2022
DOI: 10.1140/epjd/s10053-022-00429-z
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Quantum classifier for recognition and identification of leaf profile features

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Cited by 7 publications
(6 citation statements)
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“…In our work, we show the theory and applications of the QQFT. In the works of the last period of 2021 to 2022 [3][4][5], the authors used just QCFT. With these comparisons shown in Table 1, we conclude that in general, the results of the QCFT are shown in all cases to be similar to those of the FFT.…”
Section: Comparison Of Cft Qft and Qqftmentioning
confidence: 99%
See 3 more Smart Citations
“…In our work, we show the theory and applications of the QQFT. In the works of the last period of 2021 to 2022 [3][4][5], the authors used just QCFT. With these comparisons shown in Table 1, we conclude that in general, the results of the QCFT are shown in all cases to be similar to those of the FFT.…”
Section: Comparison Of Cft Qft and Qqftmentioning
confidence: 99%
“…The authors used the QCFTand the quantum complex wavelet transform and proposed a quantum image scaling scheme based on the extension of the bilinear interpolation method. Kumar et al [3] apply a quantum classifier to a computer vision system for leaf recognition which can be applied to a quantum computer. Images from 10 species of leaves, which are categorized into two groups, namely, simple and palmately, are recognized using a quantum classifier.…”
Section: Introductionmentioning
confidence: 99%
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“…By adding acids like HCl or H 2 SO 4 , the POT can be easily modified and made more effective in attracting dye molecules and nanoparticles. The process of adding these acids, known as doping, can be controlled by adding reversible acids or bases to enhance the adsorption properties of POT 31–34 . However, the long chains have a tendency to aggregate, which decreases the amount of surface area and active sites that are available for adsorption and is a disadvantage of using POT 35,36 .…”
Section: Introductionmentioning
confidence: 99%