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    An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations
    (Springer, 2023) Ahmed, Abdulaziz; Al-Maamari, Mohammed; Firouz, Mohammad; Delen, Dursun
    Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. In this paper, the metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, and ASA-CaB. Grid search (GS), a traditional approach used for machine learning fine-tuning, is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The optimized model is used to develop an e-triage tool that can be used at EDs to predict ED patients' emergency severity index (ESI). The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, and 83.2%, respectively.
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    Adoption of energy consumption in urban mobility considering digital carbon footprint: A two-phase interval-valued Fermatean fuzzy dominance methodology
    (Pergamon-Elsevier Science Ltd, 2023) Jeevaraj, S.; Gokasar, Ilgin; Deveci, Muhammet; Delen, Dursun; Zaidan, Bilal Bahaa; Wen, Xin; Shang, Wen-Long
    Interval-valued Fermatean fuzzy sets play a significant role in modelling decision-making problems with incomplete information more accurately than intuitionistic fuzzy sets. Various decision-making methods have been introduced for the different classes IFSs. In this study, we aim to introduce a novel two-phase interval-valued Fermatean fuzzy dominance method which suits the decision-making problems modelled under the IVFFS environment well and study its applications in the adoption of energy consumption in Urban mobility considering digital carbon footprint. The proposed method considers the importance and performance of one alternative with respect to all others, which is not the case with many available decision making algorithms introduced in the literature. Transportation is one of the most significant sources of global greenhouse gas (GHG) emissions. Numerous potential remedies are proposed to reduce the quantity of GHG generated by transportation activities, including regulatory measures and public transit digitalization initiatives. Decision-makers, however, should consider the digital carbon footprint of such projects. This study proposes three alternatives for reducing GHG emissions from transportation activities: incremental adoption of digital technologies to reduce energy consumption and greenhouse gases, disruptive digitalization technologies in urban mobility, and redesign of urban mobility using regulatory approaches and economic instruments. The proposed novel two-phase interval-valued Fermatean fuzzy dominance method will be utilized to rank these alternative projects in order of advantage. First, the problem is converted into a multi-criterion group decision making problem. Then a novel two-phase interval-valued Fermatean fuzzy dominance method is designed and developed to rank the alternatives. The importance and advantage of the proposed two-phase method over other existing methods are discussed by using sensitivity and comparative analysis. The results indicate that rethinking urban mobility through governmental policies and economic tools is the least advantageous choice, while incremental adoption of digital technologies is the most advantageous.
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    An analytical approach to evaluate the impact of age demographics in a pandemic
    (Springer, 2023) Abdulrashid, Ismail; Friji, Hamdi; Topuz, Kazim; Ghazzai, Hakim; Delen, Dursun; Massoud, Yehia
    The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.
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    An analytics approach to decision alternative prioritization for zero-emission zone logistics
    (Elsevier Inc., 2022) Deveci, Muhammet; Pamucar, Dragan; Gökaşar, Ilgın; Delen, Dursun; Wu, Qun; Simic, Vladimir
    Urban freight transportation requires wise management considerations since it is one of the most challenging issues cities face to attain sustainability. To help with the challenging decision process, an integrated two-stage decision analysis approach is proposed. In the first stage, the Defining Interrelationships Between Ranked criteria (DIBR) method is used to consolidate the experts’ opinions to compute the weights of the predetermined decision criteria. In the second stage, a novel approach that integrates Combined Compromise Solution (CoCoSo) with the context of type-2 neutrosophic numbers is used to identify the most optimal management decision alternative. A case study is developed to show the viability and practicability of the proposed methodology. The results indicated that “building a logistics center (for fast and cheap delivery)” is the highest-ranked decision alternative, followed by “optimized and integrated operation of urban logistics,” and “zero-emission zone implementation,” respectively. The proposed methodology can be used as a decision analysis framework for urban city authorities while selecting the most optimal policies and related solution alternatives towards achieving and sustaining low-emission urban freight transportation. © 2022 Elsevier Inc.
