Energy utilization area under Complex q-rung orthopair fuzzy soft information

Naeem Jan; Jeonghwan Gwak; Harish Garg; Younghoon Jeon; Hyoungku Kang; Naeem Jan; Jeonghwan Gwak; Harish Garg; Younghoon Jeon; Hyoungku Kang

doi:10.3934/math.2023583

AIMS Mathematics

2023, Volume 8, Issue 5: 11521-11545. doi: 10.3934/math.2023583

Previous Article Next Article

Research article Special Issues

Energy utilization area under Complex q-rung orthopair fuzzy soft information

1.
Department of Software, Korea National University of Transportation, Chungju 27469, Korea
2.
Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
3.
Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
4.
Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, Korea
5.
School of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala 147004, Punjab, India
6.
Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
7.
Department of Mathematics, Graphic Era, Deemed to be University, Dehradun, 248002, Uttarakhand, India
8.
College of Technical Engineering, The Islamic University, Najaf, Iraq
9.
Department of Electrical Engineering, Korea National University of Transportation, Chungju 27469, Korea

Received: 27 December 2022 Revised: 10 February 2023 Accepted: 20 February 2023 Published: 15 March 2023
MSC : 03E72, 68T35, 62A86, 90B50

The utilization of energy (EU) encompasses technologies aimed at discovering more effective methods for using electricity across various sectors, including residential, commercial, industrial, and transportation. Energy is an integral aspect of modern society and a driving force behind many processes in the universe. This paper aims to introduce a new concept, the Complex q-rung Orthopair Fuzzy Soft Relation (CqROFSRs), achieved through the Cartesian product of two Complex q-rung Orthopair Fuzzy Soft Sets (CqROFSSs). The proposed model has the capability to effectively capture and model graded imprecision and vagueness, which are commonly encountered in human interpretations. It provides a parameterized mathematical framework for ranking-based fuzzy modeling of two-dimensional paradoxical data. The theory integrates the CqROFS with the parametric structure of soft sets to achieve this purpose. Moreover, the utilization of complex numbers imbues these structures with the ability to effectively address phase-related and multidimensional challenges, thus conferring them with unparalleled power in managing ambiguity. Furthermore, we delved into various types of relationships, providing corresponding examples, which led to the establishment of accurate outcomes. The CqROFSRs framework is inclusive, encompassing both membership and non-membership degrees with regard to time duration. Additionally, the use of CqROFSRs techniques in selecting the optimal EU area for a daily living has been demonstrated, empowering individuals to make informed decisions and obtain verified results through the score function. To clarify the distinction, a comprehensive comparative analysis was conducted between the proposed concept and previous concepts.
- complex q-rung orthopair fuzzy set,
- complex q-rung orthopair fuzzy soft relations,
- energy utilization,
- decision making
Citation: Naeem Jan, Jeonghwan Gwak, Harish Garg, Younghoon Jeon, Hyoungku Kang. Energy utilization area under Complex q-rung orthopair fuzzy soft information[J]. AIMS Mathematics, 2023, 8(5): 11521-11545. doi: 10.3934/math.2023583

Related Papers:

Abstract

The utilization of energy (EU) encompasses technologies aimed at discovering more effective methods for using electricity across various sectors, including residential, commercial, industrial, and transportation. Energy is an integral aspect of modern society and a driving force behind many processes in the universe. This paper aims to introduce a new concept, the Complex q-rung Orthopair Fuzzy Soft Relation (CqROFSRs), achieved through the Cartesian product of two Complex q-rung Orthopair Fuzzy Soft Sets (CqROFSSs). The proposed model has the capability to effectively capture and model graded imprecision and vagueness, which are commonly encountered in human interpretations. It provides a parameterized mathematical framework for ranking-based fuzzy modeling of two-dimensional paradoxical data. The theory integrates the CqROFS with the parametric structure of soft sets to achieve this purpose. Moreover, the utilization of complex numbers imbues these structures with the ability to effectively address phase-related and multidimensional challenges, thus conferring them with unparalleled power in managing ambiguity. Furthermore, we delved into various types of relationships, providing corresponding examples, which led to the establishment of accurate outcomes. The CqROFSRs framework is inclusive, encompassing both membership and non-membership degrees with regard to time duration. Additionally, the use of CqROFSRs techniques in selecting the optimal EU area for a daily living has been demonstrated, empowering individuals to make informed decisions and obtain verified results through the score function. To clarify the distinction, a comprehensive comparative analysis was conducted between the proposed concept and previous concepts.

