Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

Zaidan, A. A.; Zaidan, B. B.; Alsalem, M. A.; Albahri, O. S.; Albahri, A. S.; Qahtan, M. Y.

doi:10.1007/s00521-019-04325-3

Cited by 69 publications

(24 citation statements)

References 109 publications

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“…The newest trend regarding the use of MCDM methods is to combine two or more methods to recoup the weaknesses of a single method [ 63 – 73 ]. AHP and TOPSIS have become a commonly integrated MCDM method [ 74 – 76 ].…”

Section: Methodsmentioning

confidence: 99%

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

et al. 2021

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As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CPtransfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

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Section: Methodsmentioning

confidence: 99%

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

et al. 2021

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“…MCDM can enhance the quality of decisions; it provides the decision-makers to choose right decisions more effectively and rationally than the traditional processes [233][234][235]. MCDM includes (A) ranking available alternatives and (B) determining the best alternative from available set of alternatives [236,237]. Accordingly, the suitable alternative(s) will be scored [238,239].…”

Section: Mcdm: Definition and Importancementioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

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Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decisionmaking theory to address the current problems in the prioritisation of patients with MCDs.

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“…The scoring of suitable alternative(s) are then performed accordingly [77], [78]. In any MADM/MCDM ranking, fundamental terms are defined, which include decision or evaluation matrix (DM/EM) alternatives and criteria [79]- [81]. A DM/EM that comprises m alternatives and n criteria is necessary [82], [83].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment and Ranking Framework for the English Skills of Pre-Service Teachers Based on Fuzzy Delphi and TOPSIS Methods

et al. 2019

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In this study, pre-service teaching refers to teaching English as a second language (TESL) to Malaysian students whose first language is not English. TESL prepares English-language learners to become future teachers of English as a second language. To date, no multi-criteria framework has been developed to evaluate and select the skills of pre-service teachers. This study presents a new framework to assess and rank the English skills of pre-service teachers on the basis of fuzzy Delphi and multi-criteria analysis. Three experiments were conducted. Firstly, criteria were identified from the literature review and the opinions of representative experts via the Delphi method. Secondly, 31 pre-service teachers were evaluated to determine the skills of pre-service teachers on the basis of Delphi criteria outcomes. English proficiency was tested through the English Language Testing Service and four language skill examinations. Each examination was evaluated by experts with vast experience in English teaching. Thirdly, pre-service teachers were ranked on the basis of a set of evaluated Delphi criteria outcomes through the technique for the order of preference by similarity to ideal solution (TOPSIS) method. Thereafter, the mean and standard deviation were utilized to ensure the identical systematic ranking of pre-service teachers. Findings are as follows. Twenty-five criteria from previous studies are representative as evaluated by the opinions of experts, which were gathered through interviews and a structured questionnaire. The validity of content was verified using a five-point Likert scale. With Delphi method outcomes, 14 criteria were selected and included in the final framework. The results of the proposed evaluation framework were tested on Malaysian pre-service teachers. TOPSIS is effective for solving the selection problems of pre-service teachers. In the final experiment, significant differences were recognized between the scores of groups, indicating identical ranking results.

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Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

Cited by 69 publications

References 109 publications

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

Assessment and Ranking Framework for the English Skills of Pre-Service Teachers Based on Fuzzy Delphi and TOPSIS Methods

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