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Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

1
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
2
Symbiosis Centre of Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India
3
College of Engineering, Design and Physical Sciences, Brunel University, London UB8 3PH, UK
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Syst. Innov. 2021, 4(1), 3; https://doi.org/10.3390/asi4010003
Received: 30 November 2020 / Revised: 31 December 2020 / Accepted: 6 January 2021 / Published: 9 January 2021
With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor. View Full-Text
Keywords: convolutional neural network; early fusion; gas detection; long-short term memory; multimodal data convolutional neural network; early fusion; gas detection; long-short term memory; multimodal data
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MDPI and ACS Style

Narkhede, P.; Walambe, R.; Mandaokar, S.; Chandel, P.; Kotecha, K.; Ghinea, G. Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion. Appl. Syst. Innov. 2021, 4, 3. https://doi.org/10.3390/asi4010003

AMA Style

Narkhede P, Walambe R, Mandaokar S, Chandel P, Kotecha K, Ghinea G. Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion. Applied System Innovation. 2021; 4(1):3. https://doi.org/10.3390/asi4010003

Chicago/Turabian Style

Narkhede, Parag, Rahee Walambe, Shruti Mandaokar, Pulkit Chandel, Ketan Kotecha, and George Ghinea. 2021. "Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion" Applied System Innovation 4, no. 1: 3. https://doi.org/10.3390/asi4010003

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