원문정보
대한산업경영학회
International Journal of Intelligent Technologies and Innovative Practices
Vol. 1 No. 1
2026.01
pp.19-23
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보