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Aspect-Level Sentiment Analysis Using WangchanBERTa for Fine-Grained Service Insight Extraction in Hotel Reviews
(2026-06-01)
Suwan, Thanachok
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Nokkaew, Manussawee
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Surawanitkun, Chayada
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Sorn-In, Kanda
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Mueanrit, Nongram
Online booking site reviews substantially influence Thai SME hotel reputations and consumer decisions. Hotels should readily extract useful information from unstructured Thai-language ratings. WangchanBERTa, a Thai deep learning model, automates hotel sentiment analysis and strategic insight development in this study. System is two-stage. Phase 1 divides 10,040 Thai hotel reviews from Agoda, Booking.com, Traveloka, and Trip.com into good and negative attitudes and determines price, service quality, and cleanliness. Phase 2 extracts aspect-level information across 11 service characteristics to discover complex trends like consumers being satisfied with service but unhappy with cost. The sentiment categorization model performed well with 91.63% accuracy and 89.69% macro F1-score in experiments. The aspect-based sentiment analysis system achieved 91.63% accuracy, 91.55% macro precision, 91.63% recall, 91.52% F1-score, and real-world insight extraction. This methodology helps hoteliers listen to customers, integrate data into business ideas, and compete in Thailand’s tourism market.
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2D-CNN Based Classification on Water Leakage Identification in Automatic Pump
(2026-01-01)
Satthamsakul, Sutham
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Rattanakun, Kritsana
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Khummongkol, Rojanee
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Tangsrirat, Worapong
This study proposes an approach for classification of unusual in automatic water pump systems, especially on a water leakage, one of the key issues that can significantly affect system efficiency and cause serious damage. The method utilizes motor current signals, which are transformed into 2D spectrograms using the Short-Time Fourier Transform (STFT). These spectrograms are then classified by a 2D Convolutional Neural Network (2D-CNN) Designed to determine between usual and unusual operating conditions with high accuracy. Experimental results indicate that the model can effectively detect unusual and leakage events when trained with appropriate parameters, such as a learning rate of 0.001 and 60 training epochs. This model serves as an efficient tool for preventing system failures and reducing maintenance costs in automatic water pump systems.
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Energy Consumption Prediction and Anomaly Detection for Boiler Feed Pump in Power Plant Using Machine Learning and Deep Learning
(2025-04-01)
Khamfoy, Polawut
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Klomwises, Yuwadee
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Srianomai, Sakuna
Enhancing energy efficiency and operational reliability is crucial in power plant management, particularly for high-energy-consuming machines such as boiler feed water pumps (BFPs). These pumps play a vital role in the continuous generation of steam and electricity and must operate 24/7 to maintain power production stability. This study proposes the development of predictive models based on machine learning and deep learning techniques to accurately predict energy consumption and applies best models to detect anomalous behaviors in BFPs, enabling timely and preventive interventions. A dataset comprising 43,082 hourly records over five years, with 18 critical operational features, was analyzed using preprocessing and feature engineering techniques. Various predictive models were trained and evaluated, including Multiple Linear Regression, Regularized Regressions (Ridge, Lasso, ElasticNet), Support Vector Regression (SVR), Decision Tree, Ensemble Methods (Random Forest, XGBoost, CatBoost, LightGBM), and Deep Learning Architectures (DNN, RNN, GRU, LSTM). Among these models, SVR demonstrated the highest accuracy (MSE: 13.5573, R²: 0.9838), followed closely by LightGBM. Feature importance analysis revealed that boiler feed pump discharge pressure and bearing housing vibration levels were the most influential variables in energy consumption prediction. Anomaly detection using the Interquartile Range (IQR) method classified deviations into two warning levels, enabling proactive maintenance strategies. Additionally, a Graphical User Interface (GUI) web application was developed for real-time monitoring, integrating predictive models, anomaly detection, and an automated email alert system to assist operators in responding to abnormal energy consumption events promptly. These results highlight the potential of predictive analytics and real-time monitoring in optimizing power plant operations, providing a foundation for extending predictive capabilities to other critical energy-intensive systems.
