This project has been developed to address medical challenges related to the process of counting and classifying blood cells from samples, a task that requires both time and high precision. To reduce the workload of medical personnel, the developers have created a platform and an artificial intelligence (AI) system capable of automatically classifying and counting cells from sample images. This system is designed to assist medical laboratory technicians by enabling them to work more efficiently and accurately, reducing the time required for analysis. Furthermore, it promotes the advancement of medical technology, ensuring effective usability from classrooms and laboratories to hospitals.
เนื่องจากคณะผู้จัดทำมีโอกาสได้พูดคุยกับบุคลากรที่ทำงานในสายอาชีพเทคนิคการแพทย์ จึงเล็งเห็นถึงปัญหาด้านระยะเวลาการทำงาน โดยเฉพาะในกระบวนการแยกและนับจำนวนเซลล์เม็ดเลือด ซึ่งเป็นขั้นตอนที่ใช้เวลาค่อนข้างมาก คณะผู้จัดทำจึงเล็งเห็นโอกาสในการนำความรู้ที่มีมาพัฒนาแนวทางใหม่เพื่อช่วยลดภาระงานของบุคลากรทางการแพทย์ หากสามารถลดระยะเวลาในกระบวนการดังกล่าวได้ ด้วยเหตุนี้เอง โครงการนี้จึงถูกริเริ่มขึ้นมา โดยมีเป้าหมายเพื่อพัฒนาเทคโนโลยีที่สามารถสนับสนุนการทำงานของบุคลากรทางการแพทย์ และยกระดับคุณภาพของกระบวนการตรวจวินิจฉัยให้มีความรวดเร็วและแม่นยำยิ่งขึ้น
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This study explores the design, production, and installation of 3D-printed modular artificial reefs (3DMARs) at Koh Khai, Chumphon Province, Thailand, through a design thinking framework. Collaborating with SCG Co., Ltd. and the Department of Marine and Coastal Resources, the research establishes design criteria and installation methods, utilizing content analysis and qualitative research. Key principles such as modularity, flexibility, environmental sustainability, and usability are identified. The user-centered approach optimizes the 3DMARs for transport and deployment, enabling local community involvement and fostering sustainable practices. The modular design supports scalability, enhancing marine habitats and coral larval settlement. Furthermore, underwater monitoring techniques enable site-specific data collection, allowing for the generation of digital twin models. This research offers a practical framework for marine ecosystem restoration and empowers coastal communities in Thailand and beyond
คณะเทคโนโลยีสารสนเทศ
Currently, the issue of developmental writing disabilities in children is a matter of great importance for school-age children. Diagnosing whether a child has developmental writing disabilities relies on writing skill assessments, which are administered to those seeking diagnosis and evaluated by medical professionals or experts. However, there are still limitations in the diagnostic process, which depends heavily on expert physicians, leading to a high demand for human resources. To address this, we have developed a method for scoring writing skill assessments using image processing technology, based on existing scoring criteria. Currently, three criteria are used for scoring: writing position, article format, and copying speed. We have also created a web application to make the system more accessible and easier to use.
คณะวิศวกรรมศาสตร์
This research aims to investigate the adulteration of Khao Dawk Mali 105 rice based on storage age using Near-Infrared Spectroscopy (NIRS) with Fourier Transform Near-Infrared Spectroscopy (FT-NIR) in the wavenumber range of 12,500 – 4,000 cm-1 (800 – 2,500 nm). Storage duration significantly impacts the quality of cooked rice. This research is divided into two parts: 1) to investigate the feasibility of separating rice according to storage age (1, 2, and 3 years) using the best model created by an Ensemble method combined with Second Derivative, which achieved an accuracy of 96.3%. 2) To investigate adulteration based on storage age by adulterating at 0% (all 2- and 3-year-old rice), 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (all 1-year-old rice). The best model was created using Gaussian Process Regression (GPR) combined with Smoothing + Multiplicative Scatter Correction (MSC), with coefficients of determination (r²), root mean square error of prediction (RMSEP), bias, and prediction ability (RPD) values of 0.92, 8.6%, 0.9%, and 3.6 respectively. This demonstrates that the adulteration model can be applied to separate rice by storage age (1, 2, and 3 years). Additionally, the color values of rice with different storage ages show differences in L* and b* values.