Tareq Zaman

The transportation sector is the primary consumer of vehicle fuel worldwide and is thus a major contributor to climate change via carbon dioxide (CO₂) emissions. In addition to severe environmental impacts, such as global warming, droughts, floods, and rising sea levels, these emissions have a negative effect on public health by increasing the prevalence of respiratory disease. Achieving environmental sustainability through regulatory oversight requires a strong understanding of vehicular fuel consumption and CO₂ emissions. However, accurate modeling of these remains challenging due to the complex non-linear relationships between various vehicular characteristics and the lack of interpretability of many predictive models. Traditional linear models often fail to capture high-dimensional data complexities, while black-box methods provide few actionable insights for policymaking. To address these gaps, we developed a robust and data-driven two-stage machine-learning (ML) framework designed to enhance model performance and reliability. First, we implemented standard data preprocessing, enhanced feature engineering, and hyperparameter tuning for 14 cutting-edge ML algorithms and three advanced modeling techniques to explore their predictive performance. Second, we introduced three interpretable explainable AI (XAI) approaches. These were evaluated on a publicly available Kaggle static dataset of 550 vehicles, dominated by gasoline-powered vehicles, with only two diesels and two electric vehicles. The tuned CatBoost model demonstrated strong predictive performance, achieving an impressive R² of 0.9260, a root mean square error (RMSE) of 1.1759, and a mean absolute error (MAE) of 0.8147. In parallel, we deterministically estimated CO₂ emissions from fuel consumption, which provide direct estimates of tailpipe emissions. To ensure transparency and model interpretability, we employed Shapley additive explanations, local interpretable model-agnostic explanations, and permutation importance to identify the key factors contributing to the model predictions. Across the explainability analyses, cylinder count, front-wheel drive (drive_fwd), and the displacement–year interaction were the primary contributors to the predicted combined miles per gallon; in other words, they strongly affected fuel consumption. Collectively, these findings demonstrate the ability of the proposed model to capture complex feature relationships; thus, it offers a valuable tool for researchers and policymakers in sustainability planning and emission control. Future research should focus on real-time driving or dynamic measurements data and enhancing practical applications to further reduce emissions and promote environmental sustainability.

The rapid advancement of generative AI has made it increasingly difficult to distinguish between authentic and fabricated visual content, creating a pressing challenge for digital forensics and content authentication. Existing detection models often depend on deep neural networks that learn global representations but fail to capture subtle structural in consistencies, especially around edges and textures, while also being computationally demanding. To overcome these challenges, we introduce a hybrid detection framework that integrates a fine-tuned Vision Transformer (ViT) with a novel edge-based image analysis module. Unlike prior methods that rely solely on learned features, the proposed edge-based module explicitly quantifies edge variance before and after smoothing to reveal characteristic artifacts of AI-generated images, such as suppressed noise and weakened boundary details. This interpretable post-processing step enhances ViT’s sensitivity to fine-grained local cues while keeping the model lightweight and efficient. Comprehensive evaluations on the CIFAKE, Artistic, and Custom Curated datasets demonstrate that our approach not only achieves state-of-the-art accuracy (0.977) and F1 score (0.977) on CIFAKE but also offers improved generalizability, transparency, and adaptability to video frame analysis. These qualities make the proposed framework a practical and trustworthy solution for real-world content verification and digital forensic applications.

The rapid advancement of generative AI has made it increasingly difficult to distinguish between authentic and fabricated visual content, creating a pressing challenge for digital forensics and content authentication. Existing detection models often depend on deep neural networks that learn global representations but fail to capture subtle structural in consistencies, especially around edges and textures, while also being computationally demanding. To overcome these challenges, we introduce a hybrid detection framework that integrates a fine-tuned Vision Transformer (ViT) with a novel edge-based image analysis module. Unlike prior methods that rely solely on learned features, the proposed edge-based module explicitly quantifies edge variance before and after smoothing to reveal characteristic artifacts of AI-generated images, such as suppressed noise and weakened boundary details. This interpretable post-processing step enhances ViT’s sensitivity to fine-grained local cues while keeping the model lightweight and efficient. Comprehensive evaluations on the CIFAKE, Artistic, and Custom Curated datasets demonstrate that our approach not only achieves state-of-the-art accuracy (0.977) and F1 score (0.977) on CIFAKE but also offers improved generalizability, transparency, and adaptability to video frame analysis. These qualities make the proposed framework a practical and trustworthy solution for real-world content verification and digital forensic applications.

The rapid advancement of generative models has led to a growing prevalence of highly realistic AI-generated images, posing significant challenges for digital forensics and content authentication. Conventional detection methods mainly rely on deep learning models that extract global features, which often overlook subtle structural inconsistencies and demand substantial computational resources. To address these limitations, we propose a hybrid detection framework that combines a fine-tuned Vision Transformer (ViT) with a novel edge-based image processing module. The edge-based module computes variance from edge-difference maps generated before and after smoothing, exploiting the observation that AI-generated images typically exhibit smoother textures, weaker edges, and reduced noise compared to real images. When applied as a post-processing step on ViT predictions, this module enhances sensitivity to fine-grained structural cues while maintaining computational efficiency. Extensive experiments on the CIFAKE, Artistic, and Custom Curated datasets demonstrate that the proposed framework achieves superior detection performance across all benchmarks, attaining 97.75% accuracy and a 97.77% F1-score on CIFAKE, surpassing widely adopted state-of-the-art models. These results establish the proposed method as a lightweight, interpretable, and effective solution for both still images and video frames, making it highly suitable for real-world applications in automated content verification and digital forensics.