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This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor’s quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (≈6-fold accuracy enhancement).

[...]enhancement methods which reported above do increase over all structure size, principally in low frequencies operation. [...]it has been a growing regard for the use of metamaterial structures such as SRR or other structures [5], in the investment of compact microwave structures employing printed circuit bodies and MIMC techniques [6, 7]. Bandpass filter with external recently proposed band stops filter (founded using three SBFs open stub, spurline, and CSRR) were connected in cascade way to realize wide stopband about to 5 /0 with adequate selectivity is presented in [16]. [...]other filters are achieved by using of SRRs and CSRRs [17-19]. [...]to get zero transmission band, a two pairs of stepped impedance stubs loaded microstrip resonator (SISLM) are located in the center of parallel coupled microstrip line. [...]to complete the whole description of simulated results it should be noted that the one of the influential parameters for BPF is selectivity (£) (see (1)) [23, 24].