Muhammad Shahzeb
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Muhammad Shahzeb has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Cao Xia
Sichuan University
1 shared publication - Hongyang Xiao
Pittsburgh Institute, Sichuan University, Chengdu 610225, China
1 shared publication - Jingfei Xu
Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
1 shared publication - Qiang Shen
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
1 shared publication - Xianzhang Zeng
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
1 shared publication - Xin Cheng
Graduate School of International Cultural Studies, Tohoku University, Sendai 980-5877, Japan
1 shared publication
Latest Publications
- Open Access
- 16 December 2024
- Published by MDPI AG in Micromachines
- Vol. 15 (12) , 1501
An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms
This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN’s accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness.
- 16 December 2024
- Published by MDPI AG in Micromachines
- Vol. 15 (12) , 1501
Open AccessAn Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms
This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN’s accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness.