Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Covers AI and ML for digital health and wellbeing in society 5.0 Presents how putting Knowledge Management systems in place can facilitate the flow of information and result in better and more informed decisions. Discusses applications of distruptive technologies in digital health and wellbeing Offers examples of implementation of Knowledge Management along with case studies Addresses the challenges associated with practical implementation and adoption of technologies in healthcare

The use of biometric identification systems is rapidly increasing across the world, owing to their potential to combat terrorism, fraud, corruption and other illegal activities. However, critics of the technology complain that the creation of an extensive central register of personal information controlled by the government will increase opportunities for the state to abuse citizens. There is also concern about the extent to which data about an individual is recorded and kept. This book reviews some of the most current and complex legal and ethical issues relating to the use of biometrics. Beginning with an overview of biometric systems, the book goes on to examine some of the theoretical underpinnings of the surveillance state, questioning whether these conceptual approaches are still relevant, particularly the integration of ubiquitous surveillance systems and devices. The book also analyses the implementation of the world's largest biometric database, Aadhaar, in detail. Additionally, the identification of individuals at border checkpoints in the United States, Australia and the EU is explored, as well as the legal and ethical debates surrounding the use of biometrics regarding: the war on terror and the current refugee crisis; violations of international human rights law principles; and mobility and privacy rights. The book concludes by addressing the collection, use and disclosure of personal information by private-sector entities such as Axciom and Facebook, and government use of these tools to profile individuals. By examining the major legal and ethical issues surrounding the debate on this rapidly emerging technology, this book will appeal to students and scholars of law, criminology and surveillance studies, as well as law enforcement and criminal law practitioners.

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product). This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumil Kaminski is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumil is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem. Pawel Pralat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators. Francois Theberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.

provides a thorough understanding of the integration of computational intelligence with information retrieval includes discussion on protecting and analysing big data on cloud platforms provides a plethora of theoretical as well as experimental research, along with surveys and impact studies

This handbook provides a computational perspective on green computing and blockchain technologies. It presents not only how to identify challenges using a practical approach but also how to develop strategies for addressing industry challenges. Handbook of Green Computing and Blockchain Technologies takes a practical-oriented approach, including solved examples and highlights standardization, industry bodies, and initiatives. Case studies provide a deeper understanding of blockchain and are related to real-time scenarios. The handbook analyzes current research and development in green computing and blockchain analytics, studies existing related standards and technologies, and provides results on implementation, challenges, and issues in today's society. FEATURES Analyzes current research developments in green computing and blockchain analytics Provides an analysis of implementation challenges and solutions Offers innovations in the decentralization process for the application of blockchain in areas such as healthcare, government services, agriculture, supply chain, financial, ecommerce, and more Discusses the impact of this technology on people's lives, the way they work and learn, and highlights standardization, industry bodies, and initiatives This handbook will benefit researchers, software developers, and undergraduate and postgraduate students in industrial systems, manufacturing, information technology, computer science, manufacturing, communications, and electrical engineering.

Introduces design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt and adapt. Free from endless derivations equations are presented and explained strategically, explaining why it is imperative to use them and how they will help in your task at hand. Illustrations and simple explanation help readers visualize and absorb easily, difficult to understand concepts.

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.

This book presents a comprehensive overview of the key topics, best practices, future opportunities and challenges in the Digital Marketing discourse. With contributions from world-renowned experts, the book covers: * Big Data, Artificial Intelligence and Analytics in Digital Marketing * Emerging technologies and how they can enhance User Experience * How 'digital' is changing servicescapes * Issues surrounding ethics and privacy * Current and future issues surrounding Social Media * Key considerations for the future of Digital Marketing * Case studies and examples from real-life organisations Unique in its rigorous, research-driven and accessible approach to the subject of Digital Marketing, this text is valuable supplementary reading for advanced undergraduate and postgraduate students studying Digital and Social Media Marketing, Customer Experience Management, Digital Analytics and Digital Transformation.

Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.

Around the third millennium B.C. in the Fertile Crescent, a world-system with a single world-economy, covering very vast regions, began to form. Mesopotamia became the center of this world-system. This was possible due to the development of common commercial law and logical competence there. The expansion of the world-economy during the Silk Road period from the 4th century B.C. to the early 5th century A.D. across various countries of Eurasia was accompanied by the spread of logical competence, first formed in Mesopotamia, as a mechanism of legal hermeneutics to draw logical conclusions without fallacies. This competence was simultaneously comprehended in different cultures connected by the Great Silk Road - in ancient Greek logic (4th - 2nd centuries B.C.); ancient Chinese proto-logic (5th - 2nd centuries B.C.); Judaic logical hermeneutics (1st - 2nd centuries A.D.); and in Indian-Buddhist logic (2nd - 6th centuries A.D.). The book analyzes the emergence of logic and its spread and early forms of its reflection. Consequently, logical competence is seen not as an innate ability, but as a social practice first established in Mesopotamia. Logic as a science became possible only after the development of logical competence as an accepted social practice. On the other hand, this view is a non-Marxist assessment of the early form of the world-system, centered on international law and logical competence, which made the world-economy and international trade then possible.

Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies

This book serves the need for developing an insight and understanding of the cutting-edge innovation in Cloud technology. It provides an understanding of cutting-edge innovations, paradigms, and security by using real-life applications, case studies, and examples. This book provides a holistic view of cloud technology theories, practices, and future applications with real-life examples. It comprehensively explains cloud technology, design principles, development trends, maintaining state-of-the-art cloud computing and software services. It describes how cloud technology can transform the operating contexts of business enterprises. It exemplifies the potential of cloud computing for next-generation computational excellence and the role it plays as a key driver for the 4th industrial revolution in Industrial Engineering and a key driver for manufacturing industries. Researchers, academicians, postgraduates, and industry specialists will find this book of interest.

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.

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.

How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive models? Build beginning-to-end workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data

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

This is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces.

From parsing to indexing, clustering to classification, retrieval to ranking, and user feedback to retrieval evaluation, all of the most important concepts are carefully introduced and exemplified. The contents and structure of the book have been carefully designed by the two main authors, with individual contributions coming from leading international authorities in the field, including Yoelle Maarek, Senior Director of Yahoo Research Israel; Dulce Ponceleon IBM Research; and Malcolm Slaney, Yahoo Research USA.

This completely reorganized, revised and enlarged second edition of "Modern Information Retrieval" contains many new chapters and double the number of pages and bibliographic references of the first edition, and a companion website www.mir2ed.org with teaching material. It will prove invaluable to students, professors, researchers, practitioners, and scholars of this fascinating field of information retrieval.

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.

The book Security of Internet of Things Nodes: Challenges, Attacks, and Countermeasures (R) covers a wide range of research topics on the security of the Internet of Things nodes along with the latest research development in the domain of Internet of Things. It also covers various algorithms, techniques, and schemes in the field of computer science with state-of-the-art tools and technologies. This book mainly focuses on the security challenges of the Internet of Things devices and the countermeasures to overcome security vulnerabilities. Also, it highlights trust management issues on the Internet of Things nodes to build secured Internet of Things systems. The book also covers the necessity of a system model for the Internet of Things devices to ensure security at the hardware level.

The book discusses the activities involved in developing an Enterprise Continuity Program (ECP) that will cover both Business Continuity Management (BCM) as well as Disaster Recovery Management (DRM). The creation of quantitative metrics for BCM are discussed as well as several models and methods that correspond to the goals and objectives of the International Standards Organisation (ISO) Technical Committee ISO/TC 292 ""Security and resilience". Significantly, the book contains the results of not only qualitative, but also quantitative, measures of Cyber Resilience which for the first time regulates organizations' activities on protecting their critical information infrastructure. The book discusses the recommendations of the ISO 22301: 2019 standard "Security and resilience - Business continuity management systems - Requirements" for improving the BCM of organizations based on the well-known "Plan-Do-Check-Act" (PDCA) model. It also discusses the recommendations of the following ISO management systems standards that are widely used to support BCM. The ISO 9001 standard ""Quality Management Systems""; ISO 14001 ""Environmental Management Systems""; ISO 31000 ""Risk Management"", ISO/IEC 20000-1 ""Information Technology - Service Management"", ISO/IEC 27001 ""Information Management security systems", ISO 28000 "Specification for security management systems for the supply chain", ASIS ORM.1-2017, NIST SP800-34, NFPA 1600: 2019, COBIT 2019, RESILIA, ITIL V4 and MOF 4.0, etc. The book expands on the best practices of the British Business Continuity Institute's Good Practice Guidelines (2018 Edition), along with guidance from the Disaster Recovery Institute's Professional Practices for Business Continuity Management (2017 Edition). Possible methods of conducting ECP projects in the field of BCM are considered in detail. Based on the practical experience of the author there are examples of Risk Assessment (RA) and Business Impact Analysis (BIA), examples of Business Continuity Plans (BCP) & Disaster Recovery Plans (DRP) and relevant BCP & DRP testing plans. This book will be useful to Chief Information Security Officers, internal and external Certified Information Systems Auditors, senior managers within companies who are responsible for ensuring business continuity and cyber stability, as well as teachers and students of MBA's, CIO and CSO programs.

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand 'spatial process' and develop spatial analytics; how to develop 'useful' predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and 'Planning' are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.

Explores the potential for disruptive technologies in different industries Addresses the challenges associated with the practical implementation or adoption of disruptive technologies Discusses topics related to IoT, Machine Learning, Sensors, Artificial Intelligence and Cloud Computing for Industry 4.0 and 5.0 Includes examples of the implementation of Industry 4.0 and 5.0 in various sectors, including engineering Contains many case studies for easy comprehension.

The term "data" being mostly used, experimented, analyzed, and researched, "Data Science and its Applications" finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications.

- 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