Abstract Healthcare is a crucial and multifaceted sector dedicated to maintaining and restoring human health through a comprehensive range of services, including preventive care, specialised treatments, and public health interventions. The integration of advanced digital technologies has transformed this sector, enhancing accessibility, efficiency, and service quality through the digitisation and centralisation of patient records. However, this transformation also introduces significant cybersecurity challenges, including risks of data breaches, unauthorised access, and cyberattacks. To address these challenges, this paper proposes a robust decision-making framework based on Multi-Criteria Decision-Making (MCDM) methodologies, specifically, the CRiteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. These are extended into an ordinal fuzzy environment to develop Ordinal-CRITIC (O-CRITIC) and Ordinal-TOPSIS (O-TOPSIS), enabling effective evaluation of healthcare system alternatives under linguistic and qualitative criteria. Seven evaluation criteria are considered: Required Expertise (C1), Tool Availability (C2), Maturity Level (C3), Learnability (C4), Scope Width (C5), Completeness (C6), and Level of Detail (C7). The proposed methods were applied to rank 22 healthcare technologies. The results indicate that alternative A6 consistently achieved the highest rank across O-TOPSIS, SAW, and classic TOPSIS (C-TOPSIS) even after the comparative analysis, validating the robustness and reliability of the proposed framework. Conversely, alternatives such as A16 and A13 ranked lowest, highlighting their limitations against the established criteria. This was followed by sensitivity analysis further demonstrating the framework’s stability under varying scenarios either for the ranking settings or following for different weighting. These findings underscore the importance of structured, objective decision-making in selecting secure and efficient healthcare systems, promoting proactive threat mitigation and resilient digital healthcare infrastructures.

ABSTRACT This study introduces a new multi‐criteria decision‐making (MCDM) framework to evaluate trauma injury detection models in intensive care units (ICUs). This research addresses the challenges associated with diverse machine learning (ML) models, inconsistencies, conflicting priorities, and the importance of metrics. The developed methodology consists of three phases: dataset identification and pre‐processing, hybrid model development, and an evaluation/benchmarking framework. Through meticulous pre‐processing, the dataset is tailored to focus on adult trauma patients. Forty hybrid models were developed by combining eight ML algorithms with four filter‐based feature‐selection methods and principal component analysis (PCA) as a dimensionality reduction method, and these models were evaluated using seven metrics. The weight coefficients for these metrics are determined using the 2‐tuple Linguistic Fermatean Fuzzy‐Weighted Zero‐Inconsistency (2TLF‐FWZIC) method. The Vlsekriterijumska Optimizcija I Kompromisno Resenje (VIKOR) approach is applied to rank the developed models. According to 2TLF‐FWZIC, classification accuracy (CA) and precision obtained the highest importance weights of 0.2439 and 0.1805, respectively, while F1, training time, and test time obtained the lowest weights of 0.1055, 0.0886, and 0.1111, respectively. The benchmarking results revealed the following top‐performing models: the Gini index with logistic regression (GI‐LR), the Gini index with a decision tree (GI_DT), and the information gain with a decision tree (IG_DT), with VIKOR Q score values of 0.016435, 0.023804, and 0.042077, respectively. The proposed MCDM framework is assessed and examined using systematic ranking, sensitivity analysis, validation of the best‐selected model using two unseen trauma datasets, and mode explainability using the SHapley Additive exPlanations (SHAP) method. We benchmarked the proposed methodology against three other benchmark studies and achieved a score of 100% across six key areas. The proposed methodology provides several insights into the empirical synthesis of this study. It contributes to advancing medical informatics by enhancing the understanding and selection of trauma injury detection models for ICUs.

