This letter proposes an advanced convolutional neural network (CNN)-based classifier for detecting the contamination level of in-service insulator strings. The goal is to enhance condition monitoring ...

Deep CNN Architecture: The model uses convolutional layers, pooling layers, and fully connected layers to extract features and classify images. Python and TensorFlow/Keras: The code is built with ...

The bottleneck features are extracted using the sandwich stacked method based on VGG16 and VGG19 pre-trained models. Afterward, the proposed hybrid classifier based on RNN-CNN has been used to ...

Due to the increased use of social networks, word-of-mouth analysis has become an effective tool in market research since firms can determine the users’ attitudes toward their brands. However, the ...

Convolutional neural network (CNN) classifiers, which perform feature extraction from brain magnetic resonance imaging (MRI) data and classify them as healthy or diseased, are very promising in aiding ...

A novel lightweight convolutional neural network architecture is proposed, denotes as VRES-CNN. Unlike most of the actual lightweight architectures VRES-CNN provides a versatile set of ...

As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a greater emphasis on understanding and addressing potential privacy risks associated with these models ...

ECG is an essential diagnostic tool that offers important insight into a person's cardiac and general health. The rise of intelligent wearable devices has opened a new avenue for clinicians and ...

In this work, a multi-task convolutional neural network (CNN) classifier was used to study the influence of various combinations of ECG leads in interpretation of 71 cardiac statements spanning ...