Complex model architectures, demanding runtime computations, and transformer-specific operations introduce unique challenges.

Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language Processing (NLP) and Computer Vision (CV) ...

A Comparative Study of AI-Powered Chatbot for Health Care. Journal of Computer and Communications, 13, 48-66. doi: 10.4236/jcc.2025.137003 . The need for this research arises from the increasing ...

Fluorescence correlation spectroscopy (FCS), known for its high sensitivity and temporal resolution, is a crucial tool for understanding molecular dynamics. This study develops a neural network-based ...

In this study, it is aimed to evaluate image rotation robustness of Convolutional Neural Networks (CNNs) and Transformer-based deep learning models: MobileNetV2, Residual Network 18 (ResNet18), Visual ...

Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models ...

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

Recent advancements in neural networks have led to significant progress in addressing many-body electron correlations in small molecules and various physical models. In this work, we propose ...