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.

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 This study proposes a new perspective of the TOPSIS and VIKOR methods using the recently introduced spherical fuzzy sets (SFSs) to handle the vagueness in subjective data and the uncertainties in objective data simultaneously. When implementing these techniques using SFSs, two main problems might arise that can lead to incorrect results. Firstly, the reference points might change with the utilized score function. Secondly, the distance between reference points might not be the largest, as known, among the available ratings. To overcome these deficiencies and increase the robustness of these two methods, they are implemented without utilizing any reference points to minimize the effect of defuzzification and without measuring the distance to eliminate the effect of distance formulas. In the proposed methods, when using an SFS to express the performance of an alternative for a criterion, this SFS per se can be viewed as a measure of proximity to the aspired level. On the other hand, the conjugate of the SFS can be viewed as a measure of proximity to the ineffectual level. Two practical applications are presented to demonstrate the proposed techniques. The first example handles a warehouse location selection problem. The second example evaluates hydrogen storage systems for automobiles with different types of data (crisp, linguistic variables, type 1 fuzzy sets). These data are transformed to SFSs to provide a more comprehensive analysis. A comparative study is conducted with earlier versions of TOPSIS and VIKOR to explicate the adequacy of the proposed methods and the consistency of the results.