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4129_Emotion argumentation.
A Privacy Preserving Matchmaking Scheme for Multiple Mobile Social Networks
4131_Flickr Circles Aesthetic Tendency Discovery by Multi-View Regularized Topic Modeling.
Fostering collective intelligence education
GASNA: Greedy algorithm for social network anonymization
Recent Advances on Graph Analytics and Its Applications in Healthcare
A light-weight dynamic ontology for Internet of Things using machine learning technique
Crowdsourcing-based Data Extraction from Visualization Charts
Prediction of purchase behaviors across heterogeneous social networks
Abstract-Electronic medical claims (EMCs) can be used to accurately predict the occurrence of a variety of diseases, which can contribute to precise medical interventions. While there is a growing interest in the application of machine learning (ML) techniques to address clinical problems, the use of deep-learning in healthcare have just gained attention recently. Deep learning, such as deep neural network (DNN), has achieved impressive results in the areas of speech recognition, computer vision, and natural language processing in recent years. However, deep learning is often difficult to comprehend due to the complexities in its framework. Furthermore, this method has not yet been demonstrated to achieve a better performance comparing to other conventional ML algorithms in disease prediction tasks using EMCs. In this study, we utilize a large population-based EMC database of around 800,000 patients to compare DNN with three other ML approaches for predicting 5-year stroke occurrence. The result shows that DNN and gradient boosting decision tree (GBDT) can result in similarly high prediction accuracies that are better compared to logistic regression (LR) and support vector machine (SVM) approaches. Meanwhile, DNN achieves optimal results by using lesser amounts of patient data when comparing to GBDT method
Expressive participation in Internet social movements: Testing the moderating effect of technology readiness and sex on student SNS use
A k-Anonymization Algorithm on Social Network Data that Reduces Distances between Nodes
Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning
Learning Social Circles in Ego-Networks Based on Multi-View Network Structure
COSI: Cloud Oriented Subgraph Identification in Massive Social Networks
Dynamics of multi-campaign propagation in online social networks
4146_Cross-Layer Resource Optimization for Wireless Relay Networks Under Dynamic Node Selfishness.
Real-time information about public transport's position using crowdsourcing
Understanding the link between social and spatial distance in the crime world
Enhanced Audit Strategies for Collaborative and Accountable Data Sharing in Social Networks
KNOWLEDGE‐BASED SYSTEMS
Evidential Missing Link Prediction in Uncertain Social Networks
Noise Mapping Through Mobile Crowdsourcing for Enhanced Living Environments
Imaging Hidden Objects with Consumer LiDAR via Motion Induced Sampling
Vehicle Cooperation Promotion Mechanism Based on Behavioral Economics Anchoring Theory
A Multimodal Guide for Virtual 3D Models of Cultural Heritage Artifacts
Group-Based Personalized Location Recommendation on Social Networks
A place next to Satoshi: foundations of blockchain and cryptocurrency research in business and economics
A Learning-based Framework to Handle Multi-round Multi-party Influence Maximization on Social Networks
An integer programming approach and visual analysis for detecting hierarchical community structures in social networks q