Journal of Practical Engineering Education (실천공학교육논문지)

Korean Institute for Pratical Engineering Education (한국실천공학교육학회)
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Implementation of a Job Prediction Program and Analysis of Vocational Training Evaluation Data Based on Artificial Intelligence

인공지능(AI) 기반 직업 훈련 평가 데이터 분석 및 취업 예측 프로그램 구현

  • Jae-Sung Chun (Department of Computer Engineering Korea University of Technology and Education) ;
  • Il-Young Moon (Department of Computer Engineering Korea University of Technology and Education)
  • 천재성 (한국기술교육대학교 컴퓨터공학과) ;
  • 문일영 (한국기술교육대학교 컴퓨터공학과)
  • Received : 2024.06.26
  • Accepted : 2024.07.29
  • Published : 2024.08.31
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Abstract

This paper utilizes artificial intelligence to analyze vocational training evaluation data for people with disabilities and selects the optimal prediction model using various machine learning algorithms. It predicts the job categories most likely to employ trainees based on data such as gender, age, education level, type of disability, and basic learning abilities. The goal is to design customized training programs based on these predictions to enhance training efficiency and employment success rates.

본 논문은 인공지능(AI)을 활용하여 장애인 직업 훈련 평가 데이터를 분석하고, 다양한 머신러닝 알고리즘을 통해 최적의 예측 모델을 선정하는 연구를 수행한다. 훈련생의 성별, 나이, 학력, 장애 유형, 기초 학습 능력 등의 데이터를 분석하여 취업 가능성이 높은 직종을 예측하고, 이를 바탕으로 맞춤형 훈련 프로그램을 설계하여 훈련 효율성과 취업 성공률을 높이는 것을 목표로 한다.

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References

  1. S. H. Moon, "Head pose estimation by using histogram and random forest," Master's thesis, Jeonnam National University, Gwangju, p. 13, 2016. 
  2. J. H. Yoo, "Peak load forecasting method for Jeju Island on alternative holiday using random forest," Master's thesis, Graduate School of Engineering, Korea University, p. 16, 2023. 
  3. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016. 
  4. S. Y. Park, J. H. Baek, S. H. Park, and J. Hur, "Implementation of a short-term wind power output forecasting model based on gradient boosting machine(GBM) algorithms," Proceedings of the Korean Institute of Electrical Engineers Conference, Gyeongbuk, pp. 305-306, May 26, 2022. 
  5. J. H. Kim and J. M. Won, "A development of the road surface decision algorithm using SVM (Support Vector Machine) clustering methods," Journal of Korean ITS Society, vol. 12, no. 5, pp. 1-12, 2013.  https://doi.org/10.12815/kits.2013.12.5.001
  6. H. Choi, T. K. Kim, G. R. Heo, S. D. Choi, and J. W. Hur, "Study of fuel pump failure prognostic based on machine learning using artificial neural network," Journal of the Korean Society of Manufacturing Process Engineers, vol. 18, no. 9, pp. 52-57, 2019.  https://doi.org/10.14775/ksmpe.2019.18.9.052
  7. J. Heo, W. K. Choi, J. M. Son, H. C. Park, and D. G. Yoon, "A study on performance improvement with minio file server on flask web server," Proceedings of the Korean Institute of Communication and Information Sciences Conference, Jeju, pp. 914-915, June 20, 2018.