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Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients' data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients' score and dose availability without sharing the patients' data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death. [ABSTRACT FROM AUTHOR]

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID‐19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters. [ABSTRACT FROM AUTHOR]

In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, whereas 'misclassification error' has the lowest weight. In the ranking results, a variation of ranking is scored by each expert, and group decision-making is applied to solve the individual ranking variety. The robustness of the formulated methods is evaluated using systematic ranking, sensitivity analysis and comparison analysis. [ABSTRACT FROM AUTHOR]

Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented. [ABSTRACT FROM AUTHOR]

Despite the wide range of research on pedestrian safety, previous studies have failed to analyse the real-time data of pedestrian walking misbehaviour on the basis of either pedestrian behaviour distraction or movements during specific activities to realise pedestrian safety for positive (normal) or aggressive pedestrians. Practically, pedestrian walking behaviour should be recognised, and aggressive pedestrians should be differentiated from normal pedestrians. This type of pedestrian behaviour recognition can be converted into a classification problem, which is the main challenge for pedestrian safety systems. In addressing the classification challenge, three issues should be considered: identification of factors, collection of data and exchange of data in the contexts of wireless communication and network failure. Thus, this work proposes a novel approach to pedestrian walking behaviour classification in the aforementioned contexts. Three useful phases are proposed for the methodology of this study. In the first phase involving factor identification, several factors of the irregular walking behaviour of mobile phone users are established by constructing a questionnaire that can determine users' options (attitudes/opinions) about mobile usage whilst walking on the street. In the second phase involving data collection, four different testing scenarios are developed to acquire the real-time data of pedestrian walking behaviour by using gyroscope sensors. In the third phase involving data exchange, the proposed approach is presented on the basis of two modules. The first module for pedestrian behaviour classification uses random forest and decision tree classifiers part of machine learning techniques via wireless communication when a server becomes available. The developed module is then trained and evaluated using five category sets to obtain the best classification of pedestrian walking behaviour. The second module is based on four standard vectors for classifying pedestrian walking behaviour when a server is unavailable. Fault-tolerant pedestrian walking behaviour is identified and is initiated when failures occur in a network. Two sets of real-time data are presented in this work. The first dataset is related to the questionnaire data from 262 sampled respondents, and the second dataset comprises data on 263 sampled participants with pedestrian walking signals. Experimental results confirm the efficacy of the proposed approach relative to previous ones. [ABSTRACT FROM AUTHOR]