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Books in Artificial intelligence

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21-30 of 523 results in All results

Uncertainty in Computational Intelligence-Based Decision Making

  • 1st Edition
  • September 27, 2024
  • Ali Ahmadian + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 4 7 5 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 4 7 6 - 9
Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others.The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science.

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.

Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments

  • 1st Edition
  • September 4, 2024
  • Xiao-Lei Zhang
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 8 5 6 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 8 5 7 - 3
Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments provides a detailed discussion of deep learning-based robust speech processing and its applications. The book begins by looking at the basics of deep learning and common deep network models, followed by front-end algorithms for deep learning-based speech denoising, speech detection, single-channel speech enhancement multi-channel speech enhancement, multi-speaker speech separation, and the applications of deep learning-based speech denoising in speaker verification and speech recognition.

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.

Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition

  • 1st Edition
  • July 13, 2024
  • Mohammadali Ahmadi
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 0 1 0 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 0 1 1 - 9
Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition: Case Studies and Code Examples presents a package for academic researchers and industries working on water resources and carbon capture and storage. This book contains fundamental knowledge on artificial intelligence related to oil and gas sustainability and the industry’s pivot to support the energy transition and provides practical applications through case studies and coding flowcharts, addressing gaps and questions raised by academic and industrial partners, including energy engineers, geologists, and environmental scientists. This timely publication provides fundamental and extensive information on advanced AI applications geared to support sustainability and the energy transition for the oil and gas industry.

Gesture Recognition

  • 1st Edition
  • July 9, 2024
  • Qiguang Miao + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 8 9 5 9 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 8 9 6 0 - 6
Gesture Recognition: Theory and Applications covers this important topic in computer science and language technology that has a goal of interpreting human gestures via mathematical algorithms. The book begins by examining the computer vision-based gesture recognition method, focusing on the theory and related research results of various recent gesture recognition technologies. The book takes the evolutions of gesture recognition technology as a clue, systematically introducing gesture recognition methods based on handcrafted features, convolutional neural networks, recurrent neural networks, multimodal data fusion, and visual attention mechanisms.Three gesture recognition-based HCI (Human Computer Interaction) practical cases are introduced. Finally, the book looks at emerging research trends and application.

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

  • 1st Edition
  • June 12, 2024
  • Rajesh Kumar Tripathy + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 1 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 0 - 9
Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered.

Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem

  • 1st Edition
  • June 7, 2024
  • Dominique J Monlezun
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 5 9 7 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 5 9 6 - 4
Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world’s first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development.Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations—amid digitalization, deglobalization, and demographic shifts—dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public–private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity’s next era—while recovering what that humanity means, and why it is worth saving.

Towards Neuromorphic Machine Intelligence

  • 1st Edition
  • June 5, 2024
  • Hong Qu
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 2 8 2 0 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 2 8 2 1 - 3
Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering. In recent years, neural networks have re-emerged in relation to AI, representing a well-grounded paradigm rooted in disciplines from physics and psychology to information science and engineering.This book represents one of the established cross-over areas where neurophysiology, cognition, and neural engineering coincide with the development of new Machine Learning and AI paradigms. There are many excellent theoretical achievements in neuron models, learning algorithms, network architecture, and so on. But these achievements are numerous and scattered, with a lack of straightforward systematic integration, making it difficult for researchers to assimilate and apply. As the third generation of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) simulate the neuron dynamics and information transmission in a biological neural system in more detail, which is a cross-product of computer science and neuroscience. The primary target audience of this book is divided into two categories: artificial intelligence researchers who know nothing about SNNs, and researchers who know a lot about SNNs. The former needs to acquire fundamental knowledge of SNNs, but the challenge is that much of the existing literature on SNNs only slightly mentions the basic knowledge of SNNs, or is too superficial, and this book gives a systematic explanation from scratch. The latter needs learning about some novel research achievements in the field of SNNs, and this book introduces the latest research results on different aspects of SNNs and provides detailed simulation processes to facilitate readers' replication. In addition, the book introduces neuromorphic hardware architecture as a further extension of the SNN system.The book starts with the birth and development of SNNs, and then introduces the main research hotspots, including spiking neuron models, learning algorithms, network architectures, and neuromorphic hardware. Therefore, the book provides readers with easy access to both the foundational concepts and recent research findings in SNNs.

Related subjects

Artificial intelligence expert systems and knowledge based systems

Artificial intelligence general

Cognitive science

Computer human interaction

Evolutionary computation genetic algorithms programming

Fuzzy logic

Machine learning

Natural language and computational linguistics

Neural networks

Planning and scheduling

Reasoning

Robotics and autonomous systems