In the constant battle between human intelligence and machine intelligence, machines are close to surpassing human intelligence. The unrestrained use of digital technologies in automating processes is one of the prime advantages of the third industrial revolution. As a result, all developed and developing nations have started to digitalize mundane tasks. Thus, digital technologies for information and communication technologies (ICT) have achieved high market space in terms of infrastructure building, employment generation, education sector reforms, funds mobilization, electronic governance, hardware manufacturing, software development, etc. Hence, it is evident that every segment of society has been penetrated by ICT or digitalization. This book attempts to spotlight areas where AI is thriving. FEATURES Impact of digitalization and AI on governance Novel AI practices being followed across the global community in security, healthcare, crime prevention and detection, education, agriculture, sensor networks, etc. Innovative techniques that can be adopted to ensure better quality and better delivery of services to the society Avenues for further research by the research community and student fraternity This book is a guide for university students (especially those from technical backgrounds), industries, NGOs, and policy makers.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process. Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment. In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy. Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.

Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data.

This book provides a first-hand account of business analytics and its implementation, and an account of the brief theoretical framework underpinning each component of business analytics. The themes of the book include (1) learning the contours and boundaries of business analytics which are in scope; (2) understanding the organization design aspects of an analytical organization; (3) providing knowledge on the domain focus of developing business activities for financial impact in functional analysis; and (4) deriving a whole gamut of business use cases in a variety of situations to apply the techniques. The book gives a complete, insightful understanding of developing and implementing analytical solution.

Convergence of Blockchain, AI and IoT: A Digital Platform discusses the convergence of three powerful technologies that play into the digital revolution and blur the lines between biological, digital, and physical objects. This book covers novel algorithms, solutions for addressing issues in applications, security, authentication, and privacy. Discusses innovative technological upgradation and significant challenges in the current era Gives an overview of clinical scientific research that enables smart diagnosis through artificial intelligence Provides an insight into how disruptive technology enabled with the self-running devices and protection mechanism is involved in an augmented reality with blockchain mechanism Talks about neural science being capable of enhancing deep brain waves to predict an overall improvement in human thoughts and behaviours Covers the digital currency mechanism in detail Enhances the knowledge of the readers about smart contract and ledger mechanism with artificial intelligence and blockchain mechanism Targeted audiences range from those interested in the technical revolution of blockchain, big data and the Internet of Things, to research scholars and the professional market.

First book to examine game analysis, modern didactic reflections on learning, and big data in a key topic in science and society today. Provides understanding on how to use game analysis when applied to different sports and how to use the approach for video, event and positional data. Presents translational work that has implications for academics, programmers and applied practitioners.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance - simulation and sampling, as well as experimental design and data collection - that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

For Database Systems and Database Design and Application courses offered at the junior, senior and graduate levels in Computer Science departments. Written by well-known computer scientists, this introduction to database systems offers a comprehensive approach, focusing on database design, database use, and implementation of database applications and database management systems. The first half of the book provides in-depth coverage of databases from the point of view of the database designer, user, and application programmer. It covers the latest database standards SQL:1999, SQL/PSM, SQL/CLI, JDBC, ODL, and XML, with broader coverage of SQL than most other texts. The second half of the book provides in-depth coverage of databases from the point of view of the DBMS implementor. It focuses on storage structures, query processing, and transaction management. The book covers the main techniques in these areas with broader coverage of query optimisation than most other texts, along with advanced topics including multidimensional and bitmap indexes, distributed transactions, and information integration techniques.

Everywhere you look people are talking about data. Buzzwords abound - 'data science', 'machine learning', 'artificial intelligence'. But what does any of it really mean, and most importantly what does it mean for your business? Long-established businesses in many industries find themselves competing with new entrants built entirely on data and analytics. This ground-breaking new book levels the playing field in dramatic fashion. The Average is Always Wrong is a completely pragmatic and hands-on guide to harnessing data to transform your business for the better. Experienced CEO and CMO Ian Shepherd takes you behind the jargon and puts together a powerful change programme anyone can enact in their business right now, to reap the rewards of simple but sophisticated uses of data. Filled with practical examples and case studies, readers will come away with a powerful understanding of the real value of data and the analytical techniques that can drive profit growth.

