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Title | Optimization of machine learning algorithms for proteomic analysis using topsis |
Authors |
Javanbakht, T.
Chakravorty, S. |
ORCID | |
Keywords |
multi-criteria decision making TOPSIS prediction proteomic analysis |
Type | Article |
Date of Issue | 2022 |
URI | https://essuir.sumdu.edu.ua/handle/123456789/90094 |
Publisher | Sumy State University |
License | Creative Commons Attribution 4.0 International License |
Citation | Javanbakht T., Chakravorty S. (2022). Optimization of machine learning algorithms for proteomic analysis using topsis. Journal of Engineering Sciences, Vol. 9(2), pp. E7-E11, doi: 10.21272/jes.2022.9(2).e2 |
Abstract |
The present study focuses on a new application of the TOPSIS method for the optimization of machine
learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for
proteomic analysis. The main hypotheses are that the change in the weights of alternatives could affect the ranking of
algorithms. The obtained data confirmed this hypothesis for their ranking. Moreover, adding labor as a cost criterion
to the list of criteria did not affect this ranking. This was because candidate 3 had better fuzzy membership degrees
than the two other candidates concerning their criteria. This work showed the importance of the value of the fuzzy
membership degrees of the cost criterion of the algorithms in their ranks. The values of the fuzzy membership degrees of the algorithms used for proteomic analysis could determine their priority according to their score differences.
One of the advantages of this study was that the studied methods could be compared according to their characteristics. Another advantage was that the obtained results could be related to the new ones after improving these methods.
The results of this work could be applied in engineering, where the analysis of proteins would be performed with
these methods. |
Appears in Collections: |
Journal of Engineering Sciences / Журнал інженерних наук |
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Canada
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China
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India
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Iran
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Javanbakht_jes_2_2022.pdf | 243.87 kB | Adobe PDF | 199595503 |
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