首页/数据集/Training performance for BD-LSTM, LSTM and BERT model using Senwave COVID-19 training dataset.

Training performance for BD-LSTM, LSTM and BERT model using Senwave COVID-19 training dataset.

NIAID Data Ecosystem2026-03-12 收录

数据链接：

https://figshare.com/articles/dataset/Training_performance_for_BD-LSTM_LSTM_and_BERT_model_using_Senwave_COVID-19_training_dataset_/15444678 数据链接链接失效反馈

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资源简介：

Note that except for the BCE and Hamming loss, higher scores shows better performance.

应用场景：

创建时间：

2021-08-19

眼科图像分析

深度学习模型性能

The number below the balanced accuracy metric, if any, indicates which model number obtained better and statistically significantly different results (Wilcoxon test, α = 0.05).

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Statistical summary of 13 DNN models trained to distinguish Abs from a specific family lineage from a large Ab set using the Robust Convolutional Autoencoder (RCAE) one-class classification method.

抗体分类

深度学习模型性能

Statistical summary of 13 DNN models trained to distinguish Abs from a specific family lineage from a large Ab set using the Robust Convolutional Autoencoder (RCAE) one-class classification method.

Figshare2021-03-29 更新40

Performance comparison between the novel customized CNN and optimized ResNet101.

深度学习模型性能

分类模型评估

Performance comparison between the novel customized CNN and optimized ResNet101.

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Comparison of classification accuracy between different deep learning models and feature extraction techniques on GTZAN and FMA datasets.

音乐分类

深度学习模型性能

It contrasts the accuracy of various models, demonstrating the superiority of the proposed CNN-based architecture with Mel-Spectrograms over traditional feature extraction methods such as MFCC and sta

Figshare2025-10-14 更新50

Performance of BC classification by the different network combinations: Case1) the complete IDSNet based on DenseNet interleaved with SENet and classification sub-network; Case2) the DenseNet combined with classification sub-network only; Case3) DenseNet combined with SENet module only.

乳腺癌影像

深度学习模型性能

The best performance is highlighted by boldface.

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