and transfer learning strategies in brain tumor classification. While single-layer models provide a foundation, multi-layer and hybrid architectures significantly enhance performance. This study builds on these findings by proposing a novel hybrid model, M-C&M-BL, which integrates CNN and BiLSTM arch...
A named entity recognition (NER) model based on multiple bidirectional long short-term memory networks (Multi-BiLSTM) and competition mechanism (CM) is proposed. This model includes three parts: word vectorization module, learning module and application module. In the word vectorization module, the...
综上所述,多头注意力机制TCN-BiLSTM-Multihead-Attention算法是一种新的多变量时间序列预测算法,它充分利用了TCN、BiLSTM和多头注意力机制的优势,并在实验中取得了显著的效果。这一算法不仅在预测准确性上有所提升,而且在处理多变量时间序列数据时具有更好的鲁棒性和泛化能力。我们相信,随着技术的不断发展和算法的不...
【2024新算法】【RBMO-BILSTM-multihead-Attention多特征分类预测】红嘴蓝鹊优化双向长短期记忆网络结合多头注意力机制多特征分类预测。(可做分类/回归/时序预测,具体私聊),可直接运行。matlab代码,2023b及其以上。1.运行环境要求MATLAB版本为2023b,多特征输入单输出的
A probabilistic CNN-BiLSTM model is developed using a deep ensemble strategy.A multi-head attention layer is added to enhance the extracted predictors of the CNN layer.The NWP model and onsite measurements are combined to improve long-term forecasting accuracy.The spatial and temporal correlations ...
To analyze comprehension performance in multilanguage smart voice systems, the integration of different data analysis methods, such as LASSO regression, SEM, PLS-SEM, CNN, and BiLSTM, provides a comprehensive approach for assessing complex interactions among the user experience, environmental variables,...
Transformer_BiLSTM model based on label embedding and attention mechanism for multi-label text classification. First, we use the R-Transformer model to obtain the global and local information of the text sequence in combination with part-of-speech embedding. At the same time, we use BiLSTM+CRF...
involve lite pre-training models such as Albert-small or Electra-small with financial corpus, knowledge of distillation and multi-stage learning. The result is that we improve the recall rate of company names recognition task from 0.73 to 0.92 and get 4 times as fast as BERT-Bilstm-CRF ...
RIME-CNN-BILSTM-multihead-Attention霜冰算法优化卷积神经网络-双向长短期记忆网络结合多头注意力机制多维时序预测,多变量输入模型。matlab代码,2021b及其以上。评价指标包括:R2、MAE、MSE、RMSE和MAPE等,代码质量极高,方便学习和替换数据。代码参考:https://mbd.pub/
【基于CNN-BiLSTM-Multihead-Attention-KDE多头注意力卷积双向长短期记忆网络多变量时间序列区间预测】基于CNN-BiLSTM-Multihead-Attention-KDE多头注意力卷积双向长短期记忆网络多变量时间序列区间预测,多图输出、点预测多指标输出(MAE、MAPE、RMSE、MSE、R2),区间预测多指比输出(区间覆盖率PICP、区间平均宽度百分比PINAW...