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    Application of MADM methods in Industry 4.0: A literature review
    (Pergamon-Elsevier Science Ltd, 2023) Zayat, Wael; Kilic, Huseyin Selcuk; Yalcin, Ahmet Selcuk; Zaim, Selim; Delen, Dursun
    Industry 4.0 has received inordinate attention from the business as well as research communities. Along with the development of Industry 4.0 applications and the diversity of potential alternatives, Multi-Attribute Decision Making (MADM) techniques have been employed by researchers as systematic approaches to support the decision-making processes. However, as the adoption of Industry 4.0 technologies requires considerable capital, and as it is relatively difficult to identify the suitable MADM method for the decision-making process in certain conditions, it becomes necessary to determine which components of Industry 4.0 are most commonly in demand of MADM applications of decision making, and which MADM techniques are mostly preferred by researchers and businesses for varied directions of Industry 4.0. Therefore, this study aims to provide a comprehensive review of MADM methods and their applications for different components of Industry 4.0. A methodology, including a review framework, is provided for the related analyses. The proposed framework includes analyses concerning methods, subtopics, and bibliometry along with the related exploratory tables and figures. Finally, the trends and research gaps are clearly stated to shed light on the further research areas taking into consideration different challenges that can be encountered by researchers, along with a set of propositions to potentially overcome them.
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    Architecture selection for 5G-radio access network using type-2 neutrosophic numbers based decision making model
    (Pergamon-Elsevier Science Ltd, 2024) Sharaf, Iman Mohamad; Alamoodi, A. H.; Albahri, O. S.; Deveci, Muhammet; Talal, Mohammed; Albahri, A. S.; Delen, Dursun
    Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, Type2 Neutrosophic Fuzzy Decision by Opinion Score Method (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C21 latency and C22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including virtualized cloud RAN coming as the optimal one, followed by fog RAN, cloud RAN, and finally heterogeneous cloud RAN. The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications.
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    A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases
    (Springer, 2023) Topuz, Kazim; Davazdahemami, Behrooz; Delen, Dursun
    During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.
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    Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets
    (Springer, 2023) Mohammed, Z. K.; Zaidan, A. A.; Aris, H. B.; Alsattar, Hassan A.; Qahtan, Sarah; Deveci, Muhammet; Delen, Dursun
    Metaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy 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 Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.
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    Business Analytics Adoption and Technological Intensity: An Efficiency Analysis
    (Springer, 2023) Bayraktar, Erkan; Tatoglu, Ekrem; Aydiner, Arafat Salih; Delen, Dursun
    Despite the overwhelming popularity of business analytics (BA) as an evidence-based decision support mechanism, the impact of its adoption on organizational performance has received scant attention from the research community. This study aims to unfold the adoption efficiencies of BA and its applications by proposing a data envelopment analysis (DEA) methodology to holistically assess the underlying factors with respect to the level of achievement regarding organizational performance, operational performance, and financial performance. Furthermore, the study unveils the firm-level and sectoral-level discrepancies in BA adoption efficiency in different industry settings. Relying on survey data obtained from 204 executives in various industries, this study provides empirical support for the cross-industry differences in BA adoption efficiencies. The results show that the firms in low-tech industries seem to achieve the highest efficiency from adopting BA regarding its influence on firm performance.
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    A critical assessment of consumer reviews: A hybrid NLP-based methodology
    (Elsevier B.V., 2022) Biswas, Baidyanath; Sengupta, Pooja; Kumar, Ajay; Delen, Dursun; Gupta, Shivam
    Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon's Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer's credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of helpfulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theoretical implications through these findings. © 2022 Elsevier B.V.
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    A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis
    (Elsevier Ltd, 2023) Miao, Zhuqi; Sealey, Meghan D.; Sathyanarayanan, Shrieraam; Delen, Dursun; Zhu, Lan; Shepherd, Scott
    Even though data preparation constitutes a large proportion of the total effort involved in electronic health record (EHR) based secondary analysis, guidelines for EHR data preparation are still insufficient to date. This study proposes a data preparation framework that can guide and validate the cleaning of EHRs for secondary analysis. The developed framework consists of three core themes—workflow, assessment and cleaning methods, and cleaning evaluation scheme. To illustrate the viability of the proposed framework, we applied it to a hip-fracture readmission scenario using the underlying data extracted from a large EHR database. The case study demonstrated the effectiveness of the proposed framework in organizing and standardizing phases and processes within an EHR data preparation workflow. Furthermore, the cleaning evaluation scheme was found to be effective in validating EHR cleaning methods, especially for those used to handle complex issues that usually appear in patient demographics, longitudinal attributes of EHRs, and the application of filtering and imputation cleaning methods.
