A Survey on Intelligent Information Processing System: A Machine Ailment Diagnosing Based on KNN Similarity Degree

Intelligent Information Processing System has successful application in informationization of traditional industry. Exact addressing the stock case´s ailment type and roots as quickly as possible has been the weight of developing information technology for veterinary. In order to assist human veterinarian expert diagnose animal ailment, this work proposes a machine diagnosing model based on KNN ailment-similarity-degree pattern recognition. The project crew devises 3 similarity distance measuring methods including Lee distance and Jaro distance, which are addressed to the uncertainty factor vector pattern and fuzzy membership pattern. In addition, the software architecture of the machine diagnosing model and diagnosing algorithm is constructed in detail. Field experimental statistics demonstrate that compared with the individual human veterinary expert, the proposed model achieve a preferable accuracy rate of diagnosis over 80%, and low a rate of misdiagnosis obviously, which is an alternate of existent ones with great potential.