This book presents futuristic trends in computational intelligence including algorithms as applicable to different application domains in health informatics covering bio-medical, bioinformatics, and biological sciences. Latest evolutionary approaches to solve optimization problems under biomedical engineering field are discussed. It provides conceptual framework with a focus on application of computational intelligence techniques in the domain of biomedical engineering and health informatics including real-time issues.

This book discusses IoT in healthcare and how it enables interoperability, machine-to-machine communication, information exchange, and data movement. It also covers how healthcare service delivery automates patient care with the help of mobility solutions, new technologies, and next-gen healthcare facilities with challenges faced and suggested solutions prescribed. Reinvention of Health Applications with IoT: Challenges and Solutions presents the latest applications of IoT in healthcare along with challenges and solutions. It looks at a comparison of advanced technologies such as Deep Learning, Machine Learning, and AI and explores the ways they can be applied to sensed data to improve prediction and decision-making in smart health services. It focuses on society 5.0 technologies and illustrates how they can improve society and the transformation of IoT in healthcare facilities to support patient independence. Case studies are included for applications such as smart eyewear, smart jackets, and smart beds. The book will also go into detail on wearable technologies and how they can communicate patient information to doctors in medical emergencies. The target audiences for this edited volume is researchers, practitioners, students, as well as key stakeholders involved in and working on healthcare engineering solutions.

This book explores the concepts and role of green computing and its recent developments for making the environment sustainable. It focuses on green automation in disciplines such as computers, nanoscience, information technology, and biochemistry. This book is characterized through descriptions of sustainability, green computing, their relevance to the environment, society, and its applications. Presents how to make the environment sustainable through engineering aspects and green computing Explores concepts and the role of green computing with recent developments Processes green automation linked with various disciplines such as nanoscience, information technology, and biochemistry Explains the concepts of green computing linked with sustainable environment through information technology This book will be of interest to researchers, libraries, students, and academicians that are interested in the concepts of green computing linked with green automation through information technology and their impacts on the future.

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.

Mobile Data Visualization is about facilitating access to and understanding of data on mobile devices. Wearable trackers, mobile phones, and tablets are used by millions of people each day to read weather maps, financial charts, or personal health meters. What is required to create e ffective visualizations for mobile devices? This book introduces key concepts of mobile data visualization and discusses opportunities and challenges from both research and practical perspectives. Mobile Data Visualization is the first book to provide an overview of how to e ffectively visualize, analyze, and communicate data on mobile devices. Drawing from the expertise, research, and experience of an international range of academics and practitioners from across the domains of Visualization, Human Computer Interaction, and Ubiquitous Computing, the book explores the challenges of mobile visualization and explains how it diff ers from traditional data visualization. It highlights opportunities for reaching new audiences with engaging, interactive, and compelling mobile content. In nine chapters, this book presents interesting perspectives on mobile data visualization including: how to characterize and classify mobile visualizations; how to interact with them while on the go and with limited attention spans; how to adapt them to various mobile contexts; specific methods on how to design and evaluate them; reflections on privacy, ethical and other challenges, as well as an outlook to a future of ubiquitous visualization. This accessible book is a valuable and rich resource for visualization designers, practitioners, researchers, and students alike.

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.

Presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research Offers a compendium of current and emerging machine learning paradigms for healthcare informatics and reflects on the diversity and complexity through the use of case studies Provides a panoramic view of data and machine learning techniques and provides an opportunity for novel insights and discovers Explores the theory and practical applications of machine learning in healthcare Includes a guided tour of machine learning algorithms, architecture design, and applications and in interdisciplinary challenges

There are many books written for the accounting and finance community. However, there are very few books written to help the non-financial career professionals who still need to understand the conceptual fundamentals of accounting and finance. In 20 years of teaching this material to non-financial professionals, Dr. Bonner has perfected a teaching approach that works to help the non-financial professional engage with the material to use financial information in leveraging their career without becoming overloaded with information that is not helpful to them. Learning this material takes repetition, application, and building the thinking processes necessary for effectiveness. Many think the challenge with finance is the math, but as this book will demonstrate, it is a conceptual problem. If you understand the conceptual framework, you will understand the math. Dr. Julie Bonner is currently a tenured professor at Central Washington University in the information technology and administrative management department. Her career has spanned business and education for over 30 years. Initially, she received a Bachelor of Science degree in accounting, whereafter she earned an MBA and then a doctorate in organizational leadership.

Provides the basic concepts of process mining techniques for pattern recognition for readers to analyze, predict, forecast, and enhance the workflow of processes Covers the entire spectrum of process mining from process discovery to operational support Discusses several process mining techniques in the context of data science and big data Contains real-life applications and case studies related to process mining theories and practices Includes detailed examples, figures, and tables for easy understanding of concepts discussed

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.

"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.

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.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: * Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. * Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. * Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.

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

"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.

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.

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.

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.

Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes with unprecedented range, scale, and speed. Law enforcement faces a challenge in keeping up and dealing with this new environment. The news is not all bad - collecting and analyzing data about criminals and their activities can provide new levels of insight into what they are doing and how they are doing it. However, using data analytics requires a change of process and new skills that (so far) many law enforcement organizations have had difficulty leveraging. Cyberspace, Data Analytics, and Policing surveys the changes that cyberspace has brought to criminality and to policing with enough technical content to expose the issues and suggest ways in which law enforcement organizations can adapt. Key Features: Provides a non-technical but robust overview of how cyberspace enables new kinds of crime and changes existing crimes. Describes how criminals exploit the ability to communicate globally to learn, form groups, and acquire cybertools. Describes how law enforcement can use the ability to collect data and apply analytics to better protect society and to discover and prosecute criminals. Provides examples from open-source data of how hot spot and intelligence-led policing can benefit law enforcement. Describes how law enforcement can exploit the ability to communicate globally to collaborate in dealing with trans-national crime.

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.

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

- 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

Convergence of Blockchain, AI, and IoT: Concepts and Challenges 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. The book provides an overview of the clinical scientific research enabling smart diagnosis equipment through AI. It presents the role these technologies play in augmented reality and blockchain, covers digital currency managed with bitcoin, and discusses deep learning and how it can enhance human thoughts and behaviors. 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.

Discusses how entrepreneurs use big data to cut costs and minimize the waste of time Covers how using big data as a way to study competitors Offers how using big data can increase efficiency Presents how big data can improve the pricing of products Provides how big data is used to help increase sales and loyalty