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Ananthaswamy Anil: Why Machines Learn /
LF

Why Machines Learn

The Elegant Maths Behind Modern AI

The Politics of Test-Bed Urbanism

Smart as a City

A Practical Approach

Applied Machine Learning in Chemical Process Engineering

Why Not Nonlinear Algebra: Math, Science, Applications And Funding

Huge Numbers

Deep Learning in Computer Vision

How We Entertained Ourselves Into a State of Emergency

Screen People

Navigating Industry 4.0 and Anticipating Industry 5.0

Decoding Explainable AI

Scaling (zooming in or out) is taking a close or distant view of an object. Infinite scaling (the scaling parameter tends to zero or infinity) may lead to a limit which is self-similar and much simpler than the original object. For instance, the classical Donsker's theorem says that all weakly dependent stationary processes with finite variance scale to Brownian motion at large scales. In the case of a random field indexed by two-dimensional parameter, both types of scaling can be anisotropic, meaning that the horizontal and vertical axes are scaled at different rate determined by the ratio γ&gt;0 of the scaling exponents along the axes. The natural questions under such scaling are whether the scaling limits exist for any γ&gt;0, and what are these limits are. The book tries to answer these questions. It introduces the concept of scaling transition and discusses its existence for a natural class of planar random fields including Gaussian, linear and some nonlinear ones. The scaling limits are identified and exhibit a surprising trichitomy at a critical point γ0, with 'unbalanced' limits having unusual path properties and dependence structure along one of the coordinate axes. Scaling transition occurs in applied sciences (telecommunications and econometrics) when aggregating independent processes with long-range dependence in which case 'unbalanced' limits are classical Gaussian or stable random fields.

Anisotropic Scaling Of Random Fields With Long-range Dependence: Scaling Limits Of Random Fields With Applications

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

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

Resistance in the Age of Artificial Intelligence

Algorithmic Capital

Distributed and Parallel Computing

Why Machines Learn

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