Recent advancements in brain imaging technology have led to a rise in the use of magnetic resonance imaging (MRI) for clinical diagnosis. Deep learning (DL) techniques have emerged as a valuable tool for automatically detecting abnormalities in brain images without manual intervention....

In this study, the authors explored deepfake detection using ResNet-50 model, with a focus on the role of data preprocessing and augmentation in improving model's robustness. Two datasets, FaceForensics++ and CelebDF, which incorporate manipulated images with differently-applied manipulations...

This work introduces an analysis to a new end-to-end hybrid model that adopts adversarial data augmentation using C-GANs in conjunction with Vision Transformers (ViT) to enhance image classification. ViT incorporates the multi-head self-attention to address the local and global features of the...

Distinguishable artificial intelligence (AI) models are employed to identify the specific scope of the task, the nature of the data, the efficient benefits of energy and time, and the precise manner of acquiring the preferred results. Laborious AI models are designed to arrive at conclusions by...

Generative Adversarial Networks (GANs), introduced by Ian Good fellow in 2014, have revolutionized the field of artificial intelligence, particularly in image generation and style transfer. GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously...

This survey paper aims at analyzing the two promising technologies in machine learning, that are GAN and Meta-Learning. GANs have brought the image synthesis and style transfer to new different level by using adversarial learning method while Meta-learning has done the same thing to few-shot...

This chapter introduces GANs and Meta-Learning as fundamental approaches to Artificial Intelligence necessary for tasks from synthetic data generation to task-based sample learning. Nevertheless, extensive research shows that both GANs and Meta-Learning suffer from important drawbacks preventing...

Generative models, span from GANs, VAEs, and Diffusion Models, are revolutionizing the-healthcare domain using fake data, enhancing medical images, and enhancing drug purposes. GANs optimize image reconstruction by increasing the PSNR by approximately 23%, whereas diffusion models produced a...

This chapter explains how combined Transfer Learning, GANs, and Meta-Learning and the artificial intelligence fields where it can help solve challenging problems. In situations where information is scanty, characteristics of a model increase through Transfer Learning of information across...

Lip reading is to deduce the meaning of speech from the movements of the lips. Lip reading has advanced significantly as a result of the introduction of large-scale datasets and the advancement of deep learning technologies. Analyzing visual signals from a speaker's lips movements to comprehend...