Selected work

Representative Papers

A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30

Jul 15, 2026

This study addresses the limited research on stock market forecasting in emerging economies by focusing on short- and long-term predictions of Egypt’s EGX30 index. A systematic evaluation of KNN, Random Forest, XGBoost, LSTM, and GRU models—augmented with ensemble learning—is conducted using RMSE, MAPE, and R² metrics. Results indicate that XGBoost achieves the best performance for one-day-ahead forecasts, while GRU excels in predictions spanning one week to two months. Notably, the ensemble approach improves two-month forecast accuracy by nearly fivefold, and KNN demonstrates unexpected strength in long-term prediction. These findings offer data-driven support for investment decision-making in Middle Eastern emerging markets and highlight substantial variations in model performance across different forecasting horizons.

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Residual Physics-Informed Neural Networks for High-Fidelity BLDC Motor Modeling

Jul 10, 2026

Accurate dynamical modeling of brushless DC (BLDC) motors is critical for high-performance robotic joint control, yet conventional approaches often incur prohibitive computational costs that hinder real-time deployment. This work proposes a physics-informed neural network (PINN) based on deep residual architectures to directly predict the six-dimensional state variables from input voltages and excitation parameters in continuous time. The model enforces fidelity to the electromechanical-thermal coupled differential equations through a composite physics-data loss. A novel curriculum scheduling strategy is introduced to progressively activate physical constraints during training, effectively mitigating premature convergence. The resulting surrogate model trains in under two minutes on a CPU and achieves inference latencies of merely 0.1–22 microseconds—yielding a 118× speedup over traditional ODE solvers—while preserving high solution fidelity, thereby enabling ultra-low-latency real-time observation and control.

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Network Analysis of the Egyptian Reddit Community

Mar 24, 2026

This study presents the first systematic investigation into the network topology of the Egyptian Reddit community, examining user interaction patterns and information diffusion mechanisms. Leveraging a dataset comprising 23,185 users and 105 Egypt-related subreddits, the research employs complex network analysis—including degree distribution and clustering coefficient—to identify influential core users, tightly knit local communities, and dominant connectivity patterns. The findings not only delineate the structural characteristics and information flow pathways of a region-specific online community but also offer empirical insights into the organizational logic of social media communities in non-Western contexts.

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Defense That Attacks: How Robust Models Become Better Attackers

Dec 02, 2025

This work reveals that adversarial training—while enhancing model robustness—unintentionally strengthens models’ capacity to generate transferable adversarial examples, thereby exacerbating ecosystem-level risks in cross-model attacks. To systematically investigate this phenomenon, the authors conduct large-scale transferability experiments across a diverse “model zoo” comprising 36 CNN and Vision Transformer (ViT) architectures, evaluating how adversarial examples crafted from models trained under different strategies migrate across architectural boundaries. Results demonstrate that perturbations generated by adversarially trained models exhibit significantly higher transferability, consistently across multiple attack methods and model families. Crucially, this study is the first to incorporate “capability of generating transferable attacks” as a formal dimension of robustness evaluation, advocating for joint assessment of both a model’s resistance to attacks and its potential as an attack source. All models, code, and experimental scripts are publicly released.

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EgMM-Corpus: A Multimodal Vision-Language Dataset for Egyptian Culture

Oct 17, 2025

To address the underrepresentation of Middle Eastern and African cultural semantics and the strong Western bias prevalent in existing multimodal datasets, this work introduces EGY-VL—the first high-quality, Egypt-specific vision-language dataset. It comprises over 3,000 culturally authentic images annotated with 313 indigenous concepts spanning landmarks, cuisine, folklore, and traditional practices; all samples undergo dual human verification for cultural fidelity and image–text alignment. Zero-shot classification evaluation on EGY-VL reveals that CLIP achieves only 21.2% Top-1 accuracy, exposing a critical systemic gap in mainstream foundation models’ non-Western cultural understanding. EGY-VL thus establishes the first regional cultural benchmark for multimodal AI evaluation and provides a rigorous, community-grounded standard to assess and advance cultural inclusivity in vision-language models.

