Simple Summary The rapidly advancing field of deep learning, specifically transformer-
based architectures and attention mechanisms, has found substantial applicability in …

Simple Summary The ongoing advancements in deep learning, notably its use in predicting
cancer survival through genomic data analysis, calls for an up-to-date review. This paper …

Generative adversarial networks (GANs) have demonstrated remarkable potential in the
realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional …

The efficient design of structures that exhibit desired properties is challenging across various
engineering and scientific applications. Traditional methods employ experts in a specific …

Despite its short history, Generative Adversarial Network (GAN) has been extensively
studied and used for various tasks, including its original purpose, ie, synthetic sample …

The generative adversarial network (GAN) has demonstrated superb performance in
generating synthetic images in recent studies. However, in the conventional framework of …

Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic
weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in …

Two evaluation metrics for GAN models have been proposed in existing studies: Inception
score (IS) and Fréchet Inception distance (FID). We propose a new GAN model based on the …

Generative adversarial networks (GANs) have recently attracted attention owing to their
impressive ability in generating high-quality and novel synthetic datasets such as signals …

Image generation using generative adversarial networks (GANs) has been extensively
researched in recent years. Despite active developments, the chronic issue of training …