H.3.2.2. Computer vision
Maryam Baghi; Kourosh Kiani; Razieh Rastgoo
Abstract
With rapid advancements in information and communication technology, recommender systems have become vital tools across a wide range of online activities and e-commerce processes. Collaborative recommender systems, which utilize user data and contributions to provide suggestions, represent a significant ...
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With rapid advancements in information and communication technology, recommender systems have become vital tools across a wide range of online activities and e-commerce processes. Collaborative recommender systems, which utilize user data and contributions to provide suggestions, represent a significant innovation in this field. In this paper, we conduct an analysis of collaborative recommender systems and evaluate their impact on enhancing the efficiency and accuracy of recommendations. To this end, we propose a deep learning approach using a Graph Convolutional Network (GCN), as a special type of Graph Neural Network (GNN). By assigning weights to edges between nodes, scores are calculated for these edges. The importance of the edges varies based on the number of neighboring nodes and their proximity to the target node. The higher the edge score, the more significant the path. To calculate edge weights, we leverage metrics such as Jaccard similarity, cosine similarity, LHN index, and Salton cosine similarity. This approach improves the identification of relationships between nodes and enhances the accuracy of the recommender system. For implementation, we utilized the well-known MovieLens dataset. Ultimately, users were clustered into 18 clusters, with a large number of nodes within each cluster. By clustering users, we increased the number and diversity of recommendations. This significantly improved the performance of the recommender system, yielding promising results.
H.3.2.3. Decision support
Fatemeh Iranmanesh; Najme Mansouri; Behnam Mohammad Hasani Zade
Abstract
The diagnosis of Alzheimer's Disease (AD) remains a significant challenge in medical research. To address the limitations of static models in capturing dynamic brain changes, this paper proposes a novel GNN-xLSTM model that integrates Graph Neural Networks (GNN) with an extended Long Short-Term Memory ...
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The diagnosis of Alzheimer's Disease (AD) remains a significant challenge in medical research. To address the limitations of static models in capturing dynamic brain changes, this paper proposes a novel GNN-xLSTM model that integrates Graph Neural Networks (GNN) with an extended Long Short-Term Memory (xLSTM) architecture. The key innovation lies in combining GNN’s ability to model spatial relationships in brain imaging data with xLSTM’s enhanced sequential learning via matrix-based memory representation and exponential gate stabilization. In the proposed approach, brain images are divided into regions, with each region represented as a graph node connected in a grid structure, and feature vectors are extracted for each node. The proposed architecture incorporates Graph Convolutional Network (GCN) layers, xLSTM cells, residual connections, batch normalization, and dropout to jointly capture global, local, and temporal dependencies. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the GNN-xLSTM model outperforms baseline models in terms of accuracy, precision, recall, and F1-score. These results demonstrate the model’s effectiveness in identifying critical brain regions and improving AD classification performance.
