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.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis. Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This new volume looks at the electrifying world of blockchain technology and how it has been revolutionizing the Internet of Things and cyber-physical systems. Aimed primarily at business users and developers who are considering blockchain-based projects, the volume provides a comprehensive introduction to the theoretical and practical aspects of blockchain technology. It presents a selection of chapters on topics that cover new information on blockchain and bitcoin security, IoT security threats and attacks, privacy issues, fault-tolerance mechanisms, and more. Some major software packages are discussed, and it also addresses the legal issues currently affecting the field. The information presented here is relevant to current and future problems relating to blockchain technology and will provide the tools to build efficient decentralized applications. Blockchain technology and the IoT can profoundly change how the world-and businesses-work, and this book provides a window into the current world of blockchain. No longer limited to just Bitcoin, blockchain technology has spread into many sectors and into a significant number of different technologies.

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

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics?including classification, clustering, statistical learning, association analysis, and link mining?in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

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.

Google's Programmable Search Engines (PSEs, previously called Custom Search Engines) provide search opportunities that are unavailable with any other tool. PSEs have advanced settings and search operators that are not supported by "regular" Google. With PSEs, it is possible to perform filtered searches within parts of the web as if they were databases! While lots of professionals use existing PSEs to source for talent or with other research goals, few people have experience creating them. Even fewer know about powerful PSE-only search operators. The main reason PSEs are not as popular as they should be is that it is not easy to get educated on PSE creation. There is little information online and no books (other than this one) on the subject. Even less info is available on the "structured" operators that allow for filtered searches. The first of its kind, this book hopes to popularize these fun and powerful tools so that many more people can include PSEs in their work. Key Features: A detailed introduction to creating PSEs, including info absent in Google's help A "hack" for creating PSEs that look for profiles in seconds An introduction to advanced PSE-only search operators allowed to perform filtered searches of parts of the web A "hack" for expanding Google's search limits to 500 terms Use cases, examples, and approaches that would be educational for those doing online research This book will be of interest to researchers, OSINT specialists, investigative journalists, Competitive Intelligence people, recruiters, and Sourcers, to name a few categories, and to the general public interested in how to search better.

i. This book will contain AI, ML, DL, big data and security never before considered ii. Innovative artificial intelligence techniques and algorithms iii. Only emerging from recent research and development, e.g. AI for big data from security perspective, which are not covered in any existing texts iv. Artificial Intelligence for big data and security Applications with advanced features v. Key new finding of machine learning and deep learning for Security Applications

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.

This book explores the phenomenon of data - big and small - in the contemporary digital, informatic and legal-bureaucratic context. Challenging the way in which legal interest in data has focused on rights and privacy concerns, this book examines the contestable, multivocal and multifaceted figure of the contemporary data subject. The book analyses "data" and "personal data" as contemporary phenomena, addressing the data realms, such as stores, institutions, systems and networks, out of which they emerge. It interrogates the role of law, regulation and governance in structuring both formal and informal definitions of the data subject, and disciplining data subjects through compliance with normative standards of conduct. Focusing on the 'personal' in and of data, the book pursues a re-evaluation of the nature, role and place of the data subject qua legal subject in on and offline societies: one that does not begin and end with the inviolability of individual rights but returns to more fundamental legal principles suited to considerations of personhood, such as stewardship, trust, property and contract. The book's concern with the production, use, abuse and alienation of personal data within the context of contemporary communicative capitalism will appeal to scholars and students of law, science and technology studies, and sociology; as well as those with broader political interests in this area.

A field manual on contextualizing cyber threats, vulnerabilities, and risks to connected cars through penetration testing and risk assessment Hacking Connected Cars deconstructs the tactics, techniques, and procedures (TTPs) used to hack into connected cars and autonomous vehicles to help you identify and mitigate vulnerabilities affecting cyber-physical vehicles. Written by a veteran of risk management and penetration testing of IoT devices and connected cars, this book provides a detailed account of how to perform penetration testing, threat modeling, and risk assessments of telematics control units and infotainment systems. This book demonstrates how vulnerabilities in wireless networking, Bluetooth, and GSM can be exploited to affect confidentiality, integrity, and availability of connected cars. Passenger vehicles have experienced a massive increase in connectivity over the past five years, and the trend will only continue to grow with the expansion of The Internet of Things and increasing consumer demand for always-on connectivity. Manufacturers and OEMs need the ability to push updates without requiring service visits, but this leaves the vehicle's systems open to attack. This book examines the issues in depth, providing cutting-edge preventative tactics that security practitioners, researchers, and vendors can use to keep connected cars safe without sacrificing connectivity. Perform penetration testing of infotainment systems and telematics control units through a step-by-step methodical guide Analyze risk levels surrounding vulnerabilities and threats that impact confidentiality, integrity, and availability Conduct penetration testing using the same tactics, techniques, and procedures used by hackers From relatively small features such as automatic parallel parking, to completely autonomous self-driving cars--all connected systems are vulnerable to attack. As connectivity becomes a way of life, the need for security expertise for in-vehicle systems is becoming increasingly urgent. Hacking Connected Cars provides practical, comprehensive guidance for keeping these vehicles secure.

