Context: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. Objective: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

This paper proposes a smart real-time health monitoring structured for hospitals' distributor based on wearable health data sensors. Health data were received from multiple heterogeneous wearable sensors, such as electrocardiogram (ECG), oxygen saturation sensor (SpO2), blood pressure monitor, and non-sensory measurement (text frame), from 500 patients with different symptoms. Triage level and healthcare services were identified based on the new four-level remote triage and package localization (4LRTPL). The numbers of healthcare services that represent hospital status were collected from 12 hospitals located in Baghdad city. This study constructed a decision matrix based on the crossover of ''multi-healthcare services'' and ''hospital list'' within Tier 4. The hospitals were then ranked using multicriteria decision-making (MCDM) techniques, namely, integrated analytic hierarchy process (AHP) and vlsekriterijumskaoptimizacija i kompromisnoresenje (VIKOR). Mean ± standard deviation was computed to ensure that the hospital ranking undergoes systematic ranking for objective validation. This research provided scenarios and checklist benchmarking to evaluate the proposed and existing health recommender frameworks. Results corroborated that: 1) the integration of AHP and VIKOR effectively solved hospital selection problems; 2) in the objective validation, significant differences were recognized between the scores of groups, indicating that the ranking results were identical; 3) in evaluation, the proposed framework exhibited an advantage over the benchmark framework with a percentage of 56.25%; and 4) hospitals with multiple healthcare services received the highest ranks, whereas hospitals with fewer healthcare services received low ranks.INDEX TERMS Real-time remote monitoring, hospital management, hospital selection, chronic heart, healthcare services, triage, wearable health sensor.

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Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical ( n = 30), social ( n = 4), economic ( n = 13) and technological ( n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling a...

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The increase in the number of open source software (OSS) users have drawn attention to improving usability. Usability is a clear concept that encompassing both task and user characteristics as well as functionality. Usability is an essential factor that affects user acceptance and OSS sustainability, which is considered as the key to the success of the OSS. To some extent, usability is one concern of the larger issue of system acceptability and sustainability. Therefore, usability is an important factor that needs to be considered since the software that is not usable is not going to be sustainable. The objective of this paper is to review researchers' efforts to improve, investigate, and evaluate the usability factor that may affect the OSS acceptability and sustainability and map the research scenery from the articles into a comprehensible structured taxonomy, which would help the researchers to identify different research gaps of this field. A survey of the usability in OSS conducted and 6033 studies identified by a search in four scholarly databases using a query that includes the keywords (usability or learnability or efficiency or satisfaction) and (open source software or OSS). A total of 46 studies are selected. By manually searching in ACM, Springer, and Google Scholar five other studies identified, and thus a total of 51 studies were the final set that includes in this paper. Based on research topics, a taxonomy created and divided into four principal categories which improve OSS usability, analyze OSS usability, evaluate OSS usability, and select and adopt OSS. A comprehensive overview and synthesis of these categories are presented as well. This paper contributes to identifying the possible opportunities and gaps for enabling the participation of interested researchers in this research area. And give possibilities for extending the use of usability research and practices to create more sustainable software. Also, helps in selecting suitable OSS among the alternatives.

Increasing demand for open-source software (OSS) has raised the value of efficient selection in terms of quality; usability is an essential quality factor that significantly affects system acceptability and sustainability. Most large and complex software packages partitioned across multiple portals and involve many users — each with their role in the software package; those users have different perspectives on the software package, defined by their knowledge, responsibilities, and commitments. Thus, a multi-perspective approach has been used in usability evaluation to overcome the challenge of inconsistency between users’ perspectives; the inconsistency challenge would lead to an ill-advised decision on the selection of a suitable OSS. This study aimed to assist the public and private organizations in evaluating and selecting the most suitable OSS. The evaluation of the OSS software packages to choose the best one is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, and (c) data variation; thus, it is considered a sophisticated multi-criteria decision making (MCDM) problem; moreover, the multi-perspective usability evaluation framework for OSS selection lacks in the current literature. Hence, this study proposes a novel multi-perspective usability evaluation framework for the selection of OSS based on the multi-criteria analysis. Integration of best-worst method (BWM) and VIKOR MCDM techniques has been used for weighting and ranking OSS alternatives. BWM is utilized for weighting of evaluation criteria, whereas VIKOR is applied to rank OSS-LMS alternatives. Individual and group decision-making contexts, and the internal and external groups aggregation were used to demonstrate the efficiency of the proposed framework. A well-organized algorithmic procedure is presented in detail, and a case study was examined to illustrate the validity and feasibility of the proposed framework. The results demonstrated that BWM and VIKOR integration works effectively to solve the OSS software package benchmarking/selection problems. Furthermore, the ranks of OSS software packages obtained from the VIKOR internal and external group decision making were similar; the best OSS-LMS based on the two ways was ‘Moodle’ software package. Among the scores of groups in the objective validation, significant differences were identified; this indicated that the ranking results of internal and external VIKOR group decision making were valid, which pointed to the validation of the framework.