Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues &amp; challenges, and recommended solutions

Yas, Qahtan M.; Zaidan, A. A.; Zaidan, B. B.; Rahmatullah, Bahbibi; Karim, Hezerul Abdul

doi:10.1016/j.measurement.2017.09.027

Cited by 62 publications

(32 citation statements)

References 113 publications

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“…The ranking aspect is the main principle for any MCDM and DM. It is based on the evaluation matrix (EM) within different criteria [30], [31]. In EM, n criteria and m alternative are created.…”

Section: Multi-criteria Decision Making (Mcdm)mentioning

confidence: 99%

“…In the scientific literature also there are many MCDM techniques including hierarchical adaptive weighting(HAW), analytic hierarchy process (AHP), the weighted product method(WPM), the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), the weighted sum model(WSM), simple additive weighting(SAW), the bestworst method (BWM), Multiplicative exponential weighting(MEW), the technique order of preference by similarity to ideal solution (TOPSIS) and the analytic network process(ANP) [31]- [43]. The huge number of MCDM methods makes the selection task more difficult as each method has its advantages and disadvantages [41], [44].…”

Section: Multi-criteria Decision Making (Mcdm)mentioning

confidence: 99%

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Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

et al. 2020

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Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value. INDEX TERMS COVID19 diagnostic, machine learning, benchmarking methodology, chest X-rays images, entropy, TOPSIS, multi-criteria decision-making. The associate editor coordinating the review of this manuscript and approving it for publication was Zheng Xiao .

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Section: Multi-criteria Decision Making (Mcdm)mentioning

confidence: 99%

Section: Multi-criteria Decision Making (Mcdm)mentioning

confidence: 99%

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

et al. 2020

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“…Kodati and Vivekanandam have presented a paper on Orange and WEKA tools of DM for analyzing heart disease [7].…”

Section: Literatute Reviewmentioning

confidence: 99%

Latest Tools for Data Mining and Machine Learning

Verma¹,

Bhardwaj²,

Arya³

et al. 2019

IJITEE

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Nowadays, Data Mining is used everywhere for extracting information from the data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns which are used to extract information and knowledge for making decisions. Many open source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for Data Mining and predictive analysis. This paper discusses about different tools available for Data Mining and Machine Learning, followed by the description, pros and cons of these tools. The article provides details of all the algorithms like classification, regression, characterization, discretization, clustering, visualization and feature selection for Data Mining and Machine Learning tools. It will help people for efficient decision making and suggests which tool is suitable according to their requirement.

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“…Detecting health conditions by using the most important criteria with respect to the appropriate weight allocated for each criteria is considered a multi-criteria decision matrix that can describe the specific problems of prioritisation [ 24 , 25 ]. Different weights are often given for various criteria which further increase the complexity of the task [ 26 ]. According to medical guidelines [ 4 , 7 ], each criterion has its reference range of examination results.…”

Section: Introductionmentioning

confidence: 99%

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Albahri

Hamid

Albahri

et al. 2021

Artificial Intelligence in Medicine

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Highlights Develop a new framework that can handle the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. The most important laboratory criteria were selected and implemented based on two feature selection approaches (i.e. data-driven and knowledge-driven). A new decision matrix was developed based on a crossover of (i) multi-laboratory characteristics criteria and (ii) lists of infected patients for patient prioritisation using integration Entropy-TOPSIS methods. Advantages of new framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

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Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions

Cited by 62 publications

References 113 publications

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Latest Tools for Data Mining and Machine Learning

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

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