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Search Result For 'Neural Networks ' , Result Number : 6

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Ahmed Mohamed Ahmed Attya
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
Neural Networks & Artificial Intelligence
Tamer Mohamed Hassan Ibrahiem Shahwan
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
A Comparison of Bayesian Methods and Artificial Neural Networks for Forecasting Chaotic Financial Time Series
Osama Abdelmoniem Ali Khashaba
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
‎44.‎ Al-Zahrani FA, Mustafa HM, Al-Hamadi A, Khashaba UA. Modeling of computer-assisted learning using ‎artificial neural networks. in: Seoyun J.Kwon (Ed.), New Developments in Artificial Neural Networks ‎Research, Ch3, ISBN: 978-1-61761-553-5, Nova Science Publishers, Inc., (2011) 41-57.‎
Osama Abdelmoniem Ali Khashaba
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
‎55.‎ Selmy AI, El-Sonbaty IA, Khashaba UA, Megahed AA. Prediction of delamination size in drilling fiber ‎reinforced polymeric composite materials using Artificial Neural Networks technique. Engineering Research‏ ‏Journal-Menoufia University, Vol.31-4, pp.369-375, (2008).‎
Fatma Desouky Mohamed Abdallah
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle Eman A. Abo Elfadl1,*, Fatma D. M. Abdallah2 1Department of Animal Husbandry and Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Egypt 2Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt *Corresponding author: emmy_f1984@yahoo.com Abstract Background & objectives: This study was undertaken to compare the accuracies of Discriminant analysis model (DA) and Artificial neural networks model (ANN) for classification and prediction of Friesian cattle fertility status by using its reproductive traits. Methods: Data was collected through field survey of 2843 animal records of Friesian breed belongs to El Dakhalia province farms, Egypt. Data was covering the period extended from 2010 to 2013. The samples of dairy production sectors were selected randomly. Data was collected from valid farm records or the structured questionnaires established by the researcher. Results: The results of classification accuracy indicated that the artificial neural network (ANN) model is more efficient than the discriminant analysis (DA) model in expressing overall classification accuracy and accuracies of correctly classified cases of fertility status for Friesian cattle. The results showed that The ANN models had shown the highest classification accuracy (93.6%) for year (2010) while, it was (79.9%) for DA. The comparison of overall classification accuracies clearly favored the supremacy of ANN over DA. The results also were confirmed by the areas under Receiver Operating Characteristic Curves (ROC) captured by ANN and DA. ROC curves are used mainly for comparing different discriminating rates. Areas under ROC curves were higher in case of ANN models across the different years compared to DA models. The differences in accuracies were also significant at 5% level of significance with p-value 0.005 by using Paired Sample t-test. From all of the above we can conclude that artificial neural network model was more accurate in prediction and classification of fertility status than a traditional statistical model (Discriminant analysis). Keywords: artificial neural networks, discriminant analysis, prediction, classification, ROC curve and fertility status Cite This Article: Eman A. Abo Elfadl, and Fatma D. M. Abdallah, “Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle.” American Journal of Applied Mathematics and Statistics, vol. 5, no. 3 (2017): 90-94. doi: 10.12691/ajams-5-3-1.
Ahmed Sabri Mohamed Ahmed
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
neural networks in civil engineering applications