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Books in Artificial intelligence

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121-130 of 523 results in All results

Designing with the Mind in Mind

  • 3rd Edition
  • August 14, 2020
  • Jeff Johnson
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 8 2 0 2 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 8 2 0 3 - 1
User interface (UI) design rules and guidelines, developed by early HCI gurus and recognized throughout the field, were based on cognitive psychology (study of mental processes such as problem solving, memory, and language), and early practitioners were well informed of its tenets. But today practitioners with backgrounds in cognitive psychology are a minority, as user interface designers and developers enter the field from a wide array of disciplines. HCI practitioners today have enough experience in UI design that they have been exposed to UI design rules, but it is essential that they understand the psychological basis behind the rules in order to effectively apply them. In Designing with the Mind in Mind, best-selling author Jeff Johnson provides designers with just enough background in perceptual and cognitive psychology that UI design guidelines make intuitive sense rather than being just a list of rules to follow.

Advanced Machine Vision Paradigms for Medical Image Analysis

  • 1st Edition
  • August 11, 2020
  • Tapan K. Gandhi + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 9 2 9 5 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 9 2 9 6 - 2
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

Ascend AI Processor Architecture and Programming

  • 1st Edition
  • July 27, 2020
  • Xiaoyao Liang
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 3 4 8 8 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 3 4 8 9 - 1
Ascend AI Processor Architecture and Programming: Principles and Applications of CANN offers in-depth AI applications using Huawei’s Ascend chip, presenting and analyzing the unique performance and attributes of this processor. The title introduces the fundamental theory of AI, the software and hardware architecture of the Ascend AI processor, related tools and programming technology, and typical application cases. It demonstrates internal software and hardware design principles, system tools and programming techniques for the processor, laying out the elements of AI programming technology needed by researchers developing AI applications. Chapters cover the theoretical fundamentals of AI and deep learning, the state of the industry, including the current state of Neural Network Processors, deep learning frameworks, and a deep learning compilation framework, the hardware architecture of the Ascend AI processor, programming methods and practices for developing the processor, and finally, detailed case studies on data and algorithms for AI.

Usability Testing Essentials: Ready, Set ...Test!

  • 2nd Edition
  • June 27, 2020
  • Carol M. Barnum
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 6 9 4 2 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 6 9 4 3 - 8
Usability Testing Essentials presents a practical, step-by-step approach to learning the entire process of planning and conducting a usability test. It explains how to analyze and apply the results and what to do when confronted with budgetary and time restrictions. This is the ideal book for anyone involved in usability or user-centered design—from students to seasoned professionals.Filled with new examples and case studies, Usability Testing Essentials, Second Edition is completely updated to reflect the latest approaches, tools and techniques needed to begin usability testing or to advance in this area.

Artificial Intelligence in Healthcare

  • 1st Edition
  • June 21, 2020
  • Adam Bohr + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 8 4 3 8 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 8 4 3 9 - 4
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction toartificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges　and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare.

Human-Machine Shared Contexts

  • 1st Edition
  • June 9, 2020
  • William Lawless + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 0 5 4 3 - 3
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 2 3 7 9 - 6
Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of “shared contexts” between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers.

Practical Machine Learning for Data Analysis Using Python

  • 1st Edition
  • June 5, 2020
  • Abdulhamit Subasi
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 1 3 7 9 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 1 3 8 0 - 3
Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.

Information Visualization

  • 4th Edition
  • December 19, 2019
  • Colin Ware
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 2 8 7 5 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 2 8 7 6 - 3
Information Visualization: Perception for Design, Fourth Edition explores the art and science of why we see objects the way we do. Based on the science of perception and vision, the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility and persuasiveness. This new edition has been revised and updated to include the latest relevant research findings. Content has been updated in areas such as the cognitive neuroscience of maps and navigation, the neuroscience of pattern perception, and the hierarchy of learned patterns. New changes to the book make it easier to apply perceptual lessons to design decisions. In addition, the book offers practical guidelines that can be applied by anyone, including interaction designers and graphic designers of all kinds.

Adaptive Neural Networks and Robot Intelligent Control in Direct or Indirect Interaction with Humans

  • 1st Edition
  • December 1, 2019
  • Boubaker Daachi + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 1 9 6 3 - 4
Adaptive Neural Networks and Robot Intelligent Control in Direct or Indirect Interaction with Humans offers a particular methodology for using neural networks to solve control problems of nonlinear systems interacting directly (mobile robot exoskeleton type) or indirectly with humans (redundant robot manipulators serial or parallel). In addition, the book provides novel perspectives and research ideas for further strengthening the presence of humans in the control loop (intention, thought, etc.). The robots used for illustration purposes were designed in collaboration with industry.

Analyzing Social Media Networks with NodeXL

  • 2nd Edition
  • May 8, 2019
  • Derek Hansen + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 7 7 5 6 - 3
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 7 7 5 7 - 0
Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Second Edition, provides readers with a thorough, practical and updated guide to NodeXL, the open-source social network analysis (SNA) plug-in for use with Excel. The book analyzes social media, provides a NodeXL tutorial, and presents network analysis case studies, all of which are revised to reflect the latest developments. Sections cover history and concepts, mapping and modeling, the detailed operation of NodeXL, and case studies, including e-mail, Twitter, Facebook, Flickr and YouTube. In addition, there are descriptions of each system and types of analysis for identifying people, documents, groups and events. This book is perfect for use as a course text in social network analysis or as a guide for practicing NodeXL users.

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