Date fruits occupy an important place among different fruits and are very important in the agricultural and food industries because of the versatile nutritional and health benefits that they offer, including their ability to reducing high cholesterol and triglyceride levels. However, the effectiveness of different date varieties in addressing these health issues remains an open question. The complexity of the evaluation of nutritional content stems from the uncertainty and inconsistency in the judgments of experts, the challenges of weighing multiple nutritional criteria, and the ability to manage the trade-offs between them. To address this, we produced a new dataset for food varieties consisting of 12 date fruit varieties (DFVs), incorporating 60 subcriteria across six primary nutritional criteria. The methodology subsequently developed a six-layered decision matrix (DM), formulated the mathematical process of spherical fuzzy numbers (SFNs), spherical fuzzy Z-numbers (SFZNs), fuzzy weighted zero-consistency (FWZIC) methods, and adaptive entropy methods to ensure the appropriate balance between the criteria and capture the trade-offs inherent in nutritional evaluation. Additionally, a modified weighted integrated sum product (WISP) method is employed to ensure precise benchmarking of the 12 DFVs. The SFZN-FWZIC results effectively handled the uncertainty and inconsistency in the fifteen expert judgments and developed reliable weights of nutritional criteria and subcriteria. The WISP model identifies Ajwa Al Madinah as the top-performing variety with a total utility of 0.8086 through 12 DFV analyses. This approach enhances fuzzy decision-making theory and provides practical guidance for stakeholders in the agricultural and food sectors. The results offer essential insights to inform decisions related to the production, consumption, and marketing of DFVs.

The emergence of large language models (LLMs) has substantially changed the artificial intelligence field, enabling its wide use over different domains. As various LLM alternatives have been developed, the current study proposes a novel decision‐support framework for evaluating and benchmarking LLMs based on multicriteria decision‐making (MCDM) techniques. In the proposed framework, an improved version of the best‐worst method (BWM) is proposed to effectively reduce the computational complexity of assigning a critical weight for the evaluation criteria of LLMs. Then, the improved BWM is integrated with the combined compromise solution (CoCoSo) method for ranking LLM alternatives. Findings show that the improved BWM successfully computes the criteria weights with low computational complexity compared to the original BWM. According to the enhanced BWM, the ‘factual errors’ criterion received the highest significant weight (0.2681), while the ‘logical inconsistencies’ criteria obtained the lowest (0.0827). The rest of the criteria were distributed in between that range. Subsequently, CoCoSo ranked the involved LLM alternatives in two different runs based on the extracted weights. Sensitivity analysis was employed to evaluate the effect of the assessment criteria on LLMs’ evaluation.

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

The degree of compaction in the levee building materials is a crucial factor that affects the piping phenomena. The density and compaction of the soil strata determine the structural soundness of the levee. Segments with reduced density or compaction can become weak spots during floods. To assess part of the Helena levee (2,500 m) in Arkansas (AR), the United States, an extensive ground-penetrating radar (GPR) fieldwork was conducted. This reliable method will undoubtedly improve the assessment procedure of the levee structure by identifying the weak spots within the structure that result from poor compaction of the levee core layers in a short time with high accuracy. However, interpreting the GPR data can be challenging and requires specialised knowledge. Obtaining meaningful insights typically involves a time-consuming process of extensive manual processing and visual inspection. To address this issue, this article proposes a novel, reliable deep-feature fusion framework for GPR data to identify horizontal variation in the soil compaction of a levee. To address data scarcity, a new type of transfer learning in the same domain is adopted, and four deep learning models (Xception, Inception, EfficientNet and MobileNet) are used to extract features. The combined features are then used to train and test five machine learning classifiers (Neural Network, Support Vector Machine, K-Nearest Neighbour, Logistic Regression, and Naive Bayes). The best combination of deep Learning and machine learning is four models with the neural network classifier which achieved the highest results by obtaining an accuracy of 98.2%, an F1 score of 97.6%, and an area under the curve of 99.9%. The proposed framework faced an additional challenge when subjected to an unseen dataset of 1,511 images reserved primarily for testing. Remarkably, it achieved an accuracy rate of 95.7% with the neural network classifier. This article presents a new research direction that has substantial potential in various domains, including civil engineering, the petroleum sector, road safety, agriculture, and more.

Semiconductor materials play a crucial role in the development of optoelectronics and power devices. However, their evaluation and selection pose a multi-attribute decision-making problem. This problem encompasses various considerations, such as multiple evaluation criteria, data variation, and the importance of criteria multiplicity. Therefore, this study proposes an integrated fuzzy multi-measurement decision-making model (IFMMDMM) to evaluate and select optimization techniques for semi-polar III-V semiconductor materials. The research methodology is designed based on three sequential phases. Firstly, four optimization techniques for semi-polar III-V semiconductor materials and four evaluation criteria are identified to construct the evaluation decision matrix. Secondly, the fuzzy-weighted zero-inconsistency method is developed to evaluate and assign weights to the defined multi-measurement criteria. Thirdly, the fuzzy decision by opinion score method is developed to select the optimization techniques for semi-polar III-V semiconductor materials. The weighting results reveal that the highest weight value was assigned to ‘root mean square under surface morphology’ ( 0.1382 ), while ‘peak-to-valley under surface morphology’ received the lowest weight value ( 0.1074 ). The selection results indicated that ‘different flux with fixed cycle NH3 treatment (D)’ ranked first, whereas ‘NH3 flux at changing V/III (A)’ had the lowest performance order. Systematic and sensitivity ranking assessments were performed to verify the efficiency of the proposed model.

