most citedPadding Module: Learning the Padding in Deep Neural Networks

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CY2025

A County-Level Similarity Network of Electric Vehicle Adoption: Integrating Predictive Modeling and Graph Theory

Fahad Alrasheedi, Hesham Ali

Electric vehicle (EV) adoption is essential for reducing carbon dioxide (CO2) emissions from internal combustion engine vehicles (ICEVs), which account for nearly half of transport…

physics.soc-ph2025

A Graph Theoretic Approach for Exploring the Relationship between EV Adoption and Charging Infrastructure Growth

Fahad S. Alrasheedi, Hesham H. Ali

The increasing global demand for conventional energy has led to significant challenges, particularly due to rising CO2 emissions and the depletion of natural resources. In the U.S.…

cs.LG2023★ 1 cited

Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary

Fahad Alrasheedi, Xin Zhong

Although Deep Neural Networks (DNNs), such as the convolutional neural networks (CNN) and Vision Transformers (ViTs), have been successfully applied in the field of computer vision…

cs.MM2023★ 1 cited

A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking

Xin Zhong, Arjon Das, Fahad Alrasheedi +1

This paper presents a comprehensive survey on deep learning-based image watermarking, a technique that entails the invisible embedding and extraction of watermarks within a cover i…

cs.CV2023★ 5 cited

Padding Module: Learning the Padding in Deep Neural Networks

Fahad Alrasheedi, Xin Zhong, Pei-Chi Huang

During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation. H…