Wiley in Intelligent Signal and Data Processing

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques. “Recent Advances in Hybrid Metaheuristics for Data Clustering” offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors-noted experts on the topic-provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: • Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts • Offers an in-depth analysis of a range of optimization algorithms • Highlights a review of data clustering • Contains a detailed overview of different standard metaheuristics in current use • Presents a step-by-step guide to the build-up of hybrid metaheuristics • Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, “Recent Advances in Hybrid Metaheuristics for Data Clustering” provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.

A comprehensive review of the most recent applications of intelligent multi-modal data processing. “Intelligent Multi-Modal Data Processing” contains a review of the most recent applications of data processing. The Editors and contributors—noted experts on the topic—offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. “Intelligent Multi-Modal Data Processing” is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: • Includes an in-depth analysis of the state-of-the-art applications of signal and data processing • Contains contributions from noted experts in the field • Offers information on hybrid differential evolution for optimal multilevel image thresholding • Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, “Intelligent Multi-Modal Data Processing” brings together in one volume the range of multi-modal data processing.

This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.

Applied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare. Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies. Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. • Provides an overview of different deep learning applications for intelligent healthcare informatics management • Describes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management • Proposes IoT solutions that disseminate essential medical information for intelligent healthcare management • Discusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques • Examines the use of exploratory data analysis in intelligent healthcare informatics systems “Applied Smart Health Care Informatics: A Computational Intelligence Perspective” is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.