AI image generation—which relies on neural networks to create new images from a variety of inputs, including text prompts—is ...

Artificial intelligence (AI) developments have revolutionized technologies and methodologies, particularly for malicious uses, especially since the advent of generative adversarial networks (GANs) in ...

In this work, we propose a hybrid deep learning approach for deepfake face detection. The base models include CNN+Bi-LSTM, ResNet34+EfficientNet+Bi-LSTM, and MobileNet+EfficientNet+Bi-LSTM. Initially, ...

This project provides an interactive demonstration of CNN-JEPA (Convolutional Neural Network Joint-Embedding Predictive Architecture), a PhD-level Artificial Machine Intelligence (AMI) that showcases ...

Automated medical image processing has significantly improved with recent advances in deep learning and imaging technologies, particularly in the area of neuroimaging-based Alzheimer's disease (AD) ...

The purpose of this study is to address the rising problem of picture modification and fraud, especially in the field of photojournalism, where the ability to recognize changed photos is essential to ...

Potato crops are vital to global food security, but they are susceptible to several diseases that hinder growth and yield. Traditional methods of detecting these diseases rely on labor-intensive lab ...

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification This project was inspired by Y. Tang's Deep Learning using Linear Support ...

Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified ...

Convolutional neural network (CNN) gained great attention for robust feature extraction and information mining. CNN had been used for variety of applications such as object recognition, image ...