The aim of the book is to serve as a text for students learning programming in 'C' on Data Structures such as array, linked list, stack, queue, trees, graph and sorting and searching methodology. The book illustrates in detail the methods, algorithms, functions and implementation of each and every concept of data structures. Algorithms are written in pseudo syntax i.e., near to 'C' language for easy understanding. It contains worked examples to amplify the material, and enhance the pedagogy. The content is not overburdened with math, and instead pays attention to the key components of the subject, especially link listing. By discussing the practical applications of the subject, the author has lessened the dry theory involved, and made the book more approachable.

AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines. Artificial intelligence technologies support discovery science in multiple different ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation - in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research, by underpinning the discovery process with AI-enabled standards and pipelines, support the management of large-scale data and knowledge resources so that they can be shared, integrated and serve as a background 'knowledge ecosystem' into which new discoveries can be embedded. However, there are limitations to what AI can achieve and its outputs can be biased and confounded thus should not be blindly trusted. The latest generation of hybrid and 'human-in-the-loop' AI technologies have as their objective a balance between human inputs and insights and the power of the number-crunching and statistical inference at massive scale that AI technologies are best at.

Universally acclaimed as the book on garbage collection. A complete and up-to-date revision of the 2012 Garbage Collection Handbook. Thorough coverage of parallel, concurrent and real-time garbage collection algortithms including C4, Garbage First, LXR, Shenandoah, Transactional Sapphire and ZGC, and garbage collection on the GPU. Clear explanation of the trickier aspects of garbage collection, including the interface to the run-time system, handling of finalisation and weak references, and support for dynamic languages. New chapters on energy aware garbage collection, and persistence and garbage collection. The e-book includes more than 40,000 hyperlinks to algorithms, figures, glossary entries, indexed items, original research papers and much more. Backed by a comprehensive online database of over 3,400 garbage collection-related publications

Published in 1996, Richard Jones's Garbage Collection was a milestone in the area of automatic memory management. The field has grown considerably since then, sparking a need for an updated look at the latest state-of-the-art developments. The Garbage Collection Handbook: The Art of Automatic Memory Management brings together a wealth of knowledge gathered by automatic memory management researchers and developers over the past fifty years. The authors compare the most important approaches and state-of-the-art techniques in a single, accessible framework. The book addresses new challenges to garbage collection made by recent advances in hardware and software. It explores the consequences of these changes for designers and implementers of high performance garbage collectors. Along with simple and traditional algorithms, the book covers parallel, incremental, concurrent, and real-time garbage collection. Algorithms and concepts are often described with pseudocode and illustrations. The nearly universal adoption of garbage collection by modern programming languages makes a thorough understanding of this topic essential for any programmer. This authoritative handbook gives expert insight on how different collectors work as well as the various issues currently facing garbage collectors. Armed with this knowledge, programmers can confidently select and configure the many choices of garbage collectors. Web ResourceThe book's online bibliographic database at www.gchandbook.org includes over 2,500 garbage collection-related publications. Continually updated, it contains abstracts for some entries and URLs or DOIs for most of the electronically available ones. The database can be searched online or downloaded as BibTeX, PostScript, or PDF. E-bookThis edition enhances the print version with copious clickable links to algorithms, figures, original papers and definitions of technical terms. In addition, each index entry links back to where it was mentioned in the text, and each entry in the bibliography includes links back to where it was cited.

Digital Intermediation offers a new framework for understanding content creation and distribution across automated media platforms - a new mediatisation process. The book draws on empirical and theoretical research to carefully identify and describe a number of unseen digital infrastructures that contribute to a predictive media production process through technologies, institutions and automation. Field data is drawn from several international sites, including Los Angeles, San Francisco, Portland, London, Amsterdam, Munich, Berlin, Hamburg, Sydney and Cartagena. By highlighting an increasingly automated content production and distribution process, the book responds to a number of regulatory debates on the societal impact of social media platforms. It highlights emerging areas of key importance that shape the production and distribution of social media content, including micro-platformization and digital first personalities. The book explains how technologies, institutions and automation are used within agencies to increase exposure for the talent they manage, while providing inside access to the processes and requirements of producers who create content for platform algorithms. Finally, it outlines user agency as a strategy for those who seek diversity in the information they access on automated social media content distribution platforms. The findings in this book provide key recommendations for policymakers working within digital media platforms, and will be invaluable reading for students and academics interested in automated media environments.

A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.

