Animal Disease Diagnosis Expert System using Convolutional Neural Networks

Author(s):  
Abishaik Mohan ◽  
R. Deepak Raju ◽  
P. Janarthanan
2012 ◽  
Vol 198-199 ◽  
pp. 1036-1041 ◽  
Author(s):  
Hua Hua Lian ◽  
Wen Xing Bao ◽  
Yun Hui Wang

There are kinds of animal diseases and a smaller number of experts in the corresponding field of disease diagnosis expert. So animal husbandry units are unable to make a rapid and accurate diagnosis for animal diseases generally. To solve this problem, the paper is proposed a model of animal disease diagnosis expert system based on HSMC-SVM. In theory, it confirms that HSMC-SVM is feasible in applying of animal diseases diagnosis expert system. Numerical experiments verify HSMC-SVM has higher accuracy and better generalization ability in the diagnosis of animal diseases.


2014 ◽  
Vol 543-547 ◽  
pp. 4161-4164
Author(s):  
Hong Juan Li ◽  
Shu Mei Zhang

Information technology includes neural networks, ontology technology, expert system, and so on, and the growth model can predict and manage growth conditions of fruit trees. The traditional expert system has shortcomings of poor self-learning ability, so the improved expert system is used to perform diagnosis of diseases and insects of fruit tree. Firstly the ontology is used to collect related symptoms of diseases and insects of fruit trees, the expert system and neural network are combined to build the prediction model of diseases and insects of fruit tree, then the conclusions of the diagnostic process are regarded as the input neurons and output neurons of neural networks, and are diagnosed by expert, so the prediction models of disease diagnosis of fruit trees are made. The models can implement the function of expert diagnosis and prediction, and provide technical support and management decision for the growth management of fruit tree, greatly improving the diagnosis efficiency of diseases and insects of fruit tree.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 219
Author(s):  
Mukhammed Garifulla ◽  
Juncheol Shin ◽  
Chanho Kim ◽  
Won Hwa Kim ◽  
Hye Jung Kim ◽  
...  

Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for healthcare professionals. Using the CNN on portable medical devices can enable a handy and accurate disease diagnosis. Unfortunately, however, the CNNs require high-performance computing resources as they involve a significant amount of computation to process big data. Thus, they are limited to being used on portable medical devices with limited computing resources. This paper discusses the network quantization techniques that reduce the size of CNN models and enable fast CNN inference with an energy-efficient CNN accelerator integrated into recent mobile processors. With extensive experiments, we show that the quantization technique reduces inference time by 97% on the mobile system integrating a CNN acceleration engine.


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