Convergence Of Deep Learning In Cyber Iot Systems And Security

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Convergence of Deep Learning in Cyber-IoT Systems and Security

Author : Rajdeep Chakraborty
Publisher : John Wiley & Sons
ISBN 13 : 111985721X
Total Pages : 485 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Convergence of Deep Learning in Cyber-IoT Systems and Security by : Rajdeep Chakraborty

Download or read book Convergence of Deep Learning in Cyber-IoT Systems and Security written by Rajdeep Chakraborty and published by John Wiley & Sons. This book was released on 2022-12-08 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. Audience Researchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.

Convergence of Deep Learning in Cyber-IoT Systems and Security

Author : Rajdeep Chakraborty
Publisher : John Wiley & Sons
ISBN 13 : 111985766X
Total Pages : 485 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Convergence of Deep Learning in Cyber-IoT Systems and Security by : Rajdeep Chakraborty

Download or read book Convergence of Deep Learning in Cyber-IoT Systems and Security written by Rajdeep Chakraborty and published by John Wiley & Sons. This book was released on 2022-11-08 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. Audience Researchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.

Convergence of Deep Learning and Artificial Intelligence in Internet of Things

Author : Ajay Rana
Publisher :
ISBN 13 : 9781032410425
Total Pages : 0 pages
Book Rating : 4.4/5 (14 download)

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Book Synopsis Convergence of Deep Learning and Artificial Intelligence in Internet of Things by : Ajay Rana

Download or read book Convergence of Deep Learning and Artificial Intelligence in Internet of Things written by Ajay Rana and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the internet of things. It further discusses applications of smart technologies in diverse sectors such as production, manufacturing, transport, and healthcare"--

Deep Learning for Security and Privacy Preservation in IoT

Author : Aaisha Makkar
Publisher : Springer Nature
ISBN 13 : 9811661863
Total Pages : 186 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Deep Learning for Security and Privacy Preservation in IoT by : Aaisha Makkar

Download or read book Deep Learning for Security and Privacy Preservation in IoT written by Aaisha Makkar and published by Springer Nature. This book was released on 2022-04-03 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.

Deep Learning Approaches for Security Threats in IoT Environments

Author : Mohamed Abdel-Basset
Publisher : John Wiley & Sons
ISBN 13 : 1119884160
Total Pages : 388 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Deep Learning Approaches for Security Threats in IoT Environments by : Mohamed Abdel-Basset

Download or read book Deep Learning Approaches for Security Threats in IoT Environments written by Mohamed Abdel-Basset and published by John Wiley & Sons. This book was released on 2022-11-22 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.

Deep Learning Techniques for IoT Security and Privacy

Author : Mohamed Abdel-Basset
Publisher : Springer Nature
ISBN 13 : 3030890252
Total Pages : 273 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Deep Learning Techniques for IoT Security and Privacy by : Mohamed Abdel-Basset

Download or read book Deep Learning Techniques for IoT Security and Privacy written by Mohamed Abdel-Basset and published by Springer Nature. This book was released on 2021-12-05 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.

Convergence of Blockchain, AI, and IoT

Author : R. Indrakumari
Publisher : CRC Press
ISBN 13 : 1000519406
Total Pages : 216 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Convergence of Blockchain, AI, and IoT by : R. Indrakumari

Download or read book Convergence of Blockchain, AI, and IoT written by R. Indrakumari and published by CRC Press. This book was released on 2021-12-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convergence of Blockchain, AI, and IoT: Concepts and Challenges discusses the convergence of three powerful technologies that play into the digital revolution and blur the lines between biological, digital, and physical objects. This book covers novel algorithms, solutions for addressing issues in applications, security, authentication, and privacy. The book provides an overview of the clinical scientific research enabling smart diagnosis equipment through AI. It presents the role these technologies play in augmented reality and blockchain, covers digital currency managed with bitcoin, and discusses deep learning and how it can enhance human thoughts and behaviors. Targeted audiences range from those interested in the technical revolution of blockchain, big data and the Internet of Things, to research scholars and the professional market.

Explainable Machine Learning Models and Architectures

Author : Suman Lata Tripathi
Publisher : John Wiley & Sons
ISBN 13 : 1394185847
Total Pages : 277 pages
Book Rating : 4.3/5 (941 download)

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Book Synopsis Explainable Machine Learning Models and Architectures by : Suman Lata Tripathi

Download or read book Explainable Machine Learning Models and Architectures written by Suman Lata Tripathi and published by John Wiley & Sons. This book was released on 2023-10-03 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Machine Learning Techniques for VLSI Chip Design

Author : Abhishek Kumar
Publisher : John Wiley & Sons
ISBN 13 : 1119910390
Total Pages : 244 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Machine Learning Techniques for VLSI Chip Design by : Abhishek Kumar

Download or read book Machine Learning Techniques for VLSI Chip Design written by Abhishek Kumar and published by John Wiley & Sons. This book was released on 2023-08-01 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and implementation of the computational architecture for ML-DL. The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.

