Air Pollution is a critical environmental issue affecting public health, climate, and ecosystems. However, accurately predicting and classifying Air Quality Index (AQI) levels across different regions remains a challenging task due to the complex nature of air pollution patterns. Conventional and ensemble ML and DL models often fail to capture the physical laws goverming the air pollution, which leads to inaccurate predictions. This study addresses these issues by introducing an approach that employs Physics-Informed Neural Networks (PINN) with Explainable AI (XAI) techniques for AQI classification (AirSense-X). The proposed approach utilizes PINN for regression, along with mapping for classification and XAI for interpretation. PINN ensures that the model learns from physical laws governing air quality rather than relying solely on data. The dataset utilized in this study is a publicly available dataset containing the AQI data at daily levels from various stations across multiple cities in India. The proposed AirSense-X approach achieves an accuracy of 98 %, with 97 % precision, 95 % recall, and an F1 score of 0.96, ensuring reliability. Similarly, the confusion matrix for the proposed approach indicated that the model correctly classified 21,306 and misclassified 268 instances. The key focuses of this study include: • Introducing a novel approach, AirSense-X, which employs PINN for accurate AQI prediction and XAI for enhanced interpretability. Additionally, the study also involves comparative analysis with conventional and ensemble ML and DL models. • Employing structure mapping technique for classification based on the predicted AQI values. • Integrating physical laws governing air pollution using a PINN model enhances prediction accuracy and ensures that the model learns beyond relying on data-driven insights.

Ensuring task safety in Human-Robot Interaction (HRI) environments is a critical requirement for reliable and trustworthy robotic systems. AI can be used effectively to estimate robot task safety. However, existing systems suffer from limited data availability and class imbalance, resulting in inaccurate detection of unsafe events. To address these issues, a hybrid speech data augmentation approach is proposed, which combines acoustic and linguistic approaches to train the ML models effectively. The experimentation involves implementing the hybrid augmentation approach, with acoustic transformations for features such as audio level and linguistic transformations for speech data. Results indicate that various Machine Learning models show enhanced performance, achieving up to 0.97 accuracy with the hybrid approach, while the other augmentation approaches achieve lower results, with accuracy ranging from 0.66 to 0.92. In addition, Explainable AI (XAI) strategies are employed to highlight key contributions of significant characteristics such as speech data, audio level, and robot position.