its 16-year history than any other school I’ve seen. Congratulations! Dr. J. Charles Jennett — Interim Dean,VCU Engineering 2 Battlefield Vital Signs 12 Breaking Through Barriers Published by Virginia Commonwealth University School of Engineering Nanomagnetic Computing to Reduce P.O.