18 citations · 32 across the 11 of their papers we have counts for
6 papers · 1 filter
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Simi Job, Xiaohui Tao, Taotao Cai +4
In machine learning, exploring data correlations to predict outcomes is a fundamental task. Recognizing causal relationships embedded within data is pivotal for a comprehensive und…
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However,…
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environmen…
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-l…
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges relat…
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted…