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| Author: Asra R. Khan, Joseph R. Geraghty | Publisher: McGraw-Hill Education / Medical | Publication Date: 29-Oct-21 | Number of Pages: 512 pages | Language: English | Binding: Paperback | ISBN-10: 1260463788 | ISBN-13: 9781260463781
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| Author: Jing Hua | Publisher: Academic Press | Publication Date: November 9, 2019 | Number of Pages: 152 pages | Language: English | Binding: Paperback | ISBN-10: 0128138424 | ISBN-13: 9780128138427
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This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.Product DetailsISBN-13: 9783540475118 Publisher: Springer Berlin Heidelberg Publication Date: 04-12-2007 Pages: 374 Product Dimensions: 6.10(w) x 9.25(h) x 0.03(d) Series: Studies in Computational Intelligence #46
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| Author: K. C. Santosh, Bharti Gawali | Publisher: Springer | Publication Date: Mar 31, 2021 | Number of Pages: 396 pages | Language: English | Binding: Paperback | ISBN-10: 9811604924 | ISBN-13: 9789811604928
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Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: Soft computing in pattern recognition and data miningA mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the setSelection of non-redundant and relevant features of real-valued data setsSelection of the minimum set of basis strings with maximum information for amino acid sequence analysisSegmen
tation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text—covering the latest findings as well as directions for future research—is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
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During the past two decades there has been a considerable growth in interest in problems of pattern recognition and image processing (PRIP). This inter est has created an increasing need for methods and techniques for the design of PRIP systems. PRIP involves analysis, classification and interpretation of data. Practical applications of PRIP include character recognition, re mote sensing, analysis of medical signals and images, fingerprint and face identification, target recognition and speech understanding. One difficulty in making PRIP systems practically feasible, and hence, more popularly used, is the requirement of computer time and storage. This situation is particularly serious when the patterns to be analyzed are quite complex. Thus it is of the utmost importance to investigate special comput er architectures and their implementations for PRIP. Since the advent of VLSI technology, it is possible to put thousands of components on one chip. This reduces the cost of processors and increases the processing speed. VLSI algorithms and their implementations have been recently developed for PRIP. This book is intended to document the recent major progress in VLSI system design for PRIP applications.
