Produktnummer:
187e77f53d382f445ea7c3cf02473b2b2d
| Themengebiete: | Behavior Analysis Deep Learning Fake News Fraud Detection Machine Learning Network Analysis in Healthcare Social Media Trending Topics |
|---|---|
| Veröffentlichungsdatum: | 21.12.2024 |
| EAN: | 9783031752032 |
| Sprache: | Englisch |
| Seitenzahl: | 336 |
| Produktart: | Gebunden |
| Herausgeber: | Alhajj, Sleiman Day, Min-Yuh Kaya, Mehmet Sailunaz, Kashfia |
| Verlag: | Springer International Publishing |
Produktinformationen "Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection"
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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