Granulation of ecological networks under fuzzy soft environment

Akram, Muhammad; Luqman, Anam

doi:10.1007/s00500-020-05083-4

Cited by 39 publications

(14 citation statements)

References 45 publications

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“…e idea of Fermatean fuzzy (FF) weighted averaging/ geometric operators was also given by Senapati and Yager [31]. For other terminologies not discussed in the paper, the readers are referred to [32][33][34][35][36][37][38][39]. ere are some incentives of this article:…”

Section: Introductionmentioning

confidence: 99%

Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Garg

Shahzadi

Akram

2020

Mathematical Problems in Engineering

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132

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This research article is devoted to establish some general aggregation operators, based on Yager’s t-norm and t-conorm, to cumulate the Fermatean fuzzy data in decision-making environments. The Fermatean fuzzy sets (FFSs), an extension of the orthopair fuzzy sets, are characterized by both membership degree (MD) and nonmembership degree (NMD) that enable them to serve as an excellent tool to represent inexact human opinions in the decision-making process. In this article, the valuable properties of the FFS are merged with the Yager operator to propose six new operators, namely, Fermatean fuzzy Yager weighted average (FFYWA), Fermatean fuzzy Yager ordered weighted average (FFYOWA), Fermatean fuzzy Yager hybrid weighted average (FFYHWA), Fermatean fuzzy Yager weighted geometric (FFYWG), Fermatean fuzzy Yager ordered weighted geometric (FFYOWG), and Fermatean fuzzy Yager hybrid weighted geometric (FFYHWG) operators. A comprehensive discussion is made to elaborate the dominant properties of the proposed operators. To verify the importance of the proposed operators, an MADM strategy is presented along with an application for selecting an authentic lab for the COVID-19 test. The superiorities of the proposed operators and limitations of the existing operators are discussed with the help of a comparative study. Moreover, we have explained comparison between the proposed theory and the Fermatean fuzzy TOPSIS method to check the accuracy and validity of the proposed operators. The influence of various values of the parameter in the Yager operator on decision-making results is also examined.

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Section: Introductionmentioning

confidence: 99%

Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Garg

Shahzadi

Akram

2020

Mathematical Problems in Engineering

Self Cite

132

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“…Then fuzzy granulation is performed on each window to obtain three time series after windowing. The membership function of fuzzy particles after granulation is (Akram et al, 2020):…”

Section: Establishment Of the Model 1fuzzy Information Granulation Of Time Seriesmentioning

confidence: 99%

Uncertainty Prediction of Mining Safety Production Situation

et al. 2021

Preprint

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In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R and Up three granular parameter sequences, according to the triangular fuzzy number; Then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; Finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of Low, R and Up sequences were 9.472 %, 16.671 % and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.

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“…These sets are still used in many studies aiming to eliminate the uncertainty (Dalkılıç 2021b , c ; Demirtaş and Dalkılıç 2020 ; Smarandache 2018 ; Ahmad et al. 2020 ; Dalkılıç and Demirtaş 2021 ; Dalkılıç 2021a ; Akram and Luqman 2020 ; Nawaz and Akram 2021 ; Siddique et al. 2021 ; Zulqarnain et al.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Covid-19 outbreak using soft set theory: estimation and application

Dalkılıç

Demirtaş

2022

Soft Comput

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Coronavirus disease (Covid-19) is a novel pandemic disease. Covid-19 originates from SARS-COV2 and represents the cause of a potentially fatal disease as a global public health problem. However, we have to renew our knowledge about the symptoms of this disease day by day. If we look generally, although the main symptoms seen in this epidemic are fever, cough and shortness of breath, cases without symptoms are also reported. Moreover, in severe cases, pneumonia, severe respiratory failure, kidney failure and death may develop. In this paper, it is suggested that all the different symptoms that may occur in various regions of the world should be taken into consideration and each region should be evaluated within itself. Moreover, in order to have an idea of the general situation, it was taken into account in the average case. For this, two algorithms were built by using soft set theory. The first of the algorithms focuses on the analysis of the relationships between the symptoms and aims to measure a possible effect of the symptoms on each other. The second one aims to identify the most dominant symptom. The results obtained by utilizing both algorithms argue that it is more useful to examine different regions in order to better manage the epidemic. Moreover, some consistent results have been obtained as to which parameters a person should show first in order to Covid test.

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Granulation of ecological networks under fuzzy soft environment

Cited by 39 publications

References 45 publications

Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Uncertainty Prediction of Mining Safety Production Situation

Algorithms for Covid-19 outbreak using soft set theory: estimation and application

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