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논문검색

Technology Convergence (TC)

Development of CNN-Transformer Hybrid Model for Odor Analysis

원문정보

초록

영어

The study identified the various causes of odor problems, the discomfort they cause, and the importance of the public health and environmental issues associated with them. To solve the odor problem, you must identify the cause and perform an accurate analysis. Therefore, we proposed a CNN-Transformer hybrid model (CTHM) that combines CNN and Transformer and evaluated its performance. It was evaluated using a dataset consisting of 120,000 odor samples, and experimental results showed that CTHM achieved an accuracy of 93.000%, a precision of 92.553%, a recall of 94.167%, an F1 score of 92.880%, and an RMSE of 0.276. Our results showed that CTHM was suitable for odor analysis and had excellent prediction performance. Utilization of this model is expected to help address odor problems and alleviate public health and environmental concerns.

목차

Abstract
1. INTRODUCTION
2. DESIGN OF MODEL
2.1 Design of CNN-Transformer Hybrid Model
2.2 Dataset
3. RESULTS OF MODEL PERFORMANCE EVALUATION
4. CONCLUSION
Acknowledgement
References

저자정보

  • Kyu-Ha Kim Department of Computer Engineering, Honam University, Korea
  • Sang-Hyun Lee Department of Computer Engineering, Honam University, Korea

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