Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion

no code implementations • 20 Sep 2023 • Thanveer Shaik, Xiaohui Tao, Lin Li, Niall Higgins, Raj Gururajan, Xujuan Zhou, Jianming Yong

In our study, we propose a novel Clustered FedStack framework based on the previously published Stacked Federated Learning (FedStack) framework.

Clustering Federated Learning +1

PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring

no code implementations • 19 Sep 2023 • Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, U R Acharya, Raj Gururajan, Xujuan Zhou

The PDRL framework is able to learn the future states of the traffic and weather forecasting and the cumulative rewards are gradually increasing over each episode.

reinforcement-learning Time Series +3

AI enabled RPM for Mental Health Facility

no code implementations • 20 Jan 2023 • Thanveer Shaik, Xiaohui Tao, Niall Higgins, Haoran Xie, Raj Gururajan, Xujuan Zhou

To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously.

Time Series Time Series Analysis

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

no code implementations • 19 Jan 2023 • Thanveer Shaik, Xiaohui Tao, Niall Higgins, Lin Li, Raj Gururajan, Xujuan Zhou, U. Rajendra Acharya

The adoption of artificial intelligence (AI) in healthcare is growing rapidly.

Federated Learning Management

FedStack: Personalized activity monitoring using stacked federated learning

no code implementations • 27 Sep 2022 • Thanveer Shaik, Xiaohui Tao, Niall Higgins, Raj Gururajan, Yuefeng Li, Xujuan Zhou, U Rajendra Acharya

The federated learning architecture was applied to these models to build local and global models capable of state of the art performances.

Federated Learning