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Found 5 papers, 1 papers with code
FakeSwarm: Improving Fake News Detection with Swarming Characteristics
We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features.
MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression
Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment.
FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction
To address this issue, we propose FINEEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings, while leveraging the intrinsic correlations among different health statuses and note categories.
BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning
Second, we demonstrate that metric learning techniques can be applied in this context to refine raw embeddings and improve classification performance.
BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns
An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e. g., misinformation, rumor, and spam) on genuine users.
