Yazar "Qahtan, Sarah" seçeneğine göre listele

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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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    Decisioning-based approach for optimising control engineering tools using digital twin capabilities and other cyber-physical metaverse manufacturing system components
    (IEEE, 2024) Mourad, Nahia; Alsattar, Hassan A.; Qahtan, Sarah; Zaidan, Aws Alaa; Deveci, Muhammet; Sangaiah, Arun Kumar; Pedrycz, Witold
    The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue; formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS.
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    Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy
    (Pergamon-Elsevier Science Ltd, 2024) Alsattar, Hassan A.; Qahtan, Sarah; Zaidan, Aws Alaa; Deveci, Muhammet; Martinez, Luis; Pamucar, Dragan; Pedrycz, Witold
    This study presents a novel dynamic localisation-based decision (DLBD) with fuzzy weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) environment to benchmark Hybrid Multi Deep Transfer and Machine Learning (HMDTML) models. The novel DLBD method is proposed to generate a dynamic localisation decision matrix based on the upper and lower boundaries and the length of the scale. The superiority of DLBD derives from its ability to manage dynamic changes with boundary value consequences. In addition, the utilization of PSVNHFS in conjunction with DLBD and FWZIC has proven to effectively address the challenges posed by vagueness, uncertainty and hesitancy in the benchmarking procedure. The proposed methodology consists of three primary three steps: i) the adaptation of 48 HMDTML models, including 4 deep transfer learning models and 12 machine learning models trained on a dataset of 936 chest Xray images obtained from both COVID-19 patients and individuals without the disease. Then, these models were evaluated based on seven evaluation criteria, and a decision matrix was proposed. ii) The development of a PSVNH-FWZIC to assign weights to the evaluation criteria. iii) The formulation of a PSVNH-DLBD for the purpose of benchmarking HMDTML models. Results of the PSVNH-FWZIC revealed that AUC and time were the most important evaluation criteria, while precision was the least important. Furthermore, the results from PSVNH-DLBD, reveal that Model M24 (Painters-Decision Tree) earned the highest rank when & lambda; = 2,3,4, 5and6, followed by Model M25 (SqueezeNet-AdaBoost) and Model M34 (DeepLoc-kNN), while Model M39 (DeepLocSVM) had the lowest rank (rank = 48) across all & lambda; values. The proposed method underwent sensitivity and comparison analyses to confirm its reliability and robustness.
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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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    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 industry 4.0 adoption strategies in small and medium enterprises: A Circular-Fermatean fuzzy decision-making approach
    (Elsevier ltd, 2025) Abu-Lail, Dareen; Mourad, Nahia; Qahtan, Sarah; Zaidan, A.A.; Alsattar, Hassan A.; Zaidan, B.B.; Pamucar, Dragan; Deveci, Muhammet; Pedrycz, Witold; Delen, Dursun
    The evaluation of Industry 4.0 (I4.0) technology adoption strategies (I4.0AS) in Small and Medium Enterprises (SMEs) from I4.0 technologies and technology-organization-environment (TOE) ecosystem perspectives poses a significant challenge due to three primary concerns: the importance of criteria, data variability for each individual I4.0AS, and uncertainty in expert opinions. This complexity arises from the consideration of diverse criteria groups for I4.0 deployment in the SMEs sector with a focus on the TOE context, which is linked to a second criteria group characterized by uncertain evaluative data. Although research in this area has increased, a comprehensive assessment methodology tailored to the unique needs of SMEs remains elusive. Addressing this gap is crucial to provide SME decision-makers with detailed insights that enhance their strategic choices. In our study, we introduce a holistic evaluation of I4.0ASs, emphasizing the performance dynamics within the TOE framework. Central to our assessment methodology is the integration of advanced Circular-Fermatean Fuzzy sets (C-FFS), designed to capture uncertain evaluative data. This three-phased methodology formulates the Circular-Fermatean Fuzzy Sets-Fuzzy-weighted zero-inconsistency (C-FFS–FWZIC) approach and evaluates I4.0ASs from both the I4.0 technology and TOE-ecosystem perspectives. Through a rigorous examination of 37 distinct I4.0ASs based on 30 I4.0 technology perspective criteria and 114 TOE-ecosystem perspective criteria, our study illuminates their efficacy across these dual perspectives. The results indicate that I4.0AS23 ranked first according to the I4.0 technologies perspective but 26th according to the TOE-ecosystem perspective, while I4.0AS1 ranked first according to the TOE-ecosystem perspective and 15th according to the I4.0 technologies perspective. I4.0AS25 yielded consistent results, scoring 6th place in both perspectives. Additionally, the resilience and versatility of our methodology are validated through an in-depth sensitivity and comparative analysis, reinforcing its potential as a valuable tool for future industry applications.
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    Neutrosophic bipolar fuzzy decision-based approach for developing sustainable circular business model innovation tools
    (Pergamon-Elsevier Science Ltd, 2024) Zaidan, Aws Alaa; Deveci, Muhammet; Alsattar, Hassan A.; Qahtan, Sarah; Shang, Wen-Long; Delen, Dursun; Mourad, Nahia
    The circular economy (CE) has been identified as a possible catalyst for sustainable development by business, academics, and policymakers. To aid company developers in creating and improving business models that incorporate circularity, a variety of tools for circular business model innovation (CBMI) have been proposed. Nevertheless, the existing tools failed to consider sustainability or CE in their advancements. Currently, there is no research that has presented a complete dataset including all potential tools that may be created based on the CE' sustainability performance attributes. Moreover, there has been a dearth of research conducted to assess and model these tools in order to determine the most efficient ones, which has resulted in a research gap. This paper constructs a decision matrix of CBMI tools by intersecting 100 CBMI tools with 10 CE' sustainability performance attributes. The modeling of CBMI tools falls under Multiple Attribute Decision Making (MADM) due to the presence of many attributes, varying importance levels of these attributes, and the and variation in data. Thus, the fuzzy weighted with zero inconsistency (FWZIC) method is reformulated under neutrosophic bipolar fuzzy sets (NBFS) to determine the weight of CE's sustainability performance attributes. The matrix that has been constructed and the resulting weight values are fed into the CODAS method in order to model CBMI tools and identify the most sustainable tool. The results indicate that the NBFS-FWZIC method gave a weight value of 0.1031 to A7, which is the greatest weight value. On the other hand, A3 had the lowest weight value of 0.0944. The CODAS method modeled the 100 CBMI tools, with Tool39 being identified as the most sustainable tool and Tool26 as the least sustainable tool. The robustness and durability of the proposed method are evaluated using a sensitivity analysis, Spearman's rank correlation test, and comparison analysis.
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    Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology
    (Pergamon-Elsevier Science Ltd, 2023) Qahtan, Sarah; Alsattar, Hassan A.; Zaidan, A. A.; Deveci, Muhammet; Pamucar, Dragan; Delen, Dursun
    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 multi-criteria 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.