ResNet-50

In agriculture, early detection of crop diseases is imperative for sustainability and maximizing yields.  Rooted in Agriculture 4.0, our innovative approach  combines pre-trained Convolutional Neural Networks (CNNs) models with data-driven solutions to address global challenges related to water scarcity.  By integrating the combined $L_{1}/L_{2}$ regularization technique to our model layers, we enhance their flexibility, reducing the risk of the overfitting effect of the model.  In the orange dataset used in our experiments, we have 1790 orange images, including a class

This study introduces an advanced Electrocardiogram (ECG) diagnostic framework that melds signal processing techniques with deep learning models to significantly boost accuracy in identifying acute myocardial infarction (MI) and MI related to left bundle branch block (LBBB).  By merging the Constant-Q Transform (CQT) with a pre-trained model, this system showcases exceptional performance, an impressive 98.99% accuracy and a remarkably low 0.0029% training loss after 100 trained epochs.  Rigorous 10-fold cross-validation substantiates and fortifies these findings.  This