Mathematics and Computer Science, Volume 2

Author : Sharmistha Ghosh
Publisher : John Wiley & Sons
ISBN 13 : 111989669X
Total Pages : 436 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Mathematics and Computer Science, Volume 2 by : Sharmistha Ghosh

Download or read book Mathematics and Computer Science, Volume 2 written by Sharmistha Ghosh and published by John Wiley & Sons. This book was released on 2023-07-13 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATHEMATICS AND COMPUTER SCIENCE This second volume in a new multi-volume set builds on the basic concepts and fundamentals laid out in the previous volume, presenting the reader with more advanced and cutting-edge topics being developed in this exciting field. This second volume in a new series from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. Building on what was presented in volume one, the chapters focus on more advanced topics in computer science, mathematics, and where the two intersect to create value for end users through practical applications. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.

Mathematics and Computer Science, Volume 1

Author : Sharmistha Ghosh
Publisher : John Wiley & Sons
ISBN 13 : 1119879817
Total Pages : 564 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Mathematics and Computer Science, Volume 1 by : Sharmistha Ghosh

Download or read book Mathematics and Computer Science, Volume 1 written by Sharmistha Ghosh and published by John Wiley & Sons. This book was released on 2023-07-19 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATHEMATICS AND COMPUTER SCIENCE This first volume in a new multi-volume set gives readers the basic concepts and applications for diverse ideas and innovations in the field of computing together with its growing interactions with mathematics. This new edited volume from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. The chapters focus on recent advances in computer science, and mathematics, and where the two intersect to create value for end users through practical applications of the theory. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.

Artificial Intelligence

Author : Arif Ahmed Sk
Publisher : Springer Nature
ISBN 13 : 303122485X
Total Pages : 347 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Artificial Intelligence by : Arif Ahmed Sk

Download or read book Artificial Intelligence written by Arif Ahmed Sk and published by Springer Nature. This book was released on 2023-01-01 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Symposium on Artificial Intelligence, ISAI 2022, held in Haldia, India, during February 17-22, 2022. The 30 full papers included in this book were carefully reviewed and selected from 75 submissions. They were organized in topical sections as follows: information systems, mathematics and data analyses; and applied artificial intelligence. .

Data Engineering and Data Science

Author : Kukatlapalli Pradeep Kumar
Publisher : John Wiley & Sons
ISBN 13 : 1119841879
Total Pages : 469 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Data Engineering and Data Science by : Kukatlapalli Pradeep Kumar

Download or read book Data Engineering and Data Science written by Kukatlapalli Pradeep Kumar and published by John Wiley & Sons. This book was released on 2023-10-03 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Supervised and Unsupervised Data Engineering for Multimedia Data

Author : Suman Kumar Swarnkar
Publisher : John Wiley & Sons
ISBN 13 : 1119786428
Total Pages : 251 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Supervised and Unsupervised Data Engineering for Multimedia Data by : Suman Kumar Swarnkar

Download or read book Supervised and Unsupervised Data Engineering for Multimedia Data written by Suman Kumar Swarnkar and published by John Wiley & Sons. This book was released on 2024-04-02 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: SUPERVISED and UNSUPERVISED DATA ENGINEERING for MULTIMEDIA DATA Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency. Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go-to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting-edge methodologies. The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. Readers will delve into a spectrum of topics, including artificial intelligence, machine learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you’re a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age.

Convergence of Deep Learning and Internet of Things: Computing and Technology

Author : Kavitha, T.
Publisher : IGI Global
ISBN 13 : 166846277X
Total Pages : 371 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis Convergence of Deep Learning and Internet of Things: Computing and Technology by : Kavitha, T.

Download or read book Convergence of Deep Learning and Internet of Things: Computing and Technology written by Kavitha, T. and published by IGI Global. This book was released on 2022-12-19 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

Cyber Security Meets Machine Learning

Author : Xiaofeng Chen
Publisher : Springer Nature
ISBN 13 : 9813367261
Total Pages : 168 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Cyber Security Meets Machine Learning by : Xiaofeng Chen

Download or read book Cyber Security Meets Machine Learning written by Xiaofeng Chen and published by Springer Nature. This book was released on 2021-07-02 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Metaheuristics for Machine Learning

Author : Kanak Kalita
Publisher : John Wiley & Sons
ISBN 13 : 1394233930
Total Pages : 272 pages
Book Rating : 4.3/5 (942 download)

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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.