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Metaheuristics In Machine Learning Theory And Applications
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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.
Book Synopsis Metaheuristics for Machine Learning by : Kanak Kalita
Download or read book Metaheuristics for Machine Learning written by Kanak Kalita and published by John Wiley & Sons. This book was released on 2024-03-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.
Book Synopsis Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems by : Essam Halim Houssein
Download or read book Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems written by Essam Halim Houssein and published by Springer Nature. This book was released on 2022-06-04 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. 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 can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.
Book Synopsis Metaheuristics for Machine Learning by : Mansour Eddaly
Download or read book Metaheuristics for Machine Learning written by Mansour Eddaly and published by Springer Nature. This book was released on 2023-03-13 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
Book Synopsis Machine Learning and Metaheuristics Algorithms, and Applications by : Sabu M. Thampi
Download or read book Machine Learning and Metaheuristics Algorithms, and Applications written by Sabu M. Thampi and published by Springer Nature. This book was released on 2020-04-04 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, held in Trivandrum, India, in December 2019. The 17 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 53 qualified submissions. The papers cover such topics as machine learning, artificial intelligence, Internet of Things, modeling and simulation, disctibuted computing methodologies, computer graphics, etc.
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.
Book Synopsis Machine Learning and Metaheuristics: Methods and Analysis by : Uma N. Dulhare
Download or read book Machine Learning and Metaheuristics: Methods and Analysis written by Uma N. Dulhare and published by Springer Nature. This book was released on 2023-12-03 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a balanced approach between theoretical understanding and real-time applications. All the topics included real-world problems which show how to explore, build, evaluate, and optimize machine learning models fusion with metaheuristic algorithms. Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.
Book Synopsis Meta-Heuristics by : Ibrahim H. Osman
Download or read book Meta-Heuristics written by Ibrahim H. Osman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.
Book Synopsis Hybrid Metaheuristics: Research And Applications by : Bhattacharyya Siddhartha
Download or read book Hybrid Metaheuristics: Research And Applications written by Bhattacharyya Siddhartha and published by World Scientific. This book was released on 2018-09-27 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and Metaheuristics: Methods and Analysis by : Uma N. Dulhare
Download or read book Machine Learning and Metaheuristics: Methods and Analysis written by Uma N. Dulhare and published by Springer. This book was released on 2023-12-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a balanced approach between theoretical understanding and real-time applications. All the topics included real-world problems which show how to explore, build, evaluate, and optimize machine learning models fusion with metaheuristic algorithms. Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.
Book Synopsis Tuning Metaheuristics by : Mauro Birattari
Download or read book Tuning Metaheuristics written by Mauro Birattari and published by Springer. This book was released on 2009-05-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning.
Book Synopsis Machine Learning Paradigms: Theory and Application by : Aboul Ella Hassanien
Download or read book Machine Learning Paradigms: Theory and Application written by Aboul Ella Hassanien and published by Springer. This book was released on 2018-12-08 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.
Book Synopsis Theory and Novel Applications of Machine Learning by : Er Meng Joo
Download or read book Theory and Novel Applications of Machine Learning written by Er Meng Joo and published by BoD – Books on Demand. This book was released on 2009-01-01 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even since computers were invented, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science. Machine Learning (ML) draws upon ideas from a diverse set of disciplines, including AI, Probability and Statistics, Computational Complexity, Information Theory, Psychology and Neurobiology, Control Theory and Philosophy. ML involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc. Real-world applications of ML are widespread such as Pattern Recognition, Data Mining, Gaming, Bio-science, Telecommunications, Control and Robotics applications. This books reports the latest developments and futuristic trends in ML.
Book Synopsis Optimization in Machine Learning and Applications by : Anand J. Kulkarni
Download or read book Optimization in Machine Learning and Applications written by Anand J. Kulkarni and published by Springer Nature. This book was released on 2019-11-29 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Book Synopsis Application of Advanced Optimization Techniques for Healthcare Analytics by : Mohamed Abdel-Basset
Download or read book Application of Advanced Optimization Techniques for Healthcare Analytics written by Mohamed Abdel-Basset and published by CRC Press. This book was released on 2023-04-11 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing. Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it. The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.
Book Synopsis 5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing by : Anandakumar Haldorai
Download or read book 5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing written by Anandakumar Haldorai and published by Springer Nature. This book was released on 2023-06-05 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features the proceedings of the 5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2022). The papers feature detail on cognitive computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. This international conference focuses on technologies from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches. Topics covered include Data Science for Cognitive Analysis, Real-Time Ubiquitous Data Science, Platform for Privacy Preserving Data Science, and Internet-Based Cognitive Platform.
Book Synopsis Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022) by : Nebojsa Bacanin
Download or read book Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022) written by Nebojsa Bacanin and published by Springer Nature. This book was released on 2023-02-10 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book.The First International Conference on Innovation in information technology and business (ICIITB) will be taking place in Muscat, Oman, on November 9th and 10th, 2022. The Conference will be carried out in a hybrid format, allowing world-scattered academicians, researchers, and industry professionals to participate in this unique Conference for Oman and the GCC region. The participants of the Conference will get an opportunity to contribute to the contemporary implementation of cutting-edge research and development in the area of artificial intelligence, data science, machine learning, and the IoT in the business environment. The participants will get a first-of-a-kind networking and knowledge sharing opportunity to be a part of an event in Oman, that will gather recognized researchers from the GCC, Europe, the USA, and other parts of the World. Select research papers will also be published in a Springer-published Conference proceedings.