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Recent publications

Latest Papers

A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30

Jul 15, 2026

This study addresses the limited research on stock market forecasting in emerging economies by focusing on short- and long-term predictions of Egypt’s EGX30 index. A systematic evaluation of KNN, Random Forest, XGBoost, LSTM, and GRU models—augmented with ensemble learning—is conducted using RMSE, MAPE, and R² metrics. Results indicate that XGBoost achieves the best performance for one-day-ahead forecasts, while GRU excels in predictions spanning one week to two months. Notably, the ensemble approach improves two-month forecast accuracy by nearly fivefold, and KNN demonstrates unexpected strength in long-term prediction. These findings offer data-driven support for investment decision-making in Middle Eastern emerging markets and highlight substantial variations in model performance across different forecasting horizons.

0 citationsRead paper

Residual Physics-Informed Neural Networks for High-Fidelity BLDC Motor Modeling

Jul 10, 2026

Accurate dynamical modeling of brushless DC (BLDC) motors is critical for high-performance robotic joint control, yet conventional approaches often incur prohibitive computational costs that hinder real-time deployment. This work proposes a physics-informed neural network (PINN) based on deep residual architectures to directly predict the six-dimensional state variables from input voltages and excitation parameters in continuous time. The model enforces fidelity to the electromechanical-thermal coupled differential equations through a composite physics-data loss. A novel curriculum scheduling strategy is introduced to progressively activate physical constraints during training, effectively mitigating premature convergence. The resulting surrogate model trains in under two minutes on a CPU and achieves inference latencies of merely 0.1–22 microseconds—yielding a 118× speedup over traditional ODE solvers—while preserving high solution fidelity, thereby enabling ultra-low-latency real-time observation and control.

0 citationsRead paper

Network Analysis of the Egyptian Reddit Community

Mar 24, 2026

This study presents the first systematic investigation into the network topology of the Egyptian Reddit community, examining user interaction patterns and information diffusion mechanisms. Leveraging a dataset comprising 23,185 users and 105 Egypt-related subreddits, the research employs complex network analysis—including degree distribution and clustering coefficient—to identify influential core users, tightly knit local communities, and dominant connectivity patterns. The findings not only delineate the structural characteristics and information flow pathways of a region-specific online community but also offer empirical insights into the organizational logic of social media communities in non-Western contexts.

0 citationsRead paper

Defense That Attacks: How Robust Models Become Better Attackers

Dec 02, 2025

This work reveals that adversarial training—while enhancing model robustness—unintentionally strengthens models’ capacity to generate transferable adversarial examples, thereby exacerbating ecosystem-level risks in cross-model attacks. To systematically investigate this phenomenon, the authors conduct large-scale transferability experiments across a diverse “model zoo” comprising 36 CNN and Vision Transformer (ViT) architectures, evaluating how adversarial examples crafted from models trained under different strategies migrate across architectural boundaries. Results demonstrate that perturbations generated by adversarially trained models exhibit significantly higher transferability, consistently across multiple attack methods and model families. Crucially, this study is the first to incorporate “capability of generating transferable attacks” as a formal dimension of robustness evaluation, advocating for joint assessment of both a model’s resistance to attacks and its potential as an attack source. All models, code, and experimental scripts are publicly released.

0 citationsRead paper

EgMM-Corpus: A Multimodal Vision-Language Dataset for Egyptian Culture

Oct 17, 2025

To address the underrepresentation of Middle Eastern and African cultural semantics and the strong Western bias prevalent in existing multimodal datasets, this work introduces EGY-VL—the first high-quality, Egypt-specific vision-language dataset. It comprises over 3,000 culturally authentic images annotated with 313 indigenous concepts spanning landmarks, cuisine, folklore, and traditional practices; all samples undergo dual human verification for cultural fidelity and image–text alignment. Zero-shot classification evaluation on EGY-VL reveals that CLIP achieves only 21.2% Top-1 accuracy, exposing a critical systemic gap in mainstream foundation models’ non-Western cultural understanding. EGY-VL thus establishes the first regional cultural benchmark for multimodal AI evaluation and provides a rigorous, community-grounded standard to assess and advance cultural inclusivity in vision-language models.

0 citationsRead paper