Telemedicine App is a prototype system that provides basic functions for communicating diagnosis between patients, nurses, and doctors via video conferencing. The system is contains different diagnostic room and it allows recording patient information. It is an open source for others to extend for further development.
เพิ่มประสิทธิภาพในการเข้าถึงการรักษา ผู้ป่วยที่อยู่ห่างไกลจากโรงพยาบาลหรือสถานพยาบาล สามารถเข้าถึงการตรวจรักษาและได้รับการวินิจฉัยจากแพทย์ผู้เชี่ยวชาญได้ทันท่วงที
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
From the current situation and uncertainty; leads to the concept of food security. It is the application of innovation and technology to create high productivity in a limited area. The unused buildings in urban areas were renovated for planting, created as a learning area for planting in urban area. The different methods of growing plants were presented. There are 35 planting innovations for disseminating knowledge, to create food security, self-reliant, supports sustainable living.
คณะอุตสาหกรรมอาหาร
The "PRIVARY" product is an innovative herbal jelly beverage designed to support weight management and promote health through the benefits of four Thai herbs: roselle, safflower, chrysanthemum, and bitter melon. These herbs are rich in active compounds such as flavonoids, beta-carotene, and anthocyanins, which help reduce blood lipids, prevent inflammation, and exhibit antioxidant properties. The product emphasizes convenience and caters to health-conscious consumers using advanced production techniques like Inverse and External Gelation to create spheres encapsulating key bioactive compounds. Additionally, the product aligns with sustainability goals by enhancing the value of Thai herbs and supporting local communities.
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.