This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.
The increasing complexity of pharmaceutical treatments requires precise pill identification to ensure patient safety. Traditional methods for pill reconciliation rely on human experts, which are time-consuming and prone to errors. Deep Convolutional Neural Networks (CNNs), particularly effective in image processing, offer a promising solution for automating and enhancing these processes.
คณะวิศวกรรมศาสตร์
ยานยนต์ไฟฟ้าดัดแปลง
คณะวิศวกรรมศาสตร์
The presented project topic is Garbage Sorting Systems. The purpose is to study the operation and develop a waste sorting system that can automatically detect the type of waste using a proximity sensor to separate the types of metal and non-metal waste, as well as an ultrasonic sensor to check the amount of waste in the bin. If the amount of waste exceeds the specified amount, the system will send a notification to the communication device connected to the system, such as a smartphone or computer. The operation of the system is designed to increase the efficiency of waste management, reduce the burden of manual waste sorting, and promote recycling. This system can be applied in various places, such as educational institutions or public places, to help reduce the amount of waste that is not properly separated and increase the opportunity to reuse waste.
คณะเทคโนโลยีสารสนเทศ
Traditional methods of public relations and learning often lack engagement and fail to provide users with a deep and immersive experience. Additionally, these methods struggle to reach a wide audience, especially those unable to visit the physical location. This project aims to solve the issues of accessibility and awareness regarding the institution’s Chalermphrakiat Hall and historical exhibition. Utilizing metaverse technology to simulate important locations allows users to explore the site and view key information in a virtual format, thereby enhancing the engagement of students staff alumni and the general public. The metaverse system is developed using Unity, a powerful game engine capable of supporting the creation of metaverse environments. This allows for the creation of an interactive and realistic virtual space. Unity also supports the management of physics, lighting, and sound, further enhancing realism. Additionally, the system is integrated with web browsers using WebGL technology, enabling the project developed in Unity to be accessed directly through a browser. Users can visit and interact with the metaverse environment from anywhere without the need to install additional software. The developers have thus created the metaverse system to provide a realistic and engaging learning experience, enhancing public relations efforts and fostering a strong connection with the institution efficiently.