Zagazig University Digital Repository
Home
Thesis & Publications
All Contents
Publications
Thesis
Graduation Projects
Research Area
Research Area Reports
Search by Research Area
Universities Thesis
ACADEMIC Links
ACADEMIC RESEARCH
Zagazig University Authors
Africa Research Statistics
Google Scholar
Research Gate
Researcher ID
CrossRef
Research Area
Research Area Filter
Search Result For 'applied mathematics' , Result Number : 14
Staff Name
Research Area
Amr Mohamed Samy Mohammed Mahdi
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
applied mathematics
Khaled Lotfy Mohamed Azab
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
Sameh Abdalazahr Hussein Mouawad
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
Khalied Mohamed Hosny
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
Salwa Amien Mohamed ebrhiem
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
Fatma Desouky Mohamed Abdallah
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics 2019, 9(1): 1-5 DOI: 10.5923/j.am.20190901.01 Role of Time Series Analysis in Forecasting Egg Production Depending on ARIMA Model Fatma D. M. Abdallah Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt Abstract The goal of this study is to show the role of time series models in predicting process and to demonstrate the suitable type of it according to the data under study. Autoregressive integrated moving averages (ARIMA) model is used as a common and a more applicable model. Univariate ARIMA model is used here to forecast egg production in some layers depending on daily data from the period of May to October 2018. Different criteria of the ARIMA model can be used to choose the suitable one such as the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute relative percentage error (MARPE). Depending on these measures the autoregressive integrated moving average model with ordering (2,2,1) is considered the best model for forecasting process. The model fit statistics such as RMSE (331.520) which was low and the lowest BIC value (11.745) indicating that the model fit the data well. The high value of R2 (0.95) and MAPE (4.542) indicated a perfect forecasting model. Also, ARIMA model with ordering (1,2,2) is good in prediction process.
Fatma Desouky Mohamed Abdallah
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Statistical Modelling of Categorical Outcome with More than Two Nominal Categories Fatma D.M. Abdallah* Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt *Corresponding author: Nour_stat2013@yahoo.com Received August 13, 2018; Revised October 04, 2018; Accepted December 04, 2018 Abstract This paper aims to explain and apply an important statistical method used for modelling categorical outcome variable with at least two unordered categories. Logistic regression model especially multinomial logistic type (MNL) model is the best choice to model unordered qualitative data. A simulation study was done to examine the efficiency of the model in representing categorical response variable. Three explanatory variables (age, species, and sex) are used for discrimination. While the outcome variable was Rose Bengal Plate Test (RBPT) results which has four outcome categories (negative, positive, false positive, and false negative). Therefore, logit model will be utilized to model this data. MNL models were fitted using SPSS packages and parameters estimated depending on maximum likelihood (MLE) by the Newton-Raphson algorithm. This model depends mainly on two estimates to interpret the results, they are the regression coefficient and the exponentiated coefficients which known as the odds ratio. This model was a good fitted for description the data of 500 values of Rose Bengal Plate Test results of Brucella in sheep and goat species. The results showed fitting of the model to the data with highly significant likelihood ratio statistic for the overall model (P value = 0.000**). Wald test was significant for all variables in positive category and this indicated that age, species and sex are good predictors for test results. The odds ratio in case of positive category for age, species and sex was 1.589, 0.214 and 0.133 respectively. Keywords: multinomial logistic regression, odds ratio, Rose Bengal Plate Test (RBPT), maximum likelihood and pseudo R2 Cite This Article: Fatma D.M. Abdallah, “Statistical Modelling of Categorical Outcome with More than Two Nominal Categories.” American Journal of Applied Mathematics and Statistics, vol. 6, no. 6 (2018): 262-265. doi: 10.12691/ajams-6-6-7.
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.
Usama Abdelhamid Ibrahim
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
21. A. Kandil, O. Tantawy and M. Abdelhakeem, Flou Topological Spaces via Ideals, International Journal of Applied Mathematics, 23(5), 873-885, 2010.
Sami Hassan Ahmed Hassn
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
Mahmoud Elsayed Mustafa Gabr
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
Nazira Mohamed Mansour Mosa
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
applied mathematics
Ahmed Nagah Saeed Majid
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
applied mathematics
Ibtesam Elsayed Mostafa Attia Eraky
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Applied Mathematics
جامعة المنصورة
جامعة الاسكندرية
جامعة القاهرة
جامعة سوهاج
جامعة الفيوم
جامعة بنها
جامعة دمياط
جامعة بورسعيد
جامعة حلوان
جامعة السويس
شراقوة
جامعة المنيا
جامعة دمنهور
جامعة المنوفية
جامعة أسوان
جامعة جنوب الوادى
جامعة قناة السويس
جامعة عين شمس
جامعة أسيوط
جامعة كفر الشيخ
جامعة السادات
جامعة طنطا
جامعة بنى سويف