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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026
Construction of Q- Rung Orthopair (M, N) Uncertainty Level
Subgroup Structures
Rathinam Nagarajan
Department of Mathematics, J.J College of Engineering and Technology, Sowdambikka group of
institutions, Tiruchirappalli- 620009, Tamilnadu, India
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150600070
Received: 18 June 2026; Accepted: 23 June 2026; Published: 06 July 2026
ABSTRACT
The main objective of this article is to introduce the idea of q-rung orthopair (m, n)-fuzzy subgroups of a
finite group. We discuss the concept of q-rung orthopair (m, n) – fuzzy subgroups as a combination of
intuitionist fuzzy subgroup and Pythagorean fuzzy subgroup. We provide the definition of q-rung orthopair
(m, n) – fuzzy subgroup and examine various properties associated with it. Finally we analyze q-rung
orthopair (m, n)- fuzzy cosets, (m, n) – fuzzy normal subgroups and (m, n)- fuzzy level subgroups.
Keywords: intuitionist fuzzy set, Pythagorean fuzzy set, q-rung orthopair (m, n)- fuzzy subgroup, q-rung
orthopair (m,n)- fuzzy Coset, normal subgroup, homomorphism.
AMS Subject classification: 20N25, 03E72, 94605.
INTRODUCTION
To handle uncertainty in real-life problems Zadeh LA [1] proposed the concept of Fuzzy Sets (FSs). In 1971
expanding the notion of FSs, Rosenfeld A [2] defined fuzzy subgroup. In recent years many researchers
studied various properties of fuzzy subgroups, t-norm fuzzy subgroups, fuzzy level subgroup, fuzzy
subgroups and fuzzy homomorphism, anti-fuzzy subgroups, fuzzy normal subgroup, fuzzy coset and fuzzy
quotient subgroup etc [3-14]. In 1986, Atanassov KT [15] introduced Intuitionistic Fuzzy Set (IFS). In
1996, Intuitionistic fuzzy subgroup was first studied by Biswas R [16]. Zhan J et al. [17] introduced
intuitionistic fuzzy M-group. Furthermore, researchers developed intuitionistic fuzzy subgroup in many
ways [18-20]. In 2013, Yager RR [21] invented Pythagorean Fuzzy Sets (PFSs). In 2018, Naz .S et al. [22]
proposed a novel approach to decision-making problem using Pythagorean fuzzy set. In 2019, Akram M et
al. [23] applied
complex Pythagorean fuzzy set in decision-making problems. Ejegwa PA [24] gave an application of
Pythagorean fuzzy set-in career placements based on academic performance using max-min-max
composition. Some results related to it were given by Peng X [25] and Yang Y [26]. First time, Bhunia S et
al. [27] proposed “On the characterization of Pythagorean fuzzy subgroups”. In certain situations, the PFSs
unable to handle the problems like if membership degree η = 0.8 and nonmembership degree θ = 0.75 then 2
2 η θ+= ≥ 1.2025 1 it is clear that PFSs fail to define this type of sets but, 1 q q µ ν+ ≤ where q > 2 . Most
recently another amazing generalization of FSs is proposed by Yager RR [28], q-Rung Orthopair Fuzzy Sets
(q-ROFSs) which models uncertain and incomplete information better than both IFSs and PFSs with high
accuracy. q-ROFSs is more fruitful in many decision-making problems. This concept is perfectly designed
to represent imprecision and ambiguity in mathematical way and to produce a formalized tool to handle
fuzziness to real problems. Pinar A et al. [29] suggested a q-rung orthopair fuzzy multi-criteria group
decision making method for supplier selection based on a novel distance. Peng X et al. [30] published
Information measures for q-rung orthopair fuzzy sets. The main objective of this article is to introduce the