Even though the semantic Web is a relatively new and dynamic area of research, a whole suite of components, standards, and tools have already been developed around it. Using a concrete approach, Introduction to the Semantic Web and Semantic Web Services builds a firm foundation in the concept of the semantic Web, its principal technologies, its real-world applications, and its relevant coding examples. This introductory yet comprehensive book covers every facet of this exciting technology. After an introduction to the semantic Web concept, it discusses its major technical enablers and the relationships among these components. The author then presents several applications of the semantic Web, including Swoogle, FOAF, and a detailed design of a semantic Web search engine. The book concludes with discussions on how to add semantics to traditional Web service descriptions and how to develop a search engine for semantic Web services. Covering the building blocks of an advanced Web technology, this practical resource equips you with the tools to further explore the world of the semantic Web on your own.

The only official body of knowledge for CCSP--the most popular cloud security credential--fully revised and updated. Certified Cloud Security Professional (CCSP) certification validates the advanced technical skills needed to design, manage, and secure data, applications, and infrastructure in the cloud. This highly sought-after global credential has been updated with revised objectives. The new third edition of The Official (ISC)2 Guide to the CCSP CBK is the authoritative, vendor-neutral common body of knowledge for cloud security professionals. This comprehensive resource provides cloud security professionals with an indispensable working reference to each of the six CCSP domains: Cloud Concepts, Architecture and Design; Cloud Data Security; Cloud Platform and Infrastructure Security; Cloud Application Security; Cloud Security Operations; and Legal, Risk and Compliance. Detailed, in-depth chapters contain the accurate information required to prepare for and achieve CCSP certification. Every essential area of cloud security is covered, including implementation, architecture, operations, controls, and immediate and long-term responses. Developed by (ISC)2, the world leader in professional cybersecurity certification and training, this indispensable guide: Covers the six CCSP domains and over 150 detailed objectives Provides guidance on real-world best practices and techniques Includes illustrated examples, tables, and diagrams The Official (ISC)2 Guide to the CCSP CBK is a vital ongoing resource for IT and information security leaders responsible for applying best practices to cloud security architecture, design, operations and service orchestration.

The IT Security Governance Guidebook with Security Program Metrics provides clear and concise explanations of key issues in information protection, describing the basic structure of information protection and enterprise protection programs. Including graphics to support the information in the text, this book includes both an overview of material as well as detailed explanations of specific issues. The accompanying downloadable resources offers a collection of metrics, formed from repeatable and comparable measurement, that are designed to correspond to the enterprise security governance model provided in the text, allowing an enterprise to measure its overall information protection program.

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

- Curating Social Data - Summarizing Social Data - Analyzing Social Data - Social Data Analytics Applications: Trust, Recommender Systems, Cognitive Analytics

This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings.

Security without Obscurity: Frequently Asked Questions (FAQ) complements Jeff Stapleton's three other Security without Obscurity books to provide clear information and answers to the most commonly asked questions about information security (IS) solutions that use or rely on cryptography and key management methods. There are good and bad cryptography, bad ways of using good cryptography, and both good and bad key management methods. Consequently, information security solutions often have common but somewhat unique issues. These common and unique issues are expressed as an FAQ organized by related topic areas. The FAQ in this book can be used as a reference guide to help address such issues. Cybersecurity is based on information technology (IT) that is managed using IS controls, but there is information, misinformation, and disinformation. Information reflects things that are accurate about security standards, models, protocols, algorithms, and products. Misinformation includes misnomers, misunderstandings, and lack of knowledge. Disinformation can occur when marketing claims either misuse or abuse terminology, alluding to things that are inaccurate or subjective. This FAQ provides information and distills misinformation and disinformation about cybersecurity. This book will be useful to security professionals, technology professionals, assessors, auditors, managers, and hopefully even senior management who want a quick, straightforward answer to their questions. It will serve as a quick reference to always have ready on an office shelf. As any good security professional knows, no one can know everything.

