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. Author manuscript; available in PMC: 2023 Dec 1.
Published in final edited form as: J Signal Process Syst. 2022 Apr 12;94(12):1515–1529. doi: 10.1007/s11265-022-01758-3

Fig. 6.

Fig. 6.

Efficacy of deep convolutional neural networks in classification of AAVs from nanopore experimental data obtained at different applied potential difference: (a) −75 mV, (b) −100 mV, (c) −125 mV, (d) −150 mV and (e) −175 mV for 1 sec time frame images. For a particular class, 70% of the data were randomly selected from all images of that class for training the network, while the rest of the images of that class were used for validation.