Title: Development of a Virtual Reality World for Avatar Thai Costume Dressing with Integrated e-Commerce System
Cover Date: 2025-01-01
Cover Display Date: 2025
DOI: 10.1109/ECTIDAMTNCON64748.2025.10962063
Description: This study presents the development and evaluation of a Virtual Reality (VR) system designed to revolutionize the shopping experience for traditional Thai textiles. The system integrates customizable avatars and e-commerce functionalities, enabling users to explore, personalize, and purchase traditional Thai attire in an immersive virtual environment. High-quality 3D models and intricate textile designs emphasize the cultural heritage and craftsmanship of Thai textiles, particularly from the Roi-Kaen-Sarn-Sin cluster, which comprises four provinces: Roi Et, Khon Kaen, Maha Sarakham, and Kalasin. Expert assessments rated the performance efficiency with an average score of 4.52, while user evaluations provided an average satisfaction score of 4.14. Although the system demonstrates significant potential, opportunities remain for optimizing scalability and user accessibility. This research underscores the transformative potential of VR technology in creating immersive shopping experiences, fostering cultural appreciation, and driving innovation in global e-commerce markets.
Citations: 0
Aggregation Type: Conference Proceeding
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Title: Improving Neural Network-Based Multi-Label Classification with Pattern Loss Penalties
Cover Date: 2024-01-01
Cover Display Date: 2024
DOI: 10.1109/ACCESS.2024.3386841
Description: This research work introduces two novel loss functions, pattern-loss (POL) and label similarity-based instance modeling (LSIM), for improving the performance of multi-label classification using artificial neural network-based techniques. These loss functions incorporate additional optimization constraints based on the distribution of multi-label class patterns and the similarity of data instances. By integrating these patterns during the network training process, the trained model is tuned to align with the existing patterns in the training data. The proposed approach decomposes the loss function into two components: the cross entropy loss and the pattern loss derived from the distribution of class-label patterns. Experimental evaluations were conducted on eight standard datasets, comparing the proposed methods with three existing techniques.The results demonstrate the effectiveness of the proposed approach, with POL and LSIM consistently achieving superior accuracy performance compared to the benchmark methods.
Citations: 3
Aggregation Type: Journal
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Title: Improving Multi-label Classification Using Feature Reconstruction Methods
Cover Date: 2023-01-01
Cover Display Date: 2023
DOI: 10.55003/cast.2022.01.23.013
Description: Multi-label classification (MLC) is a supervised classification method that allows for a data instance with more than one class label (or target). Solving MLC is still a challenging task. MLC can potentially generate complex decision boundaries as the method is a non-mutual exclusive classification method. Recently, many techniques have been proposed to cope with the complexity of MLC problems, such as the Problem transform method (PTM), the Adaptation method (AM), and the Ensemble method (EM). These techniques can generally produce good results with certain datasets. However, they have poor classification performance when the number of possible class-labels is larger, even if the dataset is well-presented (high density). The aim of this work was to solve the MLC problems by performing a feature reconstruction process on the original data features. The proposed feature reconstruction method generates a set of compact features from the original data instances. AutoEncoder is deployed to learn and encode the features of the data (as the constructed feature steps) before they are classified by learning algorithms (or classifiers). We conducted experiments using different multi-label classifiers based on and around PTM, AM, and EM, on the set of the standard dataset. The results from the experiments demonstrated that the proposed feature reconstruction technique provides promising classification results, especially with high-density data.
Citations: 1
Aggregation Type: Journal
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Title: Non-Communicable Diseases Classification using Multi-Label Learning Techniques
Cover Date: 2020-10-21
Cover Display Date: 21 October 2020
DOI: 10.1109/InCIT50588.2020.9310978
Description: Non-communicable diseases: NCDs are one of leading causes of death in the world. Multi-NCDs patients tend to undergo and suffer from multiple coexistent diseases. This research aims at classifying NCDs patients who are diagnosed with other NCDs. Multi-label classification was used in this research. There are four diseases types used in this study, i.e. diabetes, hyper-tension, cardiovascular and stroke. Binary relevance (BR), Classifier Chains (CC), The random k-Iabelsets (RAkEL) and Multi-Label k-Nearest Neighbor (ML-KNN) are adopted to transform Multi-NCDs to disease label. The experiments are conducted on the physical examination datasets collected from electronic health records. In the experiments, the comparative results of the techniques are demonstrated. The result showed that the RAkEL method outperformed other methods and achieved the best accuracy of 91.07%.
Citations: 3
Aggregation Type: Conference Proceeding
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