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คณะเทคโนโลยีการเกษตร
Climate change and the increasing unpredictability of environmental conditions have aggravated the shortage of animal feed crops during the dry season. This study examines effect of packaging thickness on the quality of corn silage during long-term storage, to maintain its nutritional value during feed shortages. The results show that packaging with thicknesses of 80, 120, 150, and 200 microns effectively maintain good physical quality, including odor, texture, color, and pH levels, during the 0–21day storage period. The silage had a fermented like fruit flavor or vinegar flavor, a silage texture, and well-preserved leaves and stems. Its color remained yellowish-green, with pH values between 3.7 and 4.7. Additionally, lactic acid analysis found that silage in 200-micron-thick packaging for 21 days had the highest lactic acid content (5.64%). However, there were no significant differences in the nutritional value of the silage across different packaging thicknesses
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.
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
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