NDLI: Comparison and validation of artificial intelligent techniques to estimate intestinal broiler microflora

Content Provider	Springer Nature Link
Author	Hemati Matin, Hamid Reza Saki, Ali Asghar Bayat Varkeshi, Maryam Zare Abyaneh, Hamid
Copyright Year	2012
Abstract	This study was carried out to investigate the ability of major mathematical methods to estimate intestinal broiler microflora population. Artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIC), and artificial neural network genetic algorithm (ANNGA) were used in this respect. The lactic acid bacteria and Enterobacteriaceae were applied as models of microflora. Input and output variables were considered as time and microflora population, respectively. The best model of ANN, CANFIC, and ANNGA was determined based on the coefficient of determination and root mean square error criteria. The results of the current study have shown that ANN, ANNGA, and CANFIS are accurate methods to estimate lactic acid bacteria and Enterobacteriaceae. The highest accuracy of microflora estimation was related to 7 days of age. The efficiency of intelligent models to lactic acid bacteria and Enterobacteriaceae has shown that ANNGA had better prediction between mentioned models. The models estimated Enterobacteriaceae population better than that for lactic acid bacteria.
Starting Page	61
Ending Page	66
Page Count	6
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	1
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-07-12
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Modeling Microflora population Broiler intestine Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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