BIDIRECTIONAL CNN-LSTM ARCHITECTURE TO PREDICT CNXIT STOCK PRICES 方法:论文探索应用双向卷积神经网络-长短期记忆网络(CNN-LSTM)架构来预测股票价格,特别关注CNXIT(Nifty IT)股票指数,以研究深度学习技术在捕捉历史股票价格数据中的复杂时间依赖性和空间模式方面的潜力。通过综合文献回顾,介绍Bidirectional CNN-LSTM模型...
【1】:《Bidirectional CNN-LSTM Architecture to Predict CNX IT Stock Prices》 该论文探索应用双向卷积神经网络-长短期记忆网络(CNN-LSTM)架构来预测股票价格,特别关注 CNX IT 股票指数。其创新点在于通过结合 CNN 和 LSTM 的双向卷积神经网络长短期记忆(CNN-LSTM)架构,提出了一种创新的预测股票价格的方法,能够捕...
BIDIRECTIONAL CNN-LSTM ARCHITECTURE TO PREDICT CNXIT STOCK PRICES 方法:论文探索应用双向卷积神经网络-长短期记忆网络(CNN-LSTM)架构来预测股票价格,特别关注CNXIT(Nifty IT)股票指数,以研究深度学习技术在捕捉历史股票价格数据中的复杂时间依赖性和空间模式方面的潜力。通过综合文献回顾,介绍Bidirectional CNN-LSTM模型...
Isolated Video-Based Sign Language Recognition Using a Hybrid CNN-LSTM Framework Based on Attention Mechanism 方法:论文提出了一种识别手语的混合模型,通过结合卷积神经网络(CNN)和基于注意力机制的长短期记忆(LSTM)神经网络来识别独立的手语词汇。该模型使用MobileNetV2作为骨干模型,通过CNN提取视频帧的空间特征,并...
实例研究包括：BIDIRECTIONAL CNN-LSTM ARCHITECTURE FOR CNXIT STOCK PRICES PREDICTION：应用双向卷积神经网络-长短期记忆架构预测CNXIT股票价格，研究深度学习捕捉复杂时间空间依赖性。ISOLATED VIDEO-BASED SIGN LANGUAGE RECOGNITION USING HYBRID CNN-LSTM WITH ATTENTION：手语识别混合模型，结合卷积神经网络和...
In this work, Convolutional Neural Network Long Short-Term Memory (CNN LSTM) architecture is proposed for modelling software reliability with time-series data. Evaluation of the model coming from 2 open source datasets that describe the development and testing of modern mobile operating systems - '...
https://github.com/Tanny1810/Human-Activity-Recognition-LSTM-CNN 您可以尝试自己实现它,通过优化模型来提高F1分数。 另:这个模型是来自于Xia Kun, Huang Jianguang, and Hanyu Wang在IEEE期刊上发表的论文LSTM-CNN Architecture for Human Activity Recognition。
其中LSTM为3层1024个cells,project为512 ,CNN+LSTM和CNN+LSTM+DNN具体的网络参数略有调整,具体如下图,另外还增加一组实验,两层CNN和三层LSTM组合,实验验证增加一层LSTM对结果有提高,但继续增加LSTM的层数对结果没有帮助。 Fig 2. CLDNN实验结构 Table 1 测试集1结果 ...
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation In this work we design a neural network for recognizing emotions in speech,\nusing the IEMOCAP dataset. Following the latest advances in audio analysis, we\nuse an architecture involving both convolutional layers, for ...
By fine-tuning the model architecture and hyperparameters, we aim to strike a balance between computational efficiency and accuracy. In addition to solely utilizing CNN, we recognize the potential of incorporating LSTM networks into our approach. By employing a hybrid CNN–LSTM architecture, we ...