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الصفحة 3
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.
Beyond the Worst-Case Analysis of Algorithms
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Beginning XSLT 2.0 : From novice to professional
This followup to Jeni Tennison's Beginning XSLT has been updated to accomodate the revised XSLT standard. Part one of this book introduces XML and XSLT at a comfortable pace, and gradually demonstrates techniques for generating HTML (plus other formats), from XML. In part two, Tennison applies theory to real-life XSLT capabilities—including generating graphics. Each chapter includes step-by-step examples (with code available online), plus review questions at the end, to help you grasp the discussed features. In fact, all of the examples and exercises revolve around an interesting common theme: making TV listings available online. This book lives up to its name, and will definitely take you from a novice to a professional, in no time!
Beginning VB 2008 Databases : From novice to professional
Beginning VB 2008 Databases teaches you everything you need to know about relational databases, SQL, and ADO.NET 2.0, giving you a sound start in developing console and Windows database applications. The book also includes chapters on the SQL Server XML data type and the LINQ enhancements to the next version of Visual Basic. In addition to teaching you database basics like using SQL to communicate with databases, this book provides you with detailed, code-practical techniques to access data in Visual Basic 2008 across a range of coding situations. Code-heavy and full of practical detail, this book has been fully revised and upgraded for .NET 3.5 and offers you the best contemporary practice in this core programming area, so that you'll find yourself using it in nearly all your .NET projects.
Beginning GIMP : From novice to professional
Beginning GIMP: From Novice to Professional explains how to use the open source image manipulation program, GIMP version 2.4. You'll learn how to install GIMP on Windows, Linux, and MacOS X platforms. Once you've installed the application, you'll learn about the interface and configuration options, and then jump into a quick–and–simple project to familiarize yourself even further. With four–color graphics and screenshots throughout, you'll learn how to prepare camera images for display on web pagesincluding functions like rescaling, cropping, and balancing color. The book also explains with great detail how to utilize layers, paths, and masks. You'll also learn how to draw lines and shapes, use patterns and gradients, and even create your own brushes, patterns, and gradients.
Autonomy oriented computing : From problem solving to complex systems modeling
Autonomy Oriented Computing explores the important theoretical and practical issues in AOC, by analyzing methodologies and presenting experimental case studies. The book serves as a comprehensive reference source for researchers, scientists, engineers, and professionals in all fields concerned with this promising new development in computer science. It can also be used as a main or supplementary text in graduate and undergraduate programs across a broad range of computer-related disciplines, including Robotics and Automation, Amorphous Computing, Image Processing and Computer Vision, Programming Paradigms, Computational Biology, and many others. The first part of the book, Fundamentals, describes the basic concepts and characteristics of an AOC system, and then it enumerates the critical design and engineering issues faced in AOC system development. The second part of the book, AOC in Depth, provides a detailed analysis of methodologies and case studies to evaluate the use of AOC in problem solving and complex system modeling. The final chapter reviews the essential features of the AOC paradigm and outlines a number of possibilities for future research and development.
Autonomous control for a reliable internet of services : Methods, models, approaches, techniques, algorithms, and tools
This open access book was prepared as a Final Publication of the COST Action IC1304 “Autonomous Control for a Reliable Internet of Services (ACROSS)”. The book contains 14 chapters and constitutes a show-case of the main outcome of the Action in line with its scientific goals. It will serve as a valuable reference for undergraduate and post-graduate students, educators, faculty members, researchers, engineers, and research strategists working in this field. The objective of this book is, by applying a systematic approach, to assess the state-of-the-art and consolidate the main research results achieved in this area.
Artificial intelligence-based Internet of things systems
Discusses the evolution of future generation technologies through Internet of Things (IoT) in the scope of Artificial Intelligence (AI). The main focus of this volume is to bring all the related technologies in a single platform, so that undergraduate and postgraduate students, researchers, academicians, and industry people can easily understand the AI algorithms, machine learning algorithms, and learning analytics in IoT-enabled technologies. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable AI-enabled IoT ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book brings together some of the top IoT-enabled AI experts throughout the world who contribute their knowledge regarding different IoT-based technology aspects. Addresses the complete functional framework workflow in AI-enabled IoT ecosystem; Presents intelligent object identification and object discovery through the IoT ecosystem and its implications to the real world ;Explores security and privacy issues and trustworthy machine learning related to data-intensive technologies in AI-based IoT ecosystems.
