Implementation of neural network algorithms in courses for forecasting the technical condition of pipeline systems
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Abstract
The present study is devoted to studying the potential of introducing neural network algorithms into educational programs for training specialists in the field of forecasting the technical condition of pipeline systems. Pipeline systems are critical infrastructure facilities that require constant monitoring and maintenance. Traditional teaching methods do not allow us to fully develop the competencies necessary to work with modern intelligent monitoring systems. The study developed and tested an integrated educational module based on neural network technologies, which includes theoretical and practical components. The results of the experimental introduction into the educational process showed a significant increase in the level of professional competencies among students in the experimental group compared with the control group: the average score in specialized disciplines increased by 18.7%, and the ability to solve complex diagnostic problems increased by 23.4%. The analysis of feedback data from students and teachers indicates increased motivation to learn and a deeper understanding of the subject area. The proposed model for integrating neural network algorithms into the educational process can be scaled for various technical disciplines and levels of education, taking into account the specifics of the subject areas.
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References
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