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    Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology
    (Routledge Journals, Taylor & Francis Ltd, 2023) Nath, Gopal; Coursey, Austin; Ekong, Joseph; Rastegari, Elham; Sengupta, Saptarshi; Dag, Asli Z.; Delen, Dursun
    Purpose Although different cancer types have been investigated from the perspective of biomedical sciences, machine learning-based studies have been scant. The present study aims to uncover the temporal effects of factors that are important for brain and central nervous system (BCNS) cancer survival, by proposing a machine learning methodology. Methods Several feature selection, data balancing, and machine learning algorithms (in addition to the sensitivity analysis) were employed to analyze the dynamic (i.e. varying) effects of several feature sets on the survival outputs. Results The results show that Gradient Boosting (GB) along with Logistic Regression (LR) and Artificial Neural Networks (ANN) outperform the other classification algorithms in this study. Furthermore, it has been observed that the importance of several features/variables varies from 1- to 5- and 10-year survival predictions. Conclusion Although the proposed hybrid methodology is validated on a large and feature-rich BCNS cancer data set, it can also be utilized to study survival prognostics of other cancer or chronic disease types.
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    A developer-oriented recommender model for the app store: A predictive network analytics approach
    (Elsevier Science Inc, 2023) Davazdahemami, Behrooz; Kalgotra, Pankush; Zolbanin, Hamed M.; Delen, Dursun
    While thousands of new mobile applications (i.e., apps) are being added to the major app markets daily, only a small portion of them attain their financial goals and survive in these competitive marketplaces. A key to the quick growth and success of relatively less popular apps is that they should make their way to the limited list of apps recommended to users of already popular apps; however, the focus of the current literature on consumers has created a void of design principles for app developers. In this study, employing a predictive network analytics approach combined with deep learning-based natural language processing and explainable artificial intelligence techniques, we shift the focus from consumers and propose a developer-oriented recommender model. We employ a set of app-specific and network-driven variables to present a novel approach for predicting potential recommendation relationships among apps, which enables app developers and marketers to characterize and target appropriate consumers. We validate the proposed model using a large (>23,000), longitudinal dataset of medical apps collected from the iOS App Store at two time points. From a total of 10,234 network links (rec-ommendations) formed between the two data collection points, the proposed approach was able to correctly predict 8,780 links (i.e., 85.8 %). We perform Shapley Additive exPlanation (SHAP) analysis to identify the most important determinants of link formations and provide insights for the app developers about the factors and design principles they can incorporate into their development process to maximize the chances of success for their apps.
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    Developing sustainable management strategies in construction and demolition wastes using a q-rung orthopair probabilistic hesitant fuzzy set-based decision modelling approach
    (Elsevier, 2023) Ghailani, Hend; Zaidan, A. A.; Qahtan, Sarah; Alsattar, Hassan A.; Al-Emran, Mostafa; Deveci, Muhammet; Delen, Dursun
    Sustainable management of construction and demolition wastes (CDWs) has become a pressing global issue in social, environmental and economic contexts, and it involves complex technological, engineering, management and regulatory challenges. Recently, many CDW management strategies have been developed based on the barrier attributes of reuse distribution. However, no strategy can simultaneously address all barrier attributes of reuse distribution. Furthermore, no research has assessed and modelled the identified CDW management strategies to determine optimality. On this basis, the presence of multiple barrier attributes, varying attribute priority and a wide range of data allow for the modelling of CDW management strategies under complex multiple-attribute decision -making (MADM) problems. This study develops the fuzzy-weighted zero inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM)-based multiplicative multiple objective optimisation by ratio analysis (MULTIMOORA) with the q-rung orthopair probabilistic hesitant fuzzy set (q-ROPHFS) to address this problem. The developed q-ROPHFS-FWZIC method prioritised and weighted the main and sub-barrier attributes of reuse distribution in CDW management strategies. The developed q-ROPHFS-FDOSM is used to score the CDW management strategies. Then, the MULTIMOORA method is used to model 51 CDW management strategies to determine the optimum one. Results showed that Strategy 46 modelled first in six q values because it had the most essential attributes (i.e. cost, market, value-for-money, experience, infrastructure, management, risk and trust). Strategy 17 and Strategy 20 are the least sustainable strategies because they had only one attribute (i.e. experience). Sensitivity analysis, systematic modelling and comparison analysis are conducted to validate and evaluate the stability and robustness of the proposed methods. The implications of this study would likely benefit various stakeholders involved in the construction industry, including construction companies, architects, engineers, policy-makers and members of the public.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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    Development of a sustainable corporate social responsibility index for performance evaluation of the energy industry: A hybrid decision-making methodology
    (Elsevier Sci Ltd, 2023) Dincer, Hasan; Yuksel, Serhat; Hacioglu, Umit; Yilmaz, Mustafa K.; Delen, Dursun
    The ever-increasing pressure from stakeholders and policymakers on energy companies to achieve Sustainable Development Goals (SDGs) and Corporate Social Responsibility (CSR) mission requires them to reinvent their policies and practices. This study aims to examine the performance of alternative business models for the oil and gas industry by employing a hybrid business analytics methodology under a fuzzy environment resulting in a generalizable model named Sustainable Development Goals-oriented CSR Index. The proposed methodology employs a hybrid framework that utilizes bipolar Q-rung Orthopair Fuzzy (q-ROF), Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA), and Elimination and Choice Translating Reality (ELECTRE) methods. The findings show that (i) the proposed model is reliable and consistent throughout the similar fuzzy set value ranges, (ii) clean energy is the most important SDG-oriented CSR Index factor for the sustainable energy industry in emerging economies, (iii) drilling is the best alternative energy sourcing for the oil and gas industry, and (iv) clean energy projects have the highest priority for energy investors. The results also highlight that global warming can be managed with effective energy practices for long-term sustainability. Finally, the findings suggest that energy companies should have the essential technological infrastructure and capable workforce to increase investment efficiency.
