Dr. Paul Augustine Ejegwa
@uam.edu.ng
Lecturer/Researcher; Department of Mathematics
Federal University of Agriculture Makurdi, Nigeria
EDUCATION
Ph.D Mathematics
M.Sc. Mathematics
PGD Education
B.Sc. (Hons) Mathematics
RESEARCH INTERESTS
Generalised fuzzy sets with applications to decision-making problems, Fuzzy algebra, Multigroup theory, Soft computing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Paul Augustine Ejegwa, Tidoo Daniel Wanzenke, Innocent Otache Ogwuche, Manasseh Terna Anum, and Kenneth Ifeanyi Isife
Springer Science and Business Media LLC
Rongfeng Li, Paul Augustine Ejegwa, Kun Li, Iorshase Agaji, Yuming Feng, and Idoko Charles Onyeke
American Institute of Mathematical Sciences (AIMS)
<abstract><p>The idea of Pythagorean fuzzy sets (PFSs) has been extensively applied in various decision-making scenarios. Many of the applications of PFSs were carried out based on similarity functions. Some methods of similarity functions for PFSs (SFPFSs) cannot be trusted for a reliable interpretations in practical cases due to some of their setbacks. In this work, a new method of SFPFSs is developed with the capacity to outsmart the efficiency of the extant SFPFSs in terms of precise results and appropriately satisfying the rules of SFs. The new method is described with some results to validate the properties of SFs. In terms of practical application, we use the newly developed method of SFPFSs to discuss the relationship between the players of the Liverpool Football Club (FC) in the 2022/2023 English Premier League (EPL) season to assess their performances in their resurgent moments within the season. Using data from BBC Sport analysis (BBCSA) on the players' rating per match in a Pythagorean fuzzy setting, we establish the players' interactions, communications, passing, contributions, and performances to ascertain the high ranking players based on performances. Similarly, a comparative analyses are presented in tables to undoubtedly express the superiority of the newly developed method of SFPFSs. Due to the flexibility of the newly developed method of SFPFSs, it can be used for clustering analysis. In addition, the new method of SFPFSs can be extended to other uncertain environments other than PFSs.</p></abstract>
Yi Zhou, Paul Augustine Ejegwa, and Samuel Ebimobowei Johnny
Springer Science and Business Media LLC
AbstractMany complex real-world problems have been resolved based on similarity operators under intuitionistic fuzzy sets (IFSs). Numerous authors have developed intuitionistic fuzzy similarity operators (IFSOs) but with some setbacks, which include imprecise results, omission of hesitation information, misleading interpretations, and outright violations of metric axioms of similarity operator. To this end, this article presents a newly developed similarity operator under IFSs to ameliorate the itemized setbacks noticed with the hitherto similarity operators. To buttress the validity of the new similarity operator, we discuss its properties in alliance with the truisms of similarity. In addition, we discuss some complex decision-making situations involving car purchase selection process, pattern recognition, and emergency management using the new similarity operator based on multiple criteria decision making (MCDM) technique and recognition principle, respectively. Finally, comparative studies are presented to argue the justification of the new similarity operator. In short, the novelty of this work includes the evaluation of the existing IFSOs to isolate their fault lines, development of a new IFSO technique with the capacity to resolve the fault lines in the existing techniques, elaboration of some properties of the newly developed IFSO, and its applications in the solution of disaster control, pattern recognition, and the process of car selection for purchasing purpose based on the recognition principle and MCDM.
Paul Augustine Ejegwa and Arun Sarkar
Springer Science and Business Media LLC
Paul Augustine Ejegwa, Yuming Feng, Shuyu Tang, Johnson Mobolaji Agbetayo, and Xiangguang Dai
Springer Science and Business Media LLC
Paul Augustine Ejegwa and Sesugh Ahemen
Springer Science and Business Media LLC
Paul Augustine Ejegwa, , Johnson Mobolaji Agbetayo, and
BON VIEW PUBLISHING PTE
The idea of intuitionistic fuzzy sets (IFSs) is a reasonable soft computing construct for resolving ambiguity and vagueness encountered in decision-making situations. Cases such as pattern recognition, diagnostic analysis, etc., have been explored based on intuitionistic fuzzy pairs via similarity-distance measures. Many similarity and distance techniques have been proposed and used to solve decision-making situations. Though the existing similarity measures and their distance counterparts are somewhat significant, they possess some weakness in terms of accuracy and their alignments with the concept of IFSs, which needed to be strengthened to enhance reliable outputs. As a consequent, this paper introduces a novel similarity-distance technique with better performance rating. A comparative analysis is presented to showcase the advantages of the novel similarity-distance over similar existing approaches. Some attributes of the similarity-distance technique are presented. Furthermore, the applications of the novel similarity-distance technique in sundry decision-making situations are explored.
