Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection (Lecture Notes in Social Networks) / Kaya, Mehmet (EDT)/ Alhajj, Sleiman (EDT)/ Sailunaz, Kashfia (EDT)

Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection (Lecture Notes in Social Networks) （2024）

個数：

Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection (Lecture Notes in Social Networks) （2024）

Kaya, Mehmet (EDT)/ Alhajj, Sleiman (EDT)/ Sailunaz, Kashfia (EDT)

ウェブストア価格 ¥33,113（本体¥30,103）
Springer International Publishing AG（2024/12発売）
外貨定価 US$ 159.99
ポイント 301pt

オンデマンド(OD/POD)版です。キャンセルは承れません。

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Hardcover:ハードカバー版／ページ数 336 p.
言語 ENG
商品コード 9783031752032
DDC分類 006.31

Full Description

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.

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.