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Books in Artificial intelligence

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61-70 of 523 results in All results

Computational Intelligence Applications for Text and Sentiment Data Analysis

  • 1st Edition
  • July 14, 2023
  • Dipankar Das + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 5 3 5 - 0
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 6 3 7 - 1
Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multifaceted data. The book investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text, i.e., exclusion of ‘neutral’ or ‘factual’ comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored.Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, the book's authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques play an important role in solving the inherent problems of sentiment analysis applications.

Is Justice Real When “Reality” is Not?

  • 1st Edition
  • July 6, 2023
  • Katherine B. Forrest + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 5 6 2 0 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 5 6 2 1 - 5
Is Justice Real When “Reality” is Not?: Constructing Ethical Digital Environments examines how frameworks and concepts of justice should evolve in virtual worlds. Directed at researchers working in, or with an interest in virtual reality, as well as those interested in the fields of artificial intelligence and justice, this book covers research regarding impacts on human psychological states existing within alternative ethical frameworks. With chapters dedicated to behavioral impacts of virtual events, robotics and "unconscious", and human psychological states of role playing and existing, readers will be well-equipped to navigate the virtual worlds in which millions of people currently spend time.

Handbook of Metaheuristic Algorithms

  • 1st Edition
  • May 30, 2023
  • Chun-Wei Tsai + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 9 1 0 8 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 9 1 0 9 - 1
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.

Artificial Intelligence in Healthcare and COVID-19

  • 1st Edition
  • May 21, 2023
  • Parag Chatterjee + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 5 3 1 - 2
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 5 7 3 - 2
Artificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical and technological visions, making it even more applied. With the advent of COVID-19, it is obvious that leading universities and medical schools must include these topics and case studies in their usual courses of health informatics to keep up with the pace of technological and medical advancements. This book will also serve professors teaching courses and industry practitioners and professionals working in the R&D team of leading medical and informatics companies who want to embrace AI and eHealth to fight COVID-19. Since AI in healthcare is a comparatively new field, there exists a vacuum of literature in this field, especially when applied to COVID-19. With the area of AI in COVID-19 being quite young, students and researchers usually face a struggle to rely on the few published papers (which are obviously too specific) or whitepapers by tech-giants (which are too wide).

Uncertainty in Data Envelopment Analysis

  • 1st Edition
  • May 19, 2023
  • Farhad Hosseinzadeh Lotfi + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 4 4 4 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 4 4 5 - 3
Classical data envelopment analysis (DEA) models use crisp data to measure the inputs and outputs of a given system. In cases such as manufacturing systems, production processes, service systems, etc., the inputs and outputs may be complex and difficult to measure with classical DEA models. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex uncertain data, then they will become more important and practical for decision makers.Uncertainty in Data Envelopment Analysis introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods, fuzzy DEA and belief degree-based uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where the inputs and outputs of systems and processes are volatile and complex, making measurement difficult.

Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods

  • 1st Edition
  • April 30, 2023
  • Kemal Polat + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 6 1 2 9 - 5
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 6 8 1 - 5
Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

Robotics for Cell Manipulation and Characterization

  • 1st Edition
  • April 20, 2023
  • Changsheng Dai + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 5 2 1 3 - 2
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 5 2 1 4 - 9
Robotics for Cell Manipulation and Characterization provides fundamental principles underpinning robotic cell manipulation and characterization, state-of-the-art technical advances in micro/nano robotics, new discoveries of cell biology enabled by robotic systems, and their applications in clinical diagnosis and treatment. This book covers several areas, including robotics, control, computer vision, biomedical engineering and life sciences using understandable figures and tables to enhance readers’ comprehension and pinpoint challenges and opportunities for biological and biomedical research.

A Handbook of Artificial Intelligence in Drug Delivery

  • 1st Edition
  • March 27, 2023
  • Anil K. Philip + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 9 9 2 5 - 3
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 3 7 3 - 8
A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more. This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies.

Machine Learning

  • 2nd Edition
  • March 1, 2023
  • Marco Gori + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 9 8 5 9 - 1
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 8 4 6 9 - 0
Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.

Explainable Deep Learning AI

  • 1st Edition
  • February 20, 2023
  • Jenny Benois-Pineau + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 6 0 9 8 - 4
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 3 8 8 - 3
Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.

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