A more accessible approach than most competitor texts, which move into advanced, research-level topics too quickly for today's students. Part I is comprehensive in providing all necessary mathematical underpinning, particularly for those who need more opportunity to develop their mathematical competence. More confident students may move directly to Part II and dip back into Part I as a reference. Ideal for use as an introductory text for courses in quantum computing. Fully worked examples illustrate the application of mathematical techniques. Exercises throughout develop concepts and enhance understanding. End-of-chapter exercises offer more practice in developing a secure foundation.

It explores a variety of modern applications in soft computing, including bioinspired computing, reconfigurable computing, fuzzy logic, fusion-based learning, intelligent healthcare systems, bioinformatics, data mining, functional approximation, genetic and evolutionary algorithms, hybrid models, machine learning, meta heuristics, neuro fuzzy system, and optimization principles. The book acts as a reference book for AI developers, researchers, and academicians as it addresses the recent technological developments in the field of soft computing.

This new volume provides the information needed to understand the simplex method, the revised simplex method, dual simplex method, and more for solving linear programming problems. Following a logical order, the book first gives a mathematical model of the linear problem programming and describes the usual assumptions under which the problem is solved. It gives a brief description of classic algorithms for solving linear programming problems as well as some theoretical results. It goes on to explain the definitions and solutions of linear programming problems, outlining the simplest geometric methods and showing how they can be implemented. Practical examples are included along the way. The book concludes with a discussion of multi-criteria decision-making methods. Advances in Optimization and Linear Programming is a highly useful guide to linear programming for professors and students in optimization and linear programming.

Discusses concepts such as Basic Programming Principles, OOP Principles, Database Programming, GUI Programming, Network Programming, Data Analytics and Visualization, Statistical Analysis, Virtual Reality, Web Development, Machine Learning, Deep Learning Provides the code and the output for all the concepts discussed Includes a case study at the end of each chapter

Optimization is presented in most multivariable calculus courses as an application of the gradient, and while this treatment makes sense for a calculus course, there is much more to the theory of optimization. Optimization problems are generated constantly, and the theory of optimization has grown and developed in response to the challenges presented by these problems. This textbook aims to show readers how optimization is done in practice and help them to develop an appreciation for the richness of the theory behind the practice. Exercises, problems (including modeling and computational problems), and implementations are incorporated throughout the text to help students learn by doing. Python notes are inserted strategically to help readers complete computational problems and implementations. The Basics of Practical Optimization, Second Edition is intended for undergraduates who have completed multivariable calculus, as well as anyone interested in optimization. The book is appropriate for a course that complements or replaces a standard linear programming course.

With the growing interest in and use of big data analytics in many industries and in many research fields around the globe, this new volume addresses the need for a comprehensive resource on the core concepts of big data analytics along with the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media. Research Practitioner's Handbook on Big Data Analytics will be a valuable addition to the libraries of practitioners in data collection in many industries along with research scholars and faculty in the domain of big data analytics. The book can also serve as a handy textbook for courses in data collection, data mining, and big data analytics.

This book is designed as a reference book and presents a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. Beginning with an introduction to optimization methods and algorithms and various enzymes, the book then moves on to provide a unified framework of process optimization for enzymes with various algorithms. The book presents current research on various applications of machine learning and discusses optimization techniques to solve real-life problems. The book compiles the different machine learning models for optimization of process parameters for production of industrially important enzymes. The production and optimization of various enzymes produced by different microorganisms are elaborated in the book It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making Covers the best-performing methods and approaches for optimization sustainable enzymes production with AI integration in a real-time environment Featuring valuable insights, the book helps readers explore new avenues leading towards multidisciplinary research discussions The book is aimed primarily at advanced undergraduates and graduates studying machine learning, data science and industrial biotechnology. Researchers and professionals will also find this book useful.

This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing. It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems. The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology. This book: * Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis. * Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems. * Presents the application of evolutionary computations for fractal visualization of sequence data. * Explores the use of genetic algorithms for pair-wise and multiple sequence alignments. * Examines the roles of efficient computational techniques in biology.

