This book features selected contributions in the areas of modeling, simulation, and optimization. The contributors discusses requirements in problem solving for modeling, simulation, and optimization. Modeling, simulation, and optimization have increased in demand in exponential ways and how potential solutions might be reached. They describe how new technologies in computing and engineering have reduced the dimension of data coverage worldwide, and how recent inventions in information and communication technology (ICT) have inched towards reducing the gaps and coverage of domains globally. The chapters cover how the digging of information in a large data and soft-computing techniques have contributed to a strength in prediction and analysis, for decision making in computer science, technology, management, social computing, green computing, and telecom. The book provides an insightful reference to the researchers in the fields of engineering and computer science. Researchers, academics, and professionals will benefit from this volume. Features selected expanded papers in modeling, simulation, and optimization from COMPSE 2016; Includes research into soft computing and its application in engineering and technology; Presents contributions from global experts in academia and industry in modeling, simulation, and optimization.

Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.

This book includes extended versions of selected works presented at the 52nd Annual Convention of Computer Society of India (CSI 2017), held at Science City, Kolkata on 19-21 January 2018. It features a collection of chapters focusing on recent trends in computational intelligence, covering topics such as ANN, neuro-fuzzy based clustering, edge detection, data mining, mobile cloud computing, intelligent scheduling, processing and authentication. It also discusses societal applications of these methods. As such it is useful for students, researchers and industry professionals working in the area of computational intelligence.

This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects the most recent achievements with respect to the automatic build-up of large lexical resources. It addresses researchers that already perform text mining, and those who want to enrich their battery of methods. Selected articles can be used to support graduate-level teaching. The book is suitable for all readers that completed undergraduate studies of computational linguistics, quantitative linguistics, computer science and computational humanities. It assumes basic knowledge of computer science and corpus processing as well as of statistics.

Web mining has become a popular area of research, integrating the different research areas of data mining and the World Wide Web. According to the taxonomy of Web mining, there are three sub-fields of Web-mining research: Web usage mining, Web content mining and Web structure mining. These three research fields cover most content and activities on the Web. With the rapid growth of the World Wide Web, Web mining has become a hot topic and is now part of the mainstream of Web - search, such as Web information systems and Web intelligence. Among all of the possible applications in Web research, e-commerce and e-services have been iden- fied as important domains for Web-mining techniques. Web-mining techniques also play an important role in e-commerce and e-services, proving to be useful tools for understanding how e-commerce and e-service Web sites and services are used, e- bling the provision of better services for customers and users. Thus, this book will focus upon Web-mining applications in e-commerce and e-services. Some chapters in this book are extended from the papers that presented in WMEE 2008 (the 2nd International Workshop for E-commerce and E-services). In addition, we also sent invitations to researchers that are famous in this research area to contr- ute for this book. The chapters of this book are introduced as follows: In chapter 1, Peter I.

The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others:

What are the key factors that affect the performance of data fusion algorithms significantly?

What conditions are favorable to data fusion algorithms?

CombSum and CombMNZ, which one is better? and why?

What is the rationale of using the linear combination method?

How can the best fusion option be found under any given circumstances?"

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

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This book reports on the latest advances on the theories, practices, standards and strategies that are related to the modern technology paradigms, the Mobile Cloud computing (MCC) and Big Data, as the pillars and their association with the emerging 5G mobile networks. The book includes 15 rigorously refereed chapters written by leading international researchers, providing the readers with technical and scientific information about various aspects of Big Data and Mobile Cloud Computing, from basic concepts to advanced findings, reporting the state-of-the-art on Big Data management. It demonstrates and discusses methods and practices to improve multi-source Big Data manipulation techniques, as well as the integration of resources availability through the 3As (Anywhere, Anything, Anytime) paradigm, using the 5G access technologies.

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data.

Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem.

Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.

This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).

This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code". Leslie A. Piegl (Review of the first edition, 2012).

This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computational methods are better suited to handle (potentially huge) group process data than traditional methodologies because of their more flexible assumptions and capability to handle real-time trace data. Indeed, the use of methods under the name of computational social science have exploded over the years. However, attention has been focused on original research rather than pedagogy, leaving those interested in obtaining computational skills lacking a much needed resource. Although the methods here can be applied to wider areas of social science, they are specifically tailored to group process research. A number of data-driven methods adapted to group process research are demonstrated in this current volume. These include text mining, relational event modeling, social simulation, machine learning, social sequence analysis, and response surface analysis. In order to take advantage of these new opportunities, this book provides clear examples (e.g., providing code) of group processes in various contexts, setting guidelines and best practices for future work to build upon. This volume will be of great benefit to those willing to learn computational methods. These include academics like graduate students and faculty, multidisciplinary professionals and researchers working on organization and management science, and consultants for various types of organizations and groups.