References

[1]	L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
[2]	V. F. Kravchenko, V. I. Ponomaryov, V. I. Pustovoit, Algorithms of three-dimensional filtration using the fuzzy-set theory for color image sequences degraded by noise, In: Doklady Physics, 2008,363–36, Maik Nauka-Interperiodica Publishing. https://doi.org/10.1134/S1028335808070070
[3]	V. Khatibi, G. A. Montazer, Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition, Artif. Intell. Med., 47 (2009), 43–52. https://doi.org/10.1016/j.artmed.2009.03.002 doi: 10.1016/j.artmed.2009.03.002
[4]	R. Krishnapuram, J. M. Keller, Fuzzy set theoretic approach to computer vision: An overview. In: IEEE International Conference on Fuzzy Systems, 1992,135–142.
[5]	J. M. Mendel, Fuzzy logic systems for engineering: A tutorial, P. IEEE, 83 (1995), 345–377. https://doi.org/10.1109/5.364485 doi: 10.1109/5.364485
[6]	K. Atanassov, Intuitionistic fuzzy sets, Int. J. Bio-Automation, 20 (2016), S1–S6. https://doi.org/10.1016/S0165-0114(86)80034-3 doi: 10.1016/S0165-0114(86)80034-3
[7]	P. Burillo, H. Bustince, Intuitionistic fuzzy relations (Part I), Mathware Soft Comput., 2 (1995), 5–38.
[8]	R. R. Yager, Properties and applications of Pythagorean fuzzy sets, In: Imprecision and Uncertainty in Information Representation and Processing, 2016,119–136. Springer, Cham. https://doi.org/10.1007/978-3-319-26302-1_9
[9]	U. Dinakaran, Analyzing online food delivery industries using Pythagorean fuzzy relation and composition, Int. J. Hosp. Tour. Syst., 14 (2021). 36.
[10]	R. R. Yager, Generalized orthopair fuzzy sets, IEEE T. Fuzzy Syst., 25 (2016), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005
[11]	H. Li, S. Yin, Y. Yang, Some preference relations based on q‐rung orthopair fuzzy sets, Int. J. Intell. Syst., 34 (2019), 2920–2936. https://doi.org/10.1002/int.22178 doi: 10.1002/int.22178
[12]	X. Peng, Z. Luo, A review of q-rung orthopair fuzzy information: Bibliometrics and future directions, Artif. Intell. Rev., 54 (2021), 3361–3430. https://doi.org/10.1007/s10462-020-09926-2 doi: 10.1007/s10462-020-09926-2
[13]	X. Peng, L. Liu, Information measures for q‐rung orthopair fuzzy sets, Int. J. Intell. Syst., 34 (2019), 1795–1834. https://doi.org/10.1002/int.22115 doi: 10.1002/int.22115
[14]	D. Ramot, R. Milo, M. Friedman, A. Kandel, Complex fuzzy sets, IEEE T. Fuzzy Syst., 10 (2002), 171–186. https://doi.org/10.1109/91.995119 doi: 10.1109/91.995119
[15]	C. Li, T. W. Chiang, Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets, IEEE T. Fuzzy Syst., 21 (2012), 567–584. https://doi.org/10.1109/TFUZZ.2012.2226890 doi: 10.1109/TFUZZ.2012.2226890
[16]	G. Zhang, T. S. Dillon, K. Y. Cai, J. Ma, J. Lu, Operation properties and δ-equalities of complex fuzzy sets, Int. J. Approx. Reason., 50 (2009), 1227–1249. https://doi.org/10.1016/j.ijar.2009.05.010 doi: 10.1016/j.ijar.2009.05.010
[17]	D. Rani, H. Garg, Distance measures between the complex intuitionistic fuzzy sets and their applications to the decision-making process, Int. J. Uncertain. Quan., 7 (2017), 423–439. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2017020356 doi: 10.1615/Int.J.UncertaintyQuantification.2017020356
[18]	N. Jan, A. Nasir, M. S. Alhilal, S. U. Khan, D. Pamucar, A. Alothaim, Investigation of Cyber-Security and Cyber-Crimes in oil and gas ssectors using the innovative structures of complex intuitionistic fuzzy relations, Entropy, 23 (2021), 1112. https://doi.org/10.3390/e23091112 doi: 10.3390/e23091112
