The two volumes of this book collect high-quality peer-reviewed research papers presented in the International Conference on ICT for Sustainable Development (ICT4SD 2015) held at Ahmedabad, India during 3 - 4 July 2015. The book discusses all areas of Information and Communication Technologies and its applications in field for engineering and management. The main focus of the volumes are on applications of ICT for Infrastructure, e-Governance, and contemporary technologies advancements on Data Mining, Security, Computer Graphics, etc. The objective of this International Conference is to provide an opportunity for the researchers, academicians, industry persons and students to interact and exchange ideas, experience and expertise in the current trend and strategies for Information and Communication Technologies.

This book constitutes the thoroughly refereed proceedings of the Fourth International Conference on Data Technologies and Applications, DATA 2015, held in Colmar, France, in July 2015. The 9 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: databases, data warehousing, data mining, data management, data security, knowledge and information systems and technologies; advanced application of data.

The two volumes of this book collect high-quality peer-reviewed research papers presented in the International Conference on ICT for Sustainable Development (ICT4SD 2015) held at Ahmedabad, India during 3 - 4 July 2015. The book discusses all areas of Information and Communication Technologies and its applications in field for engineering and management. The main focus of the volumes are on applications of ICT for Infrastructure, e-Governance, and contemporary technologies advancements on Data Mining, Security, Computer Graphics, etc. The objective of this International Conference is to provide an opportunity for the researchers, academicians, industry persons and students to interact and exchange ideas, experience and expertise in the current trend and strategies for Information and Communication Technologies.

Recently, the pressure for fast processing and efficient storage of large data with complex relations increased beyond the capability of traditional databases. Typical examples include iPhone applications, computer aided design - both electrical and mechanical, biochemistry applications, and incremental compilers. Serialization, which is sometimes used in such situations is notoriously tedious and error prone. In this book, Jiri Soukup and Petr Machacek show in detail how to write programs which store their internal data automatically and transparently to disk. Together with special data structure libraries which treat relations among objects as first-class entities, and with a UML class-diagram generator, the core application code is much simplified. The benchmark chapter shows a typical example where persistent data is faster by the order of magnitude than with a traditional database, in both traversing and accessing the data. The authors explore and exploit advanced features of object-oriented languages in a depth hardly seen in print before. Yet, you as a reader need only a basic knowledge of C++, Java, C#, or Objective C. These languages are quite similar with respect to persistency, and the authors explain their differences where necessary. The book targets professional programmers working on any industry applications, it teaches you how to design your own persistent data or how to use the existing packages efficiently. Researchers in areas like language design, compiler construction, performance evaluation, and no-SQL applications will find a wealth of novel ideas and valuable implementation tips. Under http://www.codefarms.com/book, you will find a blog and other information, including a downloadable zip file with the sources of all the listings that are longer than just a few lines - ready to compile and run.

Praise for the First Edition ...a well-written book on data analysis and data mining that provides an excellent foundation...CHOICE This is a must-read book for learning practical statistics and data analysis...Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: * Updated exercises for both manual and computer-aided implementation with accompanying worked examples * New appendices with coverage on the freely available Traceis software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance * New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches * Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

The book is a collection of high-quality peer-reviewed research papers presented in International Conference on Soft Computing Systems (ICSCS 2015) held at Noorul Islam Centre for Higher Education, Chennai, India. These research papers provide the latest developments in the emerging areas of Soft Computing in Engineering and Technology. The book is organized in two volumes and discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

This book constitutes the refereed proceedings of the 4th International Symposium on Languages, Applications and Technologies, SLATE 2015, held in Madrid, Spain, in June 2015. The 17 revised full papers presented were carefully reviewed and selected from 57 submissions. The papers are organized in topical sections on human-human languages; human-computer languages; computer-computer languages.

This important new volume presents recent research in healthcare information technology and analytics. Individual chapters look at such issues as the impact of technology failure on electronic prescribing behavior in primary care; attitudes toward electronic health records; a latent growth modeling approach to understanding lifestyle decisions based on patient historical data; designing an integrated surgical care delivery system using axiomatic design and petri net modeling; and failure in a dynamic decision environment, particularly in treating patients with a chronic disease. Other chapters look at such topics as the impact of information technology integration in integrated delivery systems; operations and supply chain control for inventory management in a health system pharmacy; decision-theoretic assistants based on contextual gesture recognition; evaluating emergency response medical information systems; clinical decision support in critical care; virtual worlds in healthcare; and natural language processing for understanding contraceptive use at the VA.

This book constitutes the proceedings of the 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, held in Yekaterinburg, Russia, in April 2016. The 23 full papers, 7 short papers, and 3 industrial papers were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on machine learning and data analysis; social networks; natural language processing; analysis of images and video.

This book constitutes the refereed proceedings of the Third International Workshop on Management of Information, Process and Cooperation, MiPAC 2016, held in Hangzhou, China, in September 2016. The 8 revised full papers were carefully reviewed and selected from 14 submissions. The papers are organized in topical sections on process modeling, process enactment, and data driven service computing.

