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Books in Machine learning

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11-20 of 54 results in All results

Metaheuristics Algorithms for Medical Applications

  • 1st Edition
  • November 24, 2023
  • Mohamed Abdel-Basset + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 3 1 4 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 3 1 5 - 2
Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.

Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems

  • 1st Edition
  • November 21, 2023
  • Yuekuan Zhou + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 1 7 7 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 1 7 8 - 3
Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy lifecycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems. This title provides critical information to students, researchers, and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity.

Energy Management in Homes and Residential Microgrids

  • 1st Edition
  • September 14, 2023
  • Reza Hemmati
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 3 7 2 8 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 3 7 2 9 - 4
Energy Management in Homes and Residential Microgrids: Short-Term Scheduling and Long-Term Planning provides an in-depth exploration of Home Energy Management Systems (HEMS), with a focus on practical applications for both short- and long-term models. Through this guide, readers will learn how to create efficient systems that facilitate the integration of renewable energy into the grid and simultaneously manage end-users' energy consumption. The short-term operation of Home Energy Management Systems is analyzed through various lenses, including renewable energy integration, energy storage integration, uncertainty in parameters, off-grid operation, outages and events, resilience, electric vehicle integration, and battery swapping strategy.The modelling of these topics is explained with step-by-step instructions, and the parameters and implications are thoroughly discussed. Additionally, the book offers insight into the long-term expansion planning for residential microgrids, providing a detailed examination of dynamic modeling, control, and stability of these small-scale energy systems. Throughout the book, simple and advanced examples are provided, and each example comes with numerical data, detailed formulation, modelling, and simulation.

Machine Learning for Biomedical Applications

  • 1st Edition
  • September 7, 2023
  • Maria Deprez + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 2 9 0 4 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 2 9 0 5 - 7
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.

Hamiltonian Monte Carlo Methods in Machine Learning

  • 1st Edition
  • February 3, 2023
  • Tshilidzi Marwala + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 9 0 3 5 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 9 0 3 6 - 0
Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitive sampling parameters and high sample autocorrelation. Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.

Data Science, Analytics and Machine Learning with R

  • 1st Edition
  • January 23, 2023
  • Luiz Paulo Favero + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 4 2 7 1 - 1
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 5 9 2 3 - 3
Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

Statistical Modeling in Machine Learning

  • 1st Edition
  • October 29, 2022
  • Tilottama Goswami + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 1 7 7 6 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 7 2 5 2 - 9
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

Digital Image Enhancement and Reconstruction

  • 1st Edition
  • October 6, 2022
  • Shyam Singh Rajput + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 8 3 7 0 - 9
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 8 5 7 8 - 9
Digital Image Enhancement and Reconstruction: Techniques and Applications explores different concepts and techniques used for the enhancement as well as reconstruction of low-quality images. Most real-life applications require good quality images to gain maximum performance, however, the quality of the images captured in real-world scenarios is often very unsatisfactory. Most commonly, images are noisy, blurry, hazy, tiny, and hence need to pass through image enhancement and/or reconstruction algorithms before they can be processed by image analysis applications. This book comprehensively explores application-specific enhancement and reconstruction techniques including satellite image enhancement, face hallucination, low-resolution face recognition, medical image enhancement and reconstruction, reconstruction of underwater images, text image enhancement, biometrics, etc. Chapters will present a detailed discussion of the challenges faced in handling each particular kind of image, analysis of the best available solutions, and an exploration of applications and future directions. The book provides readers with a deep dive into denoising, dehazing, super-resolution, and use of soft computing across a range of engineering applications.

Adversarial Robustness for Machine Learning

  • 1st Edition
  • August 20, 2022
  • Pin-Yu Chen + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 4 0 2 0 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 4 2 5 7 - 5
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems.

Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

  • 1st Edition
  • April 2, 2022
  • Qiang Li + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 4 4 5 - 2
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 4 1 7 - 9
Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning.