Jiangtian Nie

TL;DR: In this article, the authors proposed an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies.

TL;DR: Numerical results illustrate that the proposed schemes can effectively prevent data poisoning attacks and improve the privacy protection of model updates for secure and privacy-preserving traffic flow prediction.

TL;DR: This work proposes to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction that has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.

TL;DR: A new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT and an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies is proposed.

TL;DR: Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than CO VID-Net of 93.3% and a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types.