Artificial intelligence techniques for satellite image analysis
The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.
An Undergraduate Primer in Algebraic Geometry
This book consists of two parts. The first is devoted to an introduction to basic concepts in algebraic geometry: affine and projective varieties, some of their main attributes and examples. The second part is devoted to the theory of curves: local properties, affine and projective plane curves, resolution of singularities, linear equivalence of divisors and linear series, Riemann–Roch and Riemann–Hurwitz Theorems.The approach in this book is purely algebraic. The main tool is commutative algebra, from which the needed results are recalled, in most cases with proofs. The prerequisites consist of the knowledge of basics in affine and projective geometry, basic algebraic concepts regarding rings, modules, fields, linear algebra, basic notions in the theory of categories, and some elementary point–set topology.
An Invitation to Statistics in Wasserstein Space
This book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation.
An Introduction to Kolmogorov Complexity and Its Applications
Written by two experts in the field, this book is ideal for advanced undergraduate students, graduate students, and researchers in all fields of science. It is self-contained: it contains the basic requirements from mathematics, probability theory, statistics, information theory, and computer science. Included are history, theory, new developments, a wide range of applications, numerous (new) problem sets, comments, source references, and hints to solutions of problems. This is the only comprehensive treatment of the central ideas of Kolmogorov complexity and their applications.
An introduction to description logics
Designed so that domain knowledge can be described and so that computers can reason about this knowledge. DLs have recently gained increased importance since they form the logical basis of widely used ontology languages, in particular the web ontology language OWL. Written by four renowned experts, this is the first textbook on description logics. It is suitable for self-study by graduates and as the basis for a university course. Starting from a basic DL, the book introduces the reader to their syntax, semantics, reasoning problems and model theory and discusses the computational complexity of these reasoning problems and algorithms to solve them.
An Integrated Approach to Software Engineering
An Integrated Approach to Software Engineering introduces software engineering to advanced-level undergraduate and graduate students of computer science. It emphasizes a case-study approach whereby a project is developed through the course of the book, illustrating the different activities of software development. The sequence of chapters is essentially the same as the sequence of activities performed during a typical software project. All activities, including quality assurance and control activities, are described in each chapter as integral activities for that phase of development. Similarly, the author carefully introduces appropriate metrics for controlling and assessing the software process. Chapters in this revised edition, updated for today’s standards, include these new features: Software Process, Requirements Analysis and Specification, Software Architecture, Project Planning, Object Oriented Design, Coding,Testing,
Advanced technique and future perspective for next generation optical fiber communications
Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology.
Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms : A Practical Approach Using Python
Describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalise these strategies.
Advanced Data Warehouse Design : From Conventional to Spatial and Temporal Applications
This book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course.
Abstract Computing Machines : A Lambda Calculus Perspective
The book addresses ways and means of organizing computations, highlighting the relationship between algorithms and the basic mechanisms and runtime structures necessary to execute them using machines. It completely abstracts from concrete programming languages and machine architectures, taking instead the lambda calculus as the basic programming and program execution model to design various abstract machines for its correct implementation. The emphasis is on fully normalizing machines based on full-fledged beta-reductions as essential prerequisites for symbolic computations that treat functions and variables truly as first-class objects. Their weakly normalizing counterparts are shown to be functional abstract machines that sacrifice the flavors of full beta-reductions for decidedly simpler runtime structures and improved runtime efficiency. Further downgrading of the lambda calculus leads to classical imperative machines that permit side-effecting operations on the runtime environment.
A Matrix Algebra Approach to Artificial Intelligence
The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines
A First Course in Statistical Inference
Offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data. Aimed at beginning undergraduate students in mathematics, it presents the concepts underpinning frequentist statistical theory. Written in a conversational and informal style, this concise text concentrates on ideas and concepts, with key theorems stated and proved. Detailed worked examples are included and each chapter ends with a set of exercises, with full solutions given at the back of the book. Examples using R are provided throughout the book, with a brief guide to the software included. Topics covered in the book include: sampling distributions, properties of estimators, confidence intervals, hypothesis testing, ANOVA, and fitting a straight line to paired data.
