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024 7 _a10.1007/978-3-031-75204-9
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072 7 _aMAT029000
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245 1 0 _aSocial Network Analysis and Mining Applications in Healthcare and Anomaly Detection
_h[electronic resource] /
_cedited by Mehmet Kaya, Sleiman Alhajj, Kashfia Sailunaz, Min-Yuh Day.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVI, 336 p. 131 illus., 121 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
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347 _atext file
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490 1 _aLecture Notes in Social Networks,
_x2190-5436
505 0 _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.
520 _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.
650 0 _aMachine learning.
_955059
650 0 _aSocial media.
_936123
650 0 _aMedical informatics.
_960693
650 1 4 _aMachine Learning.
_955059
650 2 4 _aSocial Media.
_936123
650 2 4 _aHealth Informatics.
700 1 _aKaya, Mehmet.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
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700 1 _aAlhajj, Sleiman.
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700 1 _aSailunaz, Kashfia.
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_4http://id.loc.gov/vocabulary/relators/edt
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700 1 _aDay, Min-Yuh.
_eeditor.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031752032
776 0 8 _iPrinted edition:
_z9783031752056
776 0 8 _iPrinted edition:
_z9783031752063
830 0 _aLecture Notes in Social Networks,
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_979617
856 4 0 _uhttps://doi.org/10.1007/978-3-031-75204-9
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