Paul Augustine Ejegwa, Yanxiong Zhang, Haiqing Li, and Yuming Feng
IEEE
Distance and similarity measures are significant tools for information measure applicable in real-world under Pythagorean fuzzy domain. Some techniques for the computation of distance and similarity have been investigated between Pythagorean fuzzy sets (PFSs), notwithstanding with low performance indexes. To resolve these setbacks, this paper introduces some new measuring techniques with reliable performance in comparison to similar measures. The new techniques are confirmed with a number of theoretic results to illustrate their aptness as unfailing information measuring methods. Certain numerical experiments are carried out to ascertain the advantages of the proposed measures over the existing measures. We demonstrate the applications of the novel techniques in real-life decision-making situations pertaining to pattern recognition and disease diagnostic process where patterns and diseases are encapsulated in Pythagorean fuzzy pairs.
Paul Augustine Ejegwa and Arun Sarkar
Elsevier
Paul Augustine Ejegwa
Springer Science and Business Media LLC
Paul Augustine Ejegwa, Idoko Charles Onyeke, Nasreen Kausar, and Parameshwari Kattel
Hindawi Limited
Computation of correlation coefficient among attributes of ordinary database is important especially in the classification and analysis of data. Due to the hesitations in the process of data classification, the idea of intuitionistic fuzzy data (IFD) is appropriate for a reliable classification. To achieve a dependable correlation, the construct of partial correlation coefficient based on IFD has been considered. The construct of partial correlation coefficient of intuitionistic fuzzy sets (PCCIFSs) is reasonable since correlation coefficients of intuitionistic fuzzy sets (CCIFSs) are limited in the sense that it only expressed linear association and direction of such relation between IFD without minding the effect of other IFD. On the contrary, partial correlation coefficient finds the exact association between any two IFD by muting the effect of other IFD which could sway the result of the correlation coefficient. In previous works, the idea of PCCIFSs was introduced based on the multivariate correlation model using empirical logit transform. Besides the fact that the outputs of multivariate correlation model are not always easy to interpret, the approach also never considered the three parameters of IFSs and does not use the values of CCIFSs for the computational process. With these setbacks, we are motivated to propose a novel approach of finding PCCIFSs by incorporating the three parameters of IFD based on a modified CCIFSs approach. A comparative analysis of the robust PCCIFSs approach and the existing approach is considered to justify the novel approach. An application of the new approach of PCCIFSs is considered in the case of pattern recognition where the patterns are represented as intuitionistic fuzzy data.
Dongfang Yan, Keke Wu, Paul Augustine Ejegwa, Xianyang Xie, and Yuming Feng
MDPI AG
The process of computing correlation among attributes of an ordinary database is significant in the analysis and classification of a data set. Due to the uncertainties embedded in data classification, encapsulating correlation techniques using Pythagorean fuzzy information is appropriate to curb the uncertainties. Although correlation coefficient between Pythagorean fuzzy data (PFD) is an applicable information measure, its output is not reliable because of the intrinsic effect of other interfering PFD. Due to the fact that the correlation coefficients in a Pythagorean fuzzy environment could not remove the intrinsic effect of the interfering PFD, the notion of Pythagorean fuzzy partial correlation measure (PFPCM) is necessary to enhance the measure of precise correlation between PFD. Because of the flexibility of Pythagorean fuzzy sets (PFSs), we are motivated to initiate the study on Pythagorean fuzzy partial correlation coefficient (PFPCC) based on a modified Pythagorean fuzzy correlation measure (PFCM). Examples are given to authenticate the choice of the modified PFCM in the computational process of PFPCC. For application, we discuss a case of pattern recognition and classification using the proposed PFPCC after computing the simple correlation coefficient between the patterns based on the modified correlation technique. To be precise, the contributions of the work include the enhancement of an existing PFCC approach, development of PFPCC using the enhanced PFCC, and the application of the developed PFPCC in pattern recognition and classifications.
