Machine Learning Predictive Analytics And Optimization In Complex Systems
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Machine Learning Predictive Analytics And Optimization In Complex Systems
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Author : John Joseph, Ferdin Joe
language : en
Publisher: IGI Global
Release Date : 2025-06-27
Machine Learning Predictive Analytics And Optimization In Complex Systems written by John Joseph, Ferdin Joe and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Computers categories.
The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists.
Optimization In Machine Learning And Applications
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Author : Anand J. Kulkarni
language : en
Publisher: Springer
Release Date : 2020-12-10
Optimization In Machine Learning And Applications written by Anand J. Kulkarni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-10 with Technology & Engineering categories.
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.
Reliability Analysis And Maintenance Optimization Of Complex Systems
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Author : Qian Qian Zhao
language : en
Publisher: Springer Nature
Release Date : 2025-01-16
Reliability Analysis And Maintenance Optimization Of Complex Systems written by Qian Qian Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-16 with Technology & Engineering categories.
This book is a comprehensive guide to methodologies for analyzing reliability and optimizing maintenance in complex systems, spanning from initial design to operational stages. The book comprises 20 chapters, each addressing different research topics in the reliability and maintenance of complex systems. These chapters are authored by esteemed professors and researchers in the field of reliability engineering, and they are organized as follows: System Reliability Modeling (8 chapters), Optimal Maintenance Models (4 chapters), System Performance and Availability Analysis (3 chapters), and Reliability Testing and Accelerated Life Tests (2 chapters). The remaining chapters focus on reliability testing and life data analysis. The book offers an in-depth exploration of various techniques, algorithms, and practical industry applications, making it an invaluable resource for researchers engaged in system reliability analysis and maintenance optimization, as well as for practical engineers and industrial managers. This book will be useful to students, researchers, and engineers in understanding the latest research issues and techniques in reliability and maintenance engineering.
Machine Learning And Systems Engineering
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Author : Sio-Iong Ao
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-10-05
Machine Learning And Systems Engineering written by Sio-Iong Ao 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 2010-10-05 with Technology & Engineering categories.
A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
Metaheuristics For Machine Learning
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Author : Kanak Kalita
language : en
Publisher: John Wiley & Sons
Release Date : 2024-05-07
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 2024-05-07 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.
Data Driven Science And Engineering
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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Proceedings Of The 5th International Conference On Big Data Analytics For Cyber Physical System In Smart City Volume 1
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Author : Mohammed Atiquzzaman
language : en
Publisher: Springer Nature
Release Date : 2025-02-01
Proceedings Of The 5th International Conference On Big Data Analytics For Cyber Physical System In Smart City Volume 1 written by Mohammed Atiquzzaman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-01 with Computers categories.
This book gathers a selection of peer-reviewed papers presented at the 5th Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2023) conference, held in Fuyang, China, on December 28–29. The contributions, prepared by an international team of scientists and engineers, cover the latest advances and challenges made in the field of big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.
Optimization In Chemical Engineering
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Author : Fernando Israel Gómez-Castro
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2025-04-21
Optimization In Chemical Engineering written by Fernando Israel Gómez-Castro and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-21 with Technology & Engineering categories.
Optimization is an area in constant evolution. The search for robust optimization techniques to deal with the highly non-convex models that represent the systems related to Chemical Engineering has led to important advances in the area. The need for developing economically feasible processes which are simultaneously environmentally friendly, safe, and controllable requires for adequate optimization strategies. Moreover, finding a global optimum is still a challenge for a diversity of cases. Thus, this book presents a compilation of classic and emerging optimization techniques, focusing on their application to systems related to the Chemical Engineering. The book shows the applications of classic mathematical programming, metaheuristic optimization methods and machine learning-based strategies. The analysis of the described techniques allows the reader identifying the advantages and disadvantages of each approach. Moreover, the book will discuss the perspectives for future developments on the area.
Emerging Methods In Predictive Analytics Risk Management And Decision Making
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Author : Hsu, William H.
language : en
Publisher: IGI Global
Release Date : 2014-01-31
Emerging Methods In Predictive Analytics Risk Management And Decision Making written by Hsu, William H. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-31 with Business & Economics categories.
Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.
Exploring Probability And Random Processes Using Matlab
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Author : Roshan Trivedi
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Exploring Probability And Random Processes Using Matlab written by Roshan Trivedi and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Science categories.
"Exploring Probability and Random Processes Using MATLAB®" offers a comprehensive guide to probability theory, stochastic processes, and their practical applications, focusing on intuitive understanding and MATLAB implementation. This book provides readers with a solid foundation in probability and stochastic processes while equipping them with tools and techniques for real-world scenarios. We begin with an introduction to probability theory, covering random variables, probability distributions, and statistical measures. Readers learn how to analyze and interpret uncertainty, make probabilistic predictions, and understand statistical inference principles. Moving on to stochastic processes, we explore discrete-time and continuous-time processes, Markov chains, and other key concepts. Practical examples and MATLAB code snippets illustrate essential concepts and demonstrate their implementation in MATLAB. One distinguishing feature is the emphasis on intuitive understanding and practical application. Complex mathematical concepts are explained clearly and accessibly, making the material approachable for readers with varying mathematical backgrounds. MATLAB examples provide hands-on experience and develop proficiency in using MATLAB for probability and stochastic processes analysis. Whether you're a student building a foundation in probability theory and stochastic processes, a researcher seeking practical data analysis tools, or a practitioner in engineering or finance, this book will provide the knowledge and skills needed to succeed. With a blend of theoretical insights and practical applications, "Exploring Probability and Random Processes Using MATLAB®" is an invaluable resource.