Wiley-VCH - Computer Science

1. Field Effect Transistors

   Field Effect Transistors is an essential read for anyone interested in the future of electronics, as it provides a comprehensive yet accessible exploration of innovative semiconductor devices and their applications, making it a perfect resource for both beginners and seasoned professionals in the field. Miniaturization has become the slogan of the electronics industry. Field Effect Transistors serves as a short encyclopedia for young minds looking for solutions in the miniaturization of semiconductor devices. It explores the characteristics, novel materials used, modifications in device structure, and advancements in model FET devices. Though many devices following Moore's Law have been proposed and designed, a complete history of the existing and proposed semiconductor devices is not available. This book focuses on developments and research in emerging semiconductor FET devices and their applications, providing unique coverage of topics covering recent advancements and novel concepts in the field of miniaturized semiconductor devices. Field Effect Transistors is an easy-to-understand guide, making it excellent for those who are new to the subject, giving insight and analysis of recent developments and developed semiconductor device structures along with their applications. [528 Pages, Hardcover] Read More...

   
   
   
2. Security Yearbook 2025

   A fascinating history of the cybersecurity industry as well as an up-to-date collection of leading cybersecurity vendors from around the globe In the newly revised 2025 edition of Security Yearbook: A History and Directory of the IT Security Industry, celebrated author and information security expert Richard Stiennon delivers the latest complete desk reference for the entire cybersecurity industry. In the book, you'll find a comprehensive directory of cybersecurity vendors, updated for 2025, complete with headquarters location, category, sub-category, number of employees, and growth trends. The author has also included an insightful and concise history of important and relevant sub-sectors of the cybersecurity industry, including Distributed Denial-of-Service defense, network security, endpoint detection, identity and access management, data security, and governance risk compliance. Case studies and stories of key personalities supplement the history, showcasing the stories of significant characters who had their hands in landscape-altering events in the field. You'll also find: * Discussions of substantial IT security failures that had an impact on the industry, and on society as a whole * Major mergers and acquisitions, company failures and closures, and funding events in the cybersecurity sector * Significant developments in open-source projects with an impact on cybersecurity practitioners around the world Perfect for security architects, CISOs, freelance cybersecurity professionals, and other technical specialists, Security Yearbook 2025 is also a must-read resource for the managers, executives, and directors responsible for guiding and leading the efforts of technology professionals. New entrants to the field will want to read Security Yearbook 2025 cover-to-cover to understand how we got to where we are today. Students will enjoy Stiennon's breezy style as they learn everything the author has gleaned in his 30-year career. [528 Pages, Hardcover] Read More...

   
   
   
3. Next-Generation Systems and Secure Computing

   [480 Pages, Hardcover] Read More...

   
   
   
4. Explainable Artificial Intelligence in the Healthcare Industry

   Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource. Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models' outputs. Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings. [704 Pages, Hardcover] Read More...

   
   
   
5. Applied Machine Learning for Data Science Practitioners

   Single volume reference on using various aspects of data science to evaluate, understand, and solve business problems A reference book for anyone in the field of data science, Applied Machine Learning for Data Science Practitioners walks readers through the end-to-end process of solving any machine learning problem by identifying, choosing, and applying the right solution for the issue at hand. The text enables readers to figure out optimal validation techniques based on the use case and data orientation, choose a range of pertinent models from different types of learning, and score models to apply metrics across all the estimators evaluated. Unlike most books on data science in today's market that jump right into algorithms and coding and focus on the most-used algorithms, this text helps data scientists evaluate all pertinent techniques and algorithms to assess all these machine learning problems and suitable solutions. Readers can make an informed decision on which models and validation techniques to use based on the business problem, data availability, desired outcome, and more. Written by an internationally recognized author in the field of data science, Applied Machine Learning for Data Science Practitioners also covers topics such as: * Data preparation, including basic data cleaning, integration, transformation, and compression methods, along with data visualization and exploratory analyses * Cross-validation in model validation techniques, including independent, identically distributed, imbalanced, blocked, and grouped data * Prediction using regression models and classification using classification models, with applicable performance measurements for each * Types of clustering in clustering models based on partition, hierarchy, fuzzy theory, distribution, density, and graph theory * Detecting anomalies, including types of anomalies and key terms like noise, rare events, and outliers Applied Machine Learning for Data Science Practitioners is an essential resource for all data scientists and business professionals to cross-validate a range of different algorithms to find an optimal solution. Readers are assumed to have a basic understanding of solving business problems using data, high school level math, statistics, and coding skills. [656 Pages, Hardcover] Read More...