Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm

Šimić, Vladimir; Torkayesh, Ali Ebadi; Maghsoodi, Abteen Ijadi

doi:10.1007/s10479-022-04822-0

Cited by 30 publications

(11 citation statements)

References 104 publications

(103 reference statements)

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“…For this purpose, MABAC can be improved by an extension under extended Z-numbers (Tian et al, 2021) where experts' judgments on the decision-makers' opinions are considered in the decision-making process for more reliable and robust solutions. Another important future direction is related to the integration of the MABAC method with big data tools in order to develop big data decision support systems for various complex problems (Maghsoodi et al, 2023;Simic et al, 2022). Finally, the integration of the MABAC method with probabilistic linguistic information is anticipated to be investigated since it is natural for decision-makers to employ probabilistic linguistic information .…”

Section: Discussionmentioning

confidence: 99%

A Systematic Literature Review of MABAC Method and Applications: An Outlook for Sustainability and Circularity

Torkayesh¹,

Tirkolaee²,

Bahrini³

et al. 2023

Informatica

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Multiple Criteria Decision-Making (MCDM) is one of the most reliable and applicable decision-making tools to address real-life complex and multi-dimensional problems in accordance with the concepts of sustainable development and circular economy. Although there have been several literature reviews on several MCDM methods, there is a research gap in conducting a literature review on the Multi-Attributive Border Approximation area Comparison (MABAC) as a useful technique to deal with intelligent decision-making systems. This study attempts to present a comprehensive literature review of 117 articles on recent developments and applications of MABAC. Future outlook is provided considering challenges and current trends.

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Section: Discussionmentioning

confidence: 99%

A Systematic Literature Review of MABAC Method and Applications: An Outlook for Sustainability and Circularity

Torkayesh¹,

Tirkolaee²,

Bahrini³

et al. 2023

Informatica

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“…For future work, ensemble learning [65], [66] could be considered to improve the accuracy and robustness of a decision-making system by combining the outputs of multiple models. In addition, state-of-art feature selection technologies [67]- [69] could be applied to reduce the dimensionality of the dataset, simplify the model, and improve its accuracy and generalization ability.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Prediction for Neonatal Endotracheal Intubation Using Multimodal Transformer Network

Yoon

Shin

et al. 2023

IEEE J. Biomed. Health Inform.

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Neonates admitted to neonatal intensive care units (NICUs) are at risk for respiratory decompensation and may require endotracheal intubation. Delayed intubation is associated with increased morbidity and mortality, particularly in urgent unplanned intubation. By accurately predicting the need for intubation in real-time, additional time can be made available for preparation, thereby increasing the safety margins by avoiding high-risk late intubation. In this study, the probability of intubation in neonatal patients with respiratory problems was predicted using a deep neural network. A multimodal transformer model was developed to simultaneously analyze time-series data (1-3 h of vital signs and FiO 2 setting value) and numeric data including initial clinical information. Over a dataset including information of 128 neonatal patients who underwent noninvasive ventilation, the proposed model successfully predicted the need for intubation 3 h in advance (area under the receiver operator characteristic curve = 0.880 ± 0.051, F1-score = 0.864 ± 0.031, sensitivity = 0.886 ± 0.041, specificity = 0.849 ± 0.035, and accuracy = 0.857 ± 0.032). Moreover, the proposed model showed high generalization ability by achieving AUROC 0.890, F1-score 0.893, specificity 0.871, sensitivity 0.745, and accuracy 0.864 with an additional 91 dataset for testing.

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“…The decisionmaking process in this method primarily relies on the concept of utility function. The utility function can be conceptualized as an alternative perspective linked to optimal and suboptimal solutions [16,17]. Hence, the optimal selection is the option that exhibits the greatest proximity to the ideal point, thereby resulting in the greatest distance from the anti-ideal point.…”

Section: Ranking the Alternatives Using The Marcos Methodsmentioning

confidence: 99%

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

Badi,

Bouraima,

Qiu

et al. 2024

Decis. Mak. Adv.

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Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.

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Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm

Cited by 30 publications

References 104 publications

A Systematic Literature Review of MABAC Method and Applications: An Outlook for Sustainability and Circularity

A Systematic Literature Review of MABAC Method and Applications: An Outlook for Sustainability and Circularity

Real-Time Prediction for Neonatal Endotracheal Intubation Using Multimodal Transformer Network

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

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