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Books in Machine learning

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Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

  • 1st Edition
  • July 1, 2025
  • Manuel González Canché
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 9 6 1 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 9 6 0 - 3
Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools—just as people do not need to know C++ or TypeScript to benefit from Microsoft Word.The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

  • 1st Edition
  • June 1, 2025
  • Dinh Thai Hoang + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 0 3 2 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 0 3 3 - 6
Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security.

Data Mining

  • 5th Edition
  • April 22, 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

Dimensionality Reduction in Machine Learning

  • 1st Edition
  • April 1, 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.

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

  • 1st Edition
  • March 1, 2025
  • Abdussalam Elhanashi + 1 more
  • 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.

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.

Quantum Computing for Healthcare Data

  • 1st Edition
  • February 1, 2025
  • Nagasubramanian Gayathri + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 2 9 7 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 2 9 8 - 9
Quantum Computing for Healthcare Data: Revolutionizing the Future of Medicine presents an advanced overview of the fundamentals of quantum computing, from the transition of traditional to quantum computing, to the challenges and opportunities encountered as various industries enter into the paradigm shift. The book investigates how quantum AI, quantum data processing, and quantum data analysis can best be integrated into healthcare data systems. The book also introduces a range of case studies which feature applications of quantum computing in connected medical devices, medical simulations, robotics, medical diagnosis, and drug discovery. The book will be a valuable resource for researchers, graduate students, and professional programmers and computer engineers working in the areas of healthcare data management and analytics, blockchain, IoT, and big data analytics.

Machine Learning

  • 3rd Edition
  • December 6, 2024
  • 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.

Machine Learning Models and Architectures for Biomedical Signal Processing

  • 1st Edition
  • November 1, 2024
  • Suman Lata Tripathi + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 2 1 5 8 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 1 5 7 - 6
Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.

Artificial Intelligence of Things (AIoT)

  • 1st Edition
  • September 11, 2024
  • Fadi Al-Turjman + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 6 4 8 2 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 6 4 8 3 - 2
Artificial Intelligence of Things (AIoT): Current and Future Trends brings together researchers and developers from a wide range of domains to share ideas on how to implement technical advances, create application areas for intelligent systems, and how to develop new services and smart devices connected to the Internet. Section One covers AIoT in Everything, providing a wide range of applications for AIoT methods and technologies. Section Two gives readers comprehensive guidance on AIoT in Societal Research and Development, with practical case studies of how AIoT is impacting cultures around the world. Section Three covers the impact of AIoT in educational settings.The book also covers new capabilities such as pervasive sensing, multimedia sensing, machine learning, deep learning, and computing power. These new areas come with various requirements in terms of reliability, quality of service, and energy efficiency.