Browsing by Author "Marzan, Charlie S."

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    Multi-task deep learning for automated tobacco leaf grading in a controlled environment
    (International Journal of Computer Theory and Engineering, 2026) Marzan, Charlie S.; Ruiz, Conrado Jr.; Aran, Oya
    Grading tobacco leaves is crucial for ensuring fair pricing and quality control, however, the process is still largely carried out manually, resulting in a slow, subjective, and often inconsistent outcome. In this work, we present a multi-task deep learning approach designed to automate the grading of air-cured Burley tobacco leaves in controlled settings. The model is constructed with shared convolutional layers and separate task-specific branches, allowing it to predict stalk group, quality, and color at the same time, in line with the hierarchical grading system. To improve consistency, images were preprocessed using coin-based size normalization, rotation alignment, and segmentation. In our experiments, the multi-task model with EfficientNetB0 achieved an accuracy of 94.82% and significantly outperformed the multi-class and single-task baselines, while reducing both training time and inference delay. These findings suggest that multi-task learning can be a valuable and robust method for automated tobacco grading, showing gains in accuracy, speed, and scalability compared to other algorithms.
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    Sign to speech convolutional neural network-based Filipino sign language hand gesture recognition system
    (2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), 2021) Jarabese, Mark Benedict D.; Marzan, Charlie S.; Boado, Jenelyn Q.; Lopez, Rushaine Rica Mae F.; Ofiana, Lady Grace B.; Pilarca, Kenneth John P.
    Sign Language Recognition is a breakthrough for helping deaf-mute people and has been studied for many years. Unfortunately, every research has its own limitation and are still unable to be used commercially. In this study, we developed a real-time Filipino sign language hand gesture recognition system based on Convolutional Neural Network. A manually gathered dataset consists of 237 video clips with 20 different gestures. This dataset underwent data cleaning and augmentation using image pre-processing techniques. The Inflated 3D convolutional neural network was used to train the Filipino sign language recognition model. The experiments considered retraining the pretrained model with top layers and all layers. As a result, the model retrained with all layers using imbalanced dataset was shown to be more effective and achieving accuracy up to 95% over the model retrained with top layers to classify different signs or hand gestures. Using the Rapid Application Development model, the Filipino sign language recognition application was developed and assessed its usability by the target users. With different parameters used in the evaluation, the application found to be effective and efficient.