A new extension of FDOSM based on Pythagorean fuzzy environment for evaluating and benchmarking sign language recognition systems

Al-Samarraay, Mohammed S.; Salih, Mahmood Maher; Ahmed, Mohamed A.; Zaidan, A. A.; Albahri, O. S.; Pamučar, Dragan; Alsattar, Hassan A.; Alamoodi, A. H.; Zaidan, B. B.; Dawood, Kareem Abbas; Albahri, A. S.

doi:10.1007/s00521-021-06683-3

Cited by 31 publications

(20 citation statements)

References 124 publications

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“…And in [38] the authors extended FDOSM into intuitionistic fuzzy. Another article [39] the authors extend FDOSM using Pythagorean fuzzy numbers. Finally, [40] extended FDOSM using interval-valued Pythagorean fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Salih

Al-qaysi

Shuwandy

et al. 2022

IFS

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To date, for the purpose of solving the complex problems in the area of expert system, Multi criteria decision making is the best technique to offer the suitable solution. In the academic literature, the MCDM methods suffered from many challenges. The most important challenges are uncertainty and vagueness. One of the latest MCDM method, called the fuzzy decision by opinion score method (FDOSM). However, there are still some vagueness issues around these methods (mention some of them). According to the advantage of the Fermatean fuzzy set in solving these issues, in this research extends FDOSM into Fermatean-FDOSM so as to effectively benchmark the real-life problem. In this study, we present our methodology in two phases. The first phase presents the mathematical model of Fermatean-FDOSM which is composed of three stages of FDOSM. The second phase applied the new extension to benchmark the COVID-19 machine learning methods. The finding of Fermatean-FDOSM after comparing the result with the basic FDSOM and TOPSIS, is more logical and undergoing a systematic ranking. In the validation process, objective validation is applied to validate the final result of Fermatean-FDOSM. The result of Fermatean-FDOSM is valid, and more logical and in line with decision makers’ opinions.

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Section: Introductionmentioning

confidence: 99%

“…In the literature, many researchers mention the benefit of the intuitionistic fuzzy and the extenuations of the intuitionistic (i.e. Pythagorean fuzzy and Fermatean fuzzy) [41,42]. From the above academic literature, there is no article present the extension of FDOSM using fermatean fuzzy.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Salih

Al-qaysi

Shuwandy

et al. 2022

IFS

Self Cite

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show abstract

“…Additionally, in [38], the authors extended the FDOSM into the intuitionistic fuzzy numbers. In another article [39], the authors extended the FDOSM using Pythagorean fuzzy numbers. The authors of [40] extended the FDOSM using interval-valued Pythagorean fuzzy numbers.…”

Section: Related Workmentioning

confidence: 99%

A New Development of FDOSM Based on a 2-Tuple Fuzzy Environment: Evaluation and Benchmark of Network Protocols as a Case Study

et al. 2022

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Multicriteria decision-making (MCDM) is one of the most common methods used to select the best alternative from a set of available alternatives. Many methods in MCDM are presented in the academic literature, with the latest being the Fuzzy Decision by Opinion Score Method (FDOSM). The FDOSM can solve many challenges that are present in other MCDM methods. However, several problems still exist in the FDOSM and its extensions, such as uncertainty. One of the most significant problems in the use of the FDOSM is the loss of information during the conversion of a decision matrix into an opinion decision matrix. In this paper, the authors expanded the FDOSM into the 2-tuple-FDOSM to solve this problem. The methodology behind the development of the 2-tuple-FDOSM was presented. Within the methodology, definitions of the 2-tuple linguistic fuzzy method, which was used to solve the loss-of-information problem that is present in the FDSOM method, are presented. A network case study was used in the application of the 2-tuple-FDOSM. The final results show that the 2-tuple-FDOSM can be used to address the problem of loss of information. Finally, a comparison between the basic FDOSM, TOPSIS, and 2-tuple-FDOSM was presented.

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“…Numerous subjective weighting methods have been proposed; however, when it comes to weighting criteria, the analytic hierarchy process (AHP) [40][41][42][43][44] and best-worst method (BWM) [45,46] methods have a high success rate. Nonetheless, the inconsistency issue of their weighing techniques has been addressed [17,37,[47][48][49][50][51]. Therefore, the fuzzy-weighted with zero-inconsistency (FWZIC) method has been introduced [52].…”

Section: Introductionmentioning

confidence: 99%

“…FDOSM attempts to deal with ambiguous and fuzzy data by employing triangular fuzzy numbers (TFNs). The FDOSM technique offered a mathematical model to address MCDM issues involving a single context of decision-making followed by a group context of decision-making and has been used in [ 49 , 54 , 57 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Alqaysi

Albahri

Hamid

2022

Computational and Mathematical Methods in Medicine

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Background and Contexts. Autism spectrum disorder (ASD) is difficult to diagnose, prompting researchers to increase their efforts to find the best diagnosis by introducing machine learning (ML). Recently, several available challenges and issues have been highlighted for the diagnosis of ASD. High consideration must be taken into the feature selection (FS) approaches and classification process simultaneously by using medical tests and sociodemographic characteristic features in autism diagnostic. The constructed ML models neglected the importance of medical tests and sociodemographic features in a training and evaluation dataset, especially since some features have different contributions to the processing data and possess more relevancies to the classification information than others. However, the role of the physician’s experience towards feature contributions remains limited. In addition, the presence of many evaluation criteria, criteria trade-offs, and criteria importance categorize the evaluation and benchmarking of diagnosis ML models concerning the intersection between FS approaches and ML classification methods given under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for benchmarking the best hybrid diagnosis models to classify autism patients’ emergency levels considering multicriteria evaluation solutions. Method. The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics. Results. Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were “ReF-decision tree,” “IG-decision tree,” and “Chi2-decision tree,” with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.

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A new extension of FDOSM based on Pythagorean fuzzy environment for evaluating and benchmarking sign language recognition systems

Cited by 31 publications

References 124 publications

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

A New Development of FDOSM Based on a 2-Tuple Fuzzy Environment: Evaluation and Benchmark of Network Protocols as a Case Study

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

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