In the context of rapid economic development and globalization, traditional cultural values such as Cai Luong, a Vietnamese theatrical art, face declining engagement, particularly among youth. This study addresses the challenge by first conducting a quantitative survey of domestic and international tourists in Ho Chi Minh City, which revealed strong demand for cultural experiences enhanced by digital technologies. To meet this need, we developed VCaiLuong, a cloud-based microservice system comprising Visitor and Admin applications. The platform integrates a virtual museum, live Watch Parties, and a digital archive, enabling interactive engagement with Cai Luong heritage. Features such as 3D asset management, real-time chat, notifications, bookings, and background job automation further enhance performance and scalability. By comparing existing platforms and identifying design gaps, this study demonstrates the role of digital technologies in heritage preservation. VCaiLuong offers a sustainable model for safeguarding and promoting Cai Luong through immersive and accessible cultural experiences.
In the context of sustainable digital transformation, healthcare is a security-critical domain that requires accurate and transparent medical image analysis. This paper presents Swin-FABNET, a cardiac MRI segmentation system that integrates a Swin Transformer backbone, UPerNet decoder, and a Fuzzy Attention (FAB) block for enhanced multi-scale feature learning. 3D cine-MRI volumes are preprocessed into 2.5D stacks with online data augmentation. Explainable AI (XAI) methods such as Grad-CAM, LIME, and SHAP are employed to visualize predictions and highlight clinically relevant cardiac regions. On the ACDC 2017 dataset, Swin-FABNET achieves an average Dice score of 0.86, outperforming the baseline and alternative designs. The results demonstrate the effectiveness of fuzzy attention in improving segmentation accuracy and contribute to the development of secure, transparent, and sustainable AI applications in healthcare.
Recognizing Vietnamese handwritten text poses significant challenges due to complex diacritics, tonal variations, and limited large-scale annotated datasets. Traditional deep learning-based OCR methods, such as CRNNs designed specifically for Vietnamese handwriting, have demonstrated strong accuracy and low word error rates when trained on targeted data. In contrast, Vision-based Large Language Models (Vision-LLMs) offer broader document understanding, multimodal perception, and downstream capabilities like question answering and content interpretation. However, these models often struggle with domain adaptation, linguistic nuances, and computational efficiency, especially in the context of Vietnamese script. This paper presents a systematic comparison of OCR-specific models and Vision-LLMs for Vietnamese handwriting recognition, examining model architecture, data dependencies, recognition performance, linguistic handling, flexibility, and deployment feasibility. The results indicate that while OCR-specialized models excel in accuracy and resource efficiency within narrow domains, Vision-LLMs provide greater adaptability across tasks - albeit with higher resource demands and sensitivity to script details. Finally, we propose future directions, including hybrid approaches, dataset expansion, and optimization strategies, to advance robust and versatile Vietnamese handwriting recognition systems.
Improving environmental sound classification is crucial for enhancing intelligent surveillance systems. In this study, we propose a hybrid approach that integrates data augmentation techniques with a deep learning architecture combining a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), namely Hybrid Deep Learning–Data Augmentation (DLDA). The CNN (specialized in extracting local spatial features from spectrograms) captures discriminative patterns in the time–frequency domain, while the BLSTM (a recurrent neural network capable of learning temporal dependencies in both forward and backward directions) models long-range contextual information in audio sequences. To improve model generalization, we apply multiple data augmentation methods, including Gaussian noise addition, time shift, time stretching, pitch shifting, loudness scaling, and time delay. Experiments on the ESC-50 dataset demonstrate that our method achieves significantly higher classification accuracy compared to recent studies, confirming the effectiveness of combining advanced augmentation with our modeling.
Continuous, fine-grained water-quality monitoring is essential to prevent disease outbreaks and optimize yields in intensive shrimp aquaculture. We present an end-to-end IoT → ETL → AI framework that integrates 14 in-situ sensors, hourly cleansing and feature extraction, and sliding 10× 14 windows. Building on prior work, we adopt autoencoder-based anomaly detection trained only on normal data and contrast it with sequence forecasting, reflecting the trade-off often noted in aquaculture time-series literature between sensitivity and complexity. Our method instantiates three candidates: a 1D CNN Autoencoder, a Fully-Connected Sequence Autoencoder, and an LSTM Forecaster; anomaly thresholds are set from validation errors using μ +2σ . On a one-month dataset from an intensive shrimp farm, we apply stratified splits for train/validation/test. Results show the CNN–AE delivers the best precision–recall balance for early warning; FC–Seq–AE preserves high precision with reduced recall and smaller footprint; LSTM–Forecaster achieves zero false alarms but misses many events. The pipeline includes defense-in-depth security (mTLS-protected MQTT, AES-256 at rest, RBAC) and can support federated learning. Discussion highlights limitations (single site, early/mid-season coverage) and the need for multi-farm, late-season data and cost-sensitive learning to further improve recall and generalization.
