Abstract: Banana French Fries This project aimed to study and develop the product Banana French Fries, which is a snack made by frying bananas in a form similar to French fries, in order to add value to bananas and create new choices for consumers. The experiment consisted of selecting suitable banana varieties, developing a coating formula, and testing the taste of samples. The results of the study found that Nam Wa bananas are the most suitable for making banana French fries because they have a firm texture and naturally sweet taste. The best coating formula consists of wheat flour, eggs, and milk, which provide longer crispiness. The taste test found that most consumers gave a very good response and were satisfied with the taste and texture. This project shows that banana French fries are a product with potential to be developed as a healthy snack and can be further developed into a commercial product in the future.
เปลี่ยนจากการบริโภคมันฝรั่งจากเดิมให้มีความแตกต่างจากปกติให้ลูกค้ากลุ่มใหม่ได้รับประทานผลิตภัณฑ์รูปแบบใหม่จากล้วยและได้ช่วยให้เกษตรกรได้มีรายได้ในส่วนนี้ด้วย
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This app encourages users to clean by turning it into a fun game. Users can choose cleaning tasks, track dust levels, and earn reward points, making the cleaning process more engaging and enjoyable.
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.
คณะเทคโนโลยีการเกษตร
In the present day, interest in health and the consumption of chemical-free food has been steadily increasing, particularly in homegrown produce such as Phoenix oyster mushrooms (Pleurotus pulmonarius), which are highly nutritious and suitable for weight control. However, small-scale mushroom cultivation often faces challenges related to unsuitable environmental conditions, such as unstable temperature and humidity, which affect the growth and quality of the mushrooms. The development of an automatic temperature and humidity control system plays a crucial role in addressing these issues by utilizing sensor technology to monitor and adjust environmental conditions with precision. This helps enhance production efficiency, reduce human errors in manual control, and promote safe food production at the household level. Additionally, it helps lower production costs and supports the concept of sustainable living. The adoption of this technology is considered an important innovation in improving the quality of mushroom cultivation and increasing sustainability in food production.