Idoko Charles Onyeke and Paul Augustine Ejegwa
Springer Nature Singapore
Paul Augustine Ejegwa, Shiping Wen, Yuming Feng, Wei Zhang, and Jinkui Liu
Springer Science and Business Media LLC
Keke Wu, Paul Augustine Ejegwa, Yuming Feng, Idoko Charles Onyeke, Samuel Ebimobowei Johnny, and Sesugh Ahemen
MDPI AG
The construct of Pythagorean fuzzy distance measure (PFDM) is a competent measuring tool to curb incomplete information often encountered in decision making. PFDM possesses a wider scope of applications than distance measure under intuitionistic fuzzy information. Some Pythagorean fuzzy distance measure approaches (PFDMAs) have been developed and applied in decision making, albeit with some setbacks in terms of accuracy and precision. In this paper, some novel PFDMAs are developed with better accuracy and reliability rates compared to the already developed PFDMAs. In an effort to validate the novel PFDMAs, some of their properties are discussed in terms of theorems with proofs. In addition, some applications of the novel PFDMAs in problems of disease diagnosis and pattern recognition are discussed. Furthermore, we present comparative studies of the novel PFDMAs in conjunction to the existing PFDMAs to buttress the merit of the novel approaches in terms of consistency and precision. To end with, some new Pythagorean fuzzy similarity measuring approaches (PFDSAs) based on the novel PFDMAs are presented and applied to solve the problems of disease diagnosis and pattern recognition as well.
Paul Augustine Ejegwa and Bijan Davvaz
Springer Science and Business Media LLC
Paul Ejegwa, Shiping Wen, Yuming Feng, Wei Zhang, and Ning Tang
Institute of Electrical and Electronics Engineers (IEEE)
P. A. Ejegwa, I. C. Onyeke, B. T. Terhemen, M. P. Onoja, A. Ogiji, and C. U. Opeh
Nigerian Society of Physical Sciences
Intuitionistic fuzzy models are significant in resolving decision-making. Distance measures under intuitionistic fuzzy environment are reliable techniques deployed to express the application of IFSs. Some approaches of estimating distances between IFSs have been explored by Szmidt and Kacprzyk, where the complete parameters of IFSs are considered. Albeit, the distance operators lack reliability because of certain setbacks. In this paper, we modified Szmidt and Kacprzyk's distance operators between IFSs to enhance reliability in terms of applications. Some theorems are given to substantiate the validity of the modified intuitionistic fuzzy distance operators. Futhermore, decision-making cases of pattern recognition and disease identification are discussed using the Szmidt and Kacprzyk's distances and their improved versions where information are represented in intuitionistic fuzzy pairs. From the study, it is observed that the modified Szmidt and Kacprzyk's distance operators between IFSs yield better results compare to the Szmidt and Kacprzyk's distance operators between IFSs.
Paul Augustine Ejegwa, Victoria Adah, and Idoko Charles Onyeke
Springer Science and Business Media LLC
Paul Augustine Ejegwa
Springer Nature Singapore
Paul Augustine Ejegwa and Idoko Charles Onyeke
IGI Global
Many computing methods have been studied in intuitionistic fuzzy environment to enhance the resourcefulness of intuitionistic fuzzy sets in modelling real-life problems, among which, correlation coefficient is prominent. This paper proposes a new intuitionistic fuzzy correlation algorithm via intuitionistic fuzzy deviation, variance and covariance by taking into account the complete parameters of intuitionistic fuzzy sets. This new computing technique does not only evaluates the strength of relationship between the intuitionistic fuzzy sets but also indicates whether the intuitionistic fuzzy sets have either positive or negative linear relationship. The proposed technique is substantiated with some theoretical results, and numerically validated to be superior in terms of performance index in contrast to some hitherto methods. Multi-criteria decision-making processes involving pattern recognition and students’ admission process are determined with the aid of the proposed intuitionistic fuzzy correlation algorithm coded with JAVA programming language.
P. A. Ejegwa, G. Muhiuddin, E. A. Algehyne, J. M. Agbetayo, and D. Al-Kadi
Hindawi Limited
The idea of composition relations on Fermatean fuzzy sets based on the maximum-extreme values approach has been investigated and applied in decision making problems. However, from the perspective of the measure of central tendency, this approach is not reliable because of the information loss occasioned by the use of extreme values. Based on this limitation, we introduce an enhanced Fermatean fuzzy composition relation with a better performance rating based on the maximum-average approach. An easy-to-follow algorithm based on this approach is presented with numerical computations. An application of Fermatean fuzzy composition relations is discussed in diagnostic analysis where diseases and patients are mirrored as Fermatean fuzzy pairs characterized with some related symptoms. To ascertain the veracity of the novel Fermatean fuzzy composition relation, a comparative analysis is presented to showcase the edge of this novel Fermatean fuzzy composition relation over the existing Fermatean fuzzy composition relation.