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Showing papers on "Ranking (information retrieval) published in 2022"


Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors conducted a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization.
Abstract: Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

301 citations


Journal ArticleDOI
TL;DR: In this article , the Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) is used to rank the alternative solutions to these barriers and the overall ranking indicates that "technological complexity" ranks highest among all sub-barriers across all categories.

111 citations


Journal ArticleDOI
TL;DR: It was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field.
Abstract: This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.

94 citations


Journal ArticleDOI
TL;DR: In this paper , the authors analyzed the association between hospital Twitter metrics and the 2020 USNWR hospital cardiology and heart surgery ranking and found that a significant positive relation was observed with twitter metrics and hospital ranking.
Abstract: Since 1990, the U.S. News and World Report (USNWR) has been publishing rankings of US adult and children’s hospitals. The aim of this study was to analyze the association between hospital Twitter metrics and the 2020 USNWR hospital cardiology and heart surgery ranking. We collected data on the cardiology and heart surgery overall ranking score and expert opinion. Twitter metrics were obtained on October 20, 2020, and included time on Twitter, number of followers, accounts being followed, total tweets, reach score (difference between followers and followed), and annual tweet rate (total tweets divided by time on Twitter). The final cohort consisted of 463 hospitals (48 of which were top-ranking hospitals). A significant positive relation was observed with Twitter metrics and hospital ranking. On multivariable regression after adjusting for time on Twitter, the overall score was independently associated with annual tweet rate and reach score (β=12.45% and β=0.34% for each 1,000 tweets per year and 10,000 reach score accounts; P<.001). Similarly, expert opinion was independently associated with annual tweet rate and reach score (β=0.025% and β=0.002% for each 1000 tweets per year and 10,000 reach score accounts; P<.001). Our results emphasize how hospital leaders may leverage social media platforms as an important medium to disseminate accomplishments and increase their visibility and reputation, potentially translating to higher USNWR ranking.

82 citations


Journal ArticleDOI
Sylvia Haas1
TL;DR: In this paper , a hybrid model based on Pythagorean fuzzy DEMATEL, TOPSIS and Shapley value to find appropriate policies to improve low-carbon renewable energy projects is presented.

71 citations


Journal ArticleDOI
TL;DR: In this paper , an online tool, POSREG, was constructed to identify the optimal proteomic signature for a set of proteomic data by aggregating them to ensemble feature ranking by ensemble learning and indicating the phenotype association of discovered signature.
Abstract: Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

67 citations


Journal ArticleDOI
TL;DR: In this paper , a measure to quantify the information quality of intuitionistic fuzzy information based on a pseudo probability transformation is proposed, and its induced order is derived to rank intuitionistic values.

66 citations


Journal ArticleDOI
TL;DR: An online tool, POSREG, was constructed to identify the optimal signature for a set of proteomic data and indicates the phenotype association of discovered signature by simultaneously optimizing its reproducibility and generalizability.
Abstract: Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

66 citations


Journal ArticleDOI
TL;DR: In this article , a semi-quantitative risk assessment model has been developed in order to rank MP polymers of potential health concern emerging from marine exposure pathways, and a screening strategy was used to categorize three probability factors and two impact factors and calculate the final risk scores.

44 citations


Journal ArticleDOI
TL;DR: In this paper, the q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method was extended with q-ROFDOSM, which was used in a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution.

42 citations


Journal ArticleDOI
TL;DR: In this paper , the authors propose a method to reconcile the top tail of income and wealth distributions between survey micro-data and anonymous tax data, under the assumption that the rate of representativeness is constant, then decreasing with income.
Abstract: Household surveys often fail to capture the top tail of income and wealth distributions, as evidenced by studies based on tax data. Yet to date there is no consensus on how to best reconcile both sources of information, given the multiple biases at play. This paper contributes a novel method, rooted in standard calibration theory, to directly confront the problem of survey non-response between survey micro-data and anonymous tax data under reasonable assumptions. Our key innovation is to endogenously determine a “merging point” between the datasets, above which we start to incorporate information from tax data into the survey, under the assumption that the rate of representativeness is constant, then decreasing with income. This is followed by a “reweighting” and a “replacing” step, which preserves the microdata structure of the original survey, assuming no re-ranking of observations. We illustrate our approach with simulations, which show that our method is robust to the existence of income misreporting, and performs better than alternative methods. We also apply it to real data from five countries, both developed and less developed, finding changes to the levels and trends in income inequality. We discuss several limits to our approach and suggest some guidelines for future research.

