Currently, urban agriculture is gaining increasing attention as it helps enhance food security and expand green spaces in cities. However, some people remain uninterested in urban farming, possibly due to living in urban areas or having limited space, making them perceive agriculture as something distant from their daily lives. The development of an urban agriculture card game aims to promote learning about urban farming through an engaging and enjoyable gameplay experience.
การทำเกษตรในเมืองได้รับความสนใจอย่างแพร่หลาย โดยใช้พื้นที่ว่างในเมืองเพื่อทำการปลูกพืชหรือเลี้ยงสัตว์เพื่อเพิ่มความมั่นคงทางอาหารและพื้นที่สีเขียว การเรียนรู้ผ่านเกมช่วยให้ผู้เรียนเข้าใจเนื้อหาได้ดีขึ้นและสนุกสนาน ทั้งปัจจุบันบอร์ดเกมหรือการ์ดเกมได้รับความนิยมอย่างมากจึงมีความสนใจและเล็งเห็นวิธีการเรียนรู้ผ่านการเล่นการ์ดเกมที่ได้ทั้งความสนุกสนานและความรู้ผ่านกลไกของเกม เพื่อสร้างความเข้าใจและตระหนักถึงความสำคัญของการเกษตรในชีวิตประจำวัน
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.
คณะวิศวกรรมศาสตร์
ยานยนต์ไฟฟ้าดัดแปลง
คณะเทคโนโลยีการเกษตร
The research aims to develop chili Thai commercial varieties for resistance to anthracnose and Pepper yellow leaf curl virus disease. The varieties allowing farmer to reduce the use of chemical pesticides for disease and pest control, also increases productivity and lowers production costs for farmers. The development new varieties are under studied of undergraduate, master's, and doctoral students by using conventional and molecular plant breeding. The new chili varieties were released to farmer and commercial companies for development for Thai commercial seed industry.