Lung cancer is the most common cause of cancer-related mortality globally. Early diagnosis of this highly fatal and prevalent disease can significantly improve survival rates and prevent its progression. Computed tomography (CT) is the gold standard imaging modality for lung cancer diagnosis, offering critical insights into the assessment of lung nodules. We present a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs). By optimizing and integrating grid and block attention mechanisms with InceptionNeXt blocks, the proposed model effectively captures both fine-grained and large-scale features in CT images. This comprehensive approach enables the model not only to differentiate between malignant and benign nodules but also to identify specific cancer subtypes such as adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. The use of InceptionNeXt blocks facilitates multi-scale feature processing, making the model particularly effective for complex and diverse lung nodule patterns. Similarly, including grid attention improves the model’s capacity to identify spatial relationships across different sections of the picture, whereas block attention focuses on capturing hierarchical and contextual information, allowing for precise identification and categorization of lung nodules. To ensure robustness and generalizability, the model was trained and validated using two public datasets, Chest CT and IQ-OTH/NCCD, employing transfer learning and pre-processing techniques to improve detection accuracy. The proposed model achieved an impressive accuracy of 99.54% on the IQ-OTH/NCCD dataset and 98.41% on the Chest CT dataset, outperforming state-of-the-art CNN-based and ViT-based methods. With only 18.1 million parameters, the model provides a lightweight yet powerful solution for early lung cancer detection, potentially improving clinical outcomes and increasing patient survival rates.

Air pollution poses a critical challenge to environmental sustainability, public health, and urban planning. Accurate air quality prediction is essential for devising effective management strategies and early warning systems. This study utilized a dataset comprising hourly measurements of pollutants such as PM2.5, NO_x, CO, and benzene, sourced from five metal oxide sensors and a certified analyzer in a polluted urban area, totaling 9,357 records collected over one year (March 2004–February 2005) from the Kaggle Air Quality Data Set. A comprehensive comparison of ten machine learning regression models XGBoost, LightGBM, Random Forest, Gradient Boosting, CatBoost, Support Vector Regression (SVR) with Bayesian Optimization, Decision Tree, K-Nearest Neighbors (KNN), Elastic Net, and Bayesian Ridge was conducted. Model performance was enhanced through Bayesian optimization and randomized cross-validation, with stacking employed to leverage the strengths of base models. Experimental results showed that hyperparameter optimization and ensemble strategies significantly improved accuracy, with the SVR model optimized via Bayesian optimization achieving the highest performance: an R² score of 99.94%, MAE of 0.0120, and MSE of 0.0005. These findings underscore the methodology’s efficacy in precisely capturing the spatial and temporal dynamics of air pollution.

Background: Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magnetic resonance imaging (MRI), have shown utility in detecting the disease. However, artificial intelligence (AI) offers promise for automating this process, potentially enhancing diagnostic accuracy and accessibility. Methods: In this study, various machine learning models were used to detect Alzheimer disease, including K-nearest neighbor regression, support vector machines (SVM), AdaBoost regression, and logistic regression. A neural network was constructed and validated using data from 150 participants in the University of Washington’s Alzheimer’s Disease Research Center (Open Access Imaging Studies Series [OASIS] dataset). Cross-validation was also performed on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to assess the robustness of the models. Results: Among the models tested, K-nearest neighbor regression achieved the highest accuracy, reaching 97.33%. The cross-validation on the ADNI dataset further confirmed the effectiveness of the models, demonstrating satisfactory results in screening and diagnosing Alzheimer disease in a community-based sample. Conclusion: The findings indicate that AI-based models, particularly K-nearest neighbor regression, provide promising accuracy for the early detection of Alzheimer disease. This approach has potential for further development into practical diagnostic tools that could be applied in clinical and community settings. Export

Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime. Existing maintenance strategies often fail to accurately predict such failures, highlighting the need for novel predictive approaches. This study aims to improve the reliability of power systems by predicting transformer failures through the integration of IoT technologies and advanced machine learning techniques. The proposed hybrid model combines the LightGBM algorithm with GridSearch optimization to achieve both high predictive accuracy and computational efficiency. In addition, the model enhances interpretability by incorporating SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for transparent decision making. The study presents a detailed comparison of different classification algorithms and evaluates their performance using metrics such as accuracy, recall, and F1 score. The results show that the hybrid model outperforms other methods, achieving an accuracy of 99.91%. The SHAP and LIME analyses provide engineers and researchers with valuable insights by highlighting the most influential features in failure prediction. In addition, the model’s ability to efficiently handle large data sets enhances its practicality in real-world power systems. By proposing an innovative approach to failure prediction, this research contributes to both the theoretical foundation and practical advancement of sustainable and reliable energy infrastructures.

This study introduces a hybrid deep learning model integrating EfficientNet-B3 and Vision Transformer with an Attention Fusion mechanism for automated colon cancer detection using the Kvasir endoscopic dataset. The model leverages EfficientNet-B3’s strength in capturing fine-grained local textures and Vision Transformer’s ability to model global contextual relationships. A multi-head attention-based fusion block harmonizes these features, achieving comprehensive representations and enhanced classification stability. Model optimization was guided by the Matthews Correlation Coefficient (MCC), alongside evaluations of accuracy, F1-score, and Brier Score. Experimental results demonstrate a 96.2% accuracy and an MCC of 0.961, surpassing standalone baselines and existing benchmark architectures. Cross-validation confirmed robust generalization, while Grad-CAM analyses improved interpretability by visualizing salient histopathological regions influencing predictions. Despite slight overfitting tendencies, the model maintained strong performance across all eight image classes. These findings highlight the model’s ability to address limitations of single-architecture approaches by combining local and global feature extraction, offering rapid, objective, and reliable diagnostic support. The proposed framework shows significant promise for integration into computer-aided colonoscopy systems, paving the way for enhanced clinical diagnostics and reduced pathologist workload through AI-driven precision medicine.

This study proposes an advanced hybrid fault‐tolerant control (FTC) architecture for permanent magnet synchronous motors (PMSMs) operating under speed sensor faults (SSFs), integrating model predictive control (MPC), third‐order sliding mode control (TOR‐SMC), and a model reference adaptive system (MRAS). The key innovation lies in the synergistic combination of MPC’s predictive optimization with the robustness of TOR‐SMC and the real‐time adaptive estimation capability of MRAS, enabling reliable operation in the presence of sensor degradation or failure. A residual‐based fault detection mechanism is embedded to monitor discrepancies between actual and estimated rotor speeds, enabling rapid fault identification and seamless transition to observer‐based control. The proposed hybrid control system is designed within a hierarchical architecture, wherein MPC optimizes inverter switching actions, TOR‐SMC ensures robust disturbance rejection and chattering suppression, and MRAS delivers high‐fidelity speed estimation. Simulation studies under various operating scenarios—encompassing step changes in speed, variable torque loads, and fault scenarios—demonstrate that the system achieves a maximum speed estimation error of 1.8%, speed tracking accuracy of 97.6%, and a fault detection time of less than 2.5 ms, which is 41.3% faster than extended Kalman filter (EKF)–based schemes. Quantitatively, the proposed method reduces torque ripples by 32.5%, current overshoot by 35.7%, and transient response time by 27%, while improving overall fault tolerance by 63% compared to conventional FTC approaches. The TOR‐SMC module contributes to a 78% reduction in chattering and ensures stable electromagnetic torque behavior even under dynamic torque disturbances. In parallel, MRAS offers faster convergence (2.5 ms) and smoother transitions compared to SMO and EKF observers, maintaining control integrity despite sensor anomalies. This comprehensive and modular FTC approach addresses a critical vulnerability in PMSM drive systems and is particularly well‐suited for deployment in electric vehicles, aerospace systems, and renewable energy platforms, where high reliability, real‐time responsiveness, and robustness to sensor degradation are paramount. The results confirm the proposed hybrid MPC–TOR‐SMC–MRAS framework as a scalable and high‐performance solution for next‐generation motor control systems under fault‐prone environments.