Although we live in an era in which we are surrounded by an ever-deepening fog of data, few of us truly understand how the data are created, where data are stored, or how to retrieve or destroy data-if that is indeed possible. This book is for all of you, whatever your need or interest. Electronically Stored Information: The Complete Guide to Management, Understanding, Acquisition, Storage, Search, and Retrieval, Second Edition explains the reasons you need to know about electronic data. It also gets into great detail about the how, what, when, and where of what is known in legal circles as electronically stored information (ESI). With easy-to-understand explanations and guidelines, this book provides the practical understanding you need to effectively manage the complex world of ESI. Whether you are an attorney, judge, paralegal, business manager or owner, or just one of the ever-growing population of computer users, you will benefit from the information presented in this book.

Educational Data Mining (EDM) is one of the emerging fields in the pedagogy and andragogy paradigm, it concerns the techniques which research data coming from the educational domain. EDM is a promising discipline which has an imperative impact on predicting students' academic performance. It includes the transformation of existing, and the innovation of new approaches derived from multidisciplinary spheres of influence such as statistics, machine learning, psychometrics, scientific computing etc. An archetype that is covered in this book is that of learning by example. The intention is that reader will easily be able to replicate the given examples and then adapt them to suit their own needs of teaching-learning. The content of the book is based on the research work undertaken by the authors on the theme "Mining of Educational Data for the Analysis and Prediction of Students' Academic Performance". The basic know-how presented in this book can be treated as guide for educational data mining implementation using R and Rattle open source data mining tools. . Technical topics discussed in the book include: * Emerging Research Directions in Educational Data Mining * Design Aspects and Developmental Framework of the System * Model Development - Building Classifiers * Educational Data Analysis: Clustering Approach

The real world is perceived and broken down as data, models and algorithms in the eyes of physicists and engineers. Data is noisy by nature and classical statistical tools have so far been successful in dealing with relatively smaller levels of randomness. The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the standard concept of probabilistic independence to non-commuting random variables. Concretely worked out examples and applications to financial engineering and portfolio construction make this unique book an essential tool for physicists, engineers, data analysts, and economists.

Your logical, linear guide to the fundamentals of data science programming Data science is exploding--in a good way--with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you're a beginning student or already mid-career, get your copy now and add even more meaning to your life--and everyone else's!

The amount of data in our world has been exploding, and analyzing large data sets-so called big data-will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.

A comprehensive examination of blockchain architecture and its key characteristics Blockchain architecture is a way of recording data such that it cannot be altered or falsified. Data is recorded in a kind of digital ledger called a blockchain, copies of which are distributed and stored across a network of participating computer systems. With the advent of cryptocurrencies and NFTs, which are entirely predicated on blockchain technology, and the integration of blockchain architecture into online and high-security networked spaces more broadly, there has never been a greater need for software, network, and financial professionals to be familiar with this technology. Blockchain for Real World Applications provides a practical discussion of this subject and the key characteristics of blockchain architecture. It describes how blockchain technology gains its essential irreversibility and persistency and discusses how this technology can be applied to the information and security needs of different kinds of businesses. It offers a comprehensive overview of the ever-growing blockchain ecosystem and its burgeoning role in a connected world. Blockchain for Real World Applications readers will also find: Treatment of real-world applications such as ID management, encryption, network security, and more Discussion of the UID (Unique Identifier) and its benefits and drawbacks Detailed analysis of privacy issues such as unauthorized access and their possible blockchain-based solutions Blockchain for Real World Applications is a must for professionals in high-security industries, as well as for researchers in blockchain technologies and related areas.

The amount of data in our world has been exploding, and analyzing large data sets-so called big data-will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.

Data Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your organization's clinical, operational, administrative, financial, and research data. By following the guidelines in this book, you will be able to successfully progress through the Alpha, Beta, and Gamma versions, plus fully implement your first production release in about a year. The Alpha version allows you to implement just enough of the basic design pattern to illustrate its core capabilities while loading a small sampling of limited data for demonstration purposes. This provides an easy way for everyone involved to visualize the new warehouse paradigm by actually examining a core subset of the working system. You can finish the Alpha version, also referred to as the proof-of-concept, in as little as 3-4 weeks. The Beta version, which can be completed in about 2-3 months, adds required functionality and much more data. It allows you to get the full warehouse up and running quickly, in order to facilitate longer-term planning, user and support team training, and setup of the operational environment. The Gamma version, which is a fully-functional system-though still lacking data-can be implemented in about 3-4 months. About one year after starting, you will be ready to launch Release 1.0 as a complete and secure data warehouse.

