Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies

Adding one and one makes two, usually. But sometimes things add up to more than the sum of their parts. This observation, now frequently expressed in the maxim "more is different", is one of the characteristic features of complex systems and, in particular, complex networks. Along with their ubiquity in real world systems, the ability of networks to exhibit emergent dynamics, once they reach a certain size, has rendered them highly attractive targets for research. The resulting network hype has made the word "network" one of the most in uential buzzwords seen in almost every corner of science, from physics and biology to economy and social sciences. The theme of "more is different" appears in a different way in the present v- ume, from the viewpoint of what we call "adaptive networks." Adaptive networks uniquely combine dynamics on a network with dynamical adaptive changes of the underlying network topology, and thus they link classes of mechanisms that were previously studied in isolation. Here adding one and one certainly does not make two, but gives rise to a number of new phenomena, including highly robust se- organization of topology and dynamics and other remarkably rich dynamical beh- iors.

A systematic overview of concepts in Medical Internet of Things (MIoT) has been included. Recent research and some pointers to future advancements in areas of MIoT have been discussed. Examples and case studies have been included. Written in easily understandable style with the help of numerous figures and dataset

The key competing texts are practitioner-focused 'how to' guides, whilst our book combines rigorous theory with practical insight and examples, with authors from both the academic and business world, making it more adoptable as a student text; Unlike other books on the subject, this has a customer focus and an exploration of how big data can add value to customers as well as organisations; Enables readers to move from "big data" to "big solutions" by demonstrating how to integrate data analytics into specific goals and processes for implementation; Highly successful and well regarded both for students and practitioners

The main purpose of this book is to sum up the vital and highly topical research issue of knowledge representation on the Web and to discuss novel solutions by combining benefits of folksonomies and Web 2.0 approaches with ontologies and semantic technologies. The book contains an overview of knowledge representation approaches in past, present and future, introduction to ontologies, Web indexing and in first case the novel approaches of developing ontologies. combines aspects of knowledge representation for both the Semantic Web (ontologies) and the Web 2.0 (folksonomies). Currently there is no monographic book which provides a combined overview over these topics. focus on the topic of using knowledge representation methods for document indexing purposes. For this purpose, considerations from classical librarian interests in knowledge representation (thesauri, classification schemes etc.) are included, which are not part of most other books which have a stronger background in computer science.

This book develops an IT strategy for cloud computing that helps businesses evaluate their readiness for cloud services and calculate the ROI. The framework provided helps reduce risks involved in transitioning from traditional "on site" IT strategy to virtual "cloud computing." Since the advent of cloud computing, many organizations have made substantial gains implementing this innovation. Cloud computing allows companies to focus more on their core competencies, as IT enablement is taken care of through cloud services. Cloud Computing and ROI includes case studies covering retail, automobile and food processing industries. Each of these case studies have successfully implemented the cloud computing framework and their strategies are explained. As cloud computing may not be ideal for all businesses, criteria are also offered to help determine if this strategy should be adopted.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm's productivity.

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.

Covers evolutionary approaches to solve optimization problems in biomedical engineering. Discusses IoT, Cloud computing, data analytics in healthcare informatics. Provides computational intelligence-based solution for diagnosis of diseases. Reviews modelling and simulations in designing of biomedical equipment. Promotes machine learning based approaches to improvements in biomedical engineering problems.

Presents how to create an effective entrepreneurship business plan Provides an overview of various aspects of entrepreneurship, function and the contemporary forms Offers a real-world understand of the entrepreneurial world with new analytics thinking and what computational skills are needed for the newer generation in handling big data challenges Encompasses the concepts which an entrepreneur must know before embarking on an entrepreneurial journey Includes inspirational case studies from "Change Leaders".

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB (R), Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third EditionThis third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web ResourceThe authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.

Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science.

This brilliant textbook explains in detail the principles of conceptual modeling independently from particular methods and languages and shows how to apply them in real-world projects. The author covers all aspects of the engineering process from structural modeling over behavioral modeling to meta-modeling, and completes the presentation with an extensive case study based on the osCommerce system. Written for computer science students in classes on information systems modeling as well as for professionals feeling the need to formalize their experiences or to update their knowledge, Olive delivers here a comprehensive treatment of all aspects of the modeling process. His book is complemented by lots of exercises and additional online teaching material."

