Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Logistics Management and Optimization through Hybrid Artificial Intelligence Systems
Logistics Management and Optimization through Hybrid Artificial Intelligence Systems offers the latest research within the field of HAIS, surveying the broad topics and collecting case studies, future directions, and cutting edge analyses. Using biologically inspired algorithms such as ant colony optimization and particle swarm optimization, this text includes solutions and heuristics for practitioners and academics alike, offering a vital resource for staying abreast in this ever-burgeoning field.
R 7,613.87
Innovations in Optimization and Machine Learning
In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed decisions based on data, and capitalize on AI technologies. However, many organizations require assistance in understanding these disciplines' theoretical principles, practical implementations, and ethical implications. Innovations in Optimization and Machine Learning serve as a comprehensive solution, offering a deep dive into optimization, machine learning, and AI. By unraveling the complexities and providing practical insights, it empowers researchers, practitioners, students, and enthusiasts to understand and contribute to advancing these fields. The book covers many topics, from evolutionary algorithms to ethical AI development, ensuring a thorough understanding of key concepts and their real-world implications. By bridging the gap between theory and practice, this book equips readers with the knowledge and tools to address optimization, machine learning, and AI challenges. Whether you're looking to enhance operational efficiency, develop innovative solutions, or drive meaningful change, this book is your guide to unlocking the transformative potential of optimization, machine learning, and AI in today's dynamic landscape.
R 14,092.77
Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems
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.
R 4,969.11
Derivative-Free Optimization
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book's structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.
R 6,996.69
Optimization and Machine Learning
Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.
R 5,795.00
Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing.Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
R 10,437.19
Machine Learning and Intelligent Communication
This book constitutes the refereed post-conference proceedings of the 7th International Conference on Machine Learning and Intelligent Computing which was held in October 2022 in Jinhua, China. Due to COVID-19 pandemic the conference was held virtually. The 16 full papers of MLICOM 2022 were selected from 41 submissions and are clustered in thematical issues on applications of neural network and deep learning; intelligent massive MIMO communications; machine learning algorithms and intelligent networks.
R 2,729.18
Innovations in Optimization and Machine Learning
In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed decisions based on data, and capitalize on AI technologies. However, many organizations require assistance in understanding these disciplines' theoretical principles, practical implementations, and ethical implications. Innovations in Optimization and Machine Learning serve as a comprehensive solution, offering a deep dive into optimization, machine learning, and AI. By unraveling the complexities and providing practical insights, it empowers researchers, practitioners, students, and enthusiasts to understand and contribute to advancing these fields. The book covers many topics, from evolutionary algorithms to ethical AI development, ensuring a thorough understanding of key concepts and their real-world implications. By bridging the gap between theory and practice, this book equips readers with the knowledge and tools to address optimization, machine learning, and AI challenges. Whether you're looking to enhance operational efficiency, develop innovative solutions, or drive meaningful change, this book is your guide to unlocking the transformative potential of optimization, machine learning, and AI in today's dynamic landscape.
R 10,645.92
Handbook Of Machine Learning - Volume 2: Optimization And Decision Making
Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.
R 4,689.00
Evolutionary Approach to Machine Learning and Deep Neural Networks
This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
R 6,724.55
Brain-computer Interface And Intelligent Transportation
This book provides a systematic exploration of Brain-Computer Interface (BCI) technology's transformative role in next-generation intelligent transportation systems, emphasizing practical development and innovation within China.It comprehensively covers BCI fundamentals, its integration with neuroscience and autonomous driving, and its immediate applications in key transport challenges. Topics range from BCI signal acquisition to layered applications in driver state monitoring (fatigue, emotion recognition), optimized traffic control, and advanced vehicle operations. The content includes novel sections on BCI integration with wearable devices, remote driving, and urban planning, featuring practical Chinese case studies and ethical considerations for the field.This is a critical resource for academic researchers and graduate students specializing in intelligent transportation, transportation engineering, computer science, or neuroscience. The book is also useful for engineers and policymakers seeking to utilize BCI or understand how it can enhance safety, optimize flow, and realize green transportation networks.
R 4,323.00
Artificial Intelligence For Science: A Deep Learning Revolution
This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
R 7,175.57
Machine Intelligence 13
The founder of modern computational logic, J A Robinson, opens this volume with a chapter on the firld's great forefathers John van Neumann and Alan Turing. Stephen Muggleton follows with an analysis of Turing's legacy in logic and machine learning, conceiving these not in generality but as specific means of imparting knowledge into computers, a theme first articulated by Turing in the late 1940s.The present volume records the Machine Intelligence Workshop of 1992, held at Strathclyde University's Ross Priory retreat on Loch Lomond, Scotland. Here the series entered not only its second quarter-century but a new phase. As can be seen in these pages, machine learning emerged to declare itself as a seed-bed of new theory, as a practical tool in engineering disciplines, and as material for new mental models in human sciences.
R 10,266.37
Handbook Of Machine Learning - Volume 1: Foundation Of Artificial Intelligence
This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.
R 4,771.53
