عناصر مشابهة
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
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| المؤلف الرئيسي: | |
| مؤلفين آخرين: | , |
| المجلد/العدد: | ع2 |
| محكمة: | نعم |
| الدولة: | مصر |
| التاريخ الميلادي: | 2022 |
| الصفحات: | 295 - 314 |
| ISSN: | 2636-2562 |
| رقم MD: | 1373003 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | English |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| رابط المحتوى: |
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| 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 | ||
