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Wagner-Kaiser R: Teaching Computers to
Read

Teaching Computers to Read

Effective Best Practices in Building Valuable NLP Solutions

Artificial Intelligence and Machine Learning in Data Science and Analytics

Moving Towards Fully Autonomous and Cost-Effective Surgical Robotic Systems with AI offers advanced insights into how AI can be used to optimize the design, manufacturing, and clinical application of these systems. It covers a wide range of topics, including the introduction of surgical robotic systems design and the presentation of a fully autonomous surgical robotic system for minimally invasive surgery. Advanced medical imaging techniques integrated with surgical robots are discussed to enhance surgical precision and safety. The book also highlights the application of machine learning and deep learning models in robotic-assisted surgery, with the development of novel algorithms for autonomous surgical robots. Various AI approaches for optimizing surgical robotic systems and the design considerations for manufacturing and assembly are examined. Additionally, the book includes a thorough analysis of the life cycle and economic aspects of surgical robotic systems, as well as risk assessment and evaluation methods. The final chapters address ethics considerations and explore future enhancements in the realm of fully autonomous surgical robotic systems. Overall, this book aims to contribute to the technical understanding, design aspects, and ethical dimensions of surgical robotics, promoting safer and more efficient surgical procedures.

Moving Towards Fully Autonomous and Cost-Effective Surgical Robotic Systems with AI

Art in the Age of AI

How to See Like a Machine

Methods and Applications

Advanced Techniques for Modifying Metaheuristics

Multi-Agent Control Systems

Trusted Generative AI and Large Language Models for Health Informatics

Malware detection is exceedingly challenging, requiring deeper expertise in two domains: machine learning (ML) and malware analysis.This comprehensive collection guides ML practitioners and researchers into the state-of-the-art and underlying insights needed to work in and research problems at the intersection of machine learning and cyber security, particularly around the problem of malware targeting the Windows platform.This useful volume's contributions come from a range of experts, representing large companies, major universities, and government labs, all working at the frontiers of malware detection and remediation research.

Machine Learning For Malware Detection

Shaping the Future of Cities

AI-Driven Urban Planning

This book highlights the enduring synergy between information technology and decision-making tools, showcasing how advancements in one field drive progress in the other. Featuring the discoveries of esteemed researchers from the International Journal of Information Technology and Decision Making, it provides an insightful exploration of foundational concepts and cutting-edge techniques.This review volume opens with a broad overview of fundamental concepts like the Analytic Hierarchy Process (AHP) and Threat-Oriented Person Screening Integrated System, (TOPSIS) across the past decades. Subsequent chapters explore how theoretical advances find practical application in diverse fields.In this book, techniques from fuzzy logic find usage in uncertainty modelling, followed by the use of feature selection and recommendation in machine learning. The use of advanced optimization techniques for applications in price forecasting and the Internet of Things (IoT) are also covered. Additionally, the work presents novel methods under a neutrosophic environment as well as global and local importance used for multi-criteria decision-making. The prolific research presented helps to expand the repertoire of approaches to tackle problems in business strategy, healthcare and public policy.This comprehensive resource is an essential reference for practitioners and researchers seeking to expand their expertise in decision-making and technology-driven solutions.

Innovative Approaches To Decision Making: Bridging It And Decision Science

This comprehensive compendium addresses a critical need in the AI and machine learning era by bridging foundational information theory (IT) concepts with practical applications in statistical learning. Unlike traditional IT textbooks, this volume emphasizes how IT principles, such as entropy and source coding, underpin modern machine learning techniques like cross-entropy, decision trees, and evidence-lower bounds.This unique book connects IT with probabilistic inference, illustrated through real-world applications, such as decoding LDPC codes for error correction. The inclusion of the Lea probabilistic programming package is particularly valuable for pedagogy, offering students a hands-on tool to solve numerical problems and reinforce theoretical concepts.The useful reference text benefits professionals, researchers, academics and students in the fields of calculus and probability, communications and information sciences.

Introduction To Information Theory And Probabilistic Inference, An

Navigating Industry 4.0 and Anticipating Industry 5.0

Decoding Explainable AI

Eeg Signal Classification Using Machine Learning

Teaching Computers to Read

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