Proper analysis of image and multimedia data requires efficient extraction and segmentation techniques. Among the many computational intelligence approaches, the soft computing paradigm is best equipped with several tools and techniques that incorporate intelligent concepts and principles. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data. The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary computation, neural networks, fuzzy sets and fuzzy logic, and rough sets. They then present seven chapters that detail the application of representative techniques to complex image processing tasks such as image recognition, lighting control, target tracking, object extraction, and edge detection. These chapters follow a structured approach with detailed explanations of the problems, solutions, results, and conclusions. This is both a standalone textbook for graduates in computer science, electrical engineering, system science, and information technology, and a reference for researchers and engineers engaged with pattern recognition, image processing, and soft computing.

This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.

This book discusses the development of a theory of info-statics as a sub-theory of the general theory of information. It describes the factors required to establish a definition of the concept of information that fixes the applicable boundaries of the phenomenon of information, its linguistic structure and scientific applications. The book establishes the definitional foundations of information and how the concepts of uncertainty, data, fact, evidence and evidential things are sequential derivatives of information as the primary category, which is a property of matter and energy. The sub-definitions are extended to include the concepts of possibility, probability, expectation, anticipation, surprise, discounting, forecasting, prediction and the nature of past-present-future information structures. It shows that the factors required to define the concept of information are those that allow differences and similarities to be established among universal objects over the ontological and epistemological spaces in terms of varieties and identities. These factors are characteristic and signal dispositions on the basis of which general definitional foundations are developed to construct the general information definition (GID). The book then demonstrates that this definition is applicable to all types of information over the ontological and epistemological spaces. It also defines the concepts of uncertainty, data, fact, evidence and knowledge based on the GID. Lastly, it uses set-theoretic analytics to enhance the definitional foundations, and shows the value of the theory of info-statics to establish varieties and categorial varieties at every point of time and thus initializes the construct of the theory of info-dynamics.

This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful." Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data--now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you'll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head--an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Virtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as "big data", that is, are huge, heterogeneous, complex, distributed, etc. Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services. Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to other fields.

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

This book gathers authoritative contributions in the field of Soft Computing. Based on selected papers presented at the 7th World Conference on Soft Computing, which was held on May 29-31, 2018, in Baku, Azerbaijan, it describes new theoretical advances, as well as cutting-edge methods and applications. New theories and algorithms in fuzzy logic, cognitive modeling, graph theory and metaheuristics are discussed, and applications in data mining, social networks, control and robotics, geoscience, biomedicine and industrial management are described. This book offers a timely, broad snapshot of recent developments, including thought-provoking trends and challenges that are yielding new research directions in the diverse areas of Soft Computing.

This book gathers high-quality papers presented at the International Conference on Smart Trends for Information Technology and Computer Communications (SmartCom 2020), organized by the Global Knowledge Research Foundation (GR Foundation) from 23 to 24 January 2020. It covers the state-of-the-art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications.

This book includes the original, peer-reviewed research from the 2nd International Conference on Emerging Trends in Electrical, Communication and Information Technologies (ICECIT 2015), held in December, 2015 at Srinivasa Ramanujan Institute of Technology, Ananthapuramu, Andhra Pradesh, India. It covers the latest research trends or developments in areas of Electrical Engineering, Electronic and Communication Engineering, and Computer Science and Information.

This book brings together two major trends: data science and blockchains. It is one of the first books to systematically cover the analytics aspects of blockchains, with the goal of linking traditional data mining research communities with novel data sources. Data science and big data technologies can be considered cornerstones of the data-driven digital transformation of organizations and society. The concept of blockchain is predicted to enable and spark transformation on par with that associated with the invention of the Internet. Cryptocurrencies are the first successful use case of highly distributed blockchains, like the world wide web was to the Internet. The book takes the reader through basic data exploration topics, proceeding systematically, method by method, through supervised and unsupervised learning approaches and information visualization techniques, all the way to understanding the blockchain data from the network science perspective. Chapters introduce the cryptocurrency blockchain data model and methods to explore it using structured query language, association rules, clustering, classification, visualization, and network science. Each chapter introduces basic concepts, presents examples with real cryptocurrency blockchain data and offers exercises and questions for further discussion. Such an approach intends to serve as a good starting point for undergraduate and graduate students to learn data science topics using cryptocurrency blockchain examples. It is also aimed at researchers and analysts who already possess good analytical and data skills, but who do not yet have the specific knowledge to tackle analytic questions about blockchain transactions. The readers improve their knowledge about the essential data science techniques in order to turn mere transactional information into social, economic, and business insights.

The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.