Hybrid Metaheuristics For Image Analysis

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Hybrid Metaheuristics For Image Analysis

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Author : Siddhartha Bhattacharyya
language : en
Publisher: Springer
Release Date : 2018-07-30

Hybrid Metaheuristics For Image Analysis written by Siddhartha Bhattacharyya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-30 with Computers categories.

This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology.

Applications Of Hybrid Metaheuristic Algorithms For Image Processing

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Author : Diego Oliva
language : en
Publisher: Springer Nature
Release Date : 2020-03-27

Applications Of Hybrid Metaheuristic Algorithms For Image Processing written by Diego Oliva and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with Technology & Engineering categories.

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.

Quantum Inspired Meta Heuristics For Image Analysis

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Author : Sandip Dey
language : en
Publisher: John Wiley & Sons
Release Date : 2019-08-05

Quantum Inspired Meta Heuristics For Image Analysis written by Sandip Dey and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-05 with Technology & Engineering categories.

Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis. Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions. Provides in-depth analysis of quantum mechanical principles Offers comprehensive review of image analysis Analyzes different state-of-the-art image thresholding approaches Detailed current, popular standard meta-heuristics in use today Guides readers step by step in the build-up of quantum inspired meta-heuristics Includes a plethora of real life case studies and applications Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-à-vis their conventional counterparts Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.

Metaheuristic Algorithms And Optimizing Neural Networks For Biomedical Image Processing

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Author : Balaji, Prasanalakshmi
language : en
Publisher: IGI Global
Release Date : 2025-08-06

Metaheuristic Algorithms And Optimizing Neural Networks For Biomedical Image Processing written by Balaji, Prasanalakshmi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-06 with Medical categories.

Metaheuristic algorithms emerge as powerful tools for optimizing complex systems, particularly in neural networks, where traditional methods may cause challenges. In biomedical image processing, the integration of metaheuristics like genetic algorithms, particle swarm optimization, and differential evolution offers promising improvements in neural network performance. These algorithms help improve hyperparameters and optimize architectures, enhancing the accuracy of tasks like disease detection, image segmentation, and classification. Further research into this convergence between metaheuristic optimization and deep learning may help advance medical diagnostics and healthcare technologies. Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing explores the optimization of neural networks for biomedical image analysis. It provides valuable insights into advanced image processing for improved healthcare, advanced technology, and potential scientific and computational breakthroughs. This book covers topics such as medical imaging, genetics, and psychology, and is a useful resource for business owners, computer engineers, medical professionals, academicians, researchers, and data scientists.

Hybrid Metaheuristics Research And Applications

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Author : Siddhartha Bhattacharyya
language : en
Publisher: World Scientific
Release Date : 2018-09-28

Hybrid Metaheuristics Research And Applications written by Siddhartha Bhattacharyya and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-28 with Computers categories.

A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.Related Link(s)

Hybrid Metaheuristics

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Author : Maria José Blesa
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-27

Hybrid Metaheuristics written by Maria José Blesa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-27 with Computers categories.

This book constitutes the refereed proceedings of the 7th International Workshop on Hybrid Metaheuristics, HM 2010, held in Vienna, Austria, in October 2010. The 14 revised full papers presented were carefully reviewed and selected from 29 submissions.

Recent Advances In Hybrid Metaheuristics For Data Clustering

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Author : Sourav De
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-02

Recent Advances In Hybrid Metaheuristics For Data Clustering written by Sourav De and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-02 with Computers categories.

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.

Metaheuristics Algorithms For Medical Applications

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Author : Mohamed Abdel-Basset
language : en
Publisher: Elsevier
Release Date : 2023-11-25

Metaheuristics Algorithms For Medical Applications written by Mohamed Abdel-Basset and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-25 with Computers categories.

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

Computational Science And Technology

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Author : Rayner Alfred
language : en
Publisher: Springer Nature
Release Date : 2021-03-15

Computational Science And Technology written by Rayner Alfred and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-15 with Computers categories.

This book gathers the proceedings of the Seventh International Conference on Computational Science and Technology 2020 (ICCST 2020), held in Pattaya, Thailand, on 29–30 August 2020. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods.

Metaheuristics In Machine Learning Theory And Applications

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Author : Diego Oliva
language : en
Publisher: Springer Nature
Release Date : 2021-07-13

Metaheuristics In Machine Learning Theory And Applications written by Diego Oliva and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Computers categories.

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.