This book examines the FinTech revolution from a data privacy perspective. It analyzes key players on the FinTech market and the developments in various market segments. Particular attention is paid to an empirical analysis of the privacy statements of 505 German FinTech firms and how they were adapted after the General Data Protection Regulation (GDPR) entered into effect in May 2018. The analysis also includes 38 expert interviews with relevant stakeholders from supervisory and regulatory authorities, the financial and FinTech industry, leading consulting firms and consumer protection agencies. By adopting this approach, the book identifies key regulatory needs, offers a valuable asset for practitioners and academics alike, and shares intriguing insights for lawyers, economists and everyone interested in FinTech and data privacy.

Everyday technology is constantly changing, and it's hard to keep up with it at times. What is all this talk about automation, STEM, analytics and super-computers, and how will it really affect my daily life at work and in the home? This book is a simple guide to everyday technology and analytics written in plain language. It starts with explaining how computer networks are increasing in speed so fast that we can do more in less time than ever before. It explains the analytical jargon in plain English and why robotics in the home will be aided by the new technology of the quantum computer. Richly furnished with over 200 illustrations, photos and with minimal equations, A Simple Guide to Technology and Analytics is a ready reference book for those times when you don't really understand the technology and analytics being talked about. It explains complicated topics such as automated character recognition in a very simple way, and has simple exercises for the reader to fully understand the technology (with answers at the back). It even has explanations on how home appliances work, which are very useful the next time you go shopping for a microwave or TV. Even the Glossary at the back can be used as a quick look-up explanation for those on the go.

It is becoming increasingly important to design and develop adaptive, robust, scalable, reliable, security and privacy mechanisms for IoT applications and for Industry 4.0 related concerns. This book serves as a useful guide for researchers and industry professionals and will help beginners to learn the basics to the more advanced topics. Along with exploring security and privacy issues through the IoT ecosystem and examining its implications to the real-world, this book addresses cryptographic tools and techniques and presents the basic and high-level concepts that can serve as guidance for those in the industry as well as help beginners get a handle on both the basic and advanced aspects of security related issues. The book goes on to cover major challenges, issues, and advances in IoT and discusses data processing as well as applications for solutions, and assists in developing self-adaptive cyberphysical security systems that will help with issues brought about by new technologies within IoT and Industry 4.0. This edited book discusses the evolution of IoT and Industry 4.0 and brings security and privacy related technological tools and techniques onto a single platform so that researchers, industry professionals, graduate, postgraduate students, and academicians can easily understand the security, privacy, challenges and opportunity concepts and make then ready to use for applications in IoT and Industry 4.0.

Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.

- The book shows you what 'data science' actually is and focuses uniquely on how to minimize the negatives of (bad) data science - It discusses the actual place of data science in a variety of companies, and what that means for the process of data science - It provides 'how to' advice to both individuals and managers - It takes a critical approach to data science and provides widely-relatable examples

This book brings together multi-disciplinary research and practical evidence about the role and exploitation of big data in driving and supporting innovation in tourism. It also provides a consolidated framework and roadmap summarising the major issues that both researchers and practitioners have to address for effective big data innovation. The book proposes a process-based model to identify and implement big data innovation strategies in tourism. This process framework consists of four major parts: 1) inputs required for big data innovation; 2) processes required to implement big data innovation; 3) outcomes of big data innovation; and 4) contextual factors influencing big data exploitation and advances in big data exploitation for business innovation.

Blockchain is the popular name given to the exciting, evolving world of distributed ledger technology (DLT). Blockchains offer equitable and secure access to data, as well as transparency and immutability. Organisations can decide to use blockchain to upgrade whatever ledgers they are currently deploying (for example, relational databases, spreadsheets and cumbersome operating models) for their data and technology stack in terms of books and records, transactions, storage, production services and in many other areas. This book describes the applied use of blockchain technology in the enterprise world. Written by two expert practitioners in the field, the book is in two main parts: (1) an introduction to the history of, and a critical context explainer about, the emergence of blockchain written in natural language and providing a tour of the features, functionality and challenges of blockchain and DLT; and (2) a series of six applied organisational use cases in (i) trade finance, (ii) healthcare, (iii) retail savings & investments, (iv) real estate, (v) central bank digital currencies (CBDC) and (vi) fund management that offer the reader a straightforward, easy-to-read comparison between 'old world' technology (such as platforms, people and processes) versus what blockchain ledgers offer to enterprises and organisations in terms of improved efficiency, performance, security and access to business data. Blockchain is sometimes tainted by association to Bitcoin, Onecoin and others. But as cryptocurrencies and stock markets continue to rise and fall with volatility and the world economy emerges changed by coronavirus, working from home and the threat of inflation, many enterprises, organisations and governments are looking again at the powerful features of blockchain and wondering how DLT may help them adapt. This book is an ideal introduction to the practical and applied nature of blockchain and DLT solutions for business executives, business students, managers, C-suite senior leaders, software architects and policy makers and sets out, clearly and professionally, the benefits and challenges of the actual business applications of blockchain.

