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เนื่องจากในปัจจุบันผู้คนนิยมหันมารับประทานอาหารที่มีโพรไบโอติกกันจำนวนมาก เช่น การรับประทานคีเฟอร์ แต่การรับประทานคีเฟอร์นมนั้นก็มีรสชาติและสัมผัสที่ทำให้รับประทานได้ยาก พวกเราจึงคิดผลิตภัณฑ์ Muly mul เจลลี่คีเฟอร์นมรสมัลเบอร์รี ที่มาในรูปแบบซอง เนื้อเจลลี่นุ่มละมุน สามารถรับประทานได้ง่าย และสะดวกสบายมากขึ้น Mulylul ตอบโจทย์ต่อผู้บริโภคที่ต้องการหาขนมที่มีประโยชน์ในการรับประทาน โดยMuly Mul เจลลี่คีเฟอร์นมรสมัลเบอร์รีมีทั้งซินไบโอติกซึ่งมีส่วนช่วยในการขับถ่ายโดยการเพิ่มแบคทีเรียที่ดีให้แก่ร่างกาย ช่วยในการลดการอักเสบ กระตุ้นภูมิคุ้มกัน และยังสารต้านอนุมูลอิสระ และวิตามินต่างๆที่จำเป็นต่อร่างกายอีกด้วย นอกจากนี้Muly mul ยังใช้วัตถุดิบจากเกษตรกรในจังหวัดสระบุรี ซึ่งจะช่วยเพิ่มมูลค่าของวัตถุดิบ สนับสนุนเกษตรกรในพื้นที่และยังช่วยยกระดับเศรษฐกิจในท้องถิ่นด้วย
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.
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.
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.