Min-Yuh Day – författare

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data.

Additionally, it discusses predicting behavior, measuring and identifying influence, analyzing the impact of COVID-19 on various social aspects, and using UNet for handling various skin conditions.This book helps readers develop their own perspectives on adapting social network concepts to various applications.

This book is an excellent source of knowledge for readers interested in the latest developments in social network analysis and mining, particularly with applications in healthcare and anomaly detection. It covers topics such as sensitivity to noise in features, enhancing fraud detection in financial systems, measuring the echo-chamber phenomenon, detecting comorbidity, and evaluating the effectiveness of mitigative and preventative actions on viral spread in small communities using agent-based stochastic simulations. Additionally, it discusses predicting behavior, measuring and identifying influence, analyzing the impact of COVID-19 on various social aspects, and using UNet for handling various skin conditions.This book helps readers develop their own perspectives on adapting social network concepts to various applications. It also demonstrates how to use various machine learning techniques for tackling challenges in social network analysis and mining.

This book explores the evolution of social network analysis and mining (SNAM), a field that originated in social and business communities but has expanded significantly in recent years.

This book explores the evolution of social network analysis and mining (SNAM), a field that originated in social and business communities but has expanded significantly in recent years.

This book explores the evolution of social network analysis and mining (SNAM), a field that originated in social and business communities but has expanded significantly in recent years. The rise of online social platforms, email logs, phone records, and instant messaging systems has driven the development of advanced techniques for analyzing social networks, drawing heavily on graph theory and machine learning.As the Web increasingly becomes a social medium, it fosters human interaction, the sharing of experiences and knowledge, and the formation and evolution of communities. This transformation has amplified the importance of SNAM in fields such as academia, politics, homeland security, and business, where understanding the complex relationships between networked actors is crucial.This book presents a comprehensive collection of cutting-edge research and developments in SNAM, offering a valuable resource for researchers and practitioners seeking to deepen their understanding of social networks and their applications.

This book constitutes the refereed proceedings of the 19th International Conference on Technologies and Applications of Artificial Intelligence, held in Taipei, Taiwan, in November 2014.The 23 revised full papers, 3 short papers, and 8 workshop papers presented at the international track of the conference were carefully reviewed and selected from overall 93 submissions to the international track, domestic track, and international workshops for inclusion in this volume. The papers feature original research results and practical development experiences among researchers and application developers from the many AI related areas including machine learning, data mining, statistics, computer vision, web intelligence, information retrieval, and computer games.