A modeling of animal diseases through using artificial neural network

This paper studied the implementation of Artificial Neural Network (ANN) where it well-known recently in veterinary disease research field in Malaysia. The parameter identification under consideration is types of animal disease, types of species and locations of disease based on the Geographical Inf...

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Main Authors: Alias, Norma, Mohd. Farid, Fatin Naemah, Al-Rahmi, Waleed Mugahed, Yahaya, Noraffandy, Al-Maatouk, Qusay
Format: Article
Language:English
Published: Science Publishing Corporation Inc. 2018
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Online Access:http://eprints.utm.my/id/eprint/85200/1/NormaAlias2018_AModelingofAnimalDiseasesThroughUsingArtificial.pdf
http://eprints.utm.my/id/eprint/85200/
https://www.sciencepubco.com/index.php/ijet/article/view/21574
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spelling my.utm.852002020-03-04T01:30:45Z http://eprints.utm.my/id/eprint/85200/ A modeling of animal diseases through using artificial neural network Alias, Norma Mohd. Farid, Fatin Naemah Al-Rahmi, Waleed Mugahed Yahaya, Noraffandy Al-Maatouk, Qusay Q Science (General) This paper studied the implementation of Artificial Neural Network (ANN) where it well-known recently in veterinary disease research field in Malaysia. The parameter identification under consideration is types of animal disease, types of species and locations of disease based on the Geographical Information System (GIS) data set. There are many types of animal diseases that affect farm animals in Malaysia. In this research, the method of multilayer perceptron neural network is used as main model since it is an effective solving method in predicting the future of veterinary disease. ANN has ability to visual animal diseases involving the computational model. The model is to present the rela-tionship between causes of the species and location and consequence of animal disease without emphasizing the process, considering the initial and boundary condition and considering the nature of the relations. The data collection of animal disease is considered as a large sparse data set. Therefore method of ANN is well suited for optimizing of the data, to train the data operational and to predict the parameter identification of animal disease. The output layers of ANN are plotted in SPSS software for statistical solution and MATLAB programming for sequential ANN implemented. The ANN will be compare to genetic algorithm for the performance and effectiveness of the method. The numerical simulation of ANN helps in future prediction of animal disease based on the species and location parameters. Science Publishing Corporation Inc. 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/85200/1/NormaAlias2018_AModelingofAnimalDiseasesThroughUsingArtificial.pdf Alias, Norma and Mohd. Farid, Fatin Naemah and Al-Rahmi, Waleed Mugahed and Yahaya, Noraffandy and Al-Maatouk, Qusay (2018) A modeling of animal diseases through using artificial neural network. International Journal of Engineering & Technology, 7 (4). pp. 3255-3262. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/21574
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Alias, Norma
Mohd. Farid, Fatin Naemah
Al-Rahmi, Waleed Mugahed
Yahaya, Noraffandy
Al-Maatouk, Qusay
A modeling of animal diseases through using artificial neural network
description This paper studied the implementation of Artificial Neural Network (ANN) where it well-known recently in veterinary disease research field in Malaysia. The parameter identification under consideration is types of animal disease, types of species and locations of disease based on the Geographical Information System (GIS) data set. There are many types of animal diseases that affect farm animals in Malaysia. In this research, the method of multilayer perceptron neural network is used as main model since it is an effective solving method in predicting the future of veterinary disease. ANN has ability to visual animal diseases involving the computational model. The model is to present the rela-tionship between causes of the species and location and consequence of animal disease without emphasizing the process, considering the initial and boundary condition and considering the nature of the relations. The data collection of animal disease is considered as a large sparse data set. Therefore method of ANN is well suited for optimizing of the data, to train the data operational and to predict the parameter identification of animal disease. The output layers of ANN are plotted in SPSS software for statistical solution and MATLAB programming for sequential ANN implemented. The ANN will be compare to genetic algorithm for the performance and effectiveness of the method. The numerical simulation of ANN helps in future prediction of animal disease based on the species and location parameters.
format Article
author Alias, Norma
Mohd. Farid, Fatin Naemah
Al-Rahmi, Waleed Mugahed
Yahaya, Noraffandy
Al-Maatouk, Qusay
author_facet Alias, Norma
Mohd. Farid, Fatin Naemah
Al-Rahmi, Waleed Mugahed
Yahaya, Noraffandy
Al-Maatouk, Qusay
author_sort Alias, Norma
title A modeling of animal diseases through using artificial neural network
title_short A modeling of animal diseases through using artificial neural network
title_full A modeling of animal diseases through using artificial neural network
title_fullStr A modeling of animal diseases through using artificial neural network
title_full_unstemmed A modeling of animal diseases through using artificial neural network
title_sort modeling of animal diseases through using artificial neural network
publisher Science Publishing Corporation Inc.
publishDate 2018
url http://eprints.utm.my/id/eprint/85200/1/NormaAlias2018_AModelingofAnimalDiseasesThroughUsingArtificial.pdf
http://eprints.utm.my/id/eprint/85200/
https://www.sciencepubco.com/index.php/ijet/article/view/21574
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score 13.159267