Air quality management is one of the most important sustainability goals in the era of Industry 5.0. The magnitude of air pollution and impact of drastic pollutants increase day by day despite the significant efforts of the environmental enthusiasts and researchers. The role of Artificial Intelligence (AI) in determining the Air Quality Index (AQI) is significant with reasonable accuracy of classification achieved. The proposed model is a multi-class problem, that classifies the AQI into six different classes. Various ML models such as Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting(GB), Logistic Regression (LR). The RF provided reliable performance metrics for AQI category prediction, achieving an accuracy and Precision of 0.99. This model is selected for the implementation of Explainable AI (XAI) models such as Local Interpretable Model Agonistic Explainer (LIME) for explanation using the local surrogacy plots and SHapley Additive exPlanations (SHAP) explainer for the global surrogacy plots. The Generative Adversarial Network (GAN) can generate synthetic data, which addresses critical issues such as missing data, class imbalance, noise, and redundant data. The performance the GAN shows optimized performance in classification of the AQI data with accuracy closer to 100 %. This is mainly due to the synthetic data generated by the GAN which enhances the performance of the classification. The proposed work integrates the efforts of the GAN-AI-XAI that enhances the performance, reliability, trustworthiness and robustness of the AQI classification model.

Introduction: Cardiovascular health is increasingly at risk due to modern lifestyle factors such as obesity, smoking, stress, hypertension, and sedentary behavior. Post-pandemic health practices and medication side effects have further contributed to rising cases of early heart failure, particularly among individuals aged 25–40 years. This highlights the need for an automated and interpretable framework to predict heart disease at an early stage. Methods: In this study, body vitals acquired from a secondary dataset. Machine learning models including Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression were employed for classification. Model performance was evaluated using accuracy, F1-score, and k-fold cross-validation. Results: Among the tested models, the Random Forest classifier demonstrated superior performance with an accuracy and F1-score of 0.955. The interpretability is enhanced with model predictions were explained using Local Interpretable Model-Agnostic Explanations (LIME) for local surrogates and SHAP values for global surrogates. SHAP decision plots provided clear insights into classification behaviour and feature contributions. Discussion/Conclusion: The proposed interpretable machine learning framework successfully predicts heart disease with high accuracy while maintaining transparency in decision-making. With the integration of sensor data with cloud-based analysis and explainable AI techniques, this study contributes to reducing the incidence of early heart failures and supports more reliable decision-making in healthcare applications.

The unanimous focus of the sustainable technological development is energy conservation and environmental friendly production. Power management is an essential aspect of sustainable development. It not only support energy production and conservation, but also increases the life time of domestic appliances and thereby reducing the global electronic wastage. The existing systems involving Artificial Intelligence (AI) were mere prediction models, without the evidence on the detailing behind the prediction. Traditional AI systems have focused on predictive analysis but often lack transparency in decision-making and limiting consumer trust. This study proposes a solution combining remote power monitoring with the ZigBee module and Explainable Artificial Intelligence (XAI) to offer both predictive accuracy and interpretability. XAI models are more consumer oriented in every area of application, similar to the problem discussed, which tells about the impact of various parameters in power management in domestic appliances. Local Interpretable Model Agonistic Explainer(LIME) and SHAP explainer are used in the proposed work, providing explainability in the local and global surrogates. The proposed work applies various regression models such as Decision Tree (DT), Random Forest(RF), Support Vector Regressor (SVR), Gradient Boost Regressor (GBR) and Extreme Graident Boost Regressor (XGBR). The RF provides the best R2-Score of 94.71% , which is 1.5%–3.0% more than the rest of the models, and also with variance score of 68.82% , had been chosen for explainability. This study demonstrates how XAI can improve transparency and reliability in AI-powered domestic energy systems, offering actionable insights for more sustainable power consumption.