Application of linear and nonlinear pattern recognition techniques for discrimination of fluoroquinolones using modified AgNPs-based colorimetric sensor array

Asadzadeh, Zartosht; Bahram, Morteza; Moghtader, Mehdi

doi:10.1016/j.chemolab.2022.104747

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…A bubble plot for the selection of the best architecture for the XY‐fused model with a rectangular grid is shown in Figure 11A. The best architecture of 12 × 12 neurons with 350 training epochs is obtained from the bubble plot and is shown in red 50,51 . A very high relative frequency of 0.8 is observed for the best network.…”

Section: Resultsmentioning

confidence: 99%

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Decision support system based on a hybrid genetic algorithm–Kohonen map for combined mode conduction–radiation heat transfer in a porous medium: A comparative assessment of three variations of the Kohonen map

Ansari,

Mishra,

Sahu

et al. 2024

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A hybrid genetic algorithm (GA)–Kohonen map, with its three variants, is explored for the first time for the decision‐making system in a porous ceramic matrix (PCM)‐based burner through determination of the regime of operation. Four different attributes of PCMs such as convective coupling (P2), extinction coefficient (β), downstream porosity (ϕ2), and scattering albedo (ω) are selected for determining the regime of operation of a PCM‐based burner. Changes in any of these attributes of a PCM lead to significant changes in the temperature profiles of the gas and solid phases. Temperature profiles of the gas and solid phases are computed by developing a numerical model. Various samples corresponding to different regimes are generated and used in a hybrid GA–Kohonen map. The best architectural details such as the neuron number and training epochs are obtained from GA as output. The best Kohonen map is trained with the input data, and regimes of operation for new temperature profiles are predicted. A supervised Kohonen map is able to provide the highest average class prediction of more than 40%. All the variants are assessed under two different types of neuron grids: hexagonal and rectangular. Comparative assessments of the three different variants of Kohonen maps, in terms of CPU time and average class prediction, are carried out.

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Section: Resultsmentioning

confidence: 99%

“…The best architecture of 12 × 12 neurons with 350 training epochs is obtained from the bubble plot and is shown in red. 50,51 A very high relative Approximately 95% of the information is used in the score and loading plots of principal components for the fused X-Y network, and shown in Figure 11C.…”

Section: Rectangular Gridmentioning

confidence: 99%