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Fitting and classifying the correlated data using the neural networks
المؤلف
al-Sayyid, Ahmad Muhammad Muhammad
المصدر
Journal of Commercial and Environmental Studies
العدد
المجلد 11، العدد 1، ج. 2 (31 مارس/آذار 2020)، ص ص. 188-222، 35ص.
الناشر
جامعة قناة السويس كلية التجارة
تاريخ النشر
2020-03-31
دولة النشر
مصر
عدد الصفحات
35
التخصصات الرئيسية
الموضوعات
الملخص EN
Neural networks have been one of the fascinating machine learning models, not only because of the fancy back propagation algorithm but also because of their complexity.
Neural networks have not been popular, partly because they were, and still are in some cases, computationally expensive and partly because they did not seem to yield better results when compared with simpler methods such as support vector machines.
Nevertheless, the neural networks have raised attention and become popular.
this forest.
One of the most important procedures when forming a neural networks is data normalization.
This involves adjusting the data to a common scale so as to accurately compare predicted and actual values.
We used normalization process to scale data.
Our neural networks has been created using the training data, then compare this to the test data to gauge the accuracy of the neural network forecast.
We are used the neural networks and compares its results to tradition methods for regression and classification.
Traditionally, the average MSE for the neural networks is lower than a linear model.
But in our case the opposite is happened.
Hidden layers use back propagation to optimize the weights of the input variables in order to improve the predictive power of the model.
We study the effect of changing the number of hidden layers on the accuracy of our model.
Also, traditionally an increasing of number of the hidden layers increase the accuracy of our model.
Finally, A confusion matrix is used to determine the number of true and false generated by our predictions.
This due to the accuracy rate of classification by divided the number of true on the total objects of a test data.
A high accuracy rate expresses a good classifying.
In this paper, we are going to fit and classify neural networks using R package “neuralnet”, and fit a linear model "VGLM" and non-linear model “VGAM” models as a comparison applied on the environmental data.
These data were collected from the Hunua Ranges, this is a small forest in southern Auckland, New Zealand.
At 392 sites in the forest, the presence/absence of 17 plant species was recorded, as well as the altitude.
Each site was of area size about 200 m2.
We will study the effect of the presence/absence these species on the sea level for all areas in
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Sayyid, Ahmad Muhammad Muhammad. 2020. Fitting and classifying the correlated data using the neural networks. Journal of Commercial and Environmental Studies،Vol. 11, no. 1، ج. 2, pp.188-222.
https://search.emarefa.net/detail/BIM-1072073
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Sayyid, Ahmad Muhammad Muhammad. Fitting and classifying the correlated data using the neural networks. Journal of Commercial and Environmental Studies Vol. 11, no. 1, ج. 2 (2020), pp.188-222.
https://search.emarefa.net/detail/BIM-1072073
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Sayyid, Ahmad Muhammad Muhammad. Fitting and classifying the correlated data using the neural networks. Journal of Commercial and Environmental Studies. 2020. Vol. 11, no. 1، ج. 2, pp.188-222.
https://search.emarefa.net/detail/BIM-1072073
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
-
رقم السجل
BIM-1072073
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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