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This study relates to an Optimized Convolutional Neural Network-based Multi-gas Optical Sensor (OCNN-MOS) developed using photonic crystals. A square-shaped dielectric rod with cubic lattice index was used for the detection of different gases, namely, N 2 , CH 4  and CO. Three nanocavities were present in the proposed OCNN-MOS, with one nanocavity located at the centre (refractive index 2.6) and the other two were the sub-nanocavities located along the path of the output and input respectively. A grey wolf optimization technique was used for optimizing its hyperparameters and for the improvement of its performance. The proposed OCNN-MOS was connected with a clean air generator for diluting the flow of hydrocarbon, a flow controller, and three standard sample gas cylinders for the conduction of the experiment. A concentrated gas mixture of 60–200 ppm was used in the experiment. The proposed optimization achieved an improvement of 7.06% in the performance of CNN.

Fibre optic sensor based multi-gas detection using optimized convolutional neural network