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Books in Artificial intelligence

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91-100 of 523 results in All results

Blockchain Applications for Healthcare Informatics

  • 1st Edition
  • May 20, 2022
  • Sudeep Tanwar
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 6 1 5 - 9
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 8 2 9 - 0
Blockchain Applications for Healthcare Informatics: Beyond 5G offers a comprehensive survey of 5G-enabled technology in healthcare applications. This book investigates the latest research in blockchain technologies and seeks to answer some of the practical and methodological questions surrounding privacy and security in healthcare. It explores the most promising aspects of 5G for healthcare industries, including how hospitals and healthcare systems can do better. Chapters investigate the detailed framework needed to maintain security and privacy in 5G healthcare services using blockchain technologies, along with case studies that look at various performance evaluation metrics, such as privacy preservation, scalability and healthcare legislation.

Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

  • 1st Edition
  • April 2, 2022
  • Qiang Li + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 4 4 5 - 2
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 4 1 7 - 9
Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning.

Artificial Intelligence and Data Science in Environmental Sensing

  • 1st Edition
  • February 9, 2022
  • Mohsen Asadnia + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 5 0 8 - 4
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 5 0 7 - 7
Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.

Measuring the User Experience

  • 3rd Edition
  • February 8, 2022
  • Bill Albert + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 8 0 8 0 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 8 0 8 1 - 5
*Textbook and Academic Authors Association (TAA) Textbook Excellence Award Winner, 2024*Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics, Third Edition provides the quantitative analysis training that students and professionals need. This book presents an update on the first resource that focused on how to quantify user experience. Now in its third edition, the authors have expanded on the area of behavioral and physiological metrics, splitting that chapter into sections that cover eye-tracking and measuring emotion. The book also contains new research and updated examples, several new case studies, and new examples using the most recent version of Excel.

Deep Learning for Robot Perception and Cognition

  • 1st Edition
  • February 4, 2022
  • Alexandros Iosifidis + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 7 8 7 - 1
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 8 5 7 2 - 0
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks.

Deep Learning on Edge Computing Devices

  • 1st Edition
  • February 2, 2022
  • Xichuan Zhou + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 7 8 3 - 3
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 9 2 7 - 3
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design.

Machine Learning for Biometrics

  • 1st Edition
  • January 21, 2022
  • Partha Pratim Sarangi + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 2 0 9 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 3 3 9 - 4
Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc. In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.

Advanced Data Mining Tools and Methods for Social Computing

  • 1st Edition
  • January 14, 2022
  • Sourav De + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 7 0 8 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 5 7 0 9 - 3
Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis.

Artificial Intelligence for Healthcare Applications and Management

  • 1st Edition
  • January 13, 2022
  • Boris Galitsky + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 4 5 2 1 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 4 5 2 2 - 4
Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.

Deep Learning for Sustainable Agriculture

  • 1st Edition
  • January 9, 2022
  • Ramesh Chandra Poonia + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 2 1 4 - 2
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 3 6 2 - 2
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm.

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