Fast algorithm for feature extraction

Borowik, Grzegorz; Jankowski, J.; Kowalski, Karol J.

doi:10.1117/12.2205909

Cited by 5 publications

(1 citation statement)

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“…The features extracted are the reduct. For an information system = ( , , , ) , where is a finite nonempty set of objects and is a finite nonempty set of attributes, is a nonempty set of values, and is an information function that maps an object in to exactly one value in , the feature reduction steps is given as follows [17]:…”

Section: Feature Extraction Algorithmmentioning

confidence: 99%

Groundwater Assessment Using Feature Extraction Algorithm Combined with Complex Proportional Assessment Method and Standard Deviation

Shaaban¹

2021

IJIES

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Groundwater (GW) quality evaluation includes a variety of biological, chemical and physical parameters. The fundamental problem with water quality assessment is the difficulty with which a large number of parameters are evaluated. If all criteria have been used to evaluate the quality of GW, then computational difficulty will certainly increase. In this paper, a new hybrid three-stage assessment approach based on Feature Extraction Algorithm (FEA), standard deviation (SD) and Complex Proportional Assessment Method (COPRAS) was proposed. In the first stage the redundant criteria for GW quality assessment is removed using FEA. Secondly, the weights of the reduct parameters are evaluated based on SD. Finally, GW sites are ranked using (COPRAS). Sixteen GW samples were gathered from several GW wells. The collected samples were investigated for 12 various physicochemical water quality criteria to evaluate GW quality. The results reveal that sulfates (SO4), nitrate (NO3), Fluorides (F), sodium (Na), and Escherichia coli (E. coli) are the main parameters for GW quality assessment. Furthermore, the optimal concentrations of physicochemical parameters: (SO4), (NO3), (F), (Na), and (E. coli) are 18.9(mg/L), 8.18(mg/L), 0.222(mg/L), 21(mg/L), 1.9(MPN/100mL), respectively, with 40 WQI.The suggested approach is compared to three MCDM methods to validate the performance of the proposed methodology. The assessment results gained by the FEA combined with COPRAS and SD significantly minimize computational difficulty, reasonable and accurate. The approach presented in this study improves the system for evaluating GW quality.

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Section: Feature Extraction Algorithmmentioning

confidence: 99%