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This book provides a comprehensive survey of different kinds of Feistel ciphers, including their definition and mathematical/computational properties. Feistel Networks form the base design of the Data Encryption Standard algorithm, a former US NIST standard block cipher, originally released in 1977, and the framework used by several other symmetric ciphers ever since. The results consolidated in this volume provide an overview of this important cipher design to researchers and practitioners willing to understand the design and security analysis of Feistel ciphers.

Data encryption (Computer science). --- Mathematical statistics. --- Computer science—Mathematics. --- Computer mathematics. --- Cryptology. --- Probability and Statistics in Computer Science. --- Mathematical Applications in Computer Science.

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Algorithms. --- Computer science. --- Computer science—Mathematics. --- Mathematical statistics. --- Discrete mathematics. --- Probabilities. --- Algorithms. --- Theory of Computation. --- Probability and Statistics in Computer Science. --- Discrete Mathematics in Computer Science. --- Probability Theory.

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This book presents the latest developments in the theory and applications of time series analysis and forecasting. Comprising a selection of refereed papers, it is divided into several parts that address modern theoretical aspects of time series analysis, forecasting and prediction, with applications to various disciplines, including econometrics and energy research. The broad range of topics discussed, including matters of particular relevance for sustainable development, will give readers a modern perspective on the subject. The included contributions were originally presented at the 8th International Conference on Time Series and Forecasting, ITISE 2022, held in Gran Canaria, Spain, June 27-30, 2022. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics. .

Time-series analysis. --- Econometrics. --- Statistics. --- Computer science --- Mathematical statistics. --- Time Series Analysis. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Applied Statistics. --- Probability and Statistics in Computer Science. --- Mathematics.

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One of the superb characteristics of Intelligent Data Analysis (IDA) is that it is an interdisciplinary ?eld in which researchers and practitioners from a number of areas are involved in a typical project. This also creates a challenge in which the success of a team depends on the participation of users and domain experts who need to interact with researchers and developers of any IDA system. All this is usually re?ected in successful projects and of course on the papers that were evaluated by this year’s program committee from which the ?nal program has been developed. In our call for papers, we solicited papers on (i) applications and tools, (ii) theory and general principles, and (iii) algorithms and techniques. We received a total of 184 papers, reviewing these was a major challenge. Each paper was assigned to three reviewers. In the end 46 papers were accepted, which are all included in the proceedings and presented at the conference. This year’s papers re?ect the results of applied and theoretical researchfrom a number of disciplines all of which are related to the ?eld of Intelligent Data Analysis. To have the best combination of theoretical and applied research and also provide the best focus, we have divided this year’s IDA program into tu- rials, invited talks, panel discussions and technical sessions.

Artificial intelligence. --- Information storage and retrieval. --- Mathematical statistics. --- Pattern recognition. --- Information technology. --- Business—Data processing. --- Artificial Intelligence. --- Information Storage and Retrieval. --- Probability and Statistics in Computer Science. --- Pattern Recognition. --- IT in Business.

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This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. Matrix algebra is one of the most important areas of mathematics in data science and in statistical theory, and previous editions had essential updates and comprehensive coverage on critical topics in mathematics. This 3rd edition offers a self-contained description of relevant aspects of matrix algebra for applications in statistics. It begins with fundamental concepts of vectors and vector spaces; covers basic algebraic properties of matrices and analytic properties of vectors and matrices in multivariate calculus; and concludes with a discussion on operations on matrices, in solutions of linear systems and in eigenanalysis. It also includes discussions of the R software package, with numerous examples and exercises. Matrix Algebra considers various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes special properties of those matrices; as well as describing various applications of matrix theory in statistics, including linear models, multivariate analysis, and stochastic processes. It begins with a discussion of the basics of numerical computations and goes on to describe accurate and efficient algorithms for factoring matrices, how to solve linear systems of equations, and the extraction of eigenvalues and eigenvectors. It covers numerical linear algebra—one of the most important subjects in the field of statistical computing. The content includes greater emphases on R, and extensive coverage of statistical linear models. Matrix Algebra is ideal for graduate and advanced undergraduate students, or as a supplementary text for courses in linear models or multivariate statistics. It’s also ideal for use in a course in statistical computing, or as a supplementary text for various courses that emphasize computations.

Algebras, Linear. --- Matrices. --- Statistics. --- Algebra. --- Mathematical statistics --- Computer science --- Mathematical statistics. --- Mathematics --- Statistical Theory and Methods. --- Statistics and Computing. --- Probability and Statistics in Computer Science. --- Computational Mathematics and Numerical Analysis. --- Data processing. --- Mathematics.