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Applied Smart Health Care InformaticsExplores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcareApplied 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 systemsApplied 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.

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniquesRecent 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 applicationsWritten 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.