The Mahachanok mango sauce is crafted from low-grade mangoes sourced from Ban Nong Bua Chum in Kalasin Province. Utilizing advanced food science technology, it effectively reduces agricultural waste and enhances product quality. This sauce is enriched with prebiotic fiber that supports the growth of beneficial gut microorganisms. With low sugar content, it is a healthy choice free from artificial colors and flavors. Its rich, natural taste makes it versatile, perfect for enhancing a wide variety of dishes, both savory and sweet.
ผลิตภัณฑ์ซอสมะม่วงมหาชนกพัฒนามาจากมะม่วงที่ตกเกรดจากบ้านหนองบัวชุม อำเภอหนองกุงศรี จังหวัดกาฬสินธุ์ เป็นการนำวิทยาศาสตร์การอาหารมาประยุกต์ใช้ให้เกิดประโยชน์สูงสุดต่อการลดของเสียทางการเกษตรและการใช้ทรัพยากรอย่างมีประสิทธิภาพ ซอสมะม่วงนี้มีการเสริมด้วยใยอาหารประเภทพรีไบโอติก ซึ่งช่วยกระตุ้นการเจริญเติบโตของจุลินทรีย์ที่ดีต่อลำไส้ สูตรซอสยังมีปริมาณน้ำตาลต่ำ อีกทั้งไม่มีการแต่งสีและกลิ่น ซึ่งช่วยรักษาคุณภาพและรสธรรมชาติของมะม่วง ทำให้ผลิตภัณฑ์นี้มีความเข้มข้นของรสชาติมะม่วงมหาชนกอย่างแท้จริงและดีต่อสุขภาพ
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
With the current cost of living situation in Thailand continuously rising, many recent graduates face challenges in managing their expenses in alignment with the increasing living costs. Food expenses, even for common street food, continue to surge with no sign of decreasing, despite improvements in raw material costs. Pay-Attention is a website platform designed to help recent graduates gain insights into managing and optimizing their food expenses effectively. It provides guidance on how to spend wisely, ensuring cost-effectiveness while maintaining adequate daily nutritional intake, without falling into monotonous eating habits.
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
This research presents a Digital Twin of an Aquarium for Water Quality Monitoring, developing a virtual model that displays real-time key water parameters, including pH level, temperature, flow rate, and dissolved oxygen. Sensor data is processed and visualized through a Graphical User Interface (GUI) to reflect the real-time status of the virtual aquarium. This system enables accurate water quality monitoring and analysis while reducing reliance on expensive software solutions.