Mahmoud Badawy

Calibrated adaptive framework for trustworthy human and artificial intelligence decision systems

Maytha AL-Ali, Adam Marks, Abdallah A. Mohamed, Hossam Magdy Balaha et autres

The integration of artificial intelligence (AI) into safety-critical industrial decision-making promises substantial gains in efficiency and reliability, yet real-world deployment remains constrained by a deeper systemic problem: miscalibrated risk estimates, static trust assumptions, and unmodeled human cognitive biases jointly destabilize collaboration under …

ae, gb, sa, Égypte, us (code pays fourni par la source)

Smart Enforcement of Disability Parking: A Drone-Based License Plate Recognition and Staged Optimization Framework

Hanaa ZainEldin, Tamer Ahmed Farrag, Shymaa G. Eladl, Malik Almaliki et autres

Unauthorized occupation of parking spaces designated for individuals with disabilities remains a persistent challenge in urban environments, limiting accessibility and inclusive mobility. This paper proposes an integrated UAV-assisted enforcement framework that combines drone-based imaging, onboard license plate recognition (LPR), IoT connectivity, and …

Égypte, sa (code pays fourni par la source)

Detection of disturbances and cyber-attacks in smart grids using explainable machine learning

Mohamed Farsi, Majed M. Alwateer, Shatha Abed Alsaedi, Abdulrhman I. AlSahafi et autres

Modern power systems are subjected to natural disruptions and cyberattacks, both of which have the potential to have catastrophic consequences on the grid's stability and security. Besides, due to the sophistication of cyber-physical threats, including techniques like false data injection and command …

sa, us, Égypte, is (code pays fourni par la source)

Integrating machine learning and explainable AI for employee attrition prediction in HR analytics

Maytha AL-Ali, Majed M. Alwateer, Shatha Abed Alsaedi, Hossam Magdy Balaha et autres

Employee attrition poses significant challenges to organizations, impacting productivity, morale, and financial stability. Predicting attrition and understanding its underlying drivers are critical for implementing effective retention strategies. In this study, we propose a comprehensive framework that utilizes advanced machine learning techniques to …

ae, sa, us, Égypte, ru (code pays fourni par la source)

Green Technology: A Systematic Review of AI and IoT Solutions for a Sustainable Future

Nesma Abd El-Mawla, Mohamed Abdou Berbar, Nawal Ahmed El-Fishawy, Mohamed A. El-Rashidy et autres

Green technology offers a solution to the pressing environmental crisis. It can change the structure and generation of waste so as not to harm the earth, and people can become environmentally friendly. To address complex environmental challenges like climate change and pollution, …

Égypte (code pays fourni par la source)

A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization

Shatha Abed Alsaedi, Majed M. Alwateer, Nouf Helal Alharbi, Hossam Magdy Balaha et autres

The rapid escalation of cyber threats and the sophistication of malware demand detection frameworks that are accurate, mathematically grounded, and interpretable. Traditional deep learning approaches often rely on standard loss formulations and empirical tuning, leaving challenges such as class imbalance, overfitting, and …

sa, us, Égypte, cz (code pays fourni par la source)

Comprehensive Machine Learning Pipeline for Encrypted Traffic Classification With Bayesian Optimization and Explainability

Ahmad Reda Alzighaibi, Adam Marks, Abdullateef I. Almudaifer, Hossam Magdy Balaha et autres

Encrypted traffic classification is critical for modern cybersecurity, yet traditional approaches are ineffective and existing learning-based methods often struggle with noisy data, limited interpretability, and inefficient optimization. To address these challenges, this paper presents a comprehensive machine learning pipeline optimization framework enhanced …

sa, gb, us, Égypte (code pays fourni par la source)

Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis

Abdallah A. Mohamed, Yousry AbdulAzeem, Abdullateef I. Almudaifer, Hanaa ZainEldin et autres

Bladder cancer represents a significant health problem worldwide, with it being a major cause of death and characterized by frequent recurrences. Effective treatment hinges on early and accurate diagnosis; however, traditional methods are invasive, time-consuming, and subjective. In this research, we propose …

sa, Égypte, us (code pays fourni par la source)

Revolutionizing breast cancer diagnosis: A computer-aided diagnosis framework with Vision Transformers for multistage histopathology-based classification

Hossam Magdy Balaha, Yousry AbdulAzeem, Mansourah Aljohani, Mohammed El-Abd et autres

us, Égypte, sa, kw (code pays fourni par la source)

Prompt-Driven Multimodal Segmentation with Dynamic Fusion for Adaptive and Robust Medical Imaging with Applications to Cancer Diagnosis

Shatha Abed Alsaedi, Hossam Magdy Balaha, Mohamed Farsi, Majed M. Alwateer et autres

BACKGROUND/OBJECTIVES: Medical image segmentation is a crucial task for diagnosis, treatment planning, and monitoring of cancer; however, it remains one of the toughest nuts to crack for Artificial Intelligence (AI)-based clinical applications. Deep-learning models have shown near-perfect results for narrow tasks such …

sa, us, Égypte, is (code pays fourni par la source)

Empowering cognitive disabilities in transit: an explainable, emotion-aware ITS framework

Malik Almaliki, Amna Bamaqa, Tamer Ahmed Farrag, Hossam Magdy Balaha et autres

People with disabilities need ongoing support and a balanced lifestyle. Smart cities like NEOM are emerging worldwide. The Saudi government has implemented several disability accessibility programs in public spaces and transportation. This article addresses a critical yet often neglected challenge: accurately recognizing …

sa, us, Égypte (code pays fourni par la source)

Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics

Mohamed Farsi, Hanaa ZainEldin, Hanaa A. Sayed, Rasha F. El-Agamy et autres

Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths globally, making a timely and reliable diagnosis essential. Manual histopathology assessment, though clinically standard, is prone to observer variability, while existing computational approaches often trade accuracy for interpretability, limiting …

sa, Égypte, us (code pays fourni par la source)