Abstract: Deep learning models are highly susceptible to adversarial attacks, where subtle perturbations in the input images lead to misclassifications. Adversarial examples typically distort specific ...

It’s a familiar moment in math class—students are asked to solve a problem, and some jump in confidently while others freeze, unsure where to begin. When students don’t yet have a clear mental model ...

Abstract: In the field of image processing, autoencoder networks have emerged as potent approaches for image denoising. However, traditional autoencoder networks often struggle with imprecise noise ...

The project titled "Medical Image Classification for Disease Diagnosis Using Convolutional Neural Networks" aims to develop a robust and accurate machine learning model for the automatic ...