MACHINE LEARNING APPROACH FOR PIG DISEASE CLASSIFICATION USING RANDOM FOREST
Abstract
Significant financial loss is associated with swine farming as a result of the late identification of pig disease.Along these lines, it is vital to recognize of pig diseases. This study focuses on classifying, diagnosing and recommending treatment to Pigs using indicators of symptoms characteristics of pigs based on machine learning algorithm. The aim of this work is to develop a system for pig disease classification, diagnosis using natural language processing and random forest with the following objectives to carry out an in-depth study of the existing systems and identify it limitations, Collecting a comprehensive dataset of pig diseases, including symptoms, physical examination results, and laboratory test data.The system was developed using popular deep learning frameworks such as TensorFlow and Python Programming Language.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.