ArticleMicromachines2024
An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms.
Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Review of Hydrogen Sensors in Aerobic and Anaerobic Environments Coupled with Artificial Intelligence Tools.Sensors (Basel, Switzerland) · 2025Review
- AI‑driven photonic noses: from conventional sensors to cloud‑to-edge intelligent microsystems.Microsystems & nanoengineering · 2025Review
- [Application of Explainable Deep Learning in Differentiating Benign from Malignant Pulmonary Space-occupying Lesions and Classifying Pathological Subtypes of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
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.
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Registered trials
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