Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection
Description
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.
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Persons
Mehmet Kaya received the Ph.D. degree from Firat University, in 2003. During 2002 and 2003, he was a Visiting Scholar with the ADSA Laboratory, Department of Computer Science, University of Calgary, Canada. He is currently a Professor in the Department of Computer Engineering, Firat University. He has published over 100 papers in refereed international journals and conferences. His research interests include data mining, machine learning, natural language processing and deep learning.
Sleiman Alhajj is currently at the International School of Medicine at Istanbul Medipol University, Istanbul, Turkey. He is a member of the medical data management and analysis group. He served as program chair of HI-BI-Bi 2023. His research interests include cancer, infectious diseases, network analysis, and medical image analysis.
Kashfia Sailunaz completed her MSc and PhD in Computer Science at the University of Calgary, Alberta, Canada. She is currently a research associate at the University of Calgary, Alberta, Canada. Her research interests include machine learning, social media analysis, and medical image analysis.
Dr. Min-Yuh Day is a Professor in the Graduate Institute of Information Management at National Taipei University, Taiwan. Prior to joining the faculty at NTPU in 2020, he was an Associate Professor in the Department of Information Management at Tamkang University, Taiwan. He was a Postdoctoral Fellow in the Intelligent Agent Systems Lab, Institute of Information Science, Academia Sinica, Taiwan. He received the Ph.D. degree from the Department of Information Management at National Taiwan University, Taiwan. He received his MBA in Management Information System from Tamkang University, Taiwan. His current research interests include electronic commerce, financial technology, artificial intelligence, big data analytics, data mining and text mining, social media marketing, information systems evaluation, question answering systems, and biomedical informatics.
Content
Sensitivity to Noise in Features in Graph Neural Network Learning.- Interpretable Ensemble Model For Associative Classification.- Scalable Algorithms to Measure User Influence in Social Networks Detecting Comorbidity Using Machine Learning.- Detecting Comorbidity Using Machine Learning.- Evaluating the Effectiveness of Mitigative and Preventative Actions on Viral Spread In A Small Community Using An Agent-based Stochastic Simulation.- Evaluating the Effectiveness of Mitigative and Preventative Actions on Viral Spread In A Small Community Using An Agent-based Stochastic Simulation.- Predicting Donor Behavior using the Dynamics of Event Co-Attendance Networks Analyzing the impact of COVID-19 on Portuguese Social Media.- Analyzing the impact of COVID-19 on Portuguese Social Media.- SegSkin: An Effective Application for Skin Lesion Segmentation using Attention-Based VGG-UNet.- Segmentation and Classification of Dermoscopic Skin Images using U-Net and Handcrafted Features.- Global Prevalence Patterns of Anti-Asian Prejudice on Twitter During the COVID-19 Pandemic.- Enhancing fraud detection in SWIFT financial systems through Ontology-Based knowledge integration and Graph-Driven analysis.- A study of firm-switching of inventors in Big Tech using public patent data.- Measuring the Echo-chamber Phenomenon Through Exposure Bias.
