Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple medical image datasets. The study utilized three publicly available datasets: LIDC-IDRI, LUNA16, and TCIA, encompassing computed tomography (CT) scans and histopathological images. Data preprocessing included normalization, augmentation, and segmentation. Dual attention mechanisms enhanced ViT’s feature extraction capabilities. PSO optimized feature selection, and SVM performed classification. Model performance was evaluated on individual and combined datasets, benchmarked against CNN-based and standard ViT approaches. The DualSet ViT-PSO-SVM significantly outperformed existing methods, achieving superior accuracy rates of 97.85% (LIDC-IDRI), 98.32% (LUNA16), and 96.75% (TCIA). Cross-dataset evaluations demonstrated strong generalization capabilities and stability across similar imaging modalities. The proposed framework effectively bridges advanced deep learning techniques with clinical applicability, offering a robust diagnostic tool for lung cancer detection, reducing complexity, and improving diagnostic reliability and interpretability.

Access to safe drinking water is a fundamental determinant of global health. The presence of contaminated water affects the citizens’ health. Per- and polyfluoroalkyl substances (PFAS) are often referred to as forever chemicals. They pose a persistent and growing threat to drinking water. In the literature, machine learning methods are used to identify the forever chemicals in water. However, traditional methods are not efficient and scalable. Thus, to solve this issue. This study develops a large-scale machine-learning framework for PFAS risk screening in US public water systems. The proposed framework incorporates data ingestion, preprocessing, and feature engineering. We have used SMOTE for correcting imbalanced data. We performed experimentation and also evaluated our ensemble-based framework integrating Gradient boosting, bagging, and meta-learning strategies. The proposed framework achieves a maximum ROC-AUC of 0.9574, with the best-performing stacking ensemble achieving a precision of 0.75, a recall of 0.68, and an F1-score of 0.71. The simulation results show that the proposed ensemble learning framework is useful for screening and identifying water systems.