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Baseline Performance of Pre-trained Models on Movie Genre Classification from Spectrograms
(2025-04-01)
Visutsak, Porawat
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Treeraphapkajondet, Kavin
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Sakphet, Visaroot
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Nitinuntatip, Wachirawit
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Satthong, Pawwinkan
This study investigates the use of deep learning for classifying movie genres based on audio spectrograms. We construct a dataset of movie trailers, transform them into spectrograms, and label them by genre. Then, we utilize MATLAB's pre-trained convolutional neural networks (CNNs) for classification, comparing the performance of 9 different architectures, including MobileNet-v2, RestNet-18, DenseNet-201, Places365-GoogLeNet, VGG-16, VGG-19, Inception-RestNet-v2, Inception-v3, and NASANet-Mobile. We evaluated all models based on their ability to classify movie trailers into five genres: action, romance, drama, comedy, and thriller. Our results, based on accuracy and F1-score across genres, indicate that VGG16 achieves the highest overall performance with an accuracy of 86.27%, an F1-score of 86.69%, a recall of 86.87%, and a precision of 87.28%. This research demonstrates the potential of leveraging pre-trained CNNs, particularly VGG-16, for effcient and effective audio-based genre classification in movie trailers.
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Improving the Sound Classification Accuracy Using CNN-LSTM and MFCC with Audio Augmentation for Diagnosing Respiratory Disease
(2025-01-01)
Phankokkruad, Manop
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Wacharawichanant, Sirirat
Audio is vital information data for understanding various situations. A multitude of sound features can be explained by analysis through the audio signals. Numerous classification methods have been developed to study audio classification. This work studies the improvement of audio classification for the diagnosis of respiratory disease through the integration of audio data augmentation and CNN in conjunction with LSTM (CNN-LSTM). Furthermore, this paper focuses on audio data augmentation and feature extraction in the deep learning approach. This study proposed the CNN-LSTM model to diagnose respiratory disease by learning from the different audio datasets. The results reveal that the CNN-LSTM model attained an accuracy of 81.48%, precision of 0.8340, sensitivity of 0.6948, and F1-score of 0.7225. Considering the achieved F1-score, the CNN-LSTM model demonstrates a high level of diagnotic accuracy. Therefore, all evaluation evaluation parameters collectively indicate the robust performance of the proposed disease classification model.
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Recognition of Marijuana Plant Leaf Diseases Based on Deep Learning
(2025-01-01)
Kong, Qingye
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Tooprakai, Siraphop
Plant pests and diseases are a common problem in agricultural production, and if not handled properly, they can seriously affect crop yield and quality. The era of the Internet of Things has arrived, and modern monitoring methods have also been applied to the growth monitoring of some crops, achieving good results. However, the cultivation of marijuana still relies mainly on traditional manual monitoring and modern methods have not yet been widely used. At the same time, the prevention of diseases and pests in marijuana is the focus, and if problems are not detected early, the losses can often be severe. This article proposes using machine learning to screen for abnormal leaves and confirm whether the leaves are healthy. After repeated training, the system can compare and classify different images of marijuana leaves and identify abnormal parts. The system is designed based on Python. The test results indicate that this technology can be applied to distinguish marijuana leaves to ensure early detection of diseases and pests, and to minimize agricultural losses caused by untimely remedial measures.
