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Author: search.filters.author.Netisopakul, PonrudeeSubject: search.filters.subject.Deep Learning

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    Baseline Performance of Pre-trained Models on Movie Genre Classification from Spectrograms
    (2025-04-01)
    Visutsak, Porawat
    ;
    Treeraphapkajondet, Kavin
    ;
    Sakphet, Visaroot
    ;
    Nitinuntatip, Wachirawit
    ;
    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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    AI-Enhanced Predictive Maintenance in Manufacturing Processes
    (2022-01-01)
    Netisopakul, Ponrudee
    ;
    Phumee, Nawarat
    This research aims to apply artificial intelligence technology to a manufacturing industry, specifically, to forecast temperature and insulation values of motors from the CNC machine. Dataset from motor sensors are collected and forecasting models are trained using four deep learning models, namely, multilayer perceptron (MLP), long-short term memory (LSTM), LSTM autoencoder, and bidirectional LSTM (Bi-LSTM). Models are evaluated by measuring the deviation of forecasting values from the real values. Two measures, root mean square error (RMSE) and mean absolute error (MAE), are used to assess model's performance. Experiments are conducted and found that the Bi-LSTM yielded the lowest RMSE and MAE numbers, hence, the best model to be selected. Further development has been implemented by integrating Bi-LSTM and genetic algorithm (GA) in order to optimize the model performance. Instead of searching the huge hyperparameter space of the neural network, the integrate GA-LSTM model using RMSE as a fitness function to reduce the search space and obtain the optimal or near optimal hyperparameters. The empirically best model is found which yields a lower RMSE value of 0.041 comparing to 0.18 when not optimized.