Journal ArticleDOI
TL;DR: In this paper , the q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method was extended with q-ROFDOSM, which was used in a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution.

Journal ArticleDOI
TL;DR: In this article , a Leaky Relu activated Deep Neural Network (LRA-DNN) was proposed for emotion extraction from text, which comes under four categories, such as pre-processing, feature extraction, ranking and classification.


Journal ArticleDOI
TL;DR: In this paper , a homogeneous Pythagorean fuzzy framework was proposed for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.

Journal ArticleDOI
TL;DR: In this article , the scaling properties and underlying processes of the main cryptocurrency databases (Coinmarketcap, Coingecko, BraveNewCoin and Cryptocompare) and exchange platforms (Coinbase, Bitstamp, Bittrex, Cexio and Exmo) were analyzed.

Proceedings ArticleDOI
23 May 2022
TL;DR: The Deep Noise Suppression (DNS) challenge as discussed by the authors was the first one to provide a subjective evaluation framework based on ITU-T P.835 to rate and rank-order the challenge entries.
Abstract: The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. This is the 4th DNS challenge, with the previous editions held at INTERSPEECH 2020 [1], ICASSP 2021 [2], and INTERSPEECH 2021 [3]. We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P.835 to rate and rank-order the challenge entries. We provide access to DNS-MOS P.835 and word accuracy (WAcc) APIs to challenge participants to help with iterative model improvements. In this challenge, we introduced the following changes: (i) Included mobile device scenarios in the blind test set; (ii) Included a personalized noise suppression track with baseline; (iii) Added WAcc as an objective metric; (iv) Included DNSMOS P.835; (v) Made the training datasets and test sets fullband (48 kHz). We use an average of WAcc and subjective scores P.835 SIG, BAK, and OVRL to get the final score for ranking the DNS models. We believe that as a research community, we still have a long way to go in achieving excellent speech quality in challenging noisy real-world scenarios.

Journal ArticleDOI
Wang Zhichao1, Yan Ran1, Yifan Chen1, Xin Yang1, Genbao Zhang1 
TL;DR: In this article, a probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) are used to implement risk assessment of failure modes by a panel of specialists. And the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure mode.
Abstract: Failure mode and effects analysis (FMEA) usually requires multi-domain specialists to implement the group risk assessment for identifying and eliminating system failures. Therefore, this paper combines several multi-criteria decision making (MCDM) techniques with probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) to implement risk assessment of failure modes by a panel of specialists. It aims at overcoming some defects existing in the conventional FMEA, such as without epistemic uncertainty and group risk assessment, as well as with some questions incurring from the risk priority number (RPN). Consequently, group members utilize PHFLTSs to express their subjective uncertain risk assessments on failure modes, in which the social network analysis (SNA) and maximizing consensus method (MCM) are exploited to derive the subjective and objective weights of group members respectively, afterwards their integrated weights are employed to aggregate individual risk assessments into the collective risk assessment. Additionally, the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure modes. Finally, an example with sensitive and comparative analyses is presented to demonstrate the effectiveness of the proposed approach.

Journal ArticleDOI
TL;DR: In this paper , a probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) are used to implement risk assessment of failure modes by a panel of specialists. And the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure mode.
Abstract: Failure mode and effects analysis (FMEA) usually requires multi-domain specialists to implement the group risk assessment for identifying and eliminating system failures. Therefore, this paper combines several multi-criteria decision making (MCDM) techniques with probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) to implement risk assessment of failure modes by a panel of specialists. It aims at overcoming some defects existing in the conventional FMEA, such as without epistemic uncertainty and group risk assessment, as well as with some questions incurring from the risk priority number (RPN). Consequently, group members utilize PHFLTSs to express their subjective uncertain risk assessments on failure modes, in which the social network analysis (SNA) and maximizing consensus method (MCM) are exploited to derive the subjective and objective weights of group members respectively, afterwards their integrated weights are employed to aggregate individual risk assessments into the collective risk assessment. Additionally, the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure modes. Finally, an example with sensitive and comparative analyses is presented to demonstrate the effectiveness of the proposed approach.

Journal ArticleDOI
TL;DR: In this article , a Monte Carlo simulation procedure is developed to select the optimum location of wind farms by using major decision criteria and applying subjective judgments from decision-makers, which is applied to offshore wind farms located in Spain, and the most appropriate locations for turbine positioning are ranked.

