ارسل ملاحظاتك

ارسل ملاحظاتك لنا







Using Artificial Neural Network in MS Excel to Investigate the Causal Relationship between Petroleum Consumption and Economic Growth in Egypt

المصدر: المجلة العلمية للاقتصاد والتجارة
الناشر: جامعة عين شمس - كلية التجارة
المؤلف الرئيسي: Ahmed, Sally Hossam Eldin (Author)
مؤلفين آخرين: Mostafa, Mostafa Galal (Advisor) , Abdel Aal, Medhat Mohamed Ahmed (Advisor)
المجلد/العدد: ع2
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2022
الشهر: يوليو
الصفحات: 295 - 314
ISSN: 2636-2562
رقم MD: 1373003
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
كلمات المؤلف المفتاحية:
Artificial Neural Network | Feed Forward Neural Network | Artificial Intelligence | Back Propagation Algorithm | Mean Square Error | Petroleum Consumption | Economic Growth
رابط المحتوى:
صورة الغلاف QR قانون

عدد مرات التحميل

5

حفظ في:
LEADER 02575nam a22002417a 4500
001 2124331
041 |a eng 
044 |b مصر 
100 |a Ahmed, Sally Hossam Eldin  |e Author  |9 727146 
245 |a Using Artificial Neural Network in MS Excel to Investigate the Causal Relationship between Petroleum Consumption and Economic Growth in Egypt 
260 |b جامعة عين شمس - كلية التجارة  |c 2022  |g يوليو 
300 |a 295 - 314 
336 |a بحوث ومقالات  |b Article 
520 |b In this paper, feed forward neural network (FFNN) was used to investigate the causal relationship between disaggregated petroleum consumption and economic growth in Egypt. Petroleum consumption will be disaggregated into six main fuel types: Natural gas Gasoline, Fuel Oil, Gas Oil, Kerosene and LPG (Liquefied Petroleum Gas). Standard multilayered, feed-forward, back-propagation neural networks were designed using Microsoft Excel (MS Excel). The results of this study indicated that FFNN showed significant results in dealing with the data. Also FFNN model is capable and can be used for predicting the causal relationship between petroleum consumption and economic growth in Egypt. For natural gas model MSE in training set =0.000738 and in testing set MSE=0.005636. For gasoline model MSE in training set =0.000437 and for testing set MSE=0.008352. For gas oil model MSE in training set =0.00071 and in testing set MSE=0.005675. For fuel oil model MSE in training set =0.000807 and in testing set MSE=0.008753. For Kerosene model MSE in training set =0.00091 and in testing set MSE=0.004312. Finally, for LPG model MSE in training set =0.000649 and for testing set MSE=0.00664, which indicate very high accuracy of the predicted models. 
653 |a الأساليب الإحصائية  |a استهلاك البترول  |a الشبكات العصبية  |a برنامج جداول البيانات 
692 |b Artificial Neural Network  |b Feed Forward Neural Network  |b Artificial Intelligence  |b Back Propagation Algorithm  |b Mean Square Error  |b Petroleum Consumption  |b Economic Growth 
700 |a Mostafa, Mostafa Galal  |e Advisor  |9 727149 
700 |a Abdel Aal, Medhat Mohamed Ahmed  |e Advisor  |9 432063 
773 |4 الاقتصاد  |4 الإدارة  |6 Economics  |6 Management  |c 029  |e Scientific Journal for Economic & Commerce  |f Al-Maġallah Al-ʿilmiyyah Lil-Iqtiṣād Wal Tiğārah  |l 002  |m ع2  |o 0527  |s المجلة العلمية للاقتصاد والتجارة  |v 052  |x 2636-2562 
856 |u 0527-052-002-029.pdf 
930 |d y  |p y  |q n 
995 |a EcoLink 
999 |c 1373003  |d 1373003 

عناصر مشابهة