network (CNN) is one of the algorithms that is widely used by researchers in the field of object identification or object … identification algorithm development, and the research objects used. CNN's research innovation is growing rapidly, with improvements … being made to the identification techniques in its algorithmic architecture. The use of the CNN algorithm in the …

network (CNN) is one of the algorithms that is widely used by researchers in the field of object identification or object … identification algorithm development, and the research objects used. CNN's research innovation is growing rapidly, with improvements … being made to the identification techniques in its algorithmic architecture. The use of the CNN algorithm in the …

against standalone CNN, LSTM, and GRU architectures. Experimental results show that the hybrid CNN-LSTM-GRU model achieves … demonstrates faster convergence than LSTM and improved class balance compared to CNN and GRU, reducing false negatives for high …

. This study proposes a novel CNN-LSTM neural network that can extract potential features from sample data to effectively … predictive accuracy of the model. The CNN-LSTM model, with the inclusion of EPU, IDEMV, and both, achieves a high prediction … accuracy. Additionally, the overall prediction accuracy of the CNN-LSTM model outperforms the other proposed methods. The …

) algorithms: a nonlinear autoregressive model with exogenous inputs (NARX), a convolutional neural network (CNN), a long short …-term memory (LSTM), and a hybrid CNN-LSTM neural network in predicting real-time forces. The experimental forces predicted by the …

. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with …

-Nearest Neighbors (KNN), Random Forest (RF), Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are employed using …

-Nearest Neighbors (KNN), Random Forest (RF), Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are employed using …