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    Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support
    (ELSEVIER SCI, 2022) Amini, Mostafa; Bagheri, Ali; Delen, Dursun
    Millions of car crashes occur annually in the US, leaving tens of thousands of deaths and many more severe injuries. Thus, understanding the most impactful contributors to severe injuries in automobile crashes and mitigating their effects are of great importance in traffic safety improvement. This paper develops a hybrid framework involving predictive analytics, explainable AI, and heuristic optimization techniques to investigate and explain the injury severity risk factors in automobile crashes. First, our framework examines various machine learning models to identify the one with the best prediction performance as the base model. Then, it utilizes two popular state-of-the-art explainable AI techniques from the literature (i.e., leave-one-covariate-out and TreeEx-plainer) and our proposed explanation method based on the variable neighborhood search procedure to construe the importance of the variables. Finally, by applying an information fusion technique, our approach identifies a unified ranking list of the most important variables contributing to severe car crash injuries. Transportation safety planners and policymakers can use our findings to reduce the severity of car accidents, improve traffic safety, and save many lives.
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    Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model
    (Elsevier, 2023) Qahtan, Sarah; Alsattar, Hassan A.; Zaidan, A. A.; Deveci, Muhammet; Pamucar, Dragan; Delen, Dursun; Pedrycz, Witold
    The benchmarking of agri-food 4.0 supply chain (Agri4SC) falls under the multiple criteria problem in supply chain visibility (SCV) and supply chain resource integration (SCRI) for improving data analytics capabilities and achieving sustainable performance (SP). It is considered a multiple criteria decision -making (MCDM) problem due to three main concerns, namely, multiple Agri4SC evaluation criteria including the SCV, SCRI and SP criteria. These criteria have relative importance and trade-offs. Despite the tremendous efforts over the last years, none of the developed Agri4SCs have met all of the essential Agri4SC evaluation criteria. Another concern raised in the evaluation and benchmarking of the Agri4SC is the uncertainty of experts. Thus, the main contribution of this research is to propose an Agri4SC benchmarking framework in SCV and SCRI for improving data analytics capabilities and achieving SP based on an extension of the proposed Fermatean probabilistic hesitant fuzzy sets (FPHFSs) and MCDM methods. The methodology process is divided into six main parts. Firstly, an Agri4SC decision matrix is formulated based on the intersection of the Agri4SC alternatives and criteria to cover multiple Agri4SC evaluation criteria issues. Secondly, novel FPHFSs are proposed along with their operational laws, score function, accuracy function, Fermatean probabilistic hesitant fuzzy average mean operator and Fermatean probabilistic hesitant fuzzy weighted average operator. The FPHFS can encompass more sophisticated and uncertain evaluation information. Thirdly, Fermatean probabilistic hesitant fuzzy weighted zero inconsistency is formulated to assign weights to the evaluation criteria. Fourthly, the Fermatean probabilistic hesitant fuzzy decision by opinion score method (FPH-FDOSM) is formulated and used to score the alternatives that were evaluated subjectively based on SCV criteria. Fifthly, the FPH-FDOSM-based multi attributive ideal-real comparative analysis (MAIRCA) scoring method with equal probabilities is proposed to score Agri4SC alternatives that were evaluated subjectively based on weighted economic, environmental and social factors. Lastly, the MAIRCA ranking method with unequal probabilities is introduced to benchmark Agri4SC alternatives that were evaluated objectively based on the weighted subcriteria of SP and the trade-offs amongst the identified criteria. The robustness and reliability of the results are tested via sensitivity analysis and Spearman's correlation coefficient.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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    Evaluation of food waste treatment techniques using the complex q-rung orthopair fuzzy generalized TODIM method with weighted power geometric operator
    (Academic Press Ltd- Elsevier Science Ltd, 2024) Cao, Yushuo; Han, Xiao; Wu, Xuzhong; Deveci, Muhammet; Kadry, Seifedine; Delen, Dursun
