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Authors: S. N. Chaudhri and N. S. Rajput

Affiliation: Indian Institute of Technology (BHU), Varanasi-221005, UP, India

Keyword(s): Mirror Mosaicking, Gas Sensor Array, Electronic Nose, Gas Classification, Pattern Recognition, Convolutional Neural Network.

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, th e 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. (More)

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Paper citation in several formats:
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 - SENSORNETS; ISBN 978-989-758-489-3; ISSN 2184-4380, SciTePress, pages 86-91. DOI: 10.5220/0010251500860091

@conference{sensornets21,
author={S. N. Chaudhri. and N. S. 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 - SENSORNETS},
year={2021},
pages={86-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010251500860091},
isbn={978-989-758-489-3},
issn={2184-4380},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Sensor Networks - SENSORNETS
TI - Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network
SN - 978-989-758-489-3
IS - 2184-4380
AU - Chaudhri, S.
AU - Rajput, N.
PY - 2021
SP - 86
EP - 91
DO - 10.5220/0010251500860091
PB - SciTePress