• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.671-672
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• 2023

블록체인 기술을 통한 정확하고 투명한 데이터관리의 장점을 학습 시스템에 적용하는 사례가 증가하고 있으며 이에 따라 학습자 만족도와 참여율을 높이기 위한 학습데이터의 효율적 관리가 필요하다. 원격학습에서 학습 연계성과 만족도는 학습자의 학습참여율과 학습에서의 만족도에 변할 수 있음을 감안하여 당 변수에서 기인하는 하이퍼파라미터를 조정하여 학습자의 학습패턴과 학습연속성을 높이기 위한 노력을 하였다. 본 논문에서 제안하는 알고리즘을 적용하여 학습자 만족도를 조사한 결과, 적용 전 대비 10% 이상 학습 만족도 및 학습연계 의향률이 높아짐을 확인할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.47-55
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• 2023

This paper presents a method for predicting the threat index of combat systems using Gradient Boosting Regressors and Support Vector Regressors among machine learning models. Currently, combat systems are software that emphasizes safety and reliability, so the application of AI technology that is not guaranteed to be reliable is restricted by policy, and as a result, the electrified domestic combat systems are not equipped with AI technology. However, in order to respond to the policy direction of the Ministry of National Defense, which aims to electrify AI, we conducted a study to secure the basic technology required for the application of machine learning in combat systems. After collecting the data required for threat index evaluation, the study determined the prediction accuracy of the trained model by processing and refining the data, selecting the machine learning model, and selecting the optimal hyper-parameters. As a result, the model score for the test data was over 99 points, confirming the applicability of machine learning models to combat systems.