This fifth edition continues to improve on the features that have made it the market leader. The text offers a flexible organization, enabling instructors to adapt the book to their particular courses. The book is both complete and careful, and it continues to maintain its emphasis on algorithms and applications. Excellent exercise sets allow students to perfect skills as they practice. This new edition continues to feature numerous computer science applications-making this the ideal text for preparing students for advanced study.

As today's world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.

Voronoi diagrams partition space according to the influence certain sites exert on their environment. Since the 17th century, such structures play an important role in many areas like Astronomy, Physics, Chemistry, Biology, Ecology, Economics, Mathematics and Computer Science. They help to describe zones of political influence, to determine the hospital nearest to an accident site, to compute collision-free paths for mobile robots, to reconstruct curves and surfaces from sample points, to refine triangular meshes, and to design location strategies for competing markets. This unique book offers a state-of-the-art view of Voronoi diagrams and their structure, and it provides efficient algorithms towards their computation. Readers with an entry-level background in algorithms can enjoy a guided tour of gently increasing difficulty through a fascinating area. Lecturers might find this volume a welcome source for their courses on computational geometry. Experts are offered a broader view, including many alternative solutions, and up-to-date references to the existing literature; they might benefit in their own research or application development.

Mathematical logic is essentially related to computer science. This book describes the aspects of mathematical logic that are closely related to each other, including classical logic, constructive logic, and modal logic. This book is intended to attend to both the peculiarities of logical systems and the requirements of computer science.In this edition, the revisions essentially involve rewriting the proofs, increasing the explanations, and adopting new terms and notations.

Written by the pioneer and foremost authority on the subject, this new book is both a comprehensive university textbook and professional/research reference on the finite-difference time-domain (FD-TD) computational solution method for Maxwell's equations. It presents in-depth discussions of: The revolutionary Berenger PML absorbing boundary condition; FD-TD modelling of nonlinear, dispersive, and gain optical materials used in lasers and optical microchips; unstructured FD-TD meshes for modelling of complex systems; 2.5-dimensional body-of-revolution FD-TD algorithms; Linear and nonlinear electronic circuit models, including a seamless tie-in to SPICE; Digital signal postprocessing of FD-TD data; FD-TD modelling of microlaser cavities; and FD-TD software development for the latest Intel and Cray massively parallel computers.

In the last few years, courses on parallel computation have been developed and offered in many institutions in the UK, Europe and US as a recognition of the growing significance of this topic in mathematics and computer science. There is a clear need for texts that meet the needs of students and lecturers and this book, based on the author's lecture at ETH Zurich, is an ideal practical student guide to scientific computing on parallel computers working up from a hardware instruction level, to shared memory machines, and finally to distributed memory machines. Aimed at advanced undergraduate and graduate students in applied mathematics, computer science, and engineering, subjects covered include linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and, in some cases, Fortran. This book is also ideal for practitioners and programmers.

This book has been designed to deal with the topics which are indispensable in the advanced age of computer science. The first three chapters cover mathematical logic, sets, relations and function. Next come the chapters on ordered sets, Boolean albegra and switching circuits and matrices. Finally there are individual chapters on combinatorics, discrete numeric functions, generating functinos, recurrence relations, algebraic structures and graph theory; Graphs are binary trees. The purpose of this book is to present principles and concepts of discrete structures as relevant to student learning. The matter has been presented in as simple and lucid manner as possible and a large number of solved examples to understand the concept and principle of the theory have been introduced.

Threshold graphs have a beautiful structure and possess many important mathematical properties. They have applications in many areas including computer science and psychology. Over the last 20 years the interest in threshold graphs has increased significantly, and the subject continues to attract much attention.

The book contains many open problems and research ideas which will appeal to graduate students and researchers interested in graph theory. But above all "Threshold Graphs and Related Topics" provides a valuable source of information for all those working in this field.

The study of network theory is a highly interdisciplinary field, which has emerged as a major topic of interest in various disciplines ranging from physics and mathematics, to biology and sociology. This book promotes the diverse nature of the study of complex networks by balancing the needs of students from very different backgrounds. It references the most commonly used concepts in network theory, provides examples of their applications in solving practical problems, and clear indications on how to analyse their results. In the first part of the book, students and researchers will discover the quantitative and analytical tools necessary to work with complex networks, including the most basic concepts in network and graph theory, linear and matrix algebra, as well as the physical concepts most frequently used for studying networks. They will also find instruction on some key skills such as how to proof analytic results and how to manipulate empirical network data. The bulk of the text is focused on instructing readers on the most useful tools for modern practitioners of network theory. These include degree distributions, random networks, network fragments, centrality measures, clusters and communities, communicability, and local and global properties of networks. The combination of theory, example and method that are presented in this text, should ready the student to conduct their own analysis of networks with confidence and allow teachers to select appropriate examples and problems to teach this subject in the classroom.

Traditional machining has many limitations in today's technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking. Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today's technology-driven market.

