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Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Liapunov Functions and Stability in Control Theory
Presents a modern and self-contained treatment of the Liapunov method for stability analysis, in the framework of mathematical nonlinear control theory. A Particular focus is on the problem of the existence of Liapunov functions (converse Liapunov theorems) and their regularity, whose interest is especially motivated by applications to automatic control.
Learning from data streams : Processing techniques in sensor networks
The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.
Knowledge Cartography : Software Tools and Mapping Techniques
The authors see mapping software as a set of visual tools for reading and writing in a networked age. In an information ocean, the primary challenge is to find meaningful patterns around which we can weave plausible narratives. Maps of concepts, discussions and arguments make the connections between ideas tangible and disputable.With 17 chapters from the leading researchers and practitioners, the reader will find the current state–of-the-art in the field. Part 1 focuses on educational applications in schools and universities, before Part 2 turns to applications in professional communities.
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: Includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. / The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. / Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. / Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. / Considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
JavaScript data structures and algorithms : An Introduction to understanding and implementing core data structure and algorithm fundamentals
Combines clear explanations of data structure and algorithm theory with practical code samples, examples and exercises, all specifically relevant to JavaScript Provides background information on object-oriented programming and native JavaScript concepts to help understand how everything fits together Illustrates how these theoretical computer science concepts ties back to practical applications in software engineering
Issues in Multi-Agent Systems : The AgentCities.ES Experience
The purpose of this book is to present current status of this technology by looking at its application in different domains, such as electronic markets, e-tourism, ambience intelligence, and complex system analysis.It starts by discussing software engineering issues for the development of multi-agent systems, how much it costs to build a multi-agent system, and which methods and tools are currently available. Next chapters present some of the most relevant aspects that are considered for the development of multi-agent systems.
Computational analysis and deep learning for medical care : Principles, methods, and applications
Focuses on the sophisticated methods for improving dye extraction and dyeing properties which will minimize the use of bioresource products. This book also brings out the innovative ways of wet chemical processing to alleviate the environmental impacts arising from this sector.
Complexity of Constraints : An Overview of Current Research Themes
This state-of-the-art survey contains the papers that were invited by the organizers after conclusion of an International Dagstuhl-Seminar on Complexity of Constraints, held in Dagstuhl Castle, Germany, in October 2006.
Complex Analysis with Applications to Number Theory
The book discusses major topics in complex analysis with applications to number theory.It 's including the theory of several finitely and infinitely complex variables, hyperbolic geometry, two- and three-manifolds, and number theory. In addition to solved examples and problems, the book covers most topics of current interest, such as Cauchy theorems, Picard’s theorems, Riemann–Zeta function, Dirichlet theorem, Gamma function, and harmonic functions.
Citation Analysis in Research Evaluation
This book is written for members of the scholarly research community, and for persons involved in research evaluation and research policy. More specifically, it is directed towards the following four main groups of readers: – All scientists and scholars who have been or will be subjected to a quantitative assessment of research performance using citation analysis. – Research policy makers and managers who wish to become conversant with the basic features of citation analysis, and about its potentialities and limitations. – Members of peer review committees and other evaluators, who consider the use of citation analysis as a tool in their assessments. – Practitioners and students in the field of quantitative science and technology studies, informetrics, and library and information science. Citation analysis involves the construction and application of a series of indicators of the ‘impact’, ‘influence’ or ‘quality’ of scholarly work, derived from citation data, i.e. data on references cited in footnotes or bibliographies of scholarly research publications. Such indicators are applied both in the study of scholarly communication and in the assessment of research performance. The term ‘scholarly’ comprises all domains of science and scholarship, including not only those fields that are normally denoted as science – the natural and life sciences, mathematical and technical sciences – but also social sciences and humanities.
