The simulation tools geared towards promoting sustainability in Mobility as a Service (MaaS) evaluation is inherently a multi-criteria decision-making (MCDM) challenge due to three primary concerns: the criteria significance, data variability, and the expert opinions' uncertainty. Despite efforts in recent years, no current developed MaaS has fully addressed all evaluation criteria. Moreover, no research has evaluated the sustainability of MaaS in the context of determining its optimality. As such, this research's pivotal contribution is to present an evaluation of simulation tools for sustainable MaaS in autonomous vehicles operating in smart cities. This evaluation leans on the advanced extension of a newly proposed an interval-valued Fermatean fuzzy rough set (IVFFRS) incorporated within integrated MCDM methodologies. The IVFFRS is designed to capture intricate and uncertain evaluative data. The initial phase of the evaluation methodology involves formulating the evaluation criteria using an interval-valued Fermatean fuzzy rough set, fuzzily weighted for zero inconsistency. The subsequent phase adopts the interval-valued Fermatean fuzzy rough decision via the opinion score method to prioritize alternatives in light of data variations. This study evaluates ten distinct simulation tools for MaaS in autonomous vehicles based on seven criteria. The methodology's robustness is further ascertained through sensitivity and comparative analyses.

Internet of Things (IoT) real-time monitoring devices, which compromise sustainable sensing parameter-based climate change, are developed to minimise food loss and waste to support supply chain systems during natural disasters. Numerous studies have shown that current IoT real time monitoring devices offer remarkable prospects for future developments involving food supply chain systems with sustainable sensing parameters. Hence, modelling effective IoT real time monitoring devices to minimise food loss and waste to support supply chain systems is crucial during natural disasters. This modelling process can be classified as multiple-attribute decision-making (MADM) given three issues: the existence of multiple sensing parameter attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and circular intuitionistic fuzzy sets (C-IFS-FWZIC) with a new additive ratio assessment (ARAS) to determine ideal IoT real-time monitoring devices to minimise loss and waste and support food supply chain systems during natural disasters. The decision matrix for the study is built by intersecting 54 IoT real-time monitoring devices with 10 sustainable sensing parameter attributes. The proposed method is further developed to ascertain the importance level of the sustainable sensing parameter attributes. These data are used in ARAS. Sensitivity analysis and correlation coefficient test are performed to assess the robustness of the proposed method.

Abstract 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.

This paper proposes a novel ship energy systems (SESs) benchmarking model for performance measurement of sustainable transportation based on the extension of q-rung orthopair fuzzy rough sets (q-ROFRS) and multicriteria decision-making (MCDM) methods. The underlying research methodology consists of two main stages: (i) Formulation of the SES decision matrix between SESs and the sustainability , (ii) Development of a q-ROFRS and fuzzy-weighted zero-inconsistency (q-ROFRS–FWZIC) model to determine the weights of each criterion. The integrated model of the q-ROFRS and fuzzy decision by the opinion score method (q-ROFRS-FDOSM) is offered as a tool for benchmarking the SESs. Sixty-two SESs are evaluated and benchmarked according to the three layers of criteria concerning the five design alternatives. The analysis of the proposed q-ROFRS–FWZIC methodology revealed that decision support methods (C2) is the most important criterion with a weight of 0.4174, followed by gas emissions (C1.1.2) and economic criterion (C1.1.1) with weights of 0.1661 and 0.1498, respectively; and energy efficiency design index (C1.2.1) is the least important. Furthermore, the results from q-ROFRS-FDOSM reveal that SES62 is the most suitable SES followed by SES60, whereas SES37 is the least suitable. Finally, the robustness of the proposed method is assessed by conducting a sensitivity analysis.

