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All Journal International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Tamee, Kreangsak
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Computer Science & IT

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Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia Yothapakdee, Kriengsak; Pugtao, Yosawaj; Charoenkhun, Sarawoot; Boonnuk, Tanunchai; Tamee, Kreangsak
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1897

Abstract

This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. The neural network algorithm achieved an accuracy rate of 87.00% across different image sizes. While larger images generally yield better results, there is a decline in performance beyond a certain size. Lowering the image resolution to 32x32 pixels significantly reduces performance to 83.00% likely due to the loss of diagnostic features. Furthermore, this study emphasizes the relationship between image size and processing time, empirically revealing that both increasing and decreasing image size beyond the optimal point results in increased training and testing time. The performance was noted with 299x299 pixel images completing the process in seconds. Our results indicate a balance between efficiency, as larger images slightly improved accuracy but slowed down speed, while smaller images negatively impacted precision and effectiveness. These findings assist in optimizing chest X-ray image sizes for pneumonia prediction models by weighing diagnostic accuracy against computational resources.
Multi-visual modality for collaborative filtering-based personalized POI recommendations Arthan, Sudarat; Tamee, Kreangsak
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp978-987

Abstract

Point-of-interest (POI) recommendation systems help users discover locations that match their interests. However, these systems often suffer from data sparsity due to limited user check-in history. To address this challenge, this study proposed a novel user profiling framework that incorporates multiple visual modalities derived from user-generated photos. Three types of visual-based user profiles were constructed: image label-based, image feature-based, and a fused profile, combining both modalities through score-level fusion. We conducted extensive experiments on two real-world datasets. The results demonstrate that visual-based profiles, particularly the image feature-based profile, consistently improve recommendation performance under sparse data conditions. Although the fused profile offered stable results, it did not consistently outperform the single modality. Furthermore, performance was sensitive to the number of nearest neighbors and the amount of training data. These findings highlight the importance of modality selection and fusion strategy in visual-based POI recommendation systems.
Co-Authors Arthan, Sudarat Boonnuk, Tanunchai Charoenkhun, Sarawoot Pugtao, Yosawaj Yothapakdee, Kriengsak