000			03110nam a22002417a 4500
003			FT8787
005			20251112141939.0
008			251112b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT55 C36 2025
082			_a.
100	1		_aCandelario, Heart Angel J.; Cruz, Tricia V.; Gray, Patricia Ann R.
245			_aWeb-based application predicting cow hoof infections with 3D modelling scrapping simulator and treatment recommendations
264		1	_a. _b. _cc2025
300			_bCapstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: Veterinary students frequently encounter challenges in obtaining practical experience in evaluating cow hoof health, this was because of their limited access to live animal cases. This gap makes it difficult for future veterinarians to confidently identify lameness and related hoof issues. In addition, there is a noticeable absence of accessible, step-by-step guides or simulations for proper hoof scraping techniques, which can lead to ineffective or even harmful practices by both students and farmers. Moreover, the current reliance on traditional methods without the support of a data-driven system, results in inconsistent treatment decisions for hoof infections, affecting both animal welfare and farm productivity. To address these gaps, this study developed WounderCow, a web-based application that integrates image processing, 3D modeling, and AI-driven treatment recommendations. The system enables users to upload images of cow back posture to predict hoof infections, classify lameness types, assess severity, and receive AI-generated treatment recommendations tailored to the specific condition. A key feature of the application is its interactive 3D simulator, which provides step-by-step guidance on hoof scraping and treatment techniques, enhancing users practical understanding of hoof care procedures. The system was developed using the Agile Scrum framework, with interactive sprints focusing on the refinement of key modules including image analysis, 3D visualization, and artificial intelligence prediction models. To evaluate the performance of the system in meeting its core objectives, a confusion matrix was employed as the primary assessment tool. This allowed for a detailed analysis of the system’s classification accuracy in predicting lameness severity, type of hoof infection, and corresponding treatment plans. Key performance metrics such as accuracy, precision, recall, and F1-score were computed to assess the model’s effectiveness. The results demonstrated that the system could reliably support diagnostic decision-making offering a valuable complement to traditional veterinary education. This study highlights the application’s potential to bridge educational gaps while advancing diagnostic and treatment practices for cow hoof conditions.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37087 _d37087