Reliable water quality monitoring is critical for safeguarding public health and ensuring the sustainability of ecosystems, especially in regions facing growing environmental and industrial pressures. This paper presents a novel edge-intelligent framework that combines Graph Neural Networks with real-time IoT sensor deployments to predict and monitor multiple water quality parameters. Leveraging a two-stage spatio-temporal graph construction process grounded in Euclidean and correlation-based criteria, we model the spatial relationships and temporal dynamics of diverse water parameters. Our models achieve strong predictive performance across 14 critical parameters, including temperature, dissolved oxygen, conductivity, and microbial indicators, with R² scores as high as 0.92. Deployed on low-cost Raspberry Pi-based edge devices, our system enables real-time inference and energy-efficient operations without reliance on cloud connectivity. This work bridges the gap between deep learning and in-situ environmental monitoring, demonstrating an IoT approach for data-driven water governance. The proposed solution holds significant promise for policy-makers, researchers, and communities aiming to decentralize environmental sensing and address the global challenge of clean water access.

Session-based cyberbullying detection on social media platforms presents significant challenges owing to the scarcity of labeled data and the complex, heterogeneous nature of social media sessions and interactions between their elements. In this study, we introduce BullyHCL, an innovative unsupervised heterogeneous graph contrastive learning framework specifically designed for session-based cyberbullying detection. This framework utilizes contrastive learning by training a discriminator with binary cross-entropy to maximize the mutual information between node and summary pairs from the original graph while minimizing it for perturbed pairs from augmented views. This approach encourages the encoder to learn representations that capture the essential structural and semantic information from the original graph while being robust to perturbations. An analysis of augmentation strategies and model parameters provides valuable insights into improving unsupervised cyberbullying detection. Experiments conducted on the Instagram and the Vine datasets reveal that the BullyHCL model consistently surpasses existing unsupervised baselines and achieves performance levels comparable to those of supervised models, underscoring its practical value as a label-free alternative.