This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.

This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing Metaheuristics techniques with Machine Learning for solving biomedical problems. The book is organized to present a stepwise progression beginning with the basics of Metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of the book presents the fundamental concepts of Metaheuristics and Machine Learning, and also provides a comprehensive taxonomic view of Metaheuristics methods according to a variety of criteria such as data type, scope, method, and so forth. The second section of the book explains how to apply Metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in Metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in Metaheuristics for biomedical science. The book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice. - Introduces a new set of Metaheuristics techniques for biomedical applications - Presents basic concepts of Metaheuristics, methods and practices, followed by advanced topics and applications - Provides researchers, practitioners, and project stakeholders with a complete guide for understanding and applying metaheuristics and machine learning techniques in their projects and solutions

The use of metaheuristic algorithms (MA) has been increasing in recent years, and the image processing field is not the exempted of their application. In the last two years a big amount of MA has been introduced as alternatives for solving complex optimization problems. This book collects the most prominent MA of the 2019 and 2020 and verifies its use in image processing tasks. In addition, literature review of both MA and digital image processing is presented as part of the introductory information. Each algorithm is detailed explained with special focus in the tuning parameters and the proper implementation for the image processing tasks. Besides several examples permits to the reader explore and confirm the use of this kind of intelligent methods. Since image processing is widely used in different domains, this book considers different kinds of datasets that includes, magnetic resonance images, thermal images, agriculture images, among others. The reader then can have some ideas of implementation that complement the theory exposed of each optimization mechanism. Regarding the image processing problems this book consider the segmentation by using different metrics based on entropies or variances. In the same way, the identification of different shapes and the detection of objects are also covered in the corresponding chapters. Each chapter is complemented with a wide range of experiments and statistical analysis that permits the reader to judge about the performance of the MA. Finally, there is included a section that includes some discussion and conclusions. This section also provides some open questions and research opportunities for the audience.

This book aims to examine innovation in the fields of computer engineering and networking. The text covers important developments in areas such as artificial intelligence, machine learning, information analysis, communication system, computer modeling, internet of things. This book presents papers from the 13th International Conference on Computer Engineering and Networks (CENet2023) held in Wuxi, China on November 3-5, 2023.

This book highlights recent research on metaheuristics for biomedical engineering, addressing both theoretical and applications aspects. Given the multidisciplinary nature of bio-medical image analysis, it has now become one of the most central topics in computer science, computer engineering and electrical and electronic engineering, and attracted the interest of many researchers. To deal with these problems, many traditional and recent methods, algorithms and techniques have been proposed. Among them, metaheuristics is the most common choice. This book provides essential content for senior and young researchers interested in methodologies for implementing metaheuristics to help solve biomedical engineering problems.

This book introduces the theory and applications of metaheuristic algorithms. It also provides methods for solving practical problems in such fields as software engineering, image recognition, video networks, and in the oceans. In the theoretical section, the book introduces the information feedback model, learning-based intelligent optimization, dynamic multi-objective optimization, and multi-model optimization. In the applications section, the book presents applications of optimization algorithms to neural architecture search, fuzz testing, oceans, and image processing. The neural architecture search chapter introduces the latest NAS method. The fuzz testing chapter uses multi-objective optimization and ant colony optimization to solve the seed selection and energy allocation problems in fuzz testing. In the ocean chapter, deep learning methods such as CNN, transformer, and attention-based methods are used to describe ENSO prediction and image processing for marine fish identification, and to provide an overview of traditional classification methods and deep learning methods. Rich in examples, this book will be a great resource for students, scholars, and those interested in metaheuristic algorithms, as well as professional practitioners and researchers working on related topics.

This book consists of a collection of the high-quality research articles in the field of computer vision and robotics which are presented in the International Conference on Computer Vision and Robotics (CVR 2023), organized by BBD University Lucknow, India, during 24–25 February 2023. The book discusses applications of computer vision and robotics in the fields like medical science, defence, and smart city planning. The book presents recent works from researchers, academicians, industry, and policy makers.

The reference text introduces the principles of quantum mechanics to evolve hybrid metaheuristics-based optimization techniques useful for real world engineering and scientific problems. The text covers advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to hybrid quantum metaheuristics and their applications to engineering problems. The book will be accompanied by additional resources including video demonstration for each chapter. It will be a useful text for graduate students and professional in the field of electrical engineering, electronics and communications engineering, and computer science engineering, this text: Discusses quantum mechanical principles in detail. Emphasizes the recent and upcoming hybrid quantum metaheuristics in a comprehensive manner. Provides comparative statistical test analysis with conventional hybrid metaheuristics. Highlights real-life case studies, applications, and video demonstrations.