Modeling of Internet of Healthcare Things (IoHT) smart-system-based blockchain in terms of security and privacy requirements is considered a multicriteria decision-making (MCDM) problem. Even though literature reviews have evaluated IoHT smart-system-based blockchain, informational ambiguity, vagueness and uncertainty remain open issues. In addition, the early MCDM techniques used to model IoHT smart systems-based blockchain have substantial theoretical shortcomings. First, the tuning parameters used produce different modeling outcomes. Second, the use of optimization theory increases the complexity of the modeling and decreases the stability of the results when the alternative numbers increases. Third, distance measurement and binary decision matrix are incompatible. Fourth, the modeling method used cannot prioritize the evaluation criteria. Therefore, this article extends the fuzzy weighted with zero inconsistency (FWZIC) method with interval-valued spherical fuzzy sets (IvSFSs) for weighting security and privacy requirements. Then, the developed IvS-FWZIC method is integrated with the complex proportional assessment method to model IoHT smart-system-based blockchain. IoHT smart-system-based blockchains 15 and 11 are selected as alternatives under remote patient monitoring systems and telemedicine systems, respectively. These alternatives are evaluated by six security and privacy requirements (criteria). The stability and robustness of the proposed methods are assessed by conducting sensitivity analysis and Spearman’s rho and comparison analysis. Researchers and developers of IoHT smart-system-based blockchain can propose more secure, private solutions with the help of this work’s implications.

Recent research has focused on developing real-time sign language recognition systems (SLRSs) based on gesture recognition to classify hand motions into their equivalent meaning in spoken language, but no comprehensive system with all desirable features has been presented. The existence of different systems has hindered the process of selecting the most preferred system. Therefore, many researchers have compared and evaluated several recognition systems to identify the best one using multicriteria decision-making methods. These studies extended the fuzzy decision by opinion score method (FDOSM) using a single Likert scale under the Pythagorean fuzzy set (PFS) or one of its extensions. However, no comparative study has examined the influence of using multiple Likert scales with a single fuzzy set. Furthermore, the effect of employing multiple Likert scales on benchmarking results is a challenging task. Therefore, this paper examines the three Likert scales (five-, seven- and ten-point) under the same fuzzy environment. This paper extends FDOSM into PFSs based on the power Bonferroni mean (PBM) operator (named PFDOSM-PBM) to benchmark the real-time SLRS. The decision matrix is constructed based on 30 real-time SLRS-based wearable sensory devices and the 11 evaluation criteria. The results reveal that the five-point Likert scale is superior to other scales (i.e., seven- and ten-point) as it is flexible, easy to use and generates more accurate findings on the basis of uncertainty compared to other scales. Systematic ranking and comparative analysis are conducted to validate and evaluate the proposed method.

Building a sustainable transportation system without involving international oil companies (IOCs) is an unrealistic feat. To date, no study has determined the best IOC and low-performing ones with respect to sustainable oil transportation, which is considered a benchmarking challenge requiring an urgent solution. Despite this limitation, the benchmarking of IOCs falls under the complex multicriteria decision making (MCDM) because of the use of several evaluation criteria and their varying datasets and the varying importance of these criteria. Moreover, the issues involving the use of imprecise, unclear, and ambiguous information remain unresolved in the existing multiattribute decision-making methods. The robustness of the multiobjective optimization on the basis of ratio analysis (MULTIMOORA, i.e., an updated version of MOORA) plus full-multiplicative form method and that of the fuzzy-weighted with zero inconsistency (FWZIC) method have been proven. Therefore, in this article, we propose a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the MCDM methods to help build a sustainable transportation industry. The proposed methodology consists of two phases. The initial phase involves assigning values to the evaluation criteria of IOCs to formulate the evaluation decision matrix. The second phase involves the development of two fuzzy MCDM methods, namely, the LDFRS with the FWZIC method (hereafter called LDFRS–FWZIC) for weighting the criterion of each IOC and the LDFRS with the MULTIMOORA method (hereafter called LDFRS–MULTIMOORA) for benchmarking the IOCs. The IOCs were evaluated based on 2 criteria, 9 subcriteria, and 47 measurement items by 483 experts from 11 IOCs. Results revealed the following: 1) LDFRS–FWZIC can effectively weigh the evaluation criteria of IOCs. The highest final weight of 0.2594 was for “cost leadership” (C2-1), whereas the lowest weights of 0.1148 was for “priority of other external matters” (C1-2) and “insufficient supply”(C1-4), and 2) LDFRS–MULTIMOORA can successfully benchmark the IOCs. IOC11 ranked first, followed by IOC10 and IOC3 in the second and third ranks, respectively. IOC4 ranked the lowest (rank= 11). A sensitivity analysis was conducted to determine the robustness of the developed fuzzy MCDM methods.

