This page shows my research and engineering projects in my high school. After discovering that Artificial Intelligence is applicable in a variety of fields, I enjoy solving problems by utilizing AI. My science project was first conducted within school under my science teacher’s guidance. Afterward, it was able to compete at different levels based on its qualifications. The competition path is school level —> city level —> county level —> state level. If the project receives top awards within its current level, it will be promoted to the next level. Each of my projects has its own website link, which serves as a way to showcase it to the judges. The following shows the abstract and video presentation of each project only. For the details of each project, including the lab logs, please click on the project website link provided for each project listed below.

In the context of bone fracture detection, the research study titled “Revolutionizing Bone Fracture Detection: YOLOv5 vs. YOLOv8 Face-off” comprehensively assessed the performance of two state of-the-art YOLO (You Only Look Once) object detection algorithms, namely YOLOv5 and YOLOv8, across different model configurations, including small (s), medium (m), and large (l) variants. These six YOLO models underwent rigorous training, validation, and testing phases, employing three distinct datasets. One dataset exclusively consisted of wrist fractures, while the remaining two contained a mix of bone fracture types. The evaluation of model performance was based on precision, F1 score, and recall confidence ratings, and the results were presented through various visualization methods, including tables, images, and graphs. Ultimately, the study established that YOLOv5 and YOLOv8 demonstrate comparable detection capabilities, with YOLOv5 occasionally exhibiting superior accuracy in detecting fractures of varying sizes.