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Books in Artificial intelligence

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41-50 of 523 results in All results

Artificial Intelligence for Medicine

  • 1st Edition
  • March 14, 2024
  • Shai Ben- David + 5 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 6 7 1 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 6 7 2 - 6
Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field.This book will be beneficial for academics, professionals in the IT industry, educators, students, and anyone else involved in the use and development of AI in the medical field.

Machine Learning with Noisy Labels

  • 1st Edition
  • February 23, 2024
  • Gustavo Carneiro
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 4 4 1 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 4 4 2 - 3
Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine learning methods. Most of the modern machine learning models based on deep learning techniques depend on carefully curated and cleanly labeled training sets to be reliably trained and deployed. However, the expensive labeling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. This book defines the different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods.

Intelligent Learning Approaches for Renewable and Sustainable Energy

  • 1st Edition
  • February 21, 2024
  • Josep M. Guerrero + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 8 0 6 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 8 0 7 - 0
Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy. Sections introduce intelligent learning approaches and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. Other sections provide detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, optimization, and more.This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence.

Putting AI in the Critical Loop

  • 1st Edition
  • February 20, 2024
  • Prithviraj Dasgupta + 6 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 9 8 8 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 9 8 7 - 9
Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams takes on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge knowledge gaps, but also to advance this science to develop better solutions. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a “collective intelligence.” Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.

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.

Human-Computer Interaction

  • 2nd Edition
  • January 12, 2024
  • I. Scott MacKenzie
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 4 0 9 6 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 4 0 9 7 - 6
Human-Computer Interaction: An Empirical Research Perspective is the definitive guide to empirical research in HCI. The book begins with foundational topics, including historical context, the human factor, interaction elements, and the fundamentals of science and research. From there, the book progresses to the methods for conducting an experiment to evaluate a new computer interface or interaction technique. There are detailed discussions and how-to analyses on models of interaction, focusing on descriptive models and predictive models. Writing and publishing a research paper is explored with helpful tips for success.Throughout the book, readers will find hands-on exercises, checklists, and real-world examples. This is a must-have, comprehensive guide to empirical and experimental research in HCI – an essential addition to your HCI library.

Synthetic Data and Generative AI

  • 1st Edition
  • January 9, 2024
  • Vincent Granville
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 8 5 7 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 8 5 6 - 9
Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

Connectomic Medicine

  • 1st Edition
  • December 1, 2023
  • Michael E. Sughrue + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 9 0 8 9 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 9 0 9 0 - 2
Connectomic Medicine: A Guide to Brain AI in Treatment Decision Planning examines how to apply connectomics to clinical medicine, including discussions on techniques, applications, novel ideas, and in case examples that highlight the state-of-the-art. Written by pioneers, this volume serves as the foundation for all neuroscience clinicians/researchers venturing into the field of AI medicine, its realistic applications, and how to integrate AI connectomics into clinical practice. With widespread applications in neurology, neurosurgery and psychiatry, this book is appropriate for anyone interested in cerebral network anatomy, imaging techniques, and insights into this emerging field.

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