This book introduces computational advertising, and Internet monetization. It provides a macroscopic understanding of how consumer products in the Internet era push user experience and monetization to the limit. Part One of the book focuses on the basic problems and background knowledge of online advertising. Part Two targets the product, operations, and sales staff, as well as high-level decision makers of the Internet products. It explains the market structure, trading models, and the main products in computational advertising. Part Three targets systems, algorithms, and architects, and focuses on the key technical challenges of different advertising products. Features * Introduces computational advertising and Internet monetization * Covers data processing, utilization, and trading * Uses business logic as the driving force to explain online advertising products and technology advancement * Explores the products and the technologies of computational advertising, to provide insights on the realization of personalization systems, constrained optimization, data monetization and trading, and other practical industry problems * Includes case studies and code snippets

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.

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

1) Focuses on the concepts and implementation strategies of various Deep Learning algorithms through properly curated examples. 2) The subject area will be valid for the next 10 years or so, as Deep Learning theory/algorithms and their applications will not be outdated easily. Hence there will be demand for such a book in the market. 3) In comparison to other titles, this book rigorously covers mathematical and conceptual details of relevant topics.

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.

"In our increasingly digitally enabled education world, analytics used ethically, strategically, and with care holds the potential to help more and more diverse students be more successful on higher education journeys than ever before. Jay Liebowitz and a cadre of the fields best 'good trouble' makers in this space help shine a light on the possibilities, potential challenges, and the power of learning together in this work." -Mark David Milliron, Ph.D., Senior Vice President and Executive Dean of the Teachers College, Western Governors University Due to the COVID-19 pandemic and its aftereffects, we have begun to enter the "new normal" of education. Instead of online learning being an "added feature" of K-12 schools and universities worldwide, it will be incorporated as an essential feature in education. There are many questions and concerns from parents, students, teachers, professors, administrators, staff, accrediting bodies, and others regarding the quality of virtual learning and its impact on student learning outcomes. Online Learning Analytics is conceived on trying to answer the questions of those who may be skeptical about online learning. Through better understanding and applying learning analytics, we can assess how successful learning and student/faculty engagement, as examples, can contribute towards producing the educational outcomes needed to advance student learning for future generations. Learning analytics has proven to be successful in many areas, such as the impact of using learning analytics in asynchronous online discussions in higher education. To prepare for a future where online learning plays a major role, this book examines: Data insights for improving curriculum design, teaching practice, and learning Scaling up learning analytics in an evidence-informed way The role of trust in online learning. Online learning faces very real philosophical and operational challenges. This book addresses areas of concern about the future of education and learning. It also energizes the field of learning analytics by presenting research on a range of topics that is broad and recognizes the humanness and depth of educating and learning.

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.

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.

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.

This volume presents papers from the 10th Working Conference of the IFIP WG 8.6 on the adoption and diffusion of information systems and technologies. This book explores the dynamics of how some technological innovation efforts succeed while others fail. The book looks to expand the research agenda, paying special attention to the areas of theoretical perspectives, methodologies, and organizational sectors.

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.

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

An introduction to the Central Dogma of molecular biology and information flow in biological systems. A systematic overview of the methods for generating gene expression data. Background knowledge on statistical modeling and machine learning techniques. Detailed methodology of analyzing gene expression data with an example case study. Clustering methods for finding co-expression patterns from microarray, bulkRNA and scRNA data. A large number of practical tools, systems and repositories that are useful for computational biologists to create, analyze and validate biologically relevant gene expression patterns. Suitable for multi-disciplinary researchers and practitioners in computer science and biological sciences.

The book offers comprehensive coverage of the most essential topics, including: A general overview of pandemics and their outbreak behavior. A detailed overview of CI techniques. Intelligent modeling, prediction and diagnostic measures for pandemics. Prognostic models. Post-pandemic socio-economic structure.

The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.

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.

Dictation systems, read-aloud software for the blind, speech control of machinery, geographical information systems with speech input and output, and educational software with talking head' artificial tutorial agents are already on the market. The field is expanding rapidly, and new methods and applications emerge almost daily. But good sources of systematic information have not kept pace with the body of information needed for development and evaluation of these systems. Much of this information is widely scattered through speech and acoustic engineering, linguistics, phonetics, and experimental psychology.The Handbook of Multimodal and Spoken Dialogue Systems presents current and developing best practice in resource creation for speech input/output software and hardware. This volume brings experts in these fields together to give detailed how to' information and recommendations on planning spoken dialogue systems, designing and evaluating audiovisual and multimodal systems, and evaluating consumer off-the-shelf products.In addition to standard terminology in the field, the following topics are covered in depth: How to collect high quality data for designing, training, and evaluating multimodal and speech dialogue systems; How to evaluate real-life computer systems with speech input and output; How to describe and model human-computer dialogue precisely and in depth.Also included: A fully searchable CD-ROM containing a hypertext version of the book in HTML format for fast look-up of specific points, convenient desktop use, and lightweight mobile reference; and The first systematic medium-scale compendium of terminology with definitions.This handbook has been especially designed for theneeds of development engineers, decision-makers, researchers, and advanced level students in the fields of speech technology, multimodal interfaces, multimedia, computational linguistics, and phonetics.

Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions.

This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms.

The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described.

This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.

- 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

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

1) Make use of digital technology for social care services is the major responsibility of computing domain. Social care services require attention for health care, old age person and disables. Thus the book focuses on suggesting software solutions for supporting societal issues such as health care, support system for old age citizens, learning and monitoring mythology for disables and also technical solutions for better living. It is considered that technology is enabling people so that they could access to advances and so that there could be benefits in the health and technology 2) Interdisciplinary computing can be applicable to all the ranges. It would remove barriers to cooperation. This book would be helpful to undergraduate, post graduate and researchers. This course is offered in many universities in US, UK etc. Interdisciplinary studies are emerging as both necessary and expedient in the academy. Hence there would be a demand for such a book. This book would also help improve computational thinking to 'understand and change the world'. It will be a link between computing and variety of other fields. 3) There are several books available on computing which either focus on programming or basics of assistive technology, online social interactions, or general topics of information science in the market currently. However, none of the available books which covers the use of recent technologies to solve real life societal problems. This book not only focuses the computing technologies, basic theories, challenges, and implementation but also covers case studies. It focuses on core theories, architectures, and technologies necessary to develop and understand the computing models and its applications. The book also has the high potential to be used as recommended textbook for research scholars and post-graduate programs.

In this multidisciplinary book, experts from around the globe examine how data-driven political campaigning works, what challenges it poses for personal privacy and democracy, and how emerging practices should be regulated. The rise of big data analytics in the political process has triggered official investigations in many countries around the world, and become the subject of broad and intense debate. Political parties increasingly rely on data analytics to profile the electorate and to target specific voter groups with individualised messages based on their demographic attributes. Political micro-targeting has become a major factor in modern campaigning, because of its potential to influence opinions, to mobilise supporters and to get out votes. The book explores the legal, philosophical and political dimensions of big data analytics in the electoral process. It demonstrates that the unregulated use of big personal data for political purposes not only infringes voters' privacy rights, but also has the potential to jeopardise the future of the democratic process, and proposes reforms to address the key regulatory and ethical questions arising from the mining, use and storage of massive amounts of voter data. Providing an interdisciplinary assessment of the use and regulation of big data in the political process, this book will appeal to scholars from law, political science, political philosophy and media studies, policy makers and anyone who cares about democracy in the age of data-driven political campaigning.

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

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

This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.