Defensive Future Studies

Defensive Future Studies

Development of A Prediction Model of Effective Factors in Military Training Courses Using Artificial Neural Network Algorithm

Document Type : Original Article

Authors
Seydhossein Khodaee 1
Mohammad Abbasian 2
Mahdi Esmaeili 2
1 Department of Industrial Management, Faculty of Management, University of Yazd, Yazd, Iran.
2 Assistant Prof. Officer University of Imam Ali, Tehran, Iran.
10.22034/dfsr.2024.2010849.1730
Abstract
Objective: Predicting the results of the current quality improvement activities is one of the concerns of the officers of the armed forces officer universities. Taking advantage of the capabilities of the artificial neural network and taking into account the aforementioned concern, the present research has presented a model to predict the process of the results of the effective factors in the military educational activities of one of the officers' universities.
Methodology: The statistical population is all the young officers under training in the combat camp in Kohestan University under study (from 2018 to 2022). In the first step, the data related to the factors and parameters of the training-educational activities of the military camp in the mountains were collected for the mentioned five-year time period. In the next step, pre-processing of the data was done and using the proposed IRNN algorithm and its coding in Python, the appropriate model was built and its validation was done.
Findings: By using the built model and the available data, it was predicted the quality of the training performance of the combat camp.
Conclusion: The results showed that with the continuation of the current policies, all the factors will have an upward trend except for the factor of "updating the level of knowledge and skills of the teaching staff". Therefore, proper planning should be done in order to update the level of knowledge and skills of the educational staff of specialized educational activities
Keywords
Training camps
officer universities
artificial neural network
prediction model
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Defensive Future Studies
Volume 8, Issue 31
Winter 2024
Pages 37-67