This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

Artificial intelligence algorithms are the backbone of search and optimization, deep learning, reinforcement learning, and, of course, generative AI. Grokking AI Algorithms, Second Edition introduces the most important AI algorithms using relatable illustrations, interesting examples, and thought-provoking exercises. Written in simple language and with lots of visual references and hands-on code examples, it helps you build a natural intuition into how intelligent systems learn, plan, and adapt. This second edition has been thoroughly revised, with new chapters on large language models, image generation, and more.

This book reports on innovative research and developments in automation. Spanning a wide range of disciplines, including communication engineering, power engineering, control engineering, instrumentation, signal processing and cybersecurity, it focuses on methods and findings aimed at improving the control and monitoring of industrial and manufacturing processes as well as safety.

Our aim in this book is to present and enlarge upon those aspects of parallel computing that are needed by practitioners of computational science. Today al­ most all classical sciences, such as mathematics, physics, chemistry and biology, employ numerical methods to help gain insight into nature. In addition to the traditional numerical methods, such as matrix inversions and the like, a whole new field of computational techniques has come to assume central importance, namely the numerical simulation methods. These methods are much less fully developed than those which are usually taught in a standard numerical math­ ematics course. However, they form a whole new set of tools for research in the physical sciences and are applicable to a very wide range of problems. At the same time there have been not only enormous strides forward in the speed and capability of computers but also dramatic new developments in computer architecture, and particularly in parallel computers. These improvements offer exciting prospects for computer studies of physical systems, and it is the new techniques and methods connected with such computer simulations that we seek to present in this book, particularly in the light of the possibilities opened up by parallel computers. It is clearly not possible at this early stage to write a definitive book on simulation methods and parallel computing.

This book gathers outstanding research papers presented at the International Joint Conference on Computational Intelligence (IJCCI 2019), held at the University of Liberal Arts Bangladesh (ULAB), Dhaka, on 25–26 October 2019 and jointly organized by the University of Liberal Arts Bangladesh (ULAB), Bangladesh; Jahangirnagar University (JU), Bangladesh; and South Asian University (SAU), India. These proceedings present novel contributions in the areas of computational intelligence, and offer valuable reference material for advanced research. The topics covered include collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing.

Master Python data structures and algorithms with practical coding exercises and real interview-style problems. This course builds your problem-solving skills from fundamentals to advanced techniques used in technical interviews and real-world applications.

Completely updated for the latest version of Java, this book offers a comprehensive introduction to programming, assuming no prior knowledge of the subject. It is designed for individuals, eager to learn Java from scratch, providing a strong foundation in Algorithmic Thinking—the fundamental skill every aspiring programmer must acquire. Algorithmic Thinking encompasses more than just writing code; it entails mastering the art of problem-solving through coding.

Completely updated for the latest version of Python, this book offers a comprehensive introduction to programming, assuming no prior knowledge of the subject. It is designed for individuals eager to learn Python from scratch, providing a strong foundation in Algorithmic Thinking—the fundamental skill every aspiring programmer must acquire. Algorithmic Thinking encompasses more than just writing code; it entails mastering the art of problem-solving through coding.