DocumentCode :
3013297
Title :
Research and Application of Animal Disease Intelligent Diagnosis Based on Support Vector Machine
Author :
Wan, Long ; Bao, Wenxing
Author_Institution :
Dept. of Comput. Sci. & Eng., North Univ. for Ethnics, Yinchuan, China
Volume :
2
fYear :
2009
fDate :
11-14 Dec. 2009
Firstpage :
66
Lastpage :
70
Abstract :
Diagnosing animal disease quickly and accurately has the economic effectiveness. But livestock breeding farms usually have relatively poor condition of disease diagnosis and animal disease cannot be diagnosed quickly and accurately. Traditional diagnostic method is usually restricted by some subjective factors. The accuracy of diagnosis is closely related to the level of medical skill. In order to resolve the rapid and accurate diagnosis of animal disease, this paper put forward a model of animal disease intelligent diagnosis which was based on support vector machine (SVM). In the model, the digital data of animal disease symptoms are taken as inputs of the disease classifier and used the classifier of the model to classify (diagnose) the sheep diseases. The results showed that the model is able to carry out animal diseases diagnosis more accurately, rapidly and have good predicality on the condition of small samples and provide a new approach for animal disease diagnosis.
Keywords :
biology computing; learning (artificial intelligence); support vector machines; animal disease intelligent diagnosis; animal disease symptoms; digital data; machine learning; sheep diseases; support vector machine; Agriculture; Animals; Competitive intelligence; Computational intelligence; Diseases; Machine intelligence; Machine learning; Medical diagnostic imaging; Support vector machine classification; Support vector machines; Animal disease; Intelligent diagnosis; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security, 2009. CIS '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5411-2
Type :
conf
DOI :
10.1109/CIS.2009.199
Filename :
5375933
Link To Document :
بازگشت