Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.

In times of crisis, it is crucial that information is disseminated quickly and accurately to the appropriate channels. In today’s technological world, there is a plethora of misinformation that can negatively sway individuals and provide them with false reports. To ensure information is distributed appropriately, organizations must implement a plan to ensure their communication is effective. Further study on the best practices and challenges of managing crisis and risk communications is required to ensure organizations are prepared. The Research Anthology on Managing Crisis and Risk Communications discusses strategies and tactics to effectively manage communication in times of crisis and considers the difficulties associated with maintaining a clear line of information. The book also provides an overview of the potential future directions for this field to improve communications moving forward. Covering key topics such as misinformation, technology, leadership, and human health, this major reference work is ideal for managers, business owners, organization leaders, industry professionals, government officials, policymakers, researchers, academicians, scholars, practitioners, instructors, and students.

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Blockchain and artificial intelligence (AI) in industrial internet of things is an emerging field of research at the intersection of information science, computer science, and electronics engineering. The radical digitization of industry coupled with the explosion of the internet of things (IoT) has set up a paradigm shift for industrial and manufacturing companies. There exists a need for a comprehensive collection of original research of the best performing methods and state-of-the-art approaches in this area of blockchain, AI, and the industrial internet of things in this new era for industrial and manufacturing companies. Blockchain and AI Technology in the Industrial Internet of Things compares different approaches to the industrial internet of things and explores the direct impact blockchain and AI technology have on the betterment of the human life. The chapters provide the latest advances in the field and provide insights and concerns on the concept and growth of the industrial internet of things. While including research on security and privacy, supply chain management systems, performance analysis, and a variety of industries, this book is ideal for professionals, researchers, managers, technologists, security analysts, executives, practitioners, researchers, academicians, and students looking for advanced research and information on the newest technologies, advances, and approaches for blockchain and AI in the industrial internet of things.

When it comes to data collection and analysis, ranked set sampling (RSS) continues to increasingly be the focus of methodological research. This type of sampling is an alternative to simple random sampling and can offer substantial improvements in precision and efficient estimation. There are different methods within RSS that can be further explored and discussed. On top of being efficient, RSS is cost-efficient and can be used in situations where sample units are difficult to obtain. With new results in modeling and applications, and a growing importance in theory and practice, it is essential for modeling to be further explored and developed through research. Ranked Set Sampling Models and Methods presents an innovative look at modeling survey sampling research and new models of RSS along with the future potentials of it. The book provides a panoramic view of the state of the art of RSS by presenting some previously known and new models. The chapters illustrate how the modeling is to be developed and how they improve the efficiency of the inferences. The chapters highlight topics such as bootstrap methods, fuzzy weight ranked set sampling method, item count technique, stratified ranked set sampling, and more. This book is essential for statisticians, social and natural science scientists, physicians and all the persons involved with the use of sampling theory in their research along with practitioners, researchers, academicians, and students interested in the latest models and methods for ranked set sampling.

Besides love, money and health are the most valuable human yearnings. Therefore, blockchain technology is paramount: a new foundation of confidence for human valuable transactions. Like information sharing was catalyzed on the pre-blockchain internet, transactions are now triggered on the new internet of value. In this second digital inflection point, economic media encompasses value beside information, and individuals can privately transact digital assets for the first time in history. Decentralized but structured organizations running on blockchain networks reduce transaction costs and are particularly competitive insofar as they guarantee data authenticity, confidentiality, and integrity, providing functional autonomy with disintermediation and smart contracts. Everything changed after user data were made public on the internet and privately traded by big tech companies, and nothing will be the same once that data is made private on the internet and publicly transacted by their rightful owners. While the internet of information reshaped the world, the internet of value will reform it, and everything will depend politically on this being done freely. Political and Economic Implications of Blockchain Technology in Business and Healthcare provides relevant theoretical frameworks on the civilizational impact of blockchain technology, which redesigns human interactions concerning value transactions. It gives ideas, concepts, and instruments to advance the knowledge on cryptoeconomics and decentralized governance in the new distributed trust paradigm. The chapters explore the ethical repercussions and profound political-economic consequences to society, providing insights into business applications focusing on the healthcare sector. In a blockchain era affected by the post-COVID-19 new normal, which mixes politics, economics, and health, this book is essential for students and researchers in social and life sciences; professionals and policymakers working in the fields of public and business administration; and healthcare workers and researchers, academicians, and students interested in blockchain technology and its political and economic impacts in the industry and society.

Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.

Post COVID-19 pandemic, researchers have been evaluating the healthcare system for improvements that can be made. Understanding global healthcare systems’ operations is essential to preventative measures to be taken for the next global health crisis. A key part to bettering healthcare is the implementation of information management and One Health. The Handbook of Research on Essential Information Approaches to Aiding Global Health in the One Health Context evaluates the concepts in global health and the application of essential information management in healthcare organizational strategic contexts. This text promotes understanding in how evaluation health and information management are decisive for health planning, management, and implementation of the One Health concept. Covering topics like development partnerships, global health, and the nature of pandemics, this text is essential for health administrators, policymakers, government officials, public health officials, information systems experts, data scientists, analysts, health information science and global health scholars, researchers, practitioners, doctors, students, and academicians.

In recent decades, the industrial revolution has increased economic growth despite its immersion in global environmental issues such as climate change. Researchers emphasize the adoption of circular economy practices in global supply chains and businesses for better socio-environmental sustainability without compromising economic growth. Integrating blockchain technology into business practices could promote the circular economy as well as global environmental sustainability. Integrating Blockchain Technology Into the Circular Economy discusses the technological advancements in circular economy practices, which provide better results for both economic growth and environmental sustainability. It provides relevant theoretical frameworks and the latest empirical research findings in the applications of blockchain technology. Covering topics such as big data analytics, financial market infrastructure, and sustainable performance, this book is an essential resource for managers, operations managers, executives, manufacturers, environmentalists, researchers, industry practitioners, students and educators of higher education, and academicians.

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug design, deep learning-based treatment evaluation and outcome prediction, sensor informatics for monitoring infected patients, and machine learning for tracking and prediction models. In addition, the book presents AI solutions for hospital management during an epidemic or pandemic, along with real-world solutions and case studies of successful measures to support different types of communities. This is a valuable source for medical informaticians, bioinformaticians, clinicians and other healthcare workers and researchers who are interested in learning more on how recently developed technologies can help us fight and minimize the effects of global pandemics. - Discusses AI advancements in predictive and decision modeling and how to design mobile apps to track contagion spread - Presents the smart contract concept in blockchain and cryptography technology to guarantee security and privacy of people's data once their information has been used to fight the pandemic - Encompasses guidelines for emergency preparedness, planning, recovery and continuity management of communities to support people in emergencies like a virus outbreak