Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
เทคโนโลยี LoRa (Long Range) เป็นเทคโนโลยีที่สามารถสื่อสารข้อมูลได้ระยะไกลที่สุดในหลักกิโลเมตร ใช้พลังงานต่ำและไม่มีค่าบริการรายเดือน จึงเหมาะกับการพัฒนาการเกษตรซึ่งมีพื้นที่กว้างและWifiเข้าไม่ถึง ในรูปแบบของการทำเกษตรอัจฉริยะด้วยระบบการสื่อสารแบบLoRa
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.
คณะเทคโนโลยีสารสนเทศ
The process of treating cancer patients in the chemotherapy department at Chonburi Cancer Hospital is complicated and inconvenient due to the procedure of submitting blood test results through the personal LINE application of medical staff, which hinders workflow efficiency. Therefore, the researcher has developed a cancer patient management and tracking program in the form of a web-based application and LINE LIFF (LINE Front-end Framework) application to facilitate both medical personnel and patients. The web-based application is designed for medical personnel to monitor, schedule, and collect patient data, while the LINE application is designed for patients to submit blood test results, view appointment schedules, record symptoms after chemotherapy, log their weekly weight, and access a chatbot for consultation. This system is developed based on client-server technology, which enhances data analysis efficiency and supports automated treatment planning. As a result, the cancer treatment process becomes faster, more modern, and more efficient.
คณะอุตสาหกรรมอาหาร
The Ginbanirose project aims to develop herbal extracts for alleviating menstrual pain using key ingredients: roselle, banana inflorescence, and ginger. These ingredients contain bioactive compounds with anti-inflammatory, antioxidant, and pain-relieving properties. The extracts are enhanced through liposome encapsulation technology, which improves absorption and stability. The production process involves herbal extraction, freeze-drying, and liposome formulation using lecithin and stabilizers. Experimental results demonstrate high phenolic content and antioxidant activity via the DPPH method. Ginbanirose addresses women’s quality of life concerns while offering significant business opportunities in the rapidly growing herbal market, particularly in the Asia-Pacific region.