Classification of Gas Sensor Data Using Multiclass SVM

Gases may be detected and identified with the aid of electronic nose (EN) that employ machine learning (ML) in conjunction with sensor arrays. In the field of smart gas sensing, sensor arrays are very important because the signals they send are used by ML algorithms to learn about the volatile organic compounds (VOCs) that have been found. EN is a powerful device which have the sense ability like human to detect the different smells. Due to its detection power, a research work is done based on EN collected data, where the system is equipped with seven Figaro Taguchi Series (TGS) sensors for detecting six different gases with their concentration levels. The built EN system for the collection of datasets, allows us to make customisable mixes of these gases and gather data automatically with our sensing equipment. The obtained dataset is used to enhance the accuracy and classification performance of the overall system by using the ML models. Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (k-NN), Decision Tree (DT) and Gradient Boosting Tree (GBT) machine learning algorithms are used in this work to boost the model performance. After a deep work on designed models, SVM performs better than the others due to its multiclass classification ability and helps to measures a 99.73% of classification accuracy while the others have less performance like RF (99.70%), k-NN (92.93%), DT (98.10%) and GBT (98.90%