Books in Artificial intelligence general

Mastering Prompt Engineering

1st Edition
July 1, 2025
Anand Nayyar + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 3 3 9 0 4 - 2
eBook
9 7 8 - 0 - 4 4 3 - 3 3 9 0 5 - 9

Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs) is a comprehensive guide that takes readers on a journey through the world of Large Language Models (LLMs) and prompt engineering. Covering foundational concepts, advanced techniques, ethical considerations, and real-world case studies, this book equips both novices and experts to navigate the complex LLM landscape. It provides insights into LLM architecture, training, and prompt engineering methods, while addressing ethical concerns such as bias and privacy. Real-world case studies showcase the practical application of prompt engineering in a wide range of settings. This resource is not just for specialists but is a practical and ethically conscious guide for AI practitioners, students, scientific researchers, and anyone interested in harnessing the potential of LLMs in natural language processing and generation. Mastering Prompt Engineering serves as a gateway to a deeper understanding of LLMs and their responsible and effective utilization through its comprehensive, ethical, and practical approach.

The Decentralized Hydrogen Revolution Using Artificial Intelligence, Internet of Things, and Blockchain

1st Edition
July 1, 2025
Hossein Pourrahmani + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 4 1 4 6 7 - 1
eBook
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The Decentralized Hydrogen Revolution Using Artificial Intelligence, Internet of Things, and Blockchain is a unique and comprehensive resource that addresses the dynamic intersection of decentralized hydrogen communities and advanced technologies. The book provides a guide to understanding the transformative potential of artificial intelligence, Internet of Things, and blockchain in the context of decentralized hydrogen, from foundational principles to advanced applications. It provides practical insights into addressing environmental concerns, integrating innovative technologies, and formulating effective policies. The book begins by introducing decentralized hydrogen communities, their principles, and significance in sustainable energy. It differentiates hydrogen from fossil fuels, highlighting environmental benefits and the inclusive nature of decentralized hydrogen production. The exploration continues with electrolysis for hydrogen generation, types of electrolyzers, working principles, technological advancements, and their environmental and economic impacts. The role of fuel cells in clean energy is covered next, detailing types, advancements, integration in various applications, and environmental and economic impacts. The discussion then shifts to advanced Artificial Intelligence algorithms in hydrogen production, focusing on AI integration, smart production mechanisms, predictive analytics, and cybersecurity challenges. Blockchain trust in hydrogen networks is examined, emphasizing blockchain integration, trust and transparency, and smart contracts. The book also discusses Internet of Things connectivity for optimizing hydrogen systems and technologies, covering IoT integration, devices, real-time monitoring, and future trends. Economic considerations and policy frameworks in hydrogen production are addressed, including economic viability, policy implications, financial incentives, and regulatory challenges. Finally, the book summarises key insights, benefits, challenges, and the role of decentralized hydrogen communities in global energy. Decentralized Hydrogen: Using Artificial Intelligence, Internet of Things, and Blockchain is an invaluable reference for researchers, scientists, industry professionals, and policymakers involved in the hydrogen economy and will also be of interest to students seeking an overview of the subject.

ChatGPT

1st Edition
June 1, 2025
Ge Cheng
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 7 4 3 6 - 7
eBook
9 7 8 - 0 - 4 4 3 - 2 7 4 3 7 - 4

ChatGPT: Principles and Architecture bridges the knowledge gap between theoretical AI concepts and their practical applications. It equips industry professionals and researchers with a deeper understanding of large language models, enabling them to effectively leverage these technologies in their respective fields. In addition, it tackles the complexity of understanding large language models and their practical applications by demystifying underlying technologies and strategies used in developing ChatGPT and similar models. By combining theoretical knowledge with real-world examples, the book enables readers to grasp the nuances of AI technologies, thus paving the way for innovative applications and solutions in their professional domains.Sections focus on the principles, architecture, pretraining, transfer learning, and middleware programming techniques of ChatGPT, providing a useful resource for the research and academic communities. It is ideal for the needs of industry professionals, researchers, and students in the field of AI and computer science who face daily challenges in understanding and implementing complex large language model technologies.

Artificial Intelligence Innovations for Biomedical Engineering and Healthcare

1st Edition
June 1, 2025
Alma Y Alanis + 3 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 3 2 8 5 6 - 5
eBook
9 7 8 - 0 - 4 4 3 - 3 2 8 5 7 - 2

Artificial Intelligence Innovations for Biomedical Engineering and Healthcare bridges the evolving domains of artificial intelligence and biomedical engineering and healthcare. In an era where data-driven insights and precision medicine are essential in healthcare, this book explores emerging trends, and showcases AI's potential in transforming patient care, diagnosis, and the treatment of chronic diseases. The book goes on to examine how advanced technologies, including complex networks and AI-driven diagnostics, are reshaping the healthcare landscape. It simplifies the relationship between artificial intelligence and biomedical engineering, elucidating how these technologies are revolutionizing self-care. From decoding complex networks to revealing AI's role in treating chronic diseases, this book serves as a guide to understanding how innovation is reshaping the healthcare landscape.