[19]	R. T. Ngan, M. Ali, D. E.Tamir, N. D. Rishe, A. Kandel, Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making, Appl. Soft Comput., 87 (2020), 105961. https://doi.org/10.1016/j.asoc.2019.105961 doi: 10.1016/j.asoc.2019.105961
[20]	K. Ullah, T. Mahmood, Z. Ali, N. Jan, On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition, Complex Intell. Syst., 6 (2020), 15–27. https://doi.org/10.1007/s40747-019-0103-6 doi: 10.1007/s40747-019-0103-6
[21]	N. Jan, S. U. Rehman, A. Nasir, H. Aydi, S. U. Khan, Analysis of economic relationship using the concept of complex Pythagorean fuzzy information, Secur. Commun. Netw., 2021 (2021), Article ID 4513992. https://doi.org/10.1155/2021/4513992 doi: 10.1155/2021/4513992
[22]	H. Garg, J. Gwak, T. Mahmood, Z. Ali, Power aggregation operators and VIKOR methods for complex q-rung orthopair fuzzy sets and their applications, Mathematics, 8 (2020), 538. https://doi.org/10.3390/math8040538 doi: 10.3390/math8040538
[23]	A. Nasir, N. Jan, J. Gwak, S. U. Khan, Investigation of financial track records by uusing some novel concepts of ccomplex q-Rung orthopair fuzzy information, IEEE Access, 9 (2021), 152857–152877. https://doi.org/10.1109/ACCESS.2021.3125383 doi: 10.1109/ACCESS.2021.3125383
[24]	H. Garg, J. Gwak, T. Mahmood, Z. Ali, Power aggregation operators and VIKOR methods for complex q-rung orthopair fuzzy sets and their applications, Mathematics, 8 (22020), 538. https://doi.org/10.3390/math8040538 doi: 10.3390/math8040538
[25]	P. Liu, Z. Ali, T. Mahmood, N. Hassan, Group decision-making using complex q-rung orthopair fuzzy Bonferroni mean, Int. J. Comput. Intell. Syst., 13 (2020), 822. https://doi.org/10.2991/ijcis.d.200514.001 doi: 10.2991/ijcis.d.200514.001
[26]	P. K. Maji, R. K. Biswas, A. Roy, Fuzzy soft sets, J. Fuzzy Math., 9 (2001), 589–602.
[27]	M. I. Ali, A note on soft sets, rough soft sets and fuzzy soft sets, Appl. Soft Comput., 11 (2011), 3329–3332. https://doi.org/10.1016/j.asoc.2011.01.003 doi: 10.1016/j.asoc.2011.01.003
[28]	F. Feng, Y. B. Jun, X. Liu, L. Li, An adjustable approach to fuzzy soft set-based decision making, J. Comput. Appl. Math., 234 (2010), 10–20. https://doi.org/10.1016/j.cam.2009.11.055 doi: 10.1016/j.cam.2009.11.055
[29]	D. K. Sut, An application of fuzzy soft relation in decision making problems, Int. J. Math. Trends Technol., 3 (2012), 51–54.
[30]	Z. Haiyan, J. Jingjing, Fuzzy soft relation and its application in decision making. In: 2015 7th International Conference on Modelling, Identification and Control (ICMIC), 2015, 1–4, IEEE. https://doi.org/10.1109/ICMIC.2015.7409443
[31]	J. Močkoř, P. Hurtík, Approximations of fuzzy soft sets by fuzzy soft relations with image processing application, Soft Comput., 25 (2021), 6915–6925. https://doi.org/10.1007/s00500-021-05769-3 doi: 10.1007/s00500-021-05769-3
[32]	N. Çağman, S. Karataş, Intuitionistic fuzzy soft set theory and its decision making, J. Intell. Fuzzy Syst., 24 (2013), 829–836. https://doi.org/10.3233/IFS-2012-0601 doi: 10.3233/IFS-2012-0601
[33]	B. Dinda, T. K. Samanta, Relations on intuitionistic fuzzy soft sets, arXiv preprint arXiv: 1202.4649, 2012.
[34]	T. M. Athira, S. J. John, H. Garg, A novel entropy measure of Pythagorean fuzzy soft sets, AIMS Math., 5 (2020), 1050–1061. https://doi.org/10.3934/math.2020073 doi: 10.3934/math.2020073
