Computer Engineering http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/193 2026-06-04T21:45:08Z 2026-06-04T21:45:08Z Simon Yosboon http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1687 2026-04-20T09:31:34Z 2024-01-01T00:00:00Z

Indoor scene classification using machine learning on object-detection based features Simon Yosboon Khwunta Kirimasthong The classification of scenes from images is a fundamental task in computer vision, vital for various applications ranging from autonomous driving to surveillance systems. An ongoing challenge in this field is the identification of discriminative features for accurate classification. This study addresses this challenge by comparing the effectiveness of two approaches: object-based feature extraction and deep learning. We propose a novel methodology that leverages YOLOv3, a state-of-the-art pre-trained model for object detection, to extract object-based features from scene images. By utilizing YOLOv3, we obtain feature vectors representing the presence and characteristics of objects within each scene. These features are then used as input for four distinct machine learning algorithms to classify scenes. Concurrently, we develop a deep learning model using the original images, which typically requires more computational resources and time for training. We conduct comprehensive experiments to evaluate the performance of both approaches across various scene classification tasks. Surprisingly, our results demonstrate that simple machine learning models utilizing object-level features achieve comparable performance to deep learning methods. This finding suggests that focusing on object-based representations can effectively classify scenes while circumventing the resource-intensive nature of deep learning algorithms. Thesis (M.Eng.) -- Computer Engineering, School of Information Technology. Mae Fah Luang University, 2024

2024-01-01T00:00:00Z Pornthep Phanbua http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1683 2026-04-09T08:30:53Z 2024-01-01T00:00:00Z

Extra trees model with minority target oversampling for classification of dementia and heart failure in adults Pornthep Phanbua Punnarumol Temdee Thesis (M.Eng.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2024

2024-01-01T00:00:00Z Wachira Lawpradit http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1671 2026-04-09T04:52:55Z 2021-01-01T00:00:00Z

A study of brain wave pattern with the touching learning of blinded people Wachira Lawpradit Thongchai Yooyativong Dissertation (Ph.D.) -- Computer Engineering, School of Information Technology. Mae Fah Luang University, 2021

2021-01-01T00:00:00Z Mongkhon Sinsirimongkhon http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1624 2026-02-20T05:56:28Z 2024-01-01T00:00:00Z

Classification model for hypertension with diabetes using gradient boosting and feature engineering Mongkhon Sinsirimongkhon Punnarumol Temdee Hypertension and diabetes present significant global health challenges, impacting individual well-being and economies. Early detection and prevention are pivotal in mitigating their adverse effects. Machine learning is widely applied in various industries and has shown promise in healthcare. While machine learning has shown promise in predicting these conditions separately, limited research has focused on their co-occurrence. This study proposes a novel multiclass-classification approach to predict the coexistence of hypertension and diabetes. The methodology encompasses data collection, preprocessing, model construction, validation, and comparison. Various classifiers were employed, including Decision Tree, Support Vector Machines, Random Forests, Extra Trees, Gradient Boosting, and Long Short-Term Memory. Additionally, CTGAN was utilized to address imbalanced datasets. Results demonstrate the effectiveness of the proposed approach. Gradient Boosting emerged as the most successful among the classifiers, achieving an impressive accuracy of 92.21% and an average AUC-ROC of 96.46%. These findings underscore the potential of machine learning in accurately predicting the concurrent presence of hypertension and diabetes. This study’s significance lies in its contribution to understanding and diabetes. This study’s significance lies in its contribution to understanding and predicting complex health conditions, facilitating early intervention and personalized care strategies. The outcomes suggest a promising avenue for healthcare practitioners to enhance proactive management approaches for individuals with both hypertension and diabetes Thesis (M.Eng.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2024

2024-01-01T00:00:00Z