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Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

The COVID-19 pandemic has caused overwhelming levels of medical waste, resulting in constant threats to environmental pollution. Furthermore, many environmental issues related to medical waste have emerged. This study aims to propose an application that allows the identification and classification of hospitals that generate overwhelming levels of medical waste aftermath of the COVID-19 pandemic by using Multi-Criteria Decision-Making methods (MCDM). MCDM was designed on the integration of the Analytic Hierarchy Process (AHP), linear diophantine fuzzy set-fuzzy decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis. Ten hospital managers were interviewed to determine the volume of medical waste generated by the hospitals they manage. Five types of medical waste were identified: general waste, sharps waste, pharmaceutical waste, infectious waste, and pathological waste. Among these five types, pharmaceutical waste is appointed as one that most impacts the environment. After that 313 experts in the health sector with experience in sustainability techniques were targeted to determine the best and worst technique for the Circular Economy to manage medical waste using the neural network approach. Findings also revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporised hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most vital and effective methods to dispose of medical waste during the pandemic. Additionally, ozone was ranked first as the most Circular Economy-related method for medical waste disposal. Among the implications of this study for governments, policymakers, and practitioners identify actions that hospitals may consider regarding the Circular Economy concept. Another implication is the supportive role of policymakers in transitioning most pollutant hospitals to becoming more sustainable.

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Context: Denial-of-Service Attack countermeasure techniques (DoS A-CTs) evaluation is a multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform design, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against Denial-of-Service (DoS) attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weigh the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. Objectives: This research proposes a Fermatean-FDOSM framework for evaluating DoS A-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. Methods: The methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included eighteen defense approaches (e.g., Sniffer, SeRA, and RLAN) based on thirteen criteria (e.g., size, power, latency, and effectiveness ... etc.). Then, the decision matrix was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results: (1) CRITIC weighting shows that MPSoC NoC Routing Algorithm (XY and YX) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. ( 2) The Fermatean-FDOSM-based group ranking shows that the Collision Point Router Detection (CPRD) countermeasure technique is the first-ranked alternative compared to the Secure Model Checkers (SMCs) approach. (3) The DoS A-CTs priority ranks were subjected to a systematic ranking that was confirmed by solid correlation results throughout thirteen criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. Conclusion: The results of this research are expected to provide a specific understanding and guide for those who want to engage in MPSoCs-based IoT and NoC communication security research with decision theory.