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In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.
Book Synopsis Metaheuristics for Data Clustering and Image Segmentation by : Meera Ramadas
Download or read book Metaheuristics for Data Clustering and Image Segmentation written by Meera Ramadas and published by Springer. This book was released on 2018-12-12 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.
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.
Book Synopsis Recent Advances in Hybrid Metaheuristics for Data Clustering by : Sourav De
Download or read book Recent Advances in Hybrid Metaheuristics for Data Clustering written by Sourav De and published by John Wiley & Sons. This book was released on 2020-06-02 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Metaheuristic Algorithms for Image Segmentation: Theory and Applications by : Diego Oliva
Download or read book Metaheuristic Algorithms for Image Segmentation: Theory and Applications written by Diego Oliva and published by Springer. This book was released on 2019-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Applications of Hybrid Metaheuristic Algorithms for Image Processing by : Diego Oliva
Download or read book Applications of Hybrid Metaheuristic Algorithms for Image Processing written by Diego Oliva and published by Springer Nature. This book was released on 2020-03-27 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 provides a broad overview of genetic algorithms, clustering algorithms influenced by genetic algorithms, improvements attained in the field of image segmentation and their application by using genetic algorithms. It also explores the comparative analysis of earlier methods and the recent ones proposed with the use of genetic algorithms"--
Book Synopsis Data Clustering and Image Segmentation Through Genetic Algorithms by : Sujata Dash
Download or read book Data Clustering and Image Segmentation Through Genetic Algorithms written by Sujata Dash and published by Engineering Science Reference. This book was released on 2018-08-03 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a broad overview of genetic algorithms, clustering algorithms influenced by genetic algorithms, improvements attained in the field of image segmentation and their application by using genetic algorithms. It also explores the comparative analysis of earlier methods and the recent ones proposed with the use of genetic algorithms"--
Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.
Book Synopsis Metaheuristic Clustering by : Swagatam Das
Download or read book Metaheuristic Clustering written by Swagatam Das and published by Springer. This book was released on 2009-01-30 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.
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.
Book Synopsis Metaheuristics in Machine Learning: Theory and Applications by : Diego Oliva
Download or read book Metaheuristics in Machine Learning: Theory and Applications written by Diego Oliva and published by Springer Nature. This book was released on with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Recent Advances in Hybrid Metaheuristics for Data Clustering by : Sourav De
Download or read book Recent Advances in Hybrid Metaheuristics for Data Clustering written by Sourav De and published by John Wiley & Sons. This book was released on 2020-08-24 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Metaheuristic algorithms are present in various applications for different domains. Recently, researchers have conducted studies on the effectiveness of these algorithms in providing optimal solutions to complicated problems. Advancements in Applied Metaheuristic Computing is a crucial reference source for the latest empirical research on methods and approaches that include metaheuristics for further system improvements, and it offers outcomes of employing optimization algorithms. Featuring coverage on a broad range of topics such as manufacturing, genetic programming, and medical imaging, this publication is ideal for researchers, academicians, advanced-level students, and technology developers seeking current research on the use of optimization algorithms in several applications.
Book Synopsis Advancements in Applied Metaheuristic Computing by : Dey, Nilanjan
Download or read book Advancements in Applied Metaheuristic Computing written by Dey, Nilanjan and published by IGI Global. This book was released on 2017-11-30 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristic algorithms are present in various applications for different domains. Recently, researchers have conducted studies on the effectiveness of these algorithms in providing optimal solutions to complicated problems. Advancements in Applied Metaheuristic Computing is a crucial reference source for the latest empirical research on methods and approaches that include metaheuristics for further system improvements, and it offers outcomes of employing optimization algorithms. Featuring coverage on a broad range of topics such as manufacturing, genetic programming, and medical imaging, this publication is ideal for researchers, academicians, advanced-level students, and technology developers seeking current research on the use of optimization algorithms in several applications.
This book focuses mainly on the usages of three key technologies: IoT, big data, and AI for various day to day applications. Further, it explores the possibilities of future research based on the usages of latest information systems. This book explores the current research and challenges to be faced by different researchers for building intelligent information solutions using key technologies; IoT, big data, and AI in improving quality of lives in smart cities and explores the limitations and capabilities of these three key computing technologies. The book is organized into three major parts; each part includes chapters exploring a specific topic, and there are: PART-1: IoT for Real World Solutions , (ii) Part-2: Big Data And Cloud Computing for Innovative Solutions For Day to Day Lives, and (iii) Part-3 Artificial Intelligence for Everyday Lives. This book may be useful to the scientists, scholars, and researchers who are working in the field of computer science and engineering, and communication engineering, along with the students in these subjects who are working or willing to work on IoT, big data, and AI technologies for improving quality of everyday life. Specialists as well as student readers find the book chapters encouraging and helpful. IoT, data science & cloud, and AI all are the undergraduate (UG/ bachelor) subjects. Use of these three key technologies for building new applications for better world is helpful for UG and postgraduate (PG/ MS) Programmes students (as an elective and core course). This book may also be very useful for the Ph.D. (research scholars) during their course work and may be used as an instrument to identify the different challenges associated with information systems.
Book Synopsis IoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges by : Pradeep Kumar Singh
Download or read book IoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges written by Pradeep Kumar Singh and published by Springer Nature. This book was released on 2023-08-23 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses mainly on the usages of three key technologies: IoT, big data, and AI for various day to day applications. Further, it explores the possibilities of future research based on the usages of latest information systems. This book explores the current research and challenges to be faced by different researchers for building intelligent information solutions using key technologies; IoT, big data, and AI in improving quality of lives in smart cities and explores the limitations and capabilities of these three key computing technologies. The book is organized into three major parts; each part includes chapters exploring a specific topic, and there are: PART-1: IoT for Real World Solutions , (ii) Part-2: Big Data And Cloud Computing for Innovative Solutions For Day to Day Lives, and (iii) Part-3 Artificial Intelligence for Everyday Lives. This book may be useful to the scientists, scholars, and researchers who are working in the field of computer science and engineering, and communication engineering, along with the students in these subjects who are working or willing to work on IoT, big data, and AI technologies for improving quality of everyday life. Specialists as well as student readers find the book chapters encouraging and helpful. IoT, data science & cloud, and AI all are the undergraduate (UG/ bachelor) subjects. Use of these three key technologies for building new applications for better world is helpful for UG and postgraduate (PG/ MS) Programmes students (as an elective and core course). This book may also be very useful for the Ph.D. (research scholars) during their course work and may be used as an instrument to identify the different challenges associated with information systems.