Journal ArticleDOI
TL;DR: In this paper , a hybrid model based on q-rung orthopair fuzzy sets (q-ROFSs) which consists of three stages is presented to express the framework definition, calculate the weight coefficients of the criteria, and rank various alternatives.
Abstract: The term metaverse, which shows a 3D-designed virtual medium where people can connect through their avatars to spend time, telecommute, and socialize, has entered our lives fast. There are limitless implementations that can take place in the metaverse. Integration of another technological innovation, which is autonomous vehicles to the metaverse, is at hand. There are numerous alternative uses of autonomous vehicles in the metaverse. In this study, three alternative implementation options for autonomous vehicles in the metaverse are investigated. These alternatives are evaluated using the proposed multi-criteria decision-making (MCDM) method under twelve different criteria, which are grouped under four main aspects, namely technological, societal, legal and ethical, and transportation. A novel hybrid model based on q-rung orthopair fuzzy sets (q-ROFSs) which consists of three stages is presented to express the framework definition, calculate the weight coefficients of the criteria, and rank various alternatives. In the first stage, the structure of the problem is created. In the second stage, q-ROFSs based OPA algorithm is used to calculate the weights of the criteria. In the last stage, q-ROFSs based RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) is applied to choose the best alternative among the three alternatives. Finally, we present a case study to verify our proposed method. The results of this study have the potential to be used as a guide by decision-makers of the metaverse while integrating autonomous vehicles into the transportation system.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a clustering-and maximum consensus-based resolution framework with linguistic distribution for social network large-scale group decision making (SNLGDM) problems.

Journal ArticleDOI
TL;DR: In this article , a survey to country experts around the world, asking them to assess the performance of their country on 13 key indicators of EOL care, was conducted and the results were combined with preference weights from caregivers-proxies of recently deceased patients to generate a preference-weighted summary score.

Journal ArticleDOI
TL;DR: In this article, the authors explored and evaluated the drivers that foster agility in international high-tech small and medium-sized enterprises (SMEs) in the context of digital transformation.

Journal ArticleDOI
TL;DR: In this paper , the influence of the journal impact factor (JIF) on the FAP process was quantified by comparing its relative weight between papers with two extreme JIFs.
Abstract: A rigorous faculty appointment and promotion (FAP) system is vital for the success of any academic institution. However, studies examining the FAP system in Asian universities are lacking. We surveyed the FAP policies of Taiwan's medical schools and identified an overreliance on the CJA score (manuscript Category, Journal quality, and Author order). The potential shortcomings of this metric and recommendations for refinement were discussed.We obtained the FAP documents from all 12 medical schools in Taiwan, and analyzed their use of traditional versus non-traditional criteria for FAP according to a published methodology. The influence of the journal impact factor (JIF) on the FAP process was quantified by comparing its relative weight between papers with two extreme JIFs. To better understand the research impact and international standing of each school, we utilized the public bibliographic database to rank universities by the number of papers, and the proportions of papers within the top 10% or 50% citation.Compared with other countries, Taiwan's medical schools focus more on the quantifiable quality of the research, mostly using a "CJA" score that integrates the category, JIF or ranking, and authorship of a paper, with the JIF being the most influential factor. The CJA score for an article with a JIF of 20 can be up to three times the threshold for promotion to Assistant Professor. The emphasis on JIF is based on a presumed correlation between JIF and citation counts. However, our analysis shows that Taiwan's medical schools have lower-than-average citation counts despite a competitive rank in the number of publications.The JIF plays an unrivaled role in determining the outcome of FAP in Taiwan's medical schools, mostly via the CJA system. The questionable effectiveness of the current system in elevating the international standing of Taiwan's higher-education institutions calls for a re-examination of the FAP system. We recommend a reduction in the relative importance of CJA score in the FAP system, adopting more rigorous metrics such as the h-index for evaluating research quality, and supporting more research aimed at improving the FAP system.

