Machine learning and explainable AI techniques are powerful tools to analyze the influence of unhealthy food ads on health awareness and concern levels. This research aims to predict levels of concern about unhealthy food advertisements and ascertain significant demographic and behavioral variables that modify the need for these concerns. A survey was conducted, and responses from 1030 adolescents were gathered on their knowledge about food advertisements and what affects their perceptions toward health. Stacking Ensemble, Random Forest, and XGBoost with hyperparameter optimization and synthetic oversampling techniques were applied to predict three categories of concern levels in unhealthy food advertisements. Stacking Ensemble, utilizing Random Forest, K-Nearest Neighbors, and Support Vector Machines as base learners and Logistic Regression as the meta-learner, yielded the highest prediction accuracy of 98.54% and an F1 score of 0.99. The most relevant features for predicting the food advertisement concern levels were age, parental education and advertisement types, such as free toys and celebrity endorsement. LIME, an interpretation protocol, has been used to enable a proper understanding of how advertisement strategies influence adolescent health awareness. The developments resulted from growing research efforts in which predictive analytics is connected to explainability in understanding and mitigating the adverse influence of unhealthy food advertisements. The results bear implications at the onset of public health policy, advertising regulation, and educational campaigns against improved adolescent dietary behaviors and awareness.

Root canal treatment involves the removal of inflamed pulp from infected teeth and sealing them to prevent re-entry of bacteria and infection. Early and accurate identification of root canal issues is crucial for improving treatment outcomes and minimizing complications. This study proposes a novel method for efficiently detecting failures in root canal treatments by incorporating image filtering techniques into deep learning models. A custom dataset has been collected from Square Hospital Ltd of Dhaka, Bangladesh, comprising 1,600 annotated, filtered radiographs. These images are processed using denoising algorithms (mean, median, Gaussian, contourlet transform, or Bayesian wavelet), non-local means, and BM3D. The YOLO models used for detection included YOLOv5, YOLOv7, and YOLOv8, with YOLOv5 achieving the highest accuracy of 90.6% on the filtered datasets. This work integrates an autodistillation technique with Grounded SAM as the base model for bounding box generation, YOLOv8 as an intermediate model to bridge architectural differences, and YOLOv5 as the target model due to its lightweight design, fewer parameters, and superior performance on the filtered dataset. Knowledge distillation was employed to optimize performance on lightweight models, resulting in precision and recall values of 99.16% and 97.29%, respectively, for YOLOv5. The filtered datasets outperformed raw images, with the Bayesian wavelet and contourlet transform yielding the best results on YOLOv5. This pipeline demonstrates the potential to develop one of the most accurate and clinically applicable diagnostic tools for root canal failure detection by combining deep learning and knowledge distillation techniques with image filtering. This study demonstrates how combining deep learning with advanced image enhancement techniques can provide a highly accurate, clinically relevant tool for the early detection of root canal treatment failures, potentially aiding dentists in making faster and more consistent diagnoses.

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics.