The proposed hybrid CNN-LSTM outperformed the single models, i.e., CNN and LSTM, per the six-performance metrics and advocated by the 10-fold cross-validation technique. Furthermore, the statistical ...

AI models extract high-level features from diverse sources - satellites, ground sensors, historical climate records, and even ...

Hybrid techniques, namely, necessary snapshot ensemble learning ... making the ConvLSTM model superior to the basic CNN-LSTM model. Because of its flexibility and applicability in various fields, ...

Given the complex and nonlinear nature of exchange rates, a hybrid model integrating sentiment analysis is introduced in the present study. It utilizes Weibo text data to extract emotional features, ...

In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS ...

This research is composed of two approaches. In the first part, we propose a hybrid model named TimeDistributed-CNN-LSTM (TD- CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short ...