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.

In Mathematical Foundations of Public Key Cryptography, the authors integrate the results of more than 20 years of research and teaching experience to help students bridge the gap between math theory and crypto practice. The book provides a theoretical structure of fundamental number theory and algebra knowledge supporting public-key cryptography. Rather than simply combining number theory and modern algebra, this textbook features the interdisciplinary characteristics of cryptography-revealing the integrations of mathematical theories and public-key cryptographic applications. Incorporating the complexity theory of algorithms throughout, it introduces the basic number theoretic and algebraic algorithms and their complexities to provide a preliminary understanding of the applications of mathematical theories in cryptographic algorithms. Supplying a seamless integration of cryptography and mathematics, the book includes coverage of elementary number theory; algebraic structure and attributes of group, ring, and field; cryptography-related computing complexity and basic algorithms, as well as lattice and fundamental methods of lattice cryptanalysis. The text consists of 11 chapters. Basic theory and tools of elementary number theory, such as congruences, primitive roots, residue classes, and continued fractions, are covered in Chapters 1-6. The basic concepts of abstract algebra are introduced in Chapters 7-9, where three basic algebraic structures of groups, rings, and fields and their properties are explained. Chapter 10 is about computational complexities of several related mathematical algorithms, and hard problems such as integer factorization and discrete logarithm. Chapter 11 presents the basics of lattice theory and the lattice basis reduction algorithm-the LLL algorithm and its application in the cryptanalysis of the RSA algorithm. Containing a number of exercises on key algorithms, the book is suitable for use as a textbook for undergraduate students and first-year graduate students in information security programs. It is also an ideal reference book for cryptography professionals looking to master public-key cryptography.

It's axiomatic to state that people fear what they do not understand, and this is especially true when it comes to technology. However, despite their prevalence, computers remain shrouded in mystery, and many users feel apprehensive when interacting with them. Smartphones have only exacerbated the issue. Indeed, most users of these devices leverage only a small fraction of the power they hold in their hands. How Things Work: The Computer Science Edition is a roadmap for readers who want to overcome their technophobia and harness the full power of everyday technology. Beginning with the basics, the book demystifies the mysterious world of computer science, explains its fundamental concepts in simple terms, and answers the questions many users feel too intimidated to ask. By the end of the book, readers will understand how computers and smart devices function and, more important, how they can make these devices work for them. To complete the picture, the book also introduces readers to the darker side of modern technology: security and privacy concerns, identity theft, and threats from the Dark Web.

Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis

Data has never mattered more. Our lives are increasingly shaped by it and how it is defined, collected and used. But who counts in the collection, analysis and application of data? This important book is the first to look at queer data - defined as data relating to gender, sex, sexual orientation and trans identity/history. The author shows us how current data practices reflect an incomplete account of LGBTQ lives and helps us understand how data biases are used to delegitimise the everyday experiences of queer people. Guyan demonstrates why it is important to understand, collect and analyse queer data, the benefits and challenges involved in doing so, and how we might better use queer data in our work. Arming us with the tools for action, this book shows how greater knowledge about queer identities is instrumental in informing decisions about resource allocation, changes to legislation, access to services, representation and visibility.

A hands on guide to web scraping and text mining for both beginners and experienced users of R * Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. * Provides basic techniques to query web documents and data sets (XPath and regular expressions). * An extensive set of exercises are presented to guide the reader through each technique. * Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. * Case studies are featured throughout along with examples for each technique presented. * R code and solutions to exercises featured in the book are provided on a supporting website.

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka

Information security has a major gap when cryptography is implemented. Cryptographic algorithms are well defined, key management schemes are well known, but the actual deployment is typically overlooked, ignored, or unknown. Cryptography is everywhere. Application and network architectures are typically well-documented but the cryptographic architecture is missing. This book provides a guide to discovering, documenting, and validating cryptographic architectures. Each chapter builds on the next to present information in a sequential process. This approach not only presents the material in a structured manner, it also serves as an ongoing reference guide for future use.