Obada Al-Khatib

Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary.

The increasing integration of renewable energy sources and smart grid (SG) technologies introduces significant challenges to power system stability due to the inherent variability and uncertainty of electricity generation and demand. While many machine learning and deep learning approaches have been proposed for stability prediction, various studies focus only on predictive performance and offer limited assessment of computational efficiency, statistical significance, and sustainability-related metrics. To address these gaps, this study suggests a hybrid fusion approach that combines Convolutional Neural Networks (CNNs), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF) classifiers through a soft voting strategy for SG stability prediction. The CNN component automatically extracts representative features, while XGBoost and RF contribute complementary classification capabilities, reducing the need for manual feature engineering. In addition to predictive evaluation, a Green AI-oriented benchmarking framework is introduced to evaluate model performance using predictive accuracy, computational runtime, memory consumption, and estimated computational CO₂ emissions associated with model training and inference. The proposed framework is assessed on the UCI SG Stability dataset using stratified cross-validation and statistical significance testing, including Friedman and Nemenyi post hoc analyses. Experimental results demonstrate that the fusion model achieves 97.68% classification accuracy and an AUC of 0.991, exceeding several individual machine learning, deep learning, and ensemble baselines. Statistical analysis shows significant improvements over recurrent deep learning models such as LSTM and GRU. However, the differences from strong tree-based methods, including XGBoost and RF, are not statistically significant. Furthermore, the proposed model reaches a high sustainability score of 0.802, indicating a favorable balance between predictive performance and computational resource requirements. The findings show that the proposed framework is effective and computationally efficient to predict the stability of the smart grid on the UCI benchmark dataset and also serves as a transparent green AI benchmarking methodology for comparative studies in the future. The validation of the approach on real-world smart grid data under noisy, not fully complete, and heterogeneous operating conditions is another interesting research direction for future work.

Deep multiscale learning has emerged as a promising venue for robust image detection and multi-object tracking in adverse conditions, but the current solutions tend to be impacted by the issues of occlusion, scale variation, and background clutter, focusing on each of them separately and restricting the generalization. In a direction to address these gaps, this piece of writing proposes a unified model that incorporates HRNet to extract high-resolution features, DETR to make use of transformers for detection, and TrackFormer to identify in an identity-preserving manner. Data was based on the MOT17 benchmark dataset, which provides various urban video sequences, including annotated bounding boxes and identities, to guarantee a test that is rigorous. The approaches were selected due to their complementary advantages: HRNet keeps fine-grained spatial information, DETR allows us to locate the objects in an accurate way, and TrackFormer tracks the trajectories across fragments. Experiments show good performance, with a mean detection AP of 70.9, precision of 76.5, recall of 72.8, MOTA of 74.8, IDF1 of 70.2, and HOTA of 63.6, maintaining real-time performance of 26 FPS with a latency of 38.5 ms per frame. In general, this work offers a globally scalable, end-to-end system for problems like surveillance and self-driving, and future work aims to address outrageously dense scenes, enhance cross-dataset generalization, and come up with lightweight systems to deploy these edges.

The rapid expansion of wind energy as a key pillar of sustainable electricity generation has intensified the need for reliable and efficient wind turbine operation, particularly in minimizing failures of critical components such as gearboxes, which significantly impact maintenance costs, downtime, and overall lifecycle sustainability. This study proposes a vibration-based fault diagnosis framework integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Multiscale Convolutional Neural Network (MSCNN) for wind turbine gearbox condition monitoring. The approach decomposes non-stationary vibration signals into Intrinsic Mode Functions (IMFs) to capture meaningful oscillatory characteristics, which are then processed through parallel multiscale convolutional branches to learn both transient and long-term signal patterns. Experimental validation using the NREL Gearbox Reliability Collaborative dataset demonstrates that the proposed CEEMDAN-MSCNN model demonstrates strong performance compared to conventional machine learning methods and single-scale CNN architectures, achieving 99.50% accuracy on an unseen holdout dataset. The proposed framework supports predictive maintenance strategies by enabling reliable fault diagnosis, reducing unplanned downtime, and improving the operational efficiency and long-term sustainability of wind energy systems.

Wildfires pose escalating environmental, social, and economic challenges worldwide, intensified by climate change, prolonged droughts, and expanding human activity. This study reviews the evolution of wildfire management from traditional detection, control, and prevention methods to advanced, data-driven approaches. It highlights the limitations of historical practices, such as reliance on ground-based monitoring, satellite imaging, and fire weather indices, and explores current global and regional strategies, including the integration of remote sensing, advanced analytics, and predictive modelling. Special attention is given to the role of machine learning (ML), particularly ensemble methods such as Random Forest (RF) and XGBoost, which have demonstrated superior predictive performance by capturing complex, non-linear relationships in wildfire data. The study synthesizes key data sources, including meteorological, vegetation, land-type, and satellite datasets, and outlines methods for their integration to improve predictive accuracy. Despite significant progress, persistent challenges remain in data fusion, spatial-temporal modelling, model generalizability, and interpretability. Future research emphasizes multi-source data integration, advanced feature engineering, spatio-temporal analysis, and enhanced model explainability using SHAP and LIME to ensure transparent and actionable predictions. Ultimately, this review underscores the potential of ML-driven systems to transform wildfire prediction into a proactive and sustainable component of modern disaster management.

The rapid advancement of deepfake generation technologies presents significant challenges to the verification of digital video authenticity. These time-dependent artifacts are difficult to detect using conventional frame-based detection approaches. This paper introduces AT-HSTNet, an Action-Transformer-based Hierarchical Spatiotemporal Network designed for robust and computationally efficient deepfake video detection. The proposed framework adopts a multi-stage hierarchical architecture in which frame-level visual features are extracted using an EfficientNet-B0 backbone, short- and medium-range temporal patterns are modeled through Bidirectional Long Short-Term Memory (BiLSTM) networks, and long-range temporal dependencies are captured using an action-aware Transformer operating on temporally aggregated representations. Unlike conventional video transformers that apply self-attention directly to raw frame-level features, the proposed action-aware attention mechanism reduces redundant computation and improves stability in temporal reasoning. Extensive experiments on the balanced FFIW-10K dataset demonstrate that AT-HSTNet achieves an accuracy of 98.7%, with 98.0% precision, 96.0% recall, and a 96.9% F1-score, outperforming representative CNN–BiLSTM and CNN–Transformer baseline architectures. In addition, AT-HSTNet is highly efficient, requiring only 0.45 GFLOPs and achieving an inference speed of approximately 30 FPS on consumer-grade GPU hardware. As a result of this study, we found hierarchical temporal modeling more effective when combined with action-aware attention for any deepfake video detection.