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Books in Machine learning

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1-10 of 54 results in All results

Machine Learning Models and Architectures for Biomedical Signal Processing

  • 1st Edition
  • November 1, 2024
  • Suman Lata Tripathi + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 2 1 5 8 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 1 5 7 - 6
Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.

Artificial Intelligence of Things (AIoT)

  • 1st Edition
  • September 11, 2024
  • Fadi Al-Turjman + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 6 4 8 2 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 6 4 8 3 - 2
Artificial Intelligence of Things (AIoT): Current and Future Trends brings together researchers and developers from a wide range of domains to share ideas on how to implement technical advances, create application areas for intelligent systems, and how to develop new services and smart devices connected to the Internet. Section One covers AIoT in Everything, providing a wide range of applications for AIoT methods and technologies. Section Two gives readers comprehensive guidance on AIoT in Societal Research and Development, with practical case studies of how AIoT is impacting cultures around the world. Section Three covers the impact of AIoT in educational settings.The book also covers new capabilities such as pervasive sensing, multimedia sensing, machine learning, deep learning, and computing power. These new areas come with various requirements in terms of reliability, quality of service, and energy efficiency.

Decision-Making Models

  • 1st Edition
  • July 24, 2024
  • Tofigh Allahviranloo + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 6 1 4 7 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 6 1 4 8 - 3
Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

Distributed Optimization and Learning

  • 1st Edition
  • July 18, 2024
  • Zhongguo Li + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 6 3 6 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 6 3 7 - 4
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.

Performance Enhancement and Control of Photovoltaic Systems

  • 1st Edition
  • April 30, 2024
  • Saad Motahhir + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 3 9 2 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 3 9 3 - 0
Performance Enhancement and Control of Photovoltaic Systems brings together the latest advances in photovoltaic control and integration, with various embedded technologies applied to standalone and grid connected systems in normal and abnormal operating conditions, with new approaches intended to overcome a number of critical limitations in using PV technology. The book begins by introducing modern photovoltaic (PV) systems, system integration, materials, and thermodynamic analysis for improved performance, before examining applications in industrial processes, artificial neural network technology, and economic analysis of PV systems.In-depth chapters then demonstrate the use of advanced control and optimization techniques, covering the use of new embedded technologies, through different applications such as MPPT controllers, solar trackers, cleaning systems, cooling systems, and monitoring systems. Applications of photovoltaic energy systems in distributed generation, microgrid, and smart grid systems will be considered.

Intelligent Evolutionary Optimization

  • 1st Edition
  • April 18, 2024
  • Hua Xu + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 7 4 0 0 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 7 4 0 1 - 5
Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization.

Federated Learning

  • 1st Edition
  • February 9, 2024
  • Lam M. Nguyen + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 9 0 3 7 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 9 0 3 8 - 4
Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.

Trolley Crash

  • 1st Edition
  • January 26, 2024
  • Peggy Wu + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 9 9 1 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 9 9 2 - 3
The prolific deployment of Artificial Intelligence (AI) across different fields has introduced novel challenges for AI developers and researchers. AI is permeating decision making for the masses, and its applications range from self-driving automobiles to financial loan approvals. With AI making decisions that have ethical implications, responsibilities are now being pushed to AI designers who may be far-removed from how, where, and when these ethical decisions occur.Trolley Crash: Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers provides audiences with a catalogue of perspectives and methodologies from the latest research in ethical computing. This work integrates philosophical and computational approaches into a unified framework for ethical reasoning in the current AI landscape, specifically focusing on approaches for developing metrics. Written for AI researchers, ethicists, computer scientists, software engineers, operations researchers, and autonomous systems designers and developers, Trolley Crash will be a welcome reference for those who wish to better understand metrics for ethical reasoning in autonomous systems and related computational applications.

Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

  • 1st Edition
  • January 19, 2024
  • D. Jude Hemanth
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 2 0 0 9 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 0 1 0 - 4
Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The book's authors provide readers with an in-depth look at the challenges and associated solutions, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered that will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas.