Krungseanmuang, Woranidtha

Preferred name

Alternative Name

Krungseanmuang, W.

Main Affiliation

School of Engineering

woranidtha.kr@kmitl.ac.th

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2D and 3D LiDAR with CNN Models for Detecting Sediment Accumulation Underground after Disasters
(2025-01-01)
Krungseanmuang, Woranidtha
;
Morita, Fuka
;
Chaowalittawin, Vasutorn
;
Sathaporn, Posathip
;
Kanamori, Chisato
Global climate change impacts all regions and leads to natural disasters such as typhoons, which cause destruction, debris, and flooding. Postdisaster restoration is a very important activity that is mostly done manually and can be time-consuming and challenging, especially in subterranean environments owing to accumulated objects such as pipes, pillars, and mud distributed in confined underground areas. Therefore, in this study, we aim to utilize emerging AI technologies by comparing deep learning algorithms and evaluating four models for 2D object detection and four for 3D point cloud segmentation for detecting sediment accumulation and navigating around obstacles in underground areas after a disaster. Additionally, a custom dataset was developed to simulate underground disaster scenarios. As a result, the You Only Look Once version 11 (YOLOv11) model achieved the highest mean average precision 50 (mAP50: 91.1%) for general detection within the pillar-pipe dataset, whereas the YOLOv12 model performed the best in detecting pipes (mAP50: 87.7%). In the mud dataset, the YOLOv8 segmentation (YOLOv8-seg) model demonstrated superior performance with mAP50 scores of 93.0% (detection) and 86.4% (segmentation). For 3D point cloud segmentation, PointNet achieved the highest accuracy (98.61%), whereas RandLA-Net was optimal for pipe segmentation, achieving an intersection over union score of 37.1%. These findings highlight AI’s potential to accelerate disaster recovery, reduce manual labor, and ensure faster cleanup. Integrating deep learning models into post-typhoon restoration efforts can enable communities to recover more quickly and efficiently after climate change impacts or disaster events.
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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
(2024-01-01)
Purahong, Boonchana
;
Krungseanmuang, Woranidtha
;
Tenghongsakul, Kasi
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Archevapanich, Tuanjai
;
Khunthawiwone, Parkpoom
This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.
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Banana quality classification using lightweight CNN model with microservice integration system
(2025-06-10)
Chaowalittawin, Vasutorn
;
Krungseanmuang, Woranidtha
;
Sathaporn, Posathip
;
Morita, Fuka
;
Archevapanich, Tuanjai
Banana sorting has been performed manually, which often leads to human error due to the high volume and diverse characteristics involved. This paper presents a banana quality classification system using ConsolutechMobileNetV2 (CST-MobileNetV2) to classify banana ripeness into four categories unripe, ripe, overripe, and rotten. A lightweight deep learning model is proposed and integrated with a uniquely designed microservice system to optimize performance while minimizing computational demands. A publicly available dataset containing 13,478 images was used, and the data split into 56% for training, 14% for validation, and 30% for testing. Image normalization and augmentation techniques were applied to enhance the model's robustness. The model's performance was evaluated using a confusion matrix, achieving 98% precision, recall, and F1-score. The proposed model was compared with other deep learning models to benchmark its performance and deployed in different operating systems to evaluate its flexibility and capabilities. The LINE platform was employed as the user interface, enabling practical interaction with users. The system also demonstrated an average response time of 9.25 seconds per image, ensuring efficient processing, delivers high accuracy and scalability making it a practical and efficient solution for automated banana quality classification.
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Comparative Study of Machine Learning Models for Soil Fertilizer Classification in Precision Agriculture
(2025-01-01)
Archevapanich, Tuanjai
;
Sirikham, Thanapat
;
Chaowalittawin, Vasutorn
;
Krungseanmuang, Woranidtha
;
Sathaporn, Posathip
This study explores the machine learning techniques compare for fertilizer classification based on soil nutrient dataset aligning with the goals of precision agriculture. Five models include Random Forest, Logistic Regression, SVM, XGBoost and Neural Network(ANN) were tested using precision, accuracy, F1-score, recall and confusion matrices. The highest F1-score is XGBoost model, while the best precision performance delivered by Random Forest model. Results emphasize the significance of model selection in handling imbalanced agricultural data. The approach supports data-driven decision-making for sustainable farming aligned with Thailand's 20-Year Agricultural Strategic Plan.
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DDoS Detection Framework Using Machine Learning Optimized by Bayesian and PSO Techniques
(2026-07-01)
Sathaporn, Posathip
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Benjangkaprasert, Chawalit
;
Archevapanich, Tuanjai
This paper presents a distributed denial of service (DDoS) detection framework using machine learning techniques enhanced with hyperparameter optimization for network traffic classification and evaluated on the BCCC-cPacket-Cloud-DDoS-2024 dataset. The framework includes data preprocessing with normalization and class imbalance handling via the synthetic minority over-sampling technique. A critical contribution of this study is the rigorous analysis of the trade-off between detection accuracy and model complexity. Unlike arbitrary feature selection methods, we empirically determined the optimal feature set using information gain, identifying that the top 100 features represent the saturation point that balances high accuracy with minimal overhead. Model performance was further improved through hyperparameter optimization using particle swarm optimization and Bayesian algorithms. The extreme gradient boosting (XGBoost) model optimized using Bayesian optimization and the top 100 features achieved the highest performance, with an accuracy of 99.29% and an F1-score of 98.91%. As a result, the proposed framework improves detection performance while reducing model complexity by selecting an optimal feature set to improve model stability and efficiency.

Krungseanmuang, Woranidtha

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