Blockchain is the popular name given to the exciting, evolving world of distributed ledger technology (DLT). Blockchains offer equitable and secure access to data, as well as transparency and immutability. Organisations can decide to use blockchain to upgrade whatever ledgers they are currently deploying (for example, relational databases, spreadsheets and cumbersome operating models) for their data and technology stack in terms of books and records, transactions, storage, production services and in many other areas. This book describes the applied use of blockchain technology in the enterprise world. Written by two expert practitioners in the field, the book is in two main parts: (1) an introduction to the history of, and a critical context explainer about, the emergence of blockchain written in natural language and providing a tour of the features, functionality and challenges of blockchain and DLT; and (2) a series of six applied organisational use cases in (i) trade finance, (ii) healthcare, (iii) retail savings & investments, (iv) real estate, (v) central bank digital currencies (CBDC) and (vi) fund management that offer the reader a straightforward, easy-to-read comparison between 'old world' technology (such as platforms, people and processes) versus what blockchain ledgers offer to enterprises and organisations in terms of improved efficiency, performance, security and access to business data. Blockchain is sometimes tainted by association to Bitcoin, Onecoin and others. But as cryptocurrencies and stock markets continue to rise and fall with volatility and the world economy emerges changed by coronavirus, working from home and the threat of inflation, many enterprises, organisations and governments are looking again at the powerful features of blockchain and wondering how DLT may help them adapt. This book is an ideal introduction to the practical and applied nature of blockchain and DLT solutions for business executives, business students, managers, C-suite senior leaders, software architects and policy makers and sets out, clearly and professionally, the benefits and challenges of the actual business applications of blockchain.

In recent years, the fast-paced development of social information and networks has led to the explosive growth of data. A variety of big data have emerged, encouraging researchers to make business decisions by analysing this data. However, many challenges remain, especially concerning data security and privacy. Big data security and privacy threats permeate every link of the big data industry chain, such as data production, collection, processing, and sharing, and the causes of risk are complex and interwoven. Blockchain technology has been highly praised and recognised for its decentralised infrastructure, anonymity, security, and other characteristics, and it will change the way we access and share information. In this book, the author demonstrates how blockchain technology can overcome some limitations in big data technology and can promote the development of big data while also helping to overcome security and privacy challenges. The author investigates research into and the application of blockchain technology in the field of big data and assesses the attendant advantages and challenges while discussing the possible future directions of the convergence of blockchain and big data. After mastering concepts and technologies introduced in this work, readers will be able to understand the technical evolution, similarities, and differences between blockchain and big data technology, allowing them to further apply it in their development and research. Author: Shaoliang Peng is the Executive Director and Professor of the College of Computer Science and Electronic Engineering, National Supercomputing Centre of Hunan University, Changsha, China. His research interests are high-performance computing, bioinformatics, big data, AI, and blockchain.

Provides insight into the skill set that requires leveraging strength to move further to act as a good data analyst Discusses how big data along with deep learning holds the potential to significantly increase data understanding and in turn, helps to make decisions Covers the numerous potential applications in healthcare, education, communications, media, and the entertainment industry Offers innovative platforms for integrating big data and deep learning Presents issues related to adequate data storage, sematic indexing, data tagging, and fast information retrieval from big data

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory.* Details the pragmatic requirements for real-world General Intelligence.* Describes how machine learning fails to meet these requirements.* Provides a philosophical basis for the proposed approach.* Provides mathematical detail for a reference architecture.* Describes a research program intended to address issues of concern in contemporary AI.The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential conceptsThis is an open access book.