Achieving high performance in energy systems is crucial for sustainability . Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria ( software licenses, public source code , redistribution, public source data, and commercial software ) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight ( 0.3347 ), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score ( 0.1595 ), while the DNE21+, MARIA, and MESSAGE models have the lowest score ( 0.0646 ), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.

Due to energy's global reliance on fossil fuels and population growth, GHG emissions and their repercussions have attracted attention. Due to their cheaper cost and cleaner environment, renewable energy modes of transportation like electric vehicles are highly sought after. Electric vehicles are beneficial, but they also emit emissions indirectly in power plants that generate their electricity, which could affect small and medium communities. Thus, it is crucial to assess such modes of transportation's performance while considering key aspects and criteria. However, scholarly works in this field have not fully addressed the deployment of a comprehensive electric vehicle decision-making support system. This study addresses electric bus selection by introducing a novel approach to Multi-Criteria Decision-Making (MCDM) utilizing a developed integrated fuzzy set. We introduce an integrated approach that combines an Entropy weighting approach with a 2-tuple Linguistic T-Spherical Fuzzy Decision by Opinion Score Method (2TLTS-FDOSM). This approach is designed to tackle the challenges associated with evaluating the feasibility of electric bus models (EBMs) and addressing the theoretical challenge of MCDM in the context of the presented case study. These challenges include dealing with ambiguities and inconsistencies among decision-makers. The former method is utilized to ascertain the significance of assessment criteria, whereas the latter approach is applied to select the most favorable EBM by utilizing the weights obtained. As for the 2TLTS-FDOSM results, out of all the (n=6) EBMs considered, A3 (11-E) EBM obtained the highest score value, while the A3 (9-E) EBM had the lowest score. The robustness of the results is confirmed through sensitivity analysis.

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed.

Developing a comprehensive data-driven strategy for evaluating the organisational culture in companies to foster digital innovation involves a multi-criteria decision-making (MCDM) problem. This needs to consider various organisational culture characteristics that influence digital innovation success, assign significance weights to each characteristic, and recognise that distinct organisational cultures may excel in different aspects necessitates the proper handling of data variations. Hence, to provide organisations seeking to align cultural practises with digital innovation objectives with valuable insights, this study aims to develop an MCDM model for evaluating and benchmarking organisational culture in companies to foster digital innovation. The benchmarking decision matrix is formulated based on the intersection of evaluation characteristics and a list of organisational culture aspects in companies. The MCDM model is developed in two phases. Firstly, a new weighting model, q-rung picture fuzzy-weighted zero-inconsistency (q-RPFWZIC), is formulated for assessing the evaluation characteristics under the q-rung picture fuzzy sets environment. Secondly, the simple additive weighting (SAW) model is formulated for benchmarking the organisational culture in companies using the extracted weights of the evaluation characteristics. The results indicate that characteristic C6 (corporate entrepreneurship) has the highest weight, with a value of 0.161, while characteristic C3 (employee participation, agility and organizational structures) and C7 (digital awareness and necessity of innovations) has the lowest weight of 0.088. Company A2 secures the top rank with a score of 0.911, satisfying eight evaluation characteristics, whereas company A7 holds the last rank order, satisfying only one evaluation characteristic, obtaining a score of 0.101. In model evaluation, several scenarios were considered in a sensitivity analysis test based on a 100% increment in weight values for each characteristic to validate the reliability of the model results.

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.

