Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives

Al-Samarraay, Mohammed S.; Zaidan, A. A.; Albahri, O. S.; Pamučar, Dragan; Alsattar, Hassan A.; Alamoodi, A. H.; Albahri, A. S.

doi:10.1016/j.asoc.2021.108284

Cited by 36 publications

(8 citation statements)

References 52 publications

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“…Another article [39] the authors extend FDOSM using Pythagorean fuzzy numbers. Finally, [40] extended FDOSM using interval-valued Pythagorean fuzzy numbers. In the literature, many researchers mention the benefit of the intuitionistic fuzzy and the extenuations of the intuitionistic (i.e.…”

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

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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%

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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show abstract

“…First, patients should be triaged according to their medical laboratory tests and family characteristics and separated into different emergency conditions, such as being at high risk, requiring urgent care and being sick. In this context, a new autism triage method needs to be developed based on AI techniques 123–130 and MCDM methods 131–153 . Second, autistic patients should be evaluated and benchmarked according to their emergency level by using integrated MCDM methods 154–167 for (i) weighting the medical laboratory tests and family characteristics criteria and (ii) and then benchmarking the autism cases in each emergency category.…”

Section: Discussionmentioning

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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“… IVPFS There are both single and hybrid studies under IVPFS setting. Al-Samarraay et al [ 127 ] developed a Fuzzy Decision by Opinion Score Method (FDOSM) for evaluating and benchmarking recognition systems with IVPFSs. MULTIMOORA, EDAS, and COPRAS methods have also been used under the IVPFS environment [ 128 – 130 ].…”

Section: Interval Techniques In Mcdmmentioning

confidence: 99%

An Overview of Interval Analysis Techniques and Their Fuzzy Extensions in Multi-Criteria Decision-Making: What’s Going on and What’s Next?

Liao

Wang

Tang

et al. 2023

Int. J. Fuzzy Syst.

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Due to the diversity of information, many decision-making problems cannot be solved based on a single criterion. The complexity of assessment objects and the limitations of individual cognition cause the opinions given by experts uncertain, which further aggravates decision-making difficulties. Although fuzzy sets and intuitionistic fuzzy sets are proposed to express vague information, they still have the problem of losing information. As a tool to express uncertain information, interval techniques can effectively prevent the loss of information and improve the accuracy of decision-making. In this regard, many scholars applied interval analysis techniques and their fuzzy extensions to solve multi-criteria decision-making (MCDM) problems. This study reviews 195-related articles published from 2007 to 2022, and analyzes the research progress of interval analysis techniques and their fuzzy extensions in MCDM problems. Through bibliometrics analyses, publication and citation trends as well as productive countries can be intuitively and quantitatively obtained. Then, we review theories and methods regarding the combination of interval techniques and MCDM methods, and find that interval-valued fuzzy sets are extensively discussed and combined with TOPSIS. Next, real-world applications of these publications are reviewed, and we obtain that interval techniques are mainly used in supply chain selection. Finally, we propose future directions regarding interval techniques in MCDM problems. It is hoped that this study would be helpful for scholars and practitioners to carry out further research.

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Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives

Cited by 36 publications

References 52 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

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

An Overview of Interval Analysis Techniques and Their Fuzzy Extensions in Multi-Criteria Decision-Making: What’s Going on and What’s Next?

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