Bøger i Data-Enabled Engineering serien

This book provides a summary of the most recent developments in the research of both univariate and multivariate change point problems in the past 20 years. The emphasis is on multivariate process control and process diagnosis. The authors provide the fundamental statistical models and algorithms as well as practical solutions to applications in the manufacturing and healthcare systems. This book can be used as a reference book for anyone who is interested in statistical process control techniques, or a supplementary textbook for graduate-level courses such as Statistical Quality Control, Statistical Modeling of Manufacturing Systems, or Multivariate Statistics.

This book covers the entire research scope of Attribute-Based Encryption (ABE), from the background knowledge, to specific constructions, theoretic proofs, and applications with source codes.

This book seeks to educate the reader regarding Cyber Maneuver or Adaptive Cyber Defense.

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems.

Many professionals and students in engineering, science, business, and other application fields need to develop Windows-based and web-enabled information systems to store and use data for decision support, without help from professional programmers. However, few books are available to train professionals and students who are not professional programmers to develop these information systems. Developing Windows-Based and Web-Enabled Information Systems fills this gap, providing a self-contained, easy-to-understand, and well-illustrated text that explores current concepts, methods, and software tools for developing Windows-based and web-enabled information systems. Written in an easily accessible style, the book details current concepts, methods, and software tools for Windows-based and web-enabled information systems that store and use data. It is self-contained with easy-to-understand small examples to walk through concepts and implementation details along with large-scale case studies. The book describes data modeling methods including entity-relationship modeling, relational modeling and normalization, and object-oriented data modeling, to develop data models of a database. The author covers how to use software tools in the Microsoft application development environment, including Microsoft Access, MySQL, SQL, Visual Studio, Visual Basic, VBA, HTML, and XML, to implement databases and develop Windows-based and web-enabled applications with the database, graphical user interface, and program components. The book takes you through the entire process of developing a computer and network application for an information system, highlighting concepts and operation details. In each chapter, small data examples are used to manually walk through concepts and operational details. These features and more give you the conceptual understanding and practical skill required, even if you don''t have a computer science background, to develop Windows-based or web-enabled applications for your specialized information system.

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

This textbook covers concepts, methods, programming languages, and software tools of designing and developing both Windows-based and web-based information systems, specifically data modeling and design methods. It is designed to enable students and professionals in engineering, business, and science fields to learn up-to-date concepts, methods, and software tools for developing Windows and web-based information systems.