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision‐making (DM), these systems have found wide‐ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G‐based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria for optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation of optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selection-integrated approach for AUV optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) for optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-based optimization algorithm selection. In all three motion types – “depth”, “yaw”, and “theta” – Kp_θ had the highest weight value, with respective weights of 0.143277, 0.149578, and 0.142432. In contrast, Ki_depth consistently received the lowest weight value across all three motion types, with respective weights of 0.057375, 0.0598901, and 0.057085. Regarding the “depth” motion, the Archimedes optimization algorithm (AOA) was the highest-performing alternative with a score of 0.077364, while the eagle strategy–particle swarm optimization algorithm was the worst alternative with a score of 0.022525. The Cuckoo optimization algorithm was identified as the best alternative for the “yaw” motion with a score of 0.072785, whereas black hole optimization had the lowest score of 0.036454. The best alternative for the “theta” motion was AOA with a score of 0.067716, and the worst algorithm was sunflower optimization with a score of 0.028433. This developed approach was evaluated based on systematic ranking and sensitivity analysis, which confirmed the validity of the proposed work.

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.

Efficient evaluation for machine learning (ML)-based intrusion detection systems (IDSs) for federated learning (FL) in the Internet of Medical Things (IoMTs) environment falls under the standardisation and multicriteria decision-making (MCDM) problems. Thus, this study is developing an MCDM framework for standardising and benchmarking the ML-based IDSs used in the FL architecture of IoMT applications. In the methodology, firstly, the evaluation criteria of ML-based IDSs are standardised using the fuzzy Delphi method (FDM). Secondly, the evaluation decision matrix (DM) is formulated based on the intersection of standardised evaluation criteria and a list of ML-based IDSs. Such formulation is achieved using a dataset with 125,973 records, and each record comprises 41 features. Thirdly, the integration of MCDM methods is formulated to determine the importance weights of the main and sub standardised security and performance criteria, followed by benchmarking and selecting the optimal ML-based IDSs. In this phase, the Borda voting method is used to unify the different ranks and perform a group benchmarking context. The following results are confirmed. (1) Using FDM, 17 out of 20 evaluation criteria (14 for security and 3 for performance) reach the consensus of experts. (2) The area under curve criterion has the lowest set of weights, whilst the CPU time criterion has the highest one. (3) VIKOR group ranking shows that the BayesNet is a best classifier, whilst SVM is the last choice. For evaluation, three assessments, namely, systematic ranking, computational cost and comparative analysis, are used.

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.

This study evaluated the benchmarking process of active queue management (AQM) methods, which consider a multicriteria decision-making (MCDM) problem using multidimensional criteria. Academic studies have benchmarked the AQM methods using MCDM techniques. However, these studies have used existing MCDM techniques, which face considerable theoretical challenges. The latest MCDM method called fuzzy decision by opinion score (FDOSM) was published in the Journal of Applied Soft Computing in 2020 to address the theoretical challenges of the existing MCDM methods. However, FDOSM continues to encounter serious issues. That is, it exclusively depends on the direct aggregation MCDM approach based on arithmetic mean (AM) operator. However, performing other operators (i.e., geometric mean, harmonic mean, and root mean square), in addition to applying other MCDM approaches (i.e., distance measurement and compromise rank), may result in different ranking results. Hence, this study mainly proposes an extension of FDOSM through the following aspects: (1) application of different aggregation techniques in the direct aggregation MCDM approach, (2) discussion of the effectiveness of each type on the final AQM benchmarking, and (3) use of varying MCDM approaches on FDOSM to reach the optimum result when benchmarking the AQM methods. The current research methodology is based on two sequential phases. The first phase provides the decision matrix used in benchmarking the AQM methods. The decision matrix was constructed based on the AQM evaluation criteria and a list of AQM methods. The second phase presents two stages, namely, data transformation unit and data processing. Findings of the AQM benchmarking are as follows. (1) In the individual FDOSM, two main configurations are recommended when using the AQM benchmarking: direct aggregation MCDM approach with AM operator and compromise rank approach. Benchmarking results of both configurations based on six decision makers are nearly similar, with the AQM BLUE method being ranked the best. The exception is for the results of the compromise rank approach based on the third decision maker, which revealed that the AQM ERED method is the best. (2) Results of the group FDOSM showed a relatively similar order for the AQM methods in both configurations, with the AQM BLUE method being the best. (3) Lastly, significant differences were found among the groups' scores, thereby indicating the validity of the FDOSM-based AQM benchmarking results.

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.