[35]	A. Hussain, M. I. Ali, T. Mahmood, M. Munir, q‐Rung orthopair fuzzy soft average aggregation operators and their application in multicriteria decision‐making, Int. J. Intell. Syst., 35 (2020), 571–599. https://doi.org/10.1002/int.22217 doi: 10.1002/int.22217
[36]	P. Thirunavukarasu, R. Suresh, V. Ashokkumar, Theory of complex fuzzy soft set and its applications, Int. J. Innov. Res. Sci. Technol., 3 (2017), 13–18.
[37]	O. Yazdanbakhsh, S. Dick, A systematic review of complex fuzzy sets and logic, Fuzzy Set. Syst., 338 (2018), 1–22. https://doi.org/10.1016/j.fss.2017.01.010 doi: 10.1016/j.fss.2017.01.010
[38]	T. Kumar, R. K. Bajaj, On complex intuitionistic fuzzy soft sets with distance measures and entropies, J. Math., 2014. Article ID 972198. https://doi.org/10.1155/2014/972198 doi: 10.1155/2014/972198
[39]	J. An, A. Mikhaylov, H. Dinçer, S. Yüksel, Economic modelling of electricity generation: Long short-term memory and Q-rung orthopair fuzzy sets, Heliyon, 8 (2022), e12345. https://doi.org/10.1016/j.heliyon.2022.e12345 doi: 10.1016/j.heliyon.2022.e12345
[40]	A. Mikhaylov, I. M. Bhatti, H. Dinçer, S. Yüksel, Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spillovers, Comput. Econ., 2022, 1–34. https://doi.org/10.1007/s10614-022-10341-8
[41]	A. Mikhaylov, H. Dinçer, S. Yüksel, Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs, Financ. Innov., 9 (2023), 1–34. https://doi.org/10.1186/s40854-022-00399-6 doi: 10.1186/s40854-022-00399-6
[42]	V. Candila, D. Maximov, A. Mikhaylov, N. Moiseev, T. Senjyu, N. Tryndina, On the relationship between oil and exchange rates of oil-exporting and oil-importing countries: From the great recession period to the Covid-19 era, Energies, 14 (2021), 8046. https://doi.org/10.3390/en14238046 doi: 10.3390/en14238046
[43]	H. Dinçer, S. Yüksel, A. Mikhaylov, G. Pinter, Z. A. Shaikh, Analysis of renewable-friendly smart grid technologies for the distributed energy investment projects using a hybrid picture fuzzy rough decision-making approach, Energy Rep., 8 (2022), 11466–11477. https://doi.org/10.1016/j.egyr.2022.08.275 doi: 10.1016/j.egyr.2022.08.275
[44]	Z. Yang, Q. Li, Y. Yan, W. L. Shang, W. Ochieng, Examining influence factors of Chinese electric vehicle market demand based on online reviews under moderating effect of subsidy policy, Appl. Energy, 326 (2022), 120019. https://doi.org/10.1016/j.apenergy.2022.120019 doi: 10.1016/j.apenergy.2022.120019
[45]	Q. Liu, H. Li, W. L. Shang, K. Wang, Spatio-temporal distribution of Chinese cities' air quality and the impact of high-speed rail, Renew. Sust. Energy Rev., 170 (2022), 112970. https://doi.org/10.1016/j.rser.2022.112970 doi: 10.1016/j.rser.2022.112970
[46]	Z. Yang, S. Ahmad, A. Bernardi, W. L. Shang, J. Xuan, B. Xu, Evaluating alternative low carbon fuel technologies using a stakeholder participation-based q-rung orthopair linguistic multi-criteria framework, Appl. Energy, 332 (2023), 120492. https://doi.org/10.1016/j.apenergy.2022.120492 doi: 10.1016/j.apenergy.2022.120492
[47]	X. Yin, C. Ye, Y. Ding, Y. Song, Exploiting internet data centers as energy prosumers in integrated Electricity-Heat system, IEEE T. Smart Grid, 14 (2022), 167–182. https://doi.org/10.1109/TSG.2022.3197613 doi: 10.1109/TSG.2022.3197613
[48]	R. Ye, P. Liu, K. Shi, B. Yan, State damping control: A novel simple method of rotor UAV with high performance, IEEE Access, 8 (2020), 214346–214357. https://doi.org/10.1109/ACCESS.2020.3040779 doi: 10.1109/ACCESS.2020.3040779

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)