This book is aimed at two kinds of readers: firstly, people working in or near mathematics, who are curious about continued fractions; and secondly, senior or graduate students who would like an extensive introduction to the analytic theory of continued fractions. The book contains several recent results and new angles of approach and thus should be of interest to researchers throughout the field. The first five chapters contain an introduction to the basic theory, while the last seven chapters present a variety of applications. Finally, an appendix presents a large number of special continued fraction expansions. This very readable book also contains many valuable examples and problems.

Multisensor fusion systems are only practical if the algorithms used are practical and effective, and if there is efficient database support. The first part of this book discusses a wide range of issues related to the development of robust, context-sensitive, and efficient data fusion algorithms. The second part addresses database requirements, structures, and issues related to achieving overall computational efficiency. Featuring highly accessible notation, the processing model and database issues presented in the text are aimed at system developers working in sensor fusion, automatic target recognition, multiple-target tracking, robotic control, automated image understanding, and large-scale integration and fabrication.

This invaluable reference offers the most comprehensive introduction available to the concepts of multisensor data fusion. It introduces key algorithms, provides advice on their utilization, and raises issues associated with their implementation. With a diverse set of mathematical and heuristic techniques for combining data from multiple sources, the book shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates man-machine interface requirements an database issues.

This first volume of the Handbook of Automated Reasoning includes topics such as: the early history of automated deduction, classical logic - resolution theorem proving, and tableaux and related methods.

This book is a short, concise introduction to key mathematical ideas for computing students which develops their understanding of discrete mathematics and its application in computing. The topics are presented in a well defined, logical order that build upon each other and are constantly reinforced by worked examples. Reliance on students' previous mathematical experience is kept to a minimum, though some basic algebraic manipulation is required. This book is appropriate for CS and Math students in an undergraduate Discrete Math course.

The content constitutes an accepted core of mathematics for computer scientists (for example, the formal methods used in computer science draw heavily on the discrete methematical concepts covered here, particularly logic, sets, relations and functions). Emphasis is placed on clear and careful explanations of basic ideas and on building confidence in developing mathematical competence through carefully selected exercises. All chapters conclude with short applications/case studies relevant to computing, which provide further motivation to engage with the mathematical ideas involved, and also demonstrate how the mathematics can be applied in a computing context.

Since the publication of the first edition of this book, advances in algorithms, logic and software tools have transformed the field of data fusion. The latest edition covers these areas as well as smart agents, human computer interaction, cognitive aides to analysis and data system fusion control. Besides aiding you in selecting the appropriate algorithm for implementing a data fusion system, this book guides you through the process of determining the trade-offs among competing data fusion algorithms, selecting commercial off-the-shelf (COTS) tools, and understanding when data fusion improves systems processing. Completely new chapters in this second edition explain data fusion system control, DARPA's recently developed TRIP model, and the latest applications of data fusion in data warehousing and medical equipment, as well as defence systems.

The volume is devoted to the interaction of modern scientific computation and classical number theory. The contributions, ranging from effective finiteness results to efficient algorithms in elementary, analytical and algebraic number theory, provide a broad view of the methods and results encountered in the new and rapidly developing area of computational number theory. Topics covered include finite fields, quadratic forms, number fields, modular forms, elliptic curves and diophantine equations. In addition, two new number theoretical software packages, KANT and SIMATH, are described in detail with emphasis on algorithms in algebraic number theory.

AI Metaheuristics for Information Security in Digital Media examines the latest developments in AI-based metaheuristics algorithms with applications in information security for digital media. It highlights the importance of several security parameters, their analysis, and validations for different practical applications. Drawing on multidisciplinary research including computer vision, machine learning, artificial intelligence, modified/newly developed metaheuristics algorithms, it will enhance information security for society. It includes state-of-the-art research with illustrations and exercises throughout.

The authors focus on the mathematical models and methods that support most data mining applications and solution techniques.

This volume comprises a collection of twenty written versions of invited as well as contributed papers presented at the conference held from 20-24 May 1996 in Beijing, China. It covers many areas of logic and the foundations of mathematics, as well as computer science. Also included is an article by M. Yasugi on the Asian Logic Conference which first appeared in Japanese, to provide a glimpse into the history and development of the series.

Fibonacci Cubes have been an extremely popular area of research since the 1990s.This unique compendium features the state of research into Fibonacci Cubes. It expands the knowledge in graph theoretic and combinatorial properties of Fibonacci Cubes and their variants.By highlighting various approaches with numerous examples, it provides a fundamental source for further research in the field. This useful reference text surely benefits advanced students in computer science and mathematics and serves as an archival record of the current state of the field.

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics. Contents include: Deep Auto-Encoders Deep Neural Network Domain Adaptation Modeling Multilayer Perceptron (MLP) Natural Language Processing (NLP) Restricted Boltzmann Machines (RBM) Threat Detection

The field and topic of optimization is not only a very hot topic now, it is morphing into new approaches. Presents a very contemporary approach. Appeal to mathematicians, yet will also find use in computer science and engineering, especially in operations research. Practical approach presents a framework to be used by students and professionals alike to tackle models needed for various applications and solutions.