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

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Journal : journal of applied data sciences

 1 
AI-Driven Mobile Application for Self-Monitoring Personalized Premenstrual Symptoms and Risk Assessment of Depressive Crises in Female University Students Nuankaew, Pratya; Sorat, Jidapa; Intajak, Jindaporn; Intajak, Jirapron; Nuankaew, Wongpanya S.
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.881

Abstract

Premenstrual Syndrome (PMS) and depressive symptoms are common concerns for female university students, often triggered by hormonal fluctuations before menstruation. These conditions can severely impact academic performance, interpersonal relationships, and overall well-being, particularly when symptoms escalate into severe depressive episodes. Even though the prevalence, awareness, and self-management strategies among students are on the rise, they remain limited, particularly in cultural contexts where women's health and emotional well-being receive little attention. This study presents the development of an AI-driven mobile application designed to facilitate personalized tracking of premenstrual symptoms and assess the risk of depressive episodes. The application integrates machine learning models trained on self-reported psychological and physiological data, using validated instruments such as DASS-21 and PSST-A. The research adopted a mixed-methods approach, involving survey-based symptom identification, model training and validation, system design, and user satisfaction evaluation. This research contributes to the development of artificial intelligence-assisted self-care technology for the purpose of monitoring personal health and taking preventative psychological measures. The findings indicate that the application that was developed is beneficial in terms of forecasting the likelihood of someone suffering from depression and fostering self-awareness regarding mental health among college students. Considering this, the system has the potential to develop into a useful tool for providing aid to female students attending universities.
Mobile-Based AI Platform Integrating Image Analysis and Chatbot Technologies for Rice Variety and Weed Classification in Precision Agriculture Nuankaew, Wongpanya S.; Kuisonjai, Saweewan; Keawruangrit, Raksita; Nuankaew, Pratya
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.918

Abstract

This work presents the development of an intelligent chatbot system capable of identifying rice plants and weeds from aerial photographs captured by smartphones, thereby enhancing precision agriculture. The study involves creating an AI model that utilizes image processing and deep learning techniques. Users can access the model through a LINE chatbot, and the study will also assess users' satisfaction with the model. Researchers gathered 12,000 pictures of rice fields in Phayao Province, Thailand, to train a modified InceptionV3 model using transfer learning. The dataset included images of rice plants and various types of weeds. The model was trained using image data collected under natural lighting and augmented to improve generalization. It achieved training, validation, and testing accuracies of 98.79%, 96.08%, and 97.83%, respectively. When deployed through a LINE Chatbot, it analyzed user-submitted images to estimate rice-to-weed ratios, yielding 73.33% average accuracy with consistent rice detection. Thirty individuals who used the system reported that it functioned well, was user-friendly, and provided significant benefits for farming in real-world applications. These results suggest that the system could leverage easily accessible AI tools to enhance farming efficiency, reduce costs, and positively impact the environment.
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.
Utilization of K-means Clustering for Classifying Diabetes Risk Populations According to Health Behaviors and 3Es-2Ss Health Literacy Yodmunee, Supaporn; Nuankaew, Wongpanya S.; Sararat, Thapanapong; Nuankew, Pratya
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1042

Abstract

This study focused on classifying populations at risk for diabetes using K-means clustering integrated with the 3Es–2Ss health literacy framework: eating, exercise, emotion, smoking cessation, and alcohol cessation. Biological, behavioral, and health literacy data were analyzed. The dataset was collected from 126 participants identified as at-risk individuals in Ngao District, Lampang Province, Thailand. This relatively small, community-based sample provides valuable insights into local health behaviors but limits the generalizability and statistical power of the findings to broader populations. The K-means clustering analysis, guided by the Elbow method, identified k = 4 as the optimal number of clusters, yielding four distinct groups with different socio-demographic and health characteristics. These clusters revealed variations in health profiles, economic status, and behavioral literacy within the Thai population. Despite the small sample size and limited generalizability, missing data and inconsistencies were systematically addressed through data cleaning and normalization to maintain analytical reliability. The results suggest that K-means clustering can serve as an effective decision-support tool for public health planning, particularly for Non-Communicable Disease (NCD) prevention and diabetes management at the local level.
Co-Authors Aunban, Jirasak Intajak, Jindaporn Intajak, Jirapron Jaitem, Patdanai Kansuree, Thanapoom Keawruangrit, Raksita Khamthep, Parin Kuisonjai, Saweewan Nuankaew, Kaewpanya S. Nuankaew, Kuljira S. Nuankaew, Pratya Nuankew, Pratya Sararat, Thapanapong Sorat, Jidapa Yodmunee, Supaporn