Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network

S. Chaudhri, N. Rajput

Abstract

Limited dimensionality of the dataset obtained from an electronic nose (EN) is due to the number of elements in the sensor array used generally in the range of 4-8 elements only. Further, large number of sensor data can be generated by sampling the sensor responses both during the transient and steady states. The lower-dimensionality of sensor data prohibits the use of a convolutional neural network (CNN)-based pattern recognition techniques because the kernels of a CNN cannot be used on the obtained sample vectors to extract the features. In this paper, we have proposed a novel approach to enhance the data dimensionality keeping the sensor response characteristics absolutely unaltered. By leveraging the concept of mirror mosaicking technique, we have upscaled the input sample vectors into a 6×6 2-D input arrays to train the shallow CNN. Using the proposed approach, all the 16-unknown steady-state test samples classified accurately which are not used during the training. Moreover, the parameters of the classification report viz., Precision, Recall, and F1 score also obtained with a fraction value of 1.00. The proposed technique is a generic approach that can be used to classify various low-dimensional datasets obtained from various sensor arrays in various fields.

Download


Paper Citation


in Harvard Style

Chaudhri S. and Rajput N. (2021). Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network.In Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-489-3, pages 86-91. DOI: 10.5220/0010251500860091


in Bibtex Style

@conference{sensornets21,
author={S. Chaudhri and N. Rajput},
title={Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network},
booktitle={Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2021},
pages={86-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010251500860091},
isbn={978-989-758-489-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network
SN - 978-989-758-489-3
AU - Chaudhri S.
AU - Rajput N.
PY - 2021
SP - 86
EP - 91
DO - 10.5220/0010251500860091