In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.

Eye-tracking systems have emerged as valuable tools in various research fields, including psychology, medicine, marketing, car safety, and advertising. However, the high costs of the necessary specialized hardware prevent the widespread adoption of these systems. Appearance-based gaze estimation techniques offer a cost-effective alternative that can rely solely on RGB cameras, albeit with reduced accuracy. Therefore, the aim of our work was to present a real-time eye-tracking system with low-end hardware that leverages appearance-based techniques while overcoming their drawbacks to make reliable gaze data accessible to more users. Our system employs fast and light machine learning algorithms from an external library called MediaPipe to identify 3D facial landmarks. Additionally, it uses a series of widely recognized computer vision techniques, like morphological transformations, to effectively track eye movements. The precision and accuracy of the developed system in recognizing saccades and fixations when the eye movements are mainly horizontal were tested through a quantitative comparison with the EyeLink 1000 Plus, a professional eye tracker. Based on the encouraging registered results, we think that it is possible to adopt the presented system as a tool to quickly retrieve reliable gaze information.

In the modern landscape, millions of users rely on mapping services daily to plan trips of various kinds. While these services traditionally focus on providing the shortest routes, individual preferences often influence real-world route choices. However, most existing solutions neglect the impact of such preferences, limiting their ability to deliver truly personalized recommendations. This paper presents the Personalized Neuro-MLR (PNMLR) model, which enhances route recommendations by embedding user-specific preferences into the prediction process. Built on the Neuro-MLR (NMLR) framework [1], PNMLR leverages Graph Attention Networks (GAT) to integrate factors like user ID, time of day, and transport mode. This approach captures the variability in user behavior, offering personalized predictions for the most likely route. Extensive experiments on the Geolife GPS dataset show significant improvements across key metrics, including F1-score, precision, recall and route reachability, compared to models that do not consider user preferences. These results demonstrate the potential of PNMLR to transform route recommendation systems from generic solutions to user-centric models.