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เนื่องจากปลาหมอคางดำมีการแพร่กระจายของประชากรได้อย่างรวดเร็ว จึงต้องมีการควบคุมประชากรของปลาหมอคางดำด้วยการนำไปใช้ประโยชน์ จึงมีการทดลองนำปลาหมอคางดำมาทำเป็นผลิตภัณฑ์ลูกชิ้นปลา เพราะลูกชิ้นปลาเป็นอาหารที่เข้าถึงได้ง่าย และโปรตีนสูงอีกทั้งยังเป็นอาหารที่เหมาะสมสำหรับทุกเพศทุกวัย
คณะอุตสาหกรรมอาหาร
This study aimed to develop a formula and production process for snacks made from germinated brown rice flour and banana flour using the extrusion process. The results indicated that both germinated brown rice flour and banana flour could be effectively used as the main raw materials for snack production via extrusion. The proportion of flour in the formula and production conditions, such as moisture content of the raw materials, barrel temperature, and screw speed, significantly influenced the nutritional value, bioactive compound levels, and antioxidant activity of the final products.
คณะวิทยาศาสตร์
Nowadays, automobiles are the most widely used form of transportation. This increases the risk of accidents. Therefore, car users prefer to get insurance to reduce the risk in the event of an accident. As for the insurance company, the company will be responsible for damages according to the conditions of the policy. One of the duties of a company's claims department is to procure spare parts to control costs. However, in the case of compensation, there may be erroneous operations, such as ordering the wrong parts or ordering more than necessary. Currently, insurance companies do not have a very efficient management system. This research aims to develop a system for managing and storing automobile parts for insurance companies. The system is designed to be able to track the status of spare parts from storage to disbursement. It uses barcode technology to increase accuracy and reduce errors in data recording. Such a system will help insurance companies manage spare parts systematically, reduce unnecessary costs, and increase efficiency in providing services.
คณะวิทยาศาสตร์
This special project aims to develop and compare the performance of gold price prediction models using quantitative variables and news text data. The study incorporates nine key predictors, including Brent crude oil prices, WTI crude oil prices, silver prices, platinum prices, the U.S. Federal Reserve's policy interest rate, the Nikkei 225 index, the Dow Jones Industrial Average, the S&P 500 index, and daily news articles from Bangkok Business News. Relevant news data will be processed using Natural Language Processing (NLP) techniques and integrated with three predictive models: Gradient Boosting, Machine Learning Models, and Regression Analysis. The model performance will be evaluated using three key metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R*). This research aims to develop a predictive model that effectively utilizes both quantitative variables and news data to enhance gold price forecasting, providing valuable insights for investors and analysts.