Biometrics, Computer Security Systems and Artificial Intelligence Applications
This book presents the most recent achievements in some fascinating and rapidly developing fields within Computer Science. The scientific works presented in this book have been partitioned into three topical groups: Image Analysis and Biometrics, Computer Security Systems, and Artificial Intelligence and Applications. Image Analysis and Biometrics is the branch of Computer Science dealing with the very difficult task of artificial, visual perception of objects and surroundings, as well as the problems connected with it. Computer Security and Safety is at present a very important and intensively investigated branch of Computer Science because of the menacing activity of hackers and computer viruses.
Biometric User Authentication for IT Security : From Fundamentals to Handwriting
Biometric User Authentication for IT Security: From Fundamentals to Handwriting conveys general principals of passive (physiological trait like fingerprint, iris, face) and active (learned and trained behavior like voice, handwriting and gait) biometric recognition techniques to the reader. This professional book, unlike other publications in this area which concentrate on passive schemes, reflects a more comprehensive analysis of one particular active biometric technique: handwriting. Aspects which are thoroughly discussed include sensor characteristic dependency, attack scenarios, and the generation of cryptographic keys from handwriting.
Biomedical data mining for information retrieval : Methodologies, techniques, and applications
Discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally.
Biological and medical data analysis ; Vol. 3745 ; 6th International symposium, ISBMDA 2005, Aveiro, Portugal, November 10-11, 2005, Proceedings
The 6th International Symposium on Biological and Medical Data Analysisaimed to become a place where researchersinvolved in these diverse but increas-ingly complementary areas could meet topresent and discuss their scientificresults.The papers in this volume discuss issues from statistical models to archi-tectures and applications to bioinformatics and biomedicine. They cover bothpractical experience and novel research ideas and concepts.
Bioinformatics technologies
Solving modern biological problems requires advanced computational methods. Bioinformatics evolved from the active interaction of two fast-developing disciplines, biology and information technology. The central issue of this emerging field is the transformation of often distributed and unstructured biological data into meaningful information. This book describes the application of well-established concepts and techniques from areas like data mining, machine learning, database technologies, and visualization techniques to problems like protein data analysis, genome analysis and sequence databases. Chen has collected contributions from leading researchers in each area. The chapters can be read independently, as each offers a complete overview of its specific area, or, combined, this monograph is a comprehensive treatment that will appeal to students, researchers, and R&D professionals in industry who need a state-of-the-art introduction into this challenging and exciting young field.
Bioinformatics research and development ; 2nd International Conference, BIRD 2008 Vienna, Austria, July 7-9, 2008 Proceedings
This book constitutes the refereed proceedings of the Second International Bioinformatics Research and Development Conference, BIRD 2008, held in Vienna, Austria in July 2008.
Bioinformatics : Problem Solving Paradigms
This book highlights basic paradigms of problem analysis and algorithm design in the context of core bioinformatics problems. Mathematically demanding themes are put across to the reader by properly chosen representations with the aid of lots of illustrations.
Bioinformatics
In this textbook present mathematical models in bioinformatics and they describe the biological problems that inspire the computer science tools used to handle the enormous data sets involved. The first part of the book covers the mathematical and computational methods, while the practical applications are presented in the second part. The mathematical presentation is descriptive and avoids unnecessary formalism, and yet remains clear and precise. Emphasis is laid on motivation through biological problems and cross applications. Each of the four chapters in the first part is accompanied by exercises and problems to support an understanding of the techniques presented. Each of the six chapters of the second part is devoted to some specific application domain: sequence alignment, molecular phylogenetics and coalescence theory, genomics, proteomics, RNA, and DNA microarrays. Each chapter concludes with a problems and projects section, to deepen the reader's understanding and to allow for the design of derived methods. Many of the projects involve publicly available software and/or Web-based bioinformatics depositories. Finally, the book closes with a thorough bibliography, reaching from classic research results to very recent findings, providing many pointers for future research.Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background.
Big Data Recommender Systems ; Vol.2 : Application Paradigms
Combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools. First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters
