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Author: search.filters.author.Phongwuttisak, NuttirujHas files: No

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    Comparative Analysis Of Convolutional Neural Networks, Long Short-Term Memory Networks, and Bert For Text-Based Emotion Classification
    (2026-06-01)
    Visutsak, Porawat
    ;
    Tongbai, Tanajak
    ;
    Ongrungruaeng, Duongduen
    ;
    Phongwuttisak, Nuttiruj
    ;
    Wiriya, Surapong
    This paper presents a detailed experimental study and comparative analysis of three popular deep learning architectures (CNN, LSTM, and BERT) for emotion classification in written messages. Using a publicly available dataset of six unique emotional states (sadness, joy, love, anger, fear, and surprise), an effective ablation study was conducted to determine optimal architectural configurations, including a sequence length of 66 tokens and an embedding size of 200. To validate the results of a comparative analysis of model performance, a bootstrap technique (30 trials) and the Wilcoxon Signed-Rank test were used to eliminate potential bias. As shown by experiments, the tuned BERT architecture (with a learning rate of 2e-5) produced the most accurate and reliable result of 93.50% in classifying emotional states from texts. Moreover, with an appropriate sequence length configuration, the LSTM network (89.92%) significantly outperformed the CNN (89.65%), confirming the need to account for long-range dependencies in emotion classification. Overall, the research results show the key importance of hyperparameter tuning and the ability to handle complex information for emotion identification.
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    SkinHRNet: A Deep Learning Framework for Non-Contact Heart Rate Estimation and Arterial–Venous Sufficiency Status Classification from Short Skin Videos
    (2026-01-01)
    Traivinidsreesuk, Chetsadaporn
    ;
    Yodrabum, Nutcha
    ;
    Winaikosol, Kengkart
    ;
    Chaikangwan, Irin
    ;
    Prompattanapakdee, Jiraya
    Traditional methods for monitoring flap health in reconstructive surgery are often invasive and rely on subjective assessment. This study addresses clinically motivated monitoring challenges by evaluating two tasks using a dataset of 1,018 short skin videos: average heart rate (HR) estimation under arterial–venous sufficiency conditions and arterial–venous sufficiency status classification across sufficiency and simulated insufficiency conditions. To address these challenges, we propose SkinHRNet, a deep learning–based approach for average HR estimation and arterial–venous sufficiency status classification from short skin videos. This work contributes a short-skin-video framework that combines HR-related signal estimation with arterial–venous sufficiency classification to support the two evaluated tasks under the controlled conditions considered in this study. For average HR estimation under arterial–venous sufficiency conditions, SkinHRNet achieved a mean absolute error (MAE) of 8.66 ± 4.85 BPM. For arterial–venous sufficiency status classification, it achieved an accuracy of 0.969 ± 0.02 across the evaluated sufficiency and simulated insufficiency conditions. These findings indicate that SkinHRNet may serve as an initial research prototype for further investigation of short-video-based non-contact assessment under controlled arterial–venous sufficiency and simulated insufficiency conditions.