Designed for a proof-based course on linear algebra, this rigorous and concise textbook intentionally introduces vector spaces, inner products, and vector and matrix norms before Gaussian elimination and eigenvalues so students can quickly discover the singular value decomposition (SVD)-arguably the most enlightening and useful of all matrix factorizations. Gaussian elimination is then introduced after the SVD and the four fundamental subspaces and is presented in the context of vector spaces rather than as a computational recipe. This allows the authors to use linear independence, spanning sets and bases, and the four fundamental subspaces to explain and exploit Gaussian elimination and the LU factorization, as well as the solution of overdetermined linear systems in the least squares sense and eigenvalues and eigenvectors. This unique textbook also includes examples and problems focused on concepts rather than the mechanics of linear algebra. The problems at the end of each chapter and in an associated website encourage readers to explore how to use the notions introduced in the chapter in a variety of ways. Additional problems, quizzes, and exams will be posted on an accompanying website and updated regularly. The Less Is More Linear Algebra of Vector Spaces and Matrices is for students and researchers interested in learning linear algebra who have the mathematical maturity to appreciate abstract concepts that generalize intuitive ideas. The early introduction of the SVD makes the book particularly useful for those interested in using linear algebra in applications such as scientific computing and data science. It is appropriate for a first proof-based course in linear algebra.

1) Provides a levelling approach, bringing students at all stages of programming experience to the same point 2) Focuses Python, a general language, to an engineering and scientific context 3) Uses a classroom tested, practical approach to teaching programming 4) Teaches students and professionals how to use Python to solve engineering calculations such as differential and algebraic equations

This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence

This textbook is about computer science. It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. The authors cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science. This textbook has three key features: - A strong focus on problem solving introduces students to the fundamental data structures and algorithms by providing a very readable text without introducing an overwhelming amount of new language syntax. - Algorithm analysis in terms of Big-O running time is introduced early and applied throughout. - Python is used to facilitate the success of beginning students in using and mastering data structures and algorithms.

Describes the contribution of soft computing techniques towards a new paradigm shift Explores Soft Computing techniques in a systematic manner starting from their initial stage to recent developments in this area Presents a systematic application of fuzzy logic in mathematical sciences and decision-making. Examines the application of soft computing in health sciences and in the modeling of epidemics including the effects of vaccination Discusses the application of soft computing techniques in the modeling of infectious diseases

Focuses on the definition, engineering, and delivery of AI solutions as opposed to AI itself Reader will still gain a strong understanding of AI, but through the perspective of delivering real solutions Explores the core AI issues that impact the success of an overall solution including i. realities of dealing with data, ii. impact of AI accuracy on the ability of the solution to meet business objectives, iii. challenges in managing the quality of machine learning models Includes real world examples of enterprise scale solutions Provides a series of (optional) technical deep dives and thought experiments.

Focuses on the definition, engineering, and delivery of AI solutions as opposed to AI itself Reader will still gain a strong understanding of AI, but through the perspective of delivering real solutions Explores the core AI issues that impact the success of an overall solution including i. realities of dealing with data, ii. impact of AI accuracy on the ability of the solution to meet business objectives, iii. challenges in managing the quality of machine learning models Includes real world examples of enterprise scale solutions Provides a series of (optional) technical deep dives and thought experiments.

A best-seller in its French edition, the construction of this book is original and its success in the French market demonstrates its appeal. It is based on three principles: 1. An organization of the chapters by families of algorithms : exhaustive search, divide and conquer, etc. At the contrary, there is no chapter only devoted to a systematic exposure of, say, algorithms on strings. Some of these will be found in different chapters. 2. For each family of algorithms, an introduction is given to the mathematical principles and the issues of a rigorous design, with one or two pedagogical examples. 3. For its most part, the book details 150 problems, spanning on seven families of algorithms. For each problem, a precise and progressive statement is given. More important, a complete solution is detailed, with respect to the design principles that have been presented ; often, some classical errors are pointed at. Roughly speaking, two thirds of the book are devoted to the detailed rational construction of the solutions.

This book is for anyone who wants to gain an understanding of Blockchain technology and its potential. The book is research-oriented and covers different verticals of Blockchain technology. It discusses the characteristics and features of Blockchain, includes techniques, challenges, and future trends, along with case studies for deeper understanding. Blockchain Technology: Exploring Opportunities, Challenges, and Applications covers the core concepts related to Blockchain technology starting from scratch. The algorithms, concepts, and application areas are discussed according to current market trends and industry needs. It presents different application areas of industry and academia and discusses the characteristics and features of this technology. It also explores the challenges and future trends and provides an understanding of new opportunities. This book is for anyone at the beginner to intermediate level that wants to learn about the core concepts related to Blockchain technology.

The book describes state-of-the-art advances in simulators and emulators for quantum computing. It introduces the main concepts of quantum computing, defining q-bits, explaining the parallelism behind any quantum computation, describing measurement of the quantum state of information and explaining the process of quantum bit entanglement, collapsed state and cloning. The book reviews the concept of quantum unitary, binary and ternary quantum operators as well as the computation implied by each operator. It provides details of the quantum processor, providing its architecture, which is validated via execution simulation of some quantum instructions.