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Machine Learning and Big Data Analytics Paradigms : Analysis, Applications and Challenges
Intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
Machine learning and big data : Concepts, algorithms, tools and applications
Showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention
Machine Learning Algorithms Using Python Programming
Presents the key concepts of Machine Learning which includes Python concepts and Interpreter, Foundation of Machine Learning, Data Pre-processing, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Kernel Machine, Design and analysis of Machine Learning experiment and Data visualization. The theoretical concepts along with coding implementation are covered. This book aims to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning.
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Machine Learning : Modeling Data Locally and Globally
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.
Machine Intelligence and Big Data Analytics for Cybersecurity Applications
Presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field.
Logics of Specification Languages
Dedicated chapters address : the use of ASM (Abstract State Machines) in the classroom; the Event-B modelling method; a methodological guide to CafeOBJ logic; CASL, the Common Algebraic Specification Language; the Duration Calculus; the logic of the RAISE specification language (RSL); the specification language TLA+; the typed logic of partial functions and the Vienna Development Method (VDM); and Z logic and its applications. Each chapter is self-contained, with references, and symbol and concept indexes. Finally, in a unique feature, the book closes with short commentaries on the specification languages written by researchers closely associated with their original development.
Logical and Relational Learning
This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.
Logic Programming with Prolog
Logic Programming is the name given to a distinctive style of programming, very different from that of conventional programming languages such as C++ and Java. By far the most widely used Logic Programming language is Prolog. Prolog is a good choice for developing complex applications, especially in the field of Artificial Intelligence. This book does not assume that the reader is an experienced programmer or has a background in Mathematics, Logic or Artificial Intelligence. It starts from scratch and aims to arrive at the point where quite powerful programs can be written in the language. It is intended both as a textbook for an introductory course and as a self-study book. On completion the reader will know enough to use Prolog in their own research or practical projects. Each chapter has self-assessment exercises so that the reader may check their own progress. A glossary of the technical terms used completes the book.
Logic for Computer Scientists
This book introduces the notions and methods of formal logic from a computer science standpoint, covering propositional logic, predicate logic, and foundations of logic programming. It presents applications and themes of computer science research such as resolution, automated deduction, and logic programming in a rigorous but readable way.The style and scope of the work, rounded out by the inclusion of exercises, make this an excellent textbook for an advanced undergraduate course in logic for computer scientists.
Logging in Java with the JDK 1.4 Logging API and Apache log4j
In development scenarios where things can't be run in a debugger, or when you run the risk of masking the problem, logs are the greatest source of information about running a program. Pro Apache Log4j, Second Edition provides best practices guidelines and comprehensive coverage of the most recent release. Step by step, the book explains core concepts, from basic to advanced. Code samples are in Java and include guidelines for different application-specific needs. You'll also learn how to extend the API to write custom components and best practices for using the feature-rich log4j API. This book concludes with enterprise Java applications using log4j with JSP and J2EE.
Linked Open Data -- Creating Knowledge Out of Interlinked Data : Results of the LOD2 Project
Linked Open Data (LOD) is a pragmatic approach for realizing the Semantic Web vision of making the Web a global, distributed, semantics-based information system. This book presents an overview on the results of the research project “LOD2 -- Creating Knowledge out of Interlinked Data”. LOD2 is a large-scale integrating project co-funded by the European Commission within the FP7 Information and Communication Technologies Work Program. Commencing in September 2010, this 4-year project comprised leading Linked Open Data research groups, companies, and service providers from across 11 European countries and South Korea.
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 network programming with Java
Learn to deliver superior server-to-server communication through the networking channels / Gain expertise of the networking features of your own applications to support various network architectures such as client/server and peer-to-peer Explore the issues that impact scalability, affect security, and allow applications to work in a heterogeneous environment
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.
Learn to Tango with D
Learn to Tango with D introduces you to the powerful D language, with special attention given to the Tango software library. A concise yet thorough overview of the language's syntax and features is presented, followed by an introduction to Tango, the popular general–purpose library you'll find invaluable when building your D applications.
Learn BlackBerry 10 App Development : A Cascades-Driven Approach
Learn how to leverage the BlackBerry 10 Cascades framework to create rich native applications. Learn BlackBerry 10 App Development gives you a solid foundation for creating BlackBerry 10 apps efficiently. Along the way, you will learn how to use QML and JavaScript for designing your app’s UI, and C++/Qt for the application logic. No prior knowledge of C++ is assumed and the book covers the fundamental aspects of the language for writing BlackBerry 10 apps. Also a particular emphasis is put on how to create a visually enticing user experience with the Cascades framework, which is based on Qt and QML.
Law and the Semantic Web : Legal Ontologies, Methodologies, Legal Information Retrieval, and Applications
As part of this objective, ICT (information and communication technologies) services should become available for every citizen, and for all schools, homes and businesses. The book you have in front of you is about Semantic Web technology and law. Law is something omnipresent; all citizens — at some points in their lives — have to deal with it. In addition, law involves a large group of professionals, and is a mul- billion business world wide. Information technology is important because it that can improve citizens’ interaction with law, as well as improve legal professionals’ work environment. Legal professionals dedicate a significant amount of their time to finding, reading, analyzing and synthesizing information in order to take decisions, and prepare advice and trials, among other tasks. As part of the “Semantic-Based Knowledge and Content Systems” Strategic Objective, the European Commission is funding projects to construct technology to make the Semantic Web vision come true. 1 The articles in this book are related to two current foci of the Strategic Objective : • Knowledge acquisition and modelling, capturing knowledge from raw information and multimedia content in webs and other distributed repositories to turn poorly structured information into machi- processable knowledge.
Laser additive manufacturing: design, materials, processes and applications
Laser-based additive manufacturing (LAM) is a revolutionary advanced digital manufacturing technology developed in recent decades, which is also a key strategic technology for technological innovation and industrial sustainability. This technology unlocks the design and constraints of traditional manufacturing and meets the needs of complex geometry fabrication and high-performance part fabrication. A deeper understanding of the design, materials, processes, structures, properties and applications is desired to produce novel functional devices, as well as defect-free structurally sound and reliable LAM parts.The topics in this Special Issue reprint include macro- and micro-scale additive manufacturing with lasers, such as structure/material design, fabrication, modeling and simulation, in situ characterization of additive manufacturing processes and ex situ materials characterization and performance, with an overview that covers various applications in aerospace, biomedicine, optics and energy.
