04596nam a22005775i 4500 978-3-031-75204-9 DE-He213 20251030114631.0 cr nn 008mamaa 241221s2024 sz | s |||| 0|eng d 9783031752049 978-3-031-75204-9 10.1007/978-3-031-75204-9 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection [electronic resource] / edited by Mehmet Kaya, Sleiman Alhajj, Kashfia Sailunaz, Min-Yuh Day. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VI, 336 p. 131 illus., 121 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Lecture Notes in Social Networks, 2190-5436 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. 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. Machine learning. 55059 Social media. 36123 Medical informatics. 60693 Machine Learning. 55059 Social Media. 36123 Health Informatics. Kaya, Mehmet. editor. edt http://id.loc.gov/vocabulary/relators/edt 89314 Alhajj, Sleiman. editor. edt http://id.loc.gov/vocabulary/relators/edt 89315 Sailunaz, Kashfia. editor. edt http://id.loc.gov/vocabulary/relators/edt 89316 Day, Min-Yuh. editor. edt http://id.loc.gov/vocabulary/relators/edt 89317 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031752032 Printed edition: 9783031752056 Printed edition: 9783031752063 Lecture Notes in Social Networks, 2190-5436 79617 https://doi.org/10.1007/978-3-031-75204-9 ZDB-2-SME ZDB-2-SXM 65917 65917