The book examines patterns of participation in human rights treaties. International relations theory is divided on what motivates states to participate in treaties, specifically human rights treaties. Instead of examining the specific motivations, this dissertation examines patterns of participation. In doing so, it attempts to match theoretical expectations of state behavior with participation. The conclusion of this study is that the data suggests there are multiple motivations that lead states to participate in human rights treaties. The book is divided into five substantive chapters. After an introduction, the second chapter examines the literature on why states join treaties in general, and human rights treaties in particular. The third chapter reviews the obligations states commit to under the fifteen treaties under consideration. The fourth chapter uses basic quantitative methods to examine any differences in the participation rates between democratic and non-democratic states. The fifth chapter examines reservations, declarations, and objections made in conjuncture with the fifteen treaties. The chapter employs both quantitative and qualitative methods to determine if there are substantial differences between democratic and non-democratic states. Finally, the sixth chapter examines those states that participate in the most human rights treaties to determine if there are characteristics that help to identify these states. Additionally, the chapter examines and evaluates theoretical predictions about participation.

The Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.

Glen Goodman's goal was to retire young and wealthy, escaping the daily grind. He taught himself how to trade everything from shares to Bitcoin and made enough money to realise his dream and quit his day job while still in his 30s. In The Crypto Trader, Glen will show you exactly how he made huge profits trading Bitcoin, Ethereum, Ripple and more, so that you can do it too - without risking your shirt. Glen publicly called the top of the market in December 2017 and took his profits before the crash. But there are still tons of trading opportunities out there and Glen continues to trade crypto successfully. Inside you'll see his multi-hundred-percent gains on a raft of cryptocurrencies and learn how he builds his profits and holds onto them. Glen reveals all his trading strategies, the proven methods and rules that make him one of the most followed traders in the world on social media. (He is also frequently interviewed by the BBC, Forbes and LBC, and is a contributing expert on cryptocurrency at the London School of Economics.) It took Glen years of study and trial and error to become a consistent money maker. He learnt his trading lessons the hard way - so you don't have to. With The Crypto Trader by your side, you'll learn how to grab opportunities, make money - and keep it.

Developing and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group. The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big data and analytics in: Ticket pricing Season ticket member retention Fan engagement Sponsorship valuation Customer relationship management Digital marketing Market research Data visualization. This book examines changes in the ticketing marketplace and spotlights innovative ticketing strategies used in various sport organizations. It shows how to engage fans with social media and digital analytics, presents techniques to analyze engagement and marketing strategies, and explains how to utilize analytics to leverage fan engagement to enhance revenue for sport organizations. Filled with insightful case studies, this book benefits both sports business professionals and students. The concluding chapter on teaching sport analytics further enhances its value to academics.

Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor's manual are also provided as online supplements. This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas.

The ethics of data and analytics, in many ways, is no different than any endeavor to find the "right" answer. When a business chooses a supplier, funds a new product, or hires an employee, managers are making decisions with moral implications. The decisions in business, like all decisions, have a moral component in that people can benefit or be harmed, rules are followed or broken, people are treated fairly or not, and rights are enabled or diminished. However, data analytics introduces wrinkles or moral hurdles in how to think about ethics. Questions of accountability, privacy, surveillance, bias, and power stretch standard tools to examine whether a decision is good, ethical, or just. Dealing with these questions requires different frameworks to understand what is wrong and what could be better. Ethics of Data and Analytics: Concepts and Cases does not search for a new, different answer or to ban all technology in favor of human decision-making. The text takes a more skeptical, ironic approach to current answers and concepts while identifying and having solidarity with others. Applying this to the endeavor to understand the ethics of data and analytics, the text emphasizes finding multiple ethical approaches as ways to engage with current problems to find better solutions rather than prioritizing one set of concepts or theories. The book works through cases to understand those marginalized by data analytics programs as well as those empowered by them. Three themes run throughout the book. First, data analytics programs are value-laden in that technologies create moral consequences, reinforce or undercut ethical principles, and enable or diminish rights and dignity. This places an additional focus on the role of developers in their incorporation of values in the design of data analytics programs. Second, design is critical. In the majority of the cases examined, the purpose is to improve the design and development of data analytics programs. Third, data analytics, artificial intelligence, and machine learning are about power. The discussion of power-who has it, who gets to keep it, and who is marginalized-weaves throughout the chapters, theories, and cases. In discussing ethical frameworks, the text focuses on critical theories that question power structures and default assumptions and seek to emancipate the marginalized.

This book aims at helping software teams work more efficiently by setting up their own design processes In a world where being able to satisfy users' needs becomes a crucial element for products' existence, this book helps organizations understand design processes, allowing them efficiently deliver experiences that address the real problems of their audiences. This book offers a combination of theory and practice that will help its readers understand how to design efficient processes and apply this knowledge in their own work. A large volume of insights in the form of colorful images and doodles.