Journal ArticleDOI
TL;DR: In this article , a multi-attribute decision-analysis framework was introduced to rank and select the alternative fuel vehicles (AFVs) for a private home healthcare service provider in Chandigarh, India.
Abstract: Alternative fuel vehicles (AFVs) offer opportunities to lower fuel costs as well as to reduce greenhouse gas emissions, and, therefore, they are a feasible option for customers in the market. Due to technological advancements, decisions about suitable alternative fuel vehicles are a challenging problem for fleet operators. This paper aims to introduce a multi-attribute decision-analysis framework to rank and select the “alternative fuel vehicles (AFVs)” for a private home healthcare service provider in Chandigarh, India. The selection of AFVs can be treated as a decision-making problem, because of the presence of various qualitative and quantitative attributes. Thus, the current work introduces an integrated decision-making framework based on intuitionistic fuzzy-“method based on the removal effects of criteria (MEREC)”, “ranking sum (RS)”, and the “double normalization-based multi-aggregation (DNMA)” framework for assessing the AFVs. The combination of MEREC and RS is applied to assess the objective and subjective weighting values of various parameters for AFV assessment. The DNMA approach is utilized to prioritize the different AFVs over various significant parameters. According to the outcomes, the most significant parameters for AFV assessment are social benefits, fueling/charging infrastructure, and financial incentives, respectively. In this context, globally existing AFVs for the sustainable transportation sector are identified, and then prioritized against fifteen different criteria relevant to the environmental, economic, technological, social, and political aspects of sustainability. It is distinguished that electric vehicles (G2), hybrid electric vehicles (G1), and hydrogen vehicles (G3) achieve higher overall performance compared to the other technologies available in India. The assessment outcomes prove that electric vehicles can serve as a valuable alternative for decreasing carbon emissions and negative effects on the environment. This technology contributes to transportation sector development and job creation in less developed areas of the country. Moreover, a comparison with existing studies and a sensitivity investigation are conferred to reveal the robustness and stability of the developed framework.

Journal ArticleDOI
TL;DR: In this article , complex Fermatean fuzzy N-soft set (CFFNSfS) is proposed to handle two-dimensional information related to the degree of satisfaction and dissatisfaction implicit in the nature of human decisions.
Abstract: Decision-making methods play an important role in the real-life of human beings and consist of choosing the best options from a set of possible choices. This paper proposes the notion of complex Fermatean fuzzy N-soft set (CFFNSfS) which, by means of ranking parameters, is capable of handling two-dimensional information related to the degree of satisfaction and dissatisfaction implicit in the nature of human decisions. We define the fundamental set-theoretic operations of CFFNSfS and elaborate the CFFSfS associated with threshold. The algebraic and Yager operations on CFFNSf numbers are also defined. Several algorithms are proposed to demonstrate the applicability of CFFNSfS to multi-attribute decision making. The advanced algorithms are described and accomplished by several numerical examples. Then, a comparative study manifests the validity, feasibility, and reliability of the proposed model. This method is compared with the Fermatean fuzzy Yager weighted geometric (FFYwG) and the Fermatean fuzzy Yager weighted average (FFYwA) operators. Further, we developed a remarkable CFFNSf-TOPSIS approach by applying innovative CFFNSf weighted average operator and distance measure. The presented technique is fantastically designed for the classification of the most favorable alternative by examining the closeness of all available choices from particular ideal solutions. Afterward, we demonstrate the amenability of the initiated approach by analyzing its tremendous potential to select the best city in the USA for farming. An integrated comparative analysis with existing Fermatean fuzzy TOPSIS technique is rendered to certify the terrific capability of the established approach. Further, we decisively investigate the rationality and reliability of the presented CFFNSfS and CFFNSf-TOPSIS approach by highlighting its advantages over the existent models and TOPSIS approaches. Finally, we holistically describe the conclusion of the whole work.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a retrieval with clustering-guided contrastive learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance.

Journal ArticleDOI
TL;DR: In this paper , the authors investigated the antecedents that affect consumers' acceptance of behavioral targeting advertising (BTA) services by extending technology acceptance model 2 (TAM2) with perceived risk.

Journal ArticleDOI
TL;DR: In this article , the authors proposed a new integrated multi-criteria decision-making model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs).
Abstract: The selection of proper healthcare device suppliers in sustainable organ transplantation networks has become an essential topic of increasing life expectancy. Assessment of sustainable healthcare device suppliers can be regarded as a complex multi-criteria decision-making (MCDM) problem that consists of multiple alternative solutions with sustainable criteria. For this reason, this paper proposes a new integrated MCDM model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs). The aggregating technique of the E-VIKOR method is a strong point of this method compared to the original approach. The IVIFS is taken to cope with the uncertain situation of real-world applications. In this regard, an IVIF-similarity measure is introduced to compute weights of the decision-makers (DMs). The IVIF-Shannon entropy method is utilized to calculate the criteria weights, and a new hybrid proposed model is developed by presenting the IVIF-E-VIKOR method and IVIF-MARCOS, to calculate the ranking of sustainable supplier alternatives in organ transplantation networks to supply the surgery devices. Afterward, an illustrative example is introduced to evaluate the performance of the proposed model, and a comparative analysis is presented to confirm and validate the proposed approach. Moreover, sensitivity analysis for essential parameters of the proposed model is performed to assess their effects on outcomes.