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Real-Time White Blood Cell Classification with YOLO
(2025-01-01)
Eamkong, Anoma
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Pintavirooj, Chuchart
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Treebupachatsakul, Treesukon
White blood cell (WBC) classification plays a crucial role in diagnosing various hematological conditions, including infections, immune disorders, and leukemia. This study presents an automated approach for WBC detection and classification using the YOLOv5 deep learning model. The system integrates a 1.3MP microscope camera with a stepper motor-driven platform for real-time imaging and classification. The dataset consists of five WBC types: basophils, eosinophils, lymphocytes, monocytes, and neutrophils, with image enhancement and data augmentation applied to improve model performance. The trained YOLOv5 model achieved a classification accuracy of 92.61% and a validation accuracy of 95.86%, demonstrating high precision and recall in WBC identification. The results indicate that this system can effectively automate WBC analysis, reducing manual effort and improving diagnostic accuracy. This approach has potential applications in clinical hematology, offering a rapid and reliable method for WBC classification.
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SmartAir: Enhancing Air Quality Classification with Deep Learning and Two-State Q-Learning
(2025-01-01)
Peung-Uaypon, Supitchaya
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Jitkongchuen, Duangjai
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Thusaranon, Panita
Air pollution, especially PM 2.5, poses a growing global health threat, mainly from industrial activity, traffic, and wildfires. Traditional air quality monitoring systems are costly and lack broad coverage. This research proposes a low-cost alternative using a model that classifies air quality from outdoor images by combining Deep Learning with Reinforcement Learning. The model uses VGG19 (pre-trained on ImageNet) along with Local Binary Pattern (LBP) and RGB average values for feature extraction. A non-linear SVM classifier is enhanced with Q-Learning, which improves classification of difficult images through randomized actions: rotating images ± 15 degrees or shifting them diagonally. Experimental results show that incorporating Q-Learning increased model accuracy from 96.11% to 97.29%. This indicates that reinforcement learning helps the system adaptively correct misclassifications. The proposed model offers an efficient, accessible, and affordable tool for air quality monitoring, especially in areas lacking conventional AQI sensors.
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Dynamic System Linearization via Deep Relative Displacement Prediction for Robust IMU-WiFi Indoor Trajectory Estimation
(2025-01-01)
Chapha, Naphat
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Ploysuwan, Tuchsanai
Standalone WiFi fingerprinting and Inertial Measurement Unit (IMU) sensors are unreliable for indoor positioning due to signal instability and cumulative drift error, respectively. We present a novel sensor fusion framework that overcomes these challenges by enhancing WiFi fingerprint quality while simplifying the fusion process. Our approach filters and ranks WiFi signals to create a stable input for a Long Short-Term Memory (LSTM), while a Convolutional Neural Network (CNN) is uniquely trained to predict relative displacement from IMU data. This latter step linearizes the system dynamics, enabling the use of a simple Linear Kalman Filter and avoiding the complexity of traditional non-linear filters. Combining these innovations, the integrated framework achieves a mean position error of 4.30 m, a 32.3% improvement over the best standalone model. This work demonstrates that our approach to WiFi selection and system linearization provides a robust and highly accurate solution for real-time indoor positioning.
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Fourier Latent Transformer for Anomaly Signal with High-Frequency Reconstruction
(2025-01-01)
Chalongvorachai, Thasorn
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Woraratpanya, Kuntpong
Anomaly-related applications play a crucial role in real-world systems. However, developing effective solutions remains challenging, particularly due to missing data caused by system errors during anomaly events. Several approaches have been proposed to address this issue, including statistical methods, autoencoders, and deep learning models such as Transformers and Latent Transformers. Despite their potential, these methods often struggle to preserve high-frequency signal characteristics or require extensive training time and computational resources. To overcome these challenges, this paper proposes the Fourier Latent Transformer for Anomaly Signal with High Frequency Reconstruction. The method integrates Fourier positional encoding, which enhances the model's ability to retain high-frequency components, with a Latent Transformer architecture that reduces the need for computational resources and shortens training time. This approach not only effectively reconstructs missing highfrequency anomaly signals, but also improves overall training efficiency. Experimental results on real-world datasets show that the proposed method tremendously reduces error in anomaly data imputation, while maintaining training time comparable to baseline models.

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