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Nuankaew, Kuljira S.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Multimodal AI Framework for Sign Language Recognition and Medical Informatics in Hearing-Impaired Patients Nuankaew, Pratya; Khamthep, Parin; Jaitem, Patdanai; Nuankaew, Kuljira S.; Nuankaew, Kaewpanya S.; Nuankaew, Wongpanya S.
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1096

Abstract

This study assesses the feasibility of YOLO-based detectors for the recognition of Thai Sign Language (TSL) within clinical intake workflows. We benchmark YOLOv5 through YOLOv10 over 100 to 150 training epochs and evaluate metrics including Precision, Recall, mAP@50, mAP@50:95, alongside training and validation losses to gauge stability. The losses decrease steadily as detection metrics improve; YOLOv10 offers the optimal balance, with Precision at 0.953, Recall at 0.939, mAP@50 at 0.933, and mAP@50:95 at 0.492. The improvements observed at stricter IoU thresholds are modest, underscoring ongoing challenges in achieving accurate localization and generalization across varying lighting conditions, viewpoints, occlusions, and motion. YOLOv11 has been excluded from the primary results due to abnormal loss behavior. These findings endorse a multimodal pipeline that employs an image-based detector as the central perception component, supplemented with pose and key point cues, as well as OCR and NLP layers, to transform recognized signs into structured medical intents for triage and telemedicine applications. Future research will focus on expanding sequence-level evaluation, incorporating dialects and co-articulation in TSL, and developing compressed or distilled models to facilitate reliable on-device inference in resource-constrained environments. 
A Practical YOLO Approach to Classifying Thai Freshwater Snails of Economic Significance Nuankaew, Wongpanya S.; Aunban, Jirasak; Kansuree, Thanapoom; Nuankaew, Kuljira S.; Nuankaew, Kaewpanya S.; NUANKAEW, Pratya
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1099

Abstract

Freshwater snails are a valuable economic resource in Thailand, but species identification remains challenging due to morphological similarities that impact pricing, traceability, and aquaculture management. This study assesses an automated freshwater snail classification system using three YOLO variants trained for 100 epochs on 4,610 annotated images of six economically important species. The models were evaluated using precision, recall, mAP50, mAP50–95, inference time, and model size, revealing clear performance trade-offs. YOLOv9-tiny achieved the highest detection accuracy with an mAP50–95 of 0.9738 but incurred the largest model size and slowest inference. In contrast, YOLOv11-nano delivered the fastest inference and smallest footprint, though with lower accuracy (mAP50–95 of 0.8849), making it suitable for resource-limited or edge deployments. YOLOv8 provided a balanced alternative, offering competitive accuracy (mAP50–95 of 0.9708) with moderate computational cost. Misclassification most occurred between Bellamya sp. and Bellamya reticulata, particularly for juvenile specimens, highlighting the difficulty of distinguishing morphologically similar species and the need for more diverse training data. Overall, the results demonstrate the effectiveness of YOLO-based models for automated snail species identification, with strong potential for applications in aquaculture management, market standardization, and supply chain traceability. Future work will focus on real-world deployment, expanding datasets across diverse environments, and integrating explainable AI to improve model transparency and user trust.
Co-Authors Aunban, Jirasak Jaitem, Patdanai Kansuree, Thanapoom Khamthep, Parin Nuankaew, Kaewpanya S. Nuankaew, Pratya Nuankaew, Wongpanya S.