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.

This paper presents a hybrid optimization methodology integrating Biased Random-Key Genetic Algorithms (BRKGA), data mining, and reinforcement learning to address the Sensor Network Optimization Problem. Using statistical and clustering techniques, the method enhances BRKGA by incorporating patterns extracted from elite solutions. A reinforcement learning agent dynamically decides when and how to mine patterns, enhancing adaptivity and optimizing the process. The validated patterns are then integrated into a Quadratic Knapsack Problem (QKP) optimization model, guaranteeing compliance with budget constraints while efficiently exploring potential regions of the solution space. Experimental results demonstrate the approach's effectiveness in generating high-quality solutions for sensor allocation in Wireless Sensor Networks (WSNs), balancing exploration and exploitation, and achieving robust performance in complex optimization scenarios.

This study introduces a novel approach to the Sensor Allocation Problem (SAP) in Wireless Sensor Networks (WSNs) by integrating reinforcement learning with a multi-centrality heuristic. The goal of SAP is to optimize the network topology to minimize energy consumption while maintaining connectivity. We propose a reinforcement learning agent that interacts with a multi-centrality heuristic to dynamically select sensor placement. Extensive computational experiments were performed on both regular and semi-regular grid topologies of varying sizes. Comparisons with a Mixed-Integer Linear Programming (MILP) model reveal that our approach not only yields high-quality solutions in small to medium instances but also remains effective in larger networks, where the MILP solver often fails to produce feasible solutions. Notably, the RL module consistently improves upon the baseline heuristic allocation, demonstrating significant cost reductions while preserving full network coverage. By uniting a centrality-driven heuristic with an adaptive learning mechanism, the proposed hybrid framework addresses challenges in SAP more effectively than traditional exact methods. Consequently, it provides a promising avenue for large-scale WSN deployments, maintaining robust connectivity and efficiently managing energy consumption across various network configurations.

In this paper, we propose a method for detecting fine-scale surface changes, such as scratches and dents, by combining 3D Gaussian Splatting (3DGS) with Vision-Language Models (VLMs). While 3DGS excels at rendering high-fidelity novel views that make even subtle changes visually detectable, identifying such changes automatically remains challenging. Our approach addresses this by precisely aligning two 3DGS models of the same object, before and after potential damage, and using VLMs to analyze the rendered views for localized differences. Experiments show that our method outperforms a traditional photogrammetry-based method in detecting fine-grained surface alterations.

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

Wireless sensor networks are widely valued for their effectiveness in real-time data collection. As the amount of data exchanged within such networks grows, designing a robust network topology that maximizes area coverage with minimal sensors has become a critical challenge. The choice of topology impacts key network metrics, including sensor coverage, communication range, connectivity, inference, and installation and management costsIn this paper, we address the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations, presenting an optimization approach based on deep reinforcement learning. This problem is noteworthy, as sensors in various applications are often required to share data within distinct destinationsWe leverage deep reinforcement learning to effectively address the complex task of selecting optimal sensor locations. Our reinforcement learning agent dynamically learns network structure by iteratively adding and removing sensors, optimizing both sensor coverage and the total number of sensors used. Experiment across diverse scenarios demonstrate the effectiveness of our method for network planning problems of varying scales, achieving full coverage with fewer sensors than traditional approaches. Additionally, our approach also produce solutions for large instances where Mixed Integer Programming solvers were not able to. Overall, our method was able to reduce the number of sensors used by up to 22.3% compared to other methods.

Class imbalance and overlapping instances problems have long been recognized as one of the major causes of the performance deterioration of the classification model. Moreover, the majority class may have an irrelevant and noisy instance that shifts the decision boundary of the classification far away from the ideal one. We propose a framework for balancing the class distribution and mitigating the class overlap problem in a dataset. The key feature of our framework is its ability to detect the overlapping instances between classes and then remove the problematic instances from the majority class. Thus, it will have more precise information for the oversampling method to generate the synthetic minority instances. We evaluated the proposed framework using the Lending club and ten other datasets from the KEEL repository. We demonstrate the implementations of our framework using Tomek and Edited Nearest Neighbor for removing the overlapping instances from the majority class and SWIM-MD for generating the synthetic minority instances. Also, we used eight well-known classifiers to show that our proposed framework can improve the performance of various classifiers. Lastly, we present a detailed analysis of the experimental result that shows the superiority of our proposed framework. Our proposed framework outperformed the state-of-the-art methods in terms of geometry mean classification performance metric.