The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 - 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

This book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Agents and Artificial Intelligence, ICAART 2014, held in Angers, France, in March 2014. The 21 revised full papers presented together with one invited paper were carefully reviewed and selected from 225 submissions. The papers are organized in two topical sections on agents and on artificial intelligence.

Knowledge and expertise, especially of the kind that can shape public opinion, have been traditionally the domain of individuals holding degrees awarded by higher learning institutions or occupying formal positions in notable organizations. Expertise is validated by reputations established in an institutionalized marketplace of ideas with a limited number of "available seats" and a stringent process of selection and retention of names, ideas, topics and facts of interest. However, the social media revolution, which has enabled over two billion Internet users not only to consume, but also to produce information and knowledge, has created a secondary and very active informal marketplace of ideas and knowledge. Anchored by platforms like Wikipedia, YouTube, Facebook and Twitter, this informal marketplace has low barriers to entry and has become a gigantic and potentially questionable, knowledge resource for the public at large. Roles, Trust and Reputation in Social Media Knowledge Markets will discuss some of the emerging trends in defining, measuring and operationalizing reputation as a new and essential component of the knowledge that is generated and consumed online. The book will propose a future research agenda related to these issues. The ultimate goal of research agenda being to shape the next generation of theoretical and analytic strategies needed for understanding how knowledge markets are influenced by social interactions and reputations built around functional roles. The authors, including leading scholars and young innovators, will share with the readers some of the main lessons they have learned from their own work in these areas and will discuss the issues, topics and sub-areas that they find under-studied or that promise the greatest intellectual payoff in the future. The discussion will be placed in the context of social network analysis and "big data" research. Roles, Trust and Reputation in Social Media Knowledge Markets exposes issues that have not been satisfactorily dealt with in the current literature, as the research agenda in reputation and authorship is still emerging. In a broader sense, the volume aims to change the way in which knowledge generation in social media spaces is understood and utilized. The tools, theories and methodologies proposed by the contributors offer concrete avenues for developing the next generation of research strategies and applications that will help: tomorrow's information consumers make smarter choices, developers to create new tools and researchers to launch new research programs.

The book is a collection of high-quality peer-reviewed research papers presented in International Conference on Soft Computing Systems (ICSCS 2015) held at Noorul Islam Centre for Higher Education, Chennai, India. These research papers provide the latest developments in the emerging areas of Soft Computing in Engineering and Technology. The book is organized in two volumes and discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 29th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four revised selected regular papers. Topics covered include optimization and cluster validation processes for entity matching, business intelligence systems, and data profiling in the Semantic Web.

Statistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more. The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications.

This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient's medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.

This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.

This book constitutes the proceedings of the second Asia Pacific Requirements Engineering Symposium, APRES 2015, held in Wuhan, China, in October 2015. The 9 full papers presented together with 3 tool demos papers and one short paper, were carefully reviewed and selected from 18 submissions. The papers deal with various aspects of requirements engineering in the big data era, such as automated requirements analysis, requirements acquisition via crowdsourcing, requirement processes and specifications, requirements engineering tools.requirements engineering in the big data era, such as automated requirements analysis, requirements acquisition via crowdsourcing, requirement processes and specifications, requirements engineering tools.

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

This book constitutes the refereed post-conference proceedings of the International Conference on Machine Learning and Intelligent Communications, MLICOM 2016, held in Shanghai, China in August 2016. The 41 revised full papers were carefully reviewed and selected from 47 submissions. The papers are organized thematically: data mining in heterogeneous networks, decentralized learning for wireless communication systems, intelligent cooperative/distributed coding, intelligent cooperative networks, Intelligent massive MIMO, time coded multi-user MIMO System based on three dimensional complementary codes, intelligent positioning and navigation systems, intelligent spectrum allocation schemes, machine learning algorithm & cognitive radio networks, machine learning for multimedia.

This book constitutes the proceedings of the Third International Conference on Internet of Things (IoT) Technologies for HealthCare, HealthyIoT 2016, held in Vasteras, Sweden, October 18-19, 2016. The conference also included the First Workshop on Emerging eHealth through Internet of Things (EHIoT 2016). IoT as a set of existing and emerging technologies, notions and services provides many solutions to delivery of electronic healthcare, patient care, and medical data management. The 31 revised full papers presented along with 9 short papers were carefully reviewed and selected from 43 submissions in total. The papers cover topics such as healthcare support for the elderly, real-time monitoring systems, security, safety and communication, smart homes and smart caring environments, intelligent data processing and predictive algorithms in e-Health, emerging eHealth IoT applications, signal processing and analysis, and smartphones as a healthy thing.

Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confidentiality concerns. Moreover, the physician's interpretation of images, signals, or other technical data, is written in unstructured English which is very difficult to mine. This book addresses all these specific features.