Novel neutrosophic fuzzy max-min-based similarities and their application in clustering for educational decision-making support

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260601" target="_blank" >RIV/61989100:27240/25:10260601 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s12190-025-02520-1" target="_blank" >https://link.springer.com/article/10.1007/s12190-025-02520-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12190-025-02520-1" target="_blank" >10.1007/s12190-025-02520-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Novel neutrosophic fuzzy max-min-based similarities and their application in clustering for educational decision-making support

Popis výsledku v původním jazyce

Neutrosophic fuzzy sets merge fuzzy logic with neutrosophic theory to better handle uncertain information by employing memberships of truth, indeterminacy, and falsity to evaluate fuzzy membership. This paper proposes new measures for similarity and distance, forming the basis for a unique clustering algorithm designed for neutrosophic fuzzy environments. Applied to educational data, this algorithm helps categorize students by their grade point averages and teacher evaluations, positively influencing high school graduation exam scores. Our method, validated through the DBI and SC, proves effective in identifying relevant subgroups for practical applications. This research not only demonstrates the algorithm&apos;s ability to improve student performance but also offers insights for educational decision-making in student training.

Název v anglickém jazyce

Novel neutrosophic fuzzy max-min-based similarities and their application in clustering for educational decision-making support

Popis výsledku anglicky

Neutrosophic fuzzy sets merge fuzzy logic with neutrosophic theory to better handle uncertain information by employing memberships of truth, indeterminacy, and falsity to evaluate fuzzy membership. This paper proposes new measures for similarity and distance, forming the basis for a unique clustering algorithm designed for neutrosophic fuzzy environments. Applied to educational data, this algorithm helps categorize students by their grade point averages and teacher evaluations, positively influencing high school graduation exam scores. Our method, validated through the DBI and SC, proves effective in identifying relevant subgroups for practical applications. This research not only demonstrates the algorithm&apos;s ability to improve student performance but also offers insights for educational decision-making in student training.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Applied Mathematics and Computing

ISSN

1598-5865

e-ISSN

1865-2085

Svazek periodika

71

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

36

Strana od-do

nestránkováno

Kód UT WoS článku

001512255500001

EID výsledku v databázi Scopus

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