Driver behavior detection is essential for improving road safety in Intelligent Transportation Systems (ITSs). This paper proposes an attention-enhanced CNN integrated with Explainable Artificial Intelligence (XAI) to achieve reliable and interpretable driver behavior classification. Furthermore, the model also uses a multi-head attention mechanism to ensure that it captures local spatial features as well as long-range dependencies in the activities of the driver. The State Farm Distracted Driver dataset is experimented with using stratified 5-fold cross-validation. The proposed MHA-CNN model has a mean accuracy of 99.48% on all folds and an overall high levels of precision, recall, and F1-score in all ten distraction classes. Grad-CAM is used to produce attribution maps to improve interpretability by showing semantically important parts of the image, including hands, face, and mobile devices, and can give a visual explanation of why models make decisions. In addition, an ablation investigation is performed with k-fold validation to assess the value of the multi-head attention mechanism. Its model can run at 122 frames per second with 42.92 million parameters, showing that the suggested method is a reasonable tradeoff between accuracy, efficiency, and transparency and can be applied in real-time driver monitoring applications.
Abdullah Al Mamun, Md Shahidul Islam Shabuz, Mohamed N. Rahaman et al.· Algorithms· 0 citations
Background/Objectives: Drug–drug interactions (DDIs) are major medication-safety concerns, and experimental testing cannot cover the expanding number of drug pairs. This systematic review evaluates graph-based machine-learning methods for DDI prediction, focusing on machine-learning architectures, data integration, interpretability, reproducibility, and clinical relevance. Methods: Following PRISMA 2020, we systematically searched major databases for studies published between January 2021 and March 2026. We included studies that applied graph-based machine-learning models, particularly graph neural networks, to predict DDIs. We compared their data sources, model designs, validation methods, predictive performance, reproducibility, and clinical relevance. Because the studies used different datasets and evaluation methods, the findings were summarized narratively rather than combined statistically. Results: Sixty studies met the eligibility criteria. Methods progressed from graph convolutional networks and graph attention networks to graph transformers, contrastive learning, multimodal fusion, and LLM-enhanced representations. We found that reported improvements in prediction performance often remained study-specific. Only three studies explicitly mentioned or addressed data leakage, whereas most reviewed studies contained no explicit leakage discussion; leakage-aware drug-disjoint, temporal, and external evaluations were also uncommon. Uncertainty calibration, computational-resource reporting, complete reproducibility materials, and independently validated explanations were also limited. Conclusions: Graph-based machine learning is promising for DDI prioritization and hypothesis generation but remains insufficient for independent clinical decision-making. Future studies should use standardized benchmarks, leakage-aware validation, calibrated uncertainty, reproducible pipelines, validated explanations, and external or prospective evaluation.
Md. Tuhin Reza, Md. Abdul Kader, Wissem Inoubli et al.· Pharmaceuticals· 0 citations
