This artwork was created based on the universal concepts of global warming and post-apocalyptic world, which has caused disturbances and chaos in ecosystems, leading to the extinction of many living beings on Earth due to human actions. Repairing and restoring this world may therefore be a false hope, connected to my personal experience of losing loved ones and the sorrow from setting high hope, through the artistic process using Animation Art and Sound Art.
ข้าพเจ้าต้องการแสดงถึงปัญหาและสิ่งที่มนุษย์ได้ทิ้งเศษซากไว้ให้ผู้อื่นรับผลกระทบแทนตน เพื่อให้ตระหนักรู้ถึงความสำคัญในการเปลี่ยนแปลงการดำรงชีวิต เพื่อการดำรงอยู่ของสิ่งมีชีวิตทั่วโลก และข้าพเจ้าได้เชื่อมโยงประสบการณ์ส่วนตัวลงไปเพื่อเป็นการแสดงความรู้สึกร่วมซึ่งล้วนเป็นความทุกข์ออกไปให้ผู้อื่นได้รับทราบไม่มากก็น้อยเพื่อแบ่งเบาความทุกข์นี้ไปจากข้าพเจ้า
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.
คณะวิทยาศาสตร์
Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
คณะเทคโนโลยีการเกษตร
This research aims to evaluate the efficiency of nano-type oxygen diffusers at different pump power levels in sea bass nursery ponds. The study examines how varying power levels affect dissolved oxygen distribution in the water and their impact on the health, growth, and survival rates of sea bass. The findings indicate that pump power levels influence dissolved oxygen concentration, with the optimal power level improving oxygen distribution in the pond. This enhancement leads to higher survival and growth rates for sea bass. The results provide valuable insights for selecting appropriate oxygen diffusers and pump power levels in fish nursery pond systems. The experiment consisted of two conditions: 1. Without fish – This condition assessed the oxygenation capacity, oxygen transfer coefficient, oxygen transfer rate, and oxygen transfer efficiency of pumps at three different power levels. 2. With fish – This condition evaluated whether the oxygen supplied by pumps at three power levels was sufficient, based on the growth rate and survival rate of the fish in the pond. Blood counts were conducted to assess the immune response. The collected data were statistically analyzed using the RCBD method for the condition without fish and the CRD method for the condition with fish, employing SPSS software.