H.3.2.3. Decision support
Fateme Ghasemi; Ameneh Khadivar; Leila Moslehi; Fatemeh Abbasi
Abstract
This study investigates the effectiveness of machine learning algorithms, including Neural Networks, Bayesian Networks, Support Vector Machines, and Random Forests, in predicting football match outcomes using data from the English Premier League (2018–2022).By incorporating user-generated probabilities ...
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This study investigates the effectiveness of machine learning algorithms, including Neural Networks, Bayesian Networks, Support Vector Machines, and Random Forests, in predicting football match outcomes using data from the English Premier League (2018–2022).By incorporating user-generated probabilities for home win, away win, and draw alongside conventional features, the models were evaluated under binary and multi-class classification scenarios. The Support Vector Machine achieved the highest accuracy (69%) in the win-loss scenario, while the Neural Network reached 51% in the win-draw-loss scenario. Results indicate that user-derived features enhance predictive performance, though user predictions show a bias toward home teams, especially in uncertain cases. These findings highlight the potential of integrating user perspectives into predictive modeling and underscore the importance of addressing cognitive bias in sports analytics.
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.
H.3.2.3. Decision support
F. Moslehi; A.R. Haeri; A.R. Moini
Abstract
In today's world, most financial transactions are carried out using done through electronic instruments and in the context of the Information Technology and Internet. Disregarding the application of new technologies at this field and sufficing to traditional ways, will result in financial loss and customer ...
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In today's world, most financial transactions are carried out using done through electronic instruments and in the context of the Information Technology and Internet. Disregarding the application of new technologies at this field and sufficing to traditional ways, will result in financial loss and customer dissatisfaction. The aim of the present study is surveying and analyzing the use of electronic payment instruments in banks across the country using statistics and information retrieved from the Central Bank and data mining techniques. For this purpose, firstly, according to the volume of transactions carried out and with the help of using the K-Means algorithm, a label was dedicated to any record; then hidden patterns of the E-payment instruments transaction were detected using the CART algorithm. The obtained results of this study enable banks administrators to balance their future policies in the field of E-payment and in the bank and customers’ interest's direction based on detected patterns and provide higher quality services to their customers.