Pavement strategy is critical for achieving sustainable transportation. However, the presence of many evaluation criteria, criteria tradeoffs, criteria conflict, and criteria importance categorize the evaluation and selection of pavement strategies under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for selecting the most optimal pavement strategy to be utilized as a way to achieve sustainable transportation considering multicriteria evaluation of pavement strategies and sustainable solutions. This article presents a pavement strategy selection based on a new extension of fuzzy MCDM methods. The methodology is developed in two phases. First, the evaluation decision matrix is formulated on the basis of intersecting the “evaluation criteria” and “pavement strategies list.” Second, the proposed MCDM methods are developed: multilayer dual hesitant fuzzy weighted zero inconsistency (DH-FWZIC) to assign weights to the pavement's evaluation criteria followed by dual hesitant fuzzy decision by opinion score method (DH-FDOSM) for selecting the best pavement strategy. Four alternatives, namely, flexible/asphalt, rigid/concrete, reflective, and permeable alternatives pavement strategies, are evaluated on the basis of 30 criteria. Results show the following. 1) The multilayer DH-FWZIC method has weighted the pavement strategies’ evaluation criteria at three layers in a consistent manner, showing that a region's environment criterion has the highest final weight (0.1215) and the lowest importance value (0.0089) assigned for its windy criterion. 2) According to DH-FDOSM, the flexible/asphalt pavement achieved the first rank among the four pavement strategies. Finally, the robustness of the developed framework was assessed by conducting sensitivity analysis and comparison study.

Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

The evaluation, importance and variation nature of multiple security and privacy properties are the main issues that make the benchmarking of blockchain-based IoT healthcare Industry 4.0 systems fall under the multi-criteria decision-making (MCDM) problem. In this article, one of the recent MCDM weighting methods called fuzzy weighted with zero inconsistency (FWZIC) is effective for weighting the evaluation criteria subjectively without any inconsistency issues. However, considering the advantages of spherical fuzzy sets in providing a wide range of options to decision-makers and efficiently dealing with vagueness, hesitancy and uncertainty, this article formulated a new version of FWZIC for weighting the security and privacy properties, that is, spherical FWZIC (S-FWZIC). Moreover, an integrated MCDM framework was developed for benchmarking blockchain-based IoT healthcare Industry 4.0 systems on the basis of multi security and privacy properties. In the first phase of the methodology, a decision matrix is formulated based on the intersection of “blockchain-based Internet of Things healthcare Industry 4.0 systems” and “security and privacy properties” (i.e., user authentication, access control, privacy protection, integrity availability and anonymity). In the second phase, the weights of each security and privacy property are calculated through the S-FWZIC method. Then, these weights are employed to benchmark blockchain-based IoT healthcare Industry 4.0 systems through the combined grey relational analysis–technique for order of preference by similarity to ideal solution (GRA-TOPSIS) and the bald eagle search (BES) optimization method. Results indicate the following: First, the S-FWZIC method efficiently weighs the security and privacy properties, indicating that access control has the highest significance weight of 0.2070, while integrity has the lowest weight (0.0646); and second, the combination of the GRA-TOPSIS and the BES optimization method effectively ranks the systems. The evaluation was conducted using sensitivity analysis, revealing high correlation results over all the discussed scenarios of changing the weights of the criteria. The implications of this article can assist medical organisation administrators in selecting the most secure and appropriate system and the developers of such systems in future directions.

The benchmarking of smart e-tourism data management applications falls under the problem of multicriteria decision-making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision-making framework on the basis of the extension of interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e-tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e-tourism data management applications of each category and subcategory in smart e-tourism. In the second phase, the integration of the IT2TR-FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e-tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real-time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context-awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e-tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.