Metaheuristics For Big Data
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Metaheuristics For Big Data
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Author : Clarisse Dhaenens
language : en
Publisher: John Wiley & Sons
Release Date : 2016-08-29
Metaheuristics For Big Data written by Clarisse Dhaenens 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 2016-08-29 with Computers categories.
Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
Advanced Metaheuristic Methods In Big Data Retrieval And Analytics
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Author : Bouarara, Hadj Ahmed
language : en
Publisher: IGI Global
Release Date : 2018-11-02
Advanced Metaheuristic Methods In Big Data Retrieval And Analytics written by Bouarara, Hadj Ahmed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-02 with Computers categories.
The amount of data shared and stored on the web and other document repositories is steadily on the rise. Unfortunately, this growth increases inefficiencies and difficulties when trying to find the most relevant and up-to-date information due to unstructured data. Advanced Metaheuristic Methods in Big Data Retrieval and Analytics examines metaheuristic techniques as an important alternative model for solving complex problems that are not treatable by deterministic methods. Recent studies suggest that IR and biomimicry can be used together for several application problems in big data and internet of things, especially when conventional methods would be too expensive or difficult to implement. Featuring coverage on a broad range of topics such as ontology, plagiarism detection, and machine learning, this book is ideally designed for engineers, graduate students, IT professionals, and academicians seeking an overview of new trends in information retrieval in big data.
Cognitive Big Data Intelligence With A Metaheuristic Approach
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Author : Sushruta Mishra
language : en
Publisher: Academic Press
Release Date : 2021-11-09
Cognitive Big Data Intelligence With A Metaheuristic Approach written by Sushruta Mishra and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-09 with Computers categories.
Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks. Provides a unique opportunity to present the work on the state-of-the-art of metaheuristics approach in the area of big data processing developing automated and intelligent models Explains different, feasible applications and case studies where cognitive computing can be successfully implemented in big data analytics using metaheuristics algorithms Provides a snapshot of the latest advances in the contribution of metaheuristics frameworks in cognitive big data applications to solve optimization problems
Metaheuristics For Data Clustering And Image Segmentation
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Author : Meera Ramadas
language : en
Publisher: Springer
Release Date : 2018-12-12
Metaheuristics For Data Clustering And Image Segmentation written by Meera Ramadas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Technology & Engineering categories.
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.
Metaheuristics For Machine Learning
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Author : Mansour Eddaly
language : en
Publisher: Springer Nature
Release Date : 2023-03-13
Metaheuristics For Machine Learning written by Mansour Eddaly and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-13 with Computers categories.
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.
Metaheuristics For Enterprise Data Intelligence
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Author : Kaustubh Vaman Sakhare
language : en
Publisher:
Release Date : 2024-08-07
Metaheuristics For Enterprise Data Intelligence written by Kaustubh Vaman Sakhare and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-07 with Computers categories.
With the emergence of the Data Economy, information has become integral to business excellence. Every enterprise, irrespective of its domain of interest, carries and processes a lot of data in their day-to-day activities. Converting massive datasets into insightful information plays an important role in developing better business solutions. Data intelligence and its analysis pose several challenges in data representation, building knowledge systems, issue resolution and predictive systems for trend analysis and decision making. The data available could be of any modality, especially when data is associated with healthcare, biomedical, finance, retail, cyber security, networking, supply chain management, manufacturing and so on. Optimization of such systems is therefore crucial to leveraging the best outcomes and conclusions. To this end, AI-based nature inspired optimization methods or approximation-based optimization methods are becoming very powerful. Notable metaheuristics include Genetic Algorithms, Differential Evolution, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Grey Wolf Optimizer, Political Optimizer, Cohort Intelligence, League Championship Algorithm, and many more. This book provides a systematic discussion of AI-based Metaheuristics application in a wide range of areas including Big Data Intelligence, Predictive Analytics, Enterprise Analytics, Graph Optimization Algorithms, Machine Learning and Ensemble Learning, Computer Vision Enterprise Practices, Data Benchmarking and more.
Metaheuristics For Big Data
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Author : Clarisse Dhaenens
language : en
Publisher: John Wiley & Sons
Release Date : 2016-08-16
Metaheuristics For Big Data written by Clarisse Dhaenens 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 2016-08-16 with Computers categories.
Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
Metaheuristic Algorithms In Industry 4 0
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Author : Pritesh Shah
language : en
Publisher: CRC Press
Release Date : 2021-09-29
Metaheuristic Algorithms In Industry 4 0 written by Pritesh Shah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-29 with Computers categories.
Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces. This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more. Key features: Includes industrial case studies. Includes chapters on cyber physical systems, machine learning, deep learning, cybersecurity, robotics, smart manufacturing and predictive analytics. surveys current trends and challenges in metaheuristics and industry 4.0. Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.
Advances On Computational Intelligence In Energy
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Author : Tutut Herawan
language : en
Publisher: Springer
Release Date : 2019-07-12
Advances On Computational Intelligence In Energy written by Tutut Herawan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-12 with Technology & Engineering categories.
Addressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.
Metaheuristics For Machine Learning
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Author : Kanak Kalita
language : en
Publisher: John Wiley & Sons
Release Date : 2024-03-28
Metaheuristics For Machine Learning written by Kanak Kalita 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 2024-03-28 with Computers categories.
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.