Quantum Health AI

1st Edition
June 1, 2025
Dominique J Monlezun
English
Paperback
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eBook
9 7 8 - 0 - 4 4 3 - 3 3 3 5 4 - 5

Quantum Health AI: The Revolution of Medicine, Public Health, and Global Health is the first comprehensive book defining the transformation of the global health ecosystem by the fusion of our most powerful technologies—quantum computing and artificial intelligence—while defending an actionable human-centred approach to doing so responsibly, equitably, and sustainably. We can continue to watch wars, diseases, poverty, polarization, cyber-crime, and climate change only worsen. Our strongest technologies can remain centralized in a small number of companies and countries for their profit and power. Or we can cooperatively put quantum AI to work for the health of all of us, by better managing this technology’s overarching strategic competition between democracies and autocracies, along with the public and private sectors (balancing human security with national security, economic growth with household livelihoods, individual rights with the common good). This book draws on the decade plus of original research and first-hand perspective of the world’s first triple doctorate-trained physician-data scientist and AI ethicist. It unpacks the history, science, values, and political economics framing and driving quantum AI (including its physics, metaphysics, ethics, governance, computing, sensing, communication, materials, and security), the global health ecosystem (healthcare systems, public health agencies, biotechnology companies, and development institutions), and their growing integration, wins, and challenges. This one-stop book provides a global, inclusive, and practical guide for understanding and shaping these societal and technological trends. It thus empowers health, technology, and policy students, practitioners, professionals, researchers, and leaders in organizations, universities, companies, and governments—ultimately to make and maintain the human-centred quantum AI safeguarding and advancing humanity’s health, home, and future.

Intelligent Data Analytics for Solar Energy Prediction and Forecasting

1st Edition
May 1, 2025
Amit Kumar Yadav + 2 more
English
Paperback
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eBook
9 7 8 - 0 - 4 4 3 - 1 3 4 8 3 - 8

Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization explores the utilization of advanced neural networks, machine learning and data analytics techniques for solar radiation prediction, solar energy forecasting, installation and maximum power generation. The book addresses relevant input variable selection, solar resource assessment, tilt angle calculation, and electrical characteristics of PV modules, including detailed methods, coding, modeling and experimental analysis of PV power generation under outdoor conditions. It will be of interest to researchers, scientists and advanced students across solar energy, renewables, electrical engineering, AI, machine learning, computer science, information technology and engineers. In addition, R&D professionals and other industry personnel with an interest in applications of AI, machine learning, and data analytics within solar energy and energy systems will find this book to be a welcomed resource.

Introduction to Digital Human Modeling

1st Edition
May 1, 2025
Yujiang Xiang + 1 more
English
Paperback
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eBook
9 7 8 - 0 - 4 4 3 - 2 1 9 9 9 - 3

Introduction to Digital Human Modeling bridges the gap in current literature by providing a comprehensive resource on digital human modeling for beginners and researchers. The content includes step-by-step procedures for building a digital human model, fundamental human kinematics and dynamics, advanced topics such as motion prediction and injury prevention, and industrial applications. The book covers theoretical concepts and experimental validation, including human anatomy, degrees of freedom, skeletal and musculoskeletal modeling, equations of motion, reach envelopes, lifting prediction, muscle fatigue model, and injury analysis. It teaches readers how to build simulation-based human models, set up equations of motion, analyze human biomechanics, and utilize simulations and experiments to study worker injuries. Furthermore, the book introduces both fundamental and advanced digital human modeling methods and optimization techniques aimed at improving performance and preventing injuries in manual material handling, as well as addressing lifting and gait biomechanics and ergonomics.

Federated Learning for Medical Imaging

1st Edition
March 1, 2025
Xiaoxiao Li + 2 more
English
Paperback
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eBook
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Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging.

Artificial Neural Networks and Type-2 Fuzzy Set

1st Edition
March 1, 2025
Snehashish Chakraverty + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 3 2 8 9 4 - 7
eBook
9 7 8 - 0 - 4 4 3 - 3 2 8 9 5 - 4

Soft computing is an emerging discipline which aims to exploit tolerance for imprecision, approximate reasoning, and uncertainty to achieve robustness, tractability, and cost effectiveness for building intelligent machines. Soft computing methodologies include neural networks, fuzzy sets, genetic algorithms, Bayesian networks, and rough sets, among others. In this regard, neural networks are widely used for modeling dynamic solvers, classification of data, and prediction of solutions, whereas fuzzy sets provide a natural framework for dealing with uncertainty. Artificial Neural Networks and Type-2 Fuzzy Set: Elements of Soft Computing and Its Applications covers the fundamental concepts and the latest research on variants of Artificial Neural Networks (ANN), including scientific machine learning and Type-2 Fuzzy Set (T2FS). In addition, the book also covers different applications for solving real-world problems along with various examples and case studies. It may be noted that quite a bit of research has been done on ANN and Fuzzy Set theory/ Fuzzy logic. However, Artificial Neural Networks and Type-2 Fuzzy Set is the first book to cover the use of ANN and fuzzy set theory with regards to Type-2 Fuzzy Set in static and dynamic problems in one place. Artificial Neural Networks and Type-2 Fuzzy Sets are two of the most widely used computational intelligence techniques for solving complex problems in various domains. Both ANN and T2FS have unique characteristics that make them suitable for different types of problems. This book provides the reader with in-depth understanding of how to apply these computational intelligence techniques in various fields of science and engineering in general and static and dynamic problems in particular. Further, for validation purposes of the ANN and fuzzy models, the obtained solutions of each model in the book is compared with already existing solutions that have been obtained with numerical or analytical methods.

Dimensionality Reduction in Machine Learning

1st Edition
February 21, 2025
Jamal Amani Rad + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 3 2 8 1 8 - 3
eBook
9 7 8 - 0 - 4 4 3 - 3 2 8 1 9 - 0

Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.

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