ArticleSensors (Basel, Switzerland)2022
A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A Dual-Model Framework with Gramian Angular Field and Spatio-Temporal Attention for Rapid Gas Identification and Concentration Prediction.Sensors (Basel, Switzerland) · 2026Article
- Fast and robust mixed gas identification and recognition using tree-based machine learning and sensor array response.Scientific reports · 2025Article
- Gas concentration prediction based on SSA algorithm with CNN-BiLSTM-attention.Scientific reports · 2025Article
- Metal Oxide-Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms.ACS applied materials & interfaces · 2025Article
- Investigation and Modeling of the Behavior of Temperature Characteristics of 0.3-1.1 GHz Complementary Metal Oxide Semiconductor Class-A Broadband Power Amplifiers.Micromachines · 2024Article
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2 authors.
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Abstract
To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.
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