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Social network analysis and mining applications in healthcare and anomaly detection

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Social network analysis and mining applications in healthcare and anomaly detection/ edited by Mehmet Kaya ... [et al.].
other author:	Kaya, Mehmet.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	vi, 336 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Online social networks. -
Online resource:	https://doi.org/10.1007/978-3-031-75204-9
ISBN:	9783031752049

Social network analysis and mining applications in healthcare and anomaly detection
Social network analysis and mining applications in healthcare and anomaly detection[electronic resource] /edited by Mehmet Kaya ... [et al.]. - Cham :Springer Nature Switzerland :2024. - vi, 336 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in social networks,2190-5436. - Lecture notes in social networks..

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.

ISBN: 9783031752049

Standard No.: 10.1007/978-3-031-75204-9doiSubjects--Topical Terms:

565357
Online social networks.

LC Class. No.: HM742

Dewey Class. No.: 302.231

Social network analysis and mining applications in healthcare and anomaly detection
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520 $a 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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