These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes three modules. The first one creates a Graph Neural Network (GNN) based model to learn user profile properties, while the second module encodes news content, user historical posts, and news sharing cascading on social media as user preferences GNN-based model. The inter-dependencies between user profiles and user preferences are handled through the third module using a co-attention mechanism for capturing the relationship between the two GNN-based models. We conducted several experiments on two commonly used fake news datasets, Politifact and Gossipcop, where our approach achieved 98.53% accuracy on the Gossipcop dataset and 96.77% accuracy on the Politifact dataset. These results illustrate the effectiveness of the CMCG approach for fake news detection, as it combines various information from different modalities to achieve relatively high performances.

This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an ablation study comparing Attention U-Net with and without attention mechanisms, alongside an analysis of data augmentation effects. Training and evaluation were performed on a curated chest X-ray dataset, with segmentation performance measured using Dice score, precision, and loss functions, achieving over 98% precision and 94% Dice score. The extracted bone structures were further processed to derive unique biometric patterns, enabling robust and privacy-preserving person identification. Our findings highlight the effectiveness of attention mechanisms in improving segmentation accuracy and underscore the potential of chest bone-based biometrics in forensic and medical imaging. This work paves the way for integrating artificial intelligence into real-world forensic workflows, offering a non-invasive and reliable solution for post-mortem identification.