Earticle

초록

영어

In this paper, we propose the compared performance of normalization methods-based machine learning classification some techniques for NG leak prediction. The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. The proposed method is OrdinalEncoder(OE) based K-means clustering and OE transformation based SVM and MLP classifications for predicting NG leak. We have shown that our proposed OE based SVM method accuracy 97.82%, F1-score 98.54% and both of two normalization based MLP accuracy and F1-score also more than 96% which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

목차

Abstract
1. INTRODUCTION
2. SYSTEM OVERVIEW
3. PROPOSED ALGORITHMS
3.1. Support Vector Machine
3.2. Multilayer Perceptron
4. EVALUATION METRICS
5. EXPERIMENTAL RESULTS
6. CONCLUSION
REFERENCES

키워드

저자

• Khongorzul Dashdondov [ Professor, Department of Computer Engineering, Gachon University, Gyeonggi-do, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Intelligent Technologies and Innovative Practices

• 간기

  계간

• eISSN

  3092-412X

• 수록기간

  2026~2026

• 십진분류

  KDC 323 DDC 338