Search Results - bidirectional encoder prevention from transformers

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…Extensive experiments are conducted to assess the model’s performance compared to several state-of-the-art techniques, including standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…The proposed TF-CNN-BiLSTM model was designed with three core modules: 1) a Transformer encoder to capture long-term global dependencies via self-attention mechanisms; 2) a 1D CNN layer to extract localized price fluctuations; 3) a BiLSTM network to model bidirectional temporal relationships. …”

A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO by Chetanpal Singh, Santoso Wibowo, Srimannarayana Grandhi

“…It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. …”
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