LEADER 04407nam 22006135 450 001 9910918697503321 005 20241221115226.0 010 $a9783031752049$b(electronic bk.) 010 $z9783031752032 024 7 $a10.1007/978-3-031-75204-9 035 $a(MiAaPQ)EBC31855540 035 $a(Au-PeEL)EBL31855540 035 $a(CKB)37065136800041 035 $a(DE-He213)978-3-031-75204-9 035 $a(OCoLC)1492949783 035 $a(EXLCZ)9937065136800041 100 $a20241221d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSocial Network Analysis and Mining Applications in Healthcare and Anomaly Detection /$fedited by Mehmet Kaya, Sleiman Alhajj, Kashfia Sailunaz, Min-Yuh Day 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (338 pages) 225 1 $aLecture Notes in Social Networks,$x2190-5436 311 08$aPrint version: Kaya, Mehmet Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection Cham : Springer,c2025 9783031752032 327 $aSensitivity 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. 330 $aThis book is an excellent source of knowledge for readers interested in the latest developments in social network analysis and mining, particularly with applications in healthcare and anomaly detection. It covers topics such as sensitivity to noise in features, enhancing fraud detection in financial systems, measuring the echo-chamber phenomenon, detecting comorbidity, and evaluating the effectiveness of mitigative and preventative actions on viral spread in small communities using agent-based stochastic simulations. Additionally, it discusses predicting behavior, measuring and identifying influence, analyzing the impact of COVID-19 on various social aspects, and using UNet for handling various skin conditions. This book helps readers develop their own perspectives on adapting social network concepts to various applications. It also demonstrates how to use various machine learning techniques for tackling challenges in social network analysis and mining. 410 0$aLecture Notes in Social Networks,$x2190-5436 606 $aMachine learning 606 $aSocial media 606 $aMedical informatics 606 $aMachine Learning 606 $aSocial Media 606 $aHealth Informatics 615 0$aMachine learning. 615 0$aSocial media. 615 0$aMedical informatics. 615 14$aMachine Learning. 615 24$aSocial Media. 615 24$aHealth Informatics. 676 $a006.31 700 $aKaya$b Mehmet$01781203 701 $aAlhajj$b Sleiman$01781204 701 $aSailunaz$b Kashfia$01781205 701 $aDay$b Min-Yuh$01781206 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910918697503321 996 $aSocial Network Analysis and Mining Applications in Healthcare and Anomaly Detection$94305961 997 $aUNINA LEADER 00838nam0 2200301 450 001 9911010879303321 005 20250623102438.0 010 $a978-88-386-1237-4 100 $a20250623d2024---- kmyy0itay50 ba 101 0 $aita 102 $aIT 105 $ay 001yy 200 1 $aContabilità e bilancio$fFabrizio Cerbioni, Lino Cinquini, Ugo Sostero 205 $a7.ed. 210 $aMilano$cMcgraw -hill$d2024 215 $a534 p., E38$d24 cm 225 1 $aDisciplone aziendali 676 $a657.3 700 1$aCerbioni,$bFabrizio$0116539 701 1$aCinquini$bLino$0437328 701 1$aṢstero,$bUgo$0368770 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9911010879303321 952 $aAZRAG449A$b6106$fDECBC 959 $aDECBC 996 $aContabilità e bilancio$940174 997 $aUNINA