    Food waste has received wide attention due to its hazardous environmental effects, such as soil, water, and air pollution. Evaluating food waste treatment techniques is imperative to realize environmental sustainability. This study proposes an integrated framework, the complex q-rung orthopair fuzzy-generalized TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making) method with weighted power geometric operator, to assess the appropriate technique for food waste. The assessment of food waste treatment techniques can be divided into three phases: information processing, information fusion, and ranking alternatives. Firstly, the complex q-rung orthopair fuzzy set flexibly describes the information with periodic characteristics in the processing process with various parameters q. Then, the weighted power geometric operator is employed to calculate the weight of the expert and form the group evaluation matrix, in which the weight of each input rating depends upon the other input ratings. It can simulate the mutual support, multiplicative preferences, and interrelationship of experts. Next, the generalized TODIM method is employed to rank the food waste treatment techniques, considering experts' psychological characteristics and bounded behavior. Subsequently, a real-world application case examines the practicability of the proposed framework. Furthermore, the sensitivity analysis verifies the validity and stability of the presented framework. The comparative study highlights the effectiveness of this framework using the existing frameworks. According to the result, Anaerobic digestion (0.0043) has the highest priority among the considered alternatives, while Incineration (-0.0009) has the lowest.
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    Evaluation of intelligent transportation system implementation alternatives in metaverse using a Fermatean fuzzy distance measure-based OCRA model
    (Elsevier Science Inc, 2024) Deveci, Muhammet; Mishra, Arunodaya Raj; Rani, Pratibha; Gokasar, Ilgin; Isik, Mehtap; Delen, Dursun; Ooi, Keng-Boon
    The concept of the Metaverse, an immersive simulated world with parallels to reality, has gained significant prominence in recent times. Initially popularized through gaming, the Metaverse is now poised to infiltrate various aspects of human life. Intelligent transportation systems represent a promising yet challenging domain for Metaverse integration. Alternative implementations can create challenges in different dimensions. A comprehensive evaluation that takes challenges and opportunities for the different dimensions into account is required in decision making process of choosing the best implementation method. This study presents the development of a novel evaluation model, the Fermatean Fuzzy Operational Competitiveness Rating (OCRA) model, which incorporates the Fermatean Fuzzy Distance Measure (FF-DM) and Relative Closeness Coefficient (FF-RCC) techniques. The model is tested in a case to rank three alternative approaches, considering criteria of four key dimensions: managerial, safety, user, and urban mobility. In the first stage, the FF-DM and FF-RCC-based tool is employed to determine the criteria weights. In the second stage, an enhanced version of the Fermatean Fuzzy OCRA model, utilizing FF-DM and FFRCC, is employed to rank the alternatives. The findings indicate that policymakers' decisions in traffic management hold the potential to shape the trajectory of the Metaverse movement, representing an unparalleled opportunity with implications that extend beyond our current comprehension.
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    Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach
    (Elsevier, 2023) Pamucar, Dragan; Deveci, Muhammet; Gokasar, Ilgin; Delen, Dursun; Koppen, Mario; Pedrycz, Witold
    Sharing economy transportation applications reduce car ownership and single-vehicle occupancy, contributing to the region's environmental sustainability. Metaverse is a promising new technology that combines sharing economy applications with transportation networks. By combining these two approaches, authorities can improve the sustainability of sharing economy applications. This study aims to assist decision-makers and authorities by developing a multi-criterion decision-making (MCDM) model that prioritizes three sharing economybased metaverse integration alternatives, namely integrating safety measures, payment systems, and the optimization of operations in the metaverse. A novel multi-criteria framework, including fuzzy Schweizer-Sklar norms based on the Ordinal Priority Approach (OPA) to assess the metaverse integration alternatives, is developed. To rank the alternatives, non-linear processing of information based on the fuzzy Schweizer-Sklar weight assessment method (SWAS) is proposed. A case study is developed to provide a foundation for the experts' evaluations using twelve criteria, which are organized into four aspects namely, economic, user, operational, and advancement. Finally, the results indicate that the most favorable approach is optimized operations via the integration of the sharing economy into the metaverse.