Books in Artificial intelligence

Computational Tools for Energy Harvesting Applications

1st Edition
June 1, 2025
Abhishek Sharma + 3 more
English
eBook
9 7 8 - 0 - 4 4 3 - 1 6 0 6 1 - 5

Computational Tools for Energy Harvesting Applications presents the latest applications of energy harvesting methods for renewable energy systems. The book provides readers with the computational tools and algorithms for energy harvesting, explaining the mathematical basis behind their development and application and analyzing the latest developments. Each chapter provides a review of the fundamentals and basic theory behind each energy harvester, the working principles, and the latest practical applications.The mathematical techniques and computational tools are provided for response analysis and optimization of diverse energy harvesters, including from fixed renewable energy systems – wind, solar, etc. – and portable devices such as PEM fuel cells. This is a valuable reference for students and researchers working in the field of renewable energy harvesting and its application, as well as electrical and mechanical engineers.

Intelligent Data Analytics for Solar Energy Prediction and Forecasting

1st Edition
May 1, 2025
Amit Kumar Yadav + 2 more
English
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.

Data Mining

5th Edition
April 1, 2025
James Foulds + 4 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 1 5 8 8 8 - 9
eBook
9 7 8 - 0 - 4 4 3 - 1 5 8 8 9 - 6

Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research

Neural Network Algorithms and Their Engineering Applications

1st Edition
April 1, 2025
Chao Huang + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 9 2 0 2 - 6
eBook
9 7 8 - 0 - 4 4 3 - 2 9 2 0 3 - 3

Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book provides readers with a complete study of how to use artificial neural networks to design a population-based metaheuristic algorithm, which in turn promotes the application of artificial neural networks in the field of engineering optimization.The authors provide a deep discussion for the potential application of machine learning methods in improving the optimization performance of the neural network algorithm, helping readers understand how to use machine learning methods to design improved versions of the algorithm. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers' understanding of methods covered and facilitate readers' ability to apply the algorithms to their own research and development projects.

Dimensionality Reduction in Machine Learning

1st Edition
April 1, 2025
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.

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT)

1st Edition
March 1, 2025
Yacine Atif + 1 more
Fatos Xhafa
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 2 2 1 7 - 7
eBook
9 7 8 - 0 - 4 4 3 - 2 2 2 1 8 - 4

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms.The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies.

Federated Learning for Medical Imaging

1st Edition
March 1, 2025
Xiaoxiao Li + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 3 6 4 1 - 9
eBook
9 7 8 - 0 - 4 4 3 - 2 3 6 4 2 - 6

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.In addition, it 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 is a complete resource for computer scientists and engineers as well as clinicians and medical care policymakers wanting to learn about the application of federated learning to medical imaging.

Necrobotics for Healthcare Applications and Management

1st Edition
March 1, 2025
Hemachandran Kannan + 4 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 4 8 3 2 - 0
eBook
9 7 8 - 0 - 4 4 3 - 2 4 8 3 3 - 7

Necrobotics for Healthcare Applications and Management delves into the emerging area of necrobotics and its implications for healthcare. Exploring the convergence of robotics, technology, and healthcare, the book presents leading-edge research, practical implementations, and ethical considerations. It bridges a significant gap in healthcare literature, furnishing a contemporary and comprehensive perspective on necrobotics. Highlighting its distinct applications, management nuances, and ethical dimensions in the domain of medical robotics, the book equips readers with an in-depth grasp of this evolving field. It offers insights into technological intricacies, practical utilization, and ethical guidelines. Through real-world case studies and exemplar practices, it vividly demonstrates successful necrobotics deployments while addressing integration challenges. The book facilitates adept navigation of necrobotics complexities, spur innovation, enhance patient outcomes, and contribute to healthcare evolution. Catering to the distinct information requisites and daily obstacles encountered by engineers, healthcare practitioners, and researchers, the book offers extensive insights into necrobotics technologies, real-life case studies, and ethical reflections. It stands as a valuable resource for individuals striving to harness necrobotics' potential for efficacious healthcare solutions.

Machine Learning

3rd Edition
March 1, 2025
Sergios Theodoridis
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 9 2 3 8 - 5
eBook
9 7 8 - 0 - 4 4 3 - 2 9 2 3 9 - 2

Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms.Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.New to this editionThe new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models.

Deep Learning in Action: Image and Video Processing for Practical Use

1st Edition
March 1, 2025
English
Paperback
9 7 8 - 0 - 4 4 3 - 3 0 0 7 8 - 3
eBook
9 7 8 - 0 - 4 4 3 - 3 0 0 7 9 - 0

Artificial intelligence technology has entered an extraordinary phase of fast development and wide application. The techniques developed in traditional AI research areas, such as computer vision and object recognition, have found many innovative applications in an array of real-world settings. The general methodological contributions from AI, such as a variety of recently developed deep learning algorithms, have also been applied to a wide spectrum of fields such as surveillance applications, real-time processing, IoT devices, and health care systems. The state-of-the-art and deep learning models have wider applicability and are highly efficient. Deep Learning in Action: Image and Video Processing for Practical Use provides a comprehensive and accessible resource for both intermediate to advanced readers seeking to harness the power of deep learning in the domains of video and image processing. The book bridges the gap between theoretical concepts and practical implementation by emphasizing lightweight approaches, enabling readers to efficiently apply deep learning techniques to real-world scenarios. It focuses on resource-efficient methods, making it particularly relevant in contexts where computational constraints are a concern.

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

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health