Socioeconomic development has drawn increasing attention in academia, industries, and governments. The relationship between big data and its technologies and socioeconomic development has drawn certain attention in academia. Socioeconomic development depends not only on big data, but also on big data technologies. However, the relationship between big data and socioeconomic development is not adequately covered in current research. The Handbook of Research on Driving Socioeconomic Development With Big Data provides an original and innovative understanding of and insight into how the proposed theories, technologies, and methodologies of big data can improve socioeconomic development and sustainable development in terms of business and services, healthcare, the internet of everything, sharing economy, and more. Covering topics such as corporate social responsibility, management applications, and process mining, this major reference work is an excellent resource for data scientists, business leaders and executives, IT professionals, government officials, economists, sociologists, librarians, students, researchers, and academicians.

A smart city utilizes ICT technologies to improve the working effectiveness, share various data with the citizens, and enhance political assistance and societal wellbeing. The fundamental needs of a smart and sustainable city are utilizing smart technology for enhancing municipal activities, expanding monetary development, and improving citizens’ standards of living. The Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities discusses new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modeling, machine learning-based mathematical modeling, and more. It further delves into privacy and ethics in data analysis. Covering topics such as deep learning, optimization-based data science, and smart city automation, this premier reference source is an excellent resource for mathematicians, statisticians, computer scientists, civil engineers, government officials, students and educators of higher education, librarians, researchers, and academicians.

The construction industry is amidst a digital transformation that is focused on addressing well-documented issues and calls for significant improvements and changes through increased productivity, whole-life value, client focus, reduction of waste, and being more sustainable. The key aspect to driving change and transformation is the education and upskilling of the required workforce towards developing the required capacities. Various approaches can be taken to embed digital construction within education and through collaborative efforts in order to drive change and facilitate improvements. The Handbook of Research on Driving Transformational Change in the Digital Built Environment focuses on current developments in practice and education towards facilitating transformation in the built environment. This book provides insight, from a practice perspective, in relation to the client’s understanding, digitally enabled collaboration, interoperability and open standards, and maturity/capability. Covering topics that include digital transformation and construction, digitally enabled infrastructure, building information modelling, collaborative digital education, and the digital built environment, this book is an ideal reference source for engineers, professionals, and researchers in the field of digital transformation as well as doctoral scholars, doctoral researchers, professionals, and academicians.

Fuzzy sets have experienced multiple expansions since their conception to enhance their capacity to convey complex information. Intuitionistic fuzzy sets, image fuzzy sets, q-rung orthopair fuzzy sets, and neutrosophic sets are a few of these extensions. Researchers and academics have acquired a lot of information about their theories and methods for making decisions. However, only a small number of research findings discuss how neutrosophic sets theory and their extensions (NSTEs) are used in education. The Handbook of Research on the Applications of Neutrosophic Sets Theory and Their Extensions in Education implements fresh scientific approaches to enhance the quality of decisions under neutrosophic environments, particularly within education. Covering key topics such as data modeling, educational technologies, decision making, and learning management systems, this major reference work is ideal for instructional designers, researchers, academicians, scholars, practitioners, instructors, and students.

The subject of Artificial Intelligence (AI) is continuing on its journey of affecting each and every individual and will keep on this path in the times to come. This handbook is a collection of topics on the application of artificial intelligence applications for sustainability in different areas. It provides an insight into the various uses of concepts and practical examples for different domains all in one place, which makes it unique and important for the potential reader. Handbook of Artificial Intelligence Applications for Industrial Sustainability: Concepts and Practical Examples examines the influence of AI and how it can be used in several industries to improve corporate performance, reduce security concerns, improve customer experience, and ultimately generate value for customers and maximize profits. The handbook offers practical examples, concepts, and applications that provide an easy understanding and implementation process. It provides AI applications in many fields, such as sustainable credit decisions, cyber security and fraud prevention, warehouse management, and much more. This handbook will provide insight to customers, managers, professionals, engineers, researchers, and students on the various uses of AI and sustainability in different domains. All of this needed information compiled into one handbook makes it unique and important for the engineering, business, and computer science communities.

In the wake of the COVID-19 pandemic, educational institutions worldwide were compelled to embrace online learning, leading to a significant shift in the dynamics of education. As schools, colleges, and universities adapted to virtual learning environments, teachers and learners alike found themselves navigating unfamiliar terrain. Balance and Boundaries in Creating Meaningful Relationships in Online Higher Education explores the art of forging connections in virtual classrooms. This book provides educators with valuable guidance and strategies for cultivating relationships in virtual learning environments. It covers synchronous, asynchronous, and hybrid learning, offering a comprehensive understanding of relationship-building techniques for higher education and beyond. Addressing the unique challenges of online instruction, it empowers faculty members to create classrooms based on trust, connection, and support. With practical ideas and resources, it serves as a critical reference for transitioning to online teaching. Essential for cross-departmental higher education faculty and graduate-level students, it revolutionizes the field by empowering educators to thrive in the evolving landscape of online instruction.

This Handbook provides a thorough discussion of the most recent wave of technological (and organisational) innovations, frequently called “smart” and based on the digitisation of information. The acronym stands for "Self-Monitoring, Analysis and Reporting Technology". This new wave is one in a row of waves that have shaken up and transformed the economy, society and culture since the first Industrial Revolution and have left a huge impact on how we live, think, communicate and work: they have deeply affected the socioeconomic metabolism from within and humankind’s footprint on our planet. The Handbook analyses the origins of the current wave, its roots in earlier ones and its path-dependent nature; its current forms and actual manifestations; its multifarious impact on economy and society; and it puts forward some guesstimates regarding the probable directions of its further development. In short, the Handbook studies the past, the present and the future of smart technologies and digitalisation. This cutting-edge reference will appeal to a broad audience, including but not limited to, researchers from various disciplines with a focus on technological innovation and their impact on the socioeconomic system; students across different fields but especially from economics, social sciences and law studying questions related to radical technological change and its consequences, as well as professionals around the globe interested in the debate of smart technologies and socioeconomic transformation, from a multi- and interdisciplinary perspective.

Today, cloud computing, big data, and the internet of things (IoT) are becoming indubitable parts of modern information and communication systems. They cover not only information and communication technology but also all types of systems in society including within the realms of business, finance, industry, manufacturing, and management. Therefore, it is critical to remain up-to-date on the latest advancements and applications, as well as current issues and challenges. The Handbook of Research on Cloud Computing and Big Data Applications in IoT is a pivotal reference source that provides relevant theoretical frameworks and the latest empirical research findings on principles, challenges, and applications of cloud computing, big data, and IoT. While highlighting topics such as fog computing, language interaction, and scheduling algorithms, this publication is ideally designed for software developers, computer engineers, scientists, professionals, academicians, researchers, and students.

Recent evolutions, such as pervasive networking and other enabling technologies, have been increasingly changing human life, knowledge acquisition, and the way works are performed and students learn. In this societal change, educational institutions must maintain their leading role. They have therefore embraced digitally enhanced learning to provide increased flexibility and access for their students. The Handbook of Research on Redesigning Teaching, Learning, and Assessment in the Digital Era provides insights into the transformation of education in the digital era and responds to the needs of learners of any context and background through relevant studies that include sound pedagogical and content knowledge. Covering key topics such as hybrid learning, media, remote learning, and social media, this major reference work is ideal for administrators, policymakers, academicians, researchers, scholars, practitioners, librarians, instructors, and students.

The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse.