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คณะเทคโนโลยีการเกษตร
This project involves the development of a plant care system for dormitories using IoT (Internet of Things). The system is implemented through programming on an ESP-32 board and controlled via sensors for automated watering. The commands are operated through smartphones, supporting both iOS and Android. It is expected that this project will make plant care in dormitories easier and more convenient.
คณะวิทยาศาสตร์
Otitis Media is an infection of the middle ear that can occur in individuals of all ages. Diagnosis typically involves analyzing images taken with an otoscope by specialized physicians, which relies heavily on medical experience to expedite the process. This research introduces computer vision technology to assist in the preliminary diagnosis, aiding expert decision-making. By utilizing deep learning techniques and convolutional neural networks, specifically the YOLOv8 and Inception v3 architectures, the study aims to classify the disease and its five characteristics used by physicians: color, transparency, fluid, retraction, and perforation. Additionally, image segmentation and classification methods were employed to analyze and predict the types of Otitis Media, which are categorized into four types: Otitis Media with Effusion, Acute Otitis Media with Effusion, Perforation, and Normal. Experimental results indicate that the classification model performs moderately well in directly classifying Otitis Media, with an accuracy of 65.7%, a recall of 65.7%, and a precision of 67.6%. Moreover, the model provides the best results for classifying the perforation characteristic, with an accuracy of 91.8%, a recall of 91.8%, and a precision of 92.1%. In contrast, the classification model that incorporates image segmentation techniques achieved the best overall performance, with an mAP50-95 of 79.63%, a recall of 100%, and a precision of 99.8%. However, this model has not yet been tested for classifying the different types of Otitis Media.
คณะวิทยาศาสตร์
This project focuses on the study and development of a short-term investment framework via gold trading in the foreign exchange market. Machine learning techniques are applied to analyze and forecast pricing trends. Moreover, we develop the system using a stochastic process to determine optimal stop-loss points, with the aim of maximizing expected returns. Additionally, we apply game theory to guide the decision-making process regarding order holding or closure. The system is implemented and tested on the MetaTrader 5 (MT5) platform. This project outlined the clear process that includes data preparation, machine learning model training, probabilistic modeling of gold price movements, stop-loss strategy formulation, strategic decision modeling based on game theory, the development of an automated trading program, and backtesting to evaluate system performance.