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The rapid advancement of technology and the rise of data-driven innovations have profoundly shaped research across a variety of fields. This edited book consolidates pioneering studies and analyses that utilize cutting-edge approaches such as machine learning, statistical techniques, and data-centric methodologies. From predictive analytics in healthcare to breakthroughs in cyber security and Internet of Things (IoT) applications, the content presents a wealth of insights aimed at tackling challenges in today’s fast-paced, digitally transformed world. It underscores the transformative role of artificial intelligence, big data analytics, and block chain technologies in revolutionizing sectors like healthcare, finance, e-commerce, and climate research. This collection of chapters spans a diverse range of interdisciplinary subjects. It features healthcare studies that explore predictive models for conditions such as cervical and lung cancers, as well as thyroid disorders, showcasing the revolutionary impact of artificial intelligence in improving diagnostic precision and treatment strategies. Concurrently, research on IoT, cloud computing, and block chain highlights the growing necessity of secure and interconnected infrastructures in paving the way for smart living and decentralized systems. Statistical methodologies, including time series analysis, Bayesian models, and survival analysis, are explored in real-world contexts, offering valuable insights into climatic trends, consumer behavior, and industrial advancements. This book is the result of a collaborative effort by esteemed researchers and practitioners, whose expertise provides innovative solutions to real-world challenges. By bridging theoretical advancements with practical implementations, the volume serves as a comprehensive resource for scholars, industry experts, and students. We trust that this work will inspire further research and catalyze meaningful progress in the domains of technology, healthcare, and beyond.
Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science.
This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.
With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Second International Conference on Emerging Trends in IOT and Computing Technologies (ICEICT – 2023) is organised with a vision to address the various issues to promote the creation of intelligent solution for the future. It is expected that researchers will bring new prospects for collaboration across disciplines and gain ideas facilitating novel concepts. Second International Conference of Emerging Trends in IoT and Computer Technologies (ICEICT-2023) is an inventive event organised in Goel Institute of Technology and Management, Lucknow, India, with motive to make available an open International forum for the researches, academicians, technocrats, scientist, engineers, industrialist and students around the globe to exchange their innovations and share the research outcomes which may lead the young researchers, academicians and industrialist to contribute to the global society. The conference ICEICT- 2023 is being organised at Goel Institute of Technology and Management, Lucknow, Uttar Pradesh, during 12-13 January 2024. It will feature world-class keynote speakers, special sessions, along with the regular/oral paper presentations. The conference welcomes paper submissions from researcher, practitioners, academicians and students will cover numerous tracks in the field of Computer Science and Engineering and associated research areas.