Combination of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods in Pythagorean <i>m</i>-Polar Fuzzy Environment: A Case Study of Sign Language Recognition Systems

Albahri, O. S.; Alsattar, Hassan A.; Garfan, Salem; Qahtan, Sarah; Zaidan, A. A.; Ahmaro, Ibraheem Y.Y.; Alamoodi, A. H.; Zaidan, B. B.; Albahri, A. S.; Al-Samarraay, Mohammed S.; Jasim, Ali Najm

doi:10.1142/s0219622022500183

Cited by 20 publications

(5 citation statements)

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“…Journal of Mathematics extension of these two approaches. Albahri et al [38] provide another extended two MCDM methods for a case study of sign language. The two methods, Pythagorean mm-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) and Pythagorean mm-polar fuzzy decision by opinion score (Pm-PFDOSM), are designed to weigh the assessment of sign language criteria and to determine alternate rankings in a progressive manner.…”

Section: Calculate the Magnitude Of Each Component Of The Alternative...mentioning

confidence: 99%

Two New Approaches (RAMS-RATMI) in Multi-Criteria Decision-Making Tactics

Abdulaal

Bafail

2022

Journal of Mathematics

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When a decision must be made, a tool called multi-criteria decision-making (MCDM) is used to assess and select alternatives among numerous criteria. For a wide variety of complex problems, MCDM methods have demonstrated usefulness in finding the optimal solutions. Despite the abundance of MCDM methods available today, there has been slow progress in developing new methodologies in MCDM in the past decade. In this context, this paper presents new MCDM tools which ranks alternatives based on median similarity (RAMS) between optimal alternatives and other alternatives. RAMS is an extension to the most recently developed technique that used perimeter similarity (RAPS). This paper also introduces a further tool that combines the RAMS method with the multiple criteria ranking by alternative trace (MCRAT) methodology using a majority index and the concept of the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. This tool is ranking the alternatives based on the trace to median index (RATMI). An illustration of the use of RAMS and RATMI is given through a case study of ranking different materials for the selection of break booster valve body in a vehicle. The validity of the new two techniques was tested against seven well-known MCDM techniques (ARAS, SAW, TOPSIS, COPRAS, VIKOR, WASPAS, and MOORA) using fifteen real problems data taken from the literature. The RATMI technique was more promising than RAPS and RAMS for 87% and 93% of the fifteen difficulties, respectively, according to the results of the correlation coefficient tests between the developed techniques and the selected seven techniques.

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Section: Calculate the Magnitude Of Each Component Of The Alternative...mentioning

confidence: 99%

Two New Approaches (RAMS-RATMI) in Multi-Criteria Decision-Making Tactics

Abdulaal

Bafail

2022

Journal of Mathematics

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“…Numerous subjective weighting methods have been proposed; however, when it comes to weighting criteria, the analytic hierarchy process (AHP) 54 and best‐worst method (BWM) 55 methods have a high success rate. Nonetheless, the inconsistency of their weighing techniques has not been addressed 56–62 . A fuzzy weighted with zero inconsistency (FWZIC) method has been introduced 27 .…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the inconsistency of their weighing techniques has not been addressed. [56][57][58][59][60][61][62] A fuzzy weighted with zero inconsistency (FWZIC) method has been introduced. 27 This method can calculate the weights of criteria with zero inconsistency.…”

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confidence: 99%

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Albahri

Zaidan

Alsattar³

et al. 2022

Computational Intelligence

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Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high‐risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi‐criteria decision‐making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre‐possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T‐spherical fuzzy‐weighted zero‐inconsistency (CT‐SFWZIC) method. CT‐SFWZIC was developed based on a new fuzzy set (i.e., complex T‐spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T‐spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10‐fold cross‐validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively.

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“…FDOSM weighs the criteria values of each application implicitly and is limited to weighting each criterion explicitly. To solve this issue, FDOSM was supported by fuzzy-weighted zeroinconsistency (FWZIC), which is a powerful weighting MCDM method for providing exact weights for criteria with zero inconstancies [58][59][60][61][62][63]. FWZIC and FDOSM were used together to address the uncertainty and vagueness issues by integrating many fuzzy environments in complex case studies like COVID-19 vaccine distribution using Pythagorean fuzzy set [64], T-spherical fuzzy sets [65] and Q-rung orthopair fuzzy sets (q-ROFS) [66].…”

Section: Introductionmentioning

confidence: 99%

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment

Alamoodi

Albahri

Zaidan

et al. 2022

Neural Comput & Applic

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This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.

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Combination of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods in Pythagorean m-Polar Fuzzy Environment: A Case Study of Sign Language Recognition Systems

Cited by 20 publications

References 97 publications

Two New Approaches (RAMS-RATMI) in Multi-Criteria Decision-Making Tactics

Two New Approaches (RAMS-RATMI) in Multi-Criteria Decision-Making Tactics

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment

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