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

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Abstract

This study aimed to develop a new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin detectors based on Internet of things (IoT) by using multi-criteria decision-making (MCDM). The novelty of this work is in the use of an evaluation matrix for the performance evaluation of real-time skin detectors that are based on IoT. Nevertheless, an issue with the performance evaluation of real-time skin detector approaches is the determination of sensible criteria for performance metrics and the trade-off amongst them on the basis of different colour spaces. An experiment was conducted on the basis of three phases. In the first phase, a real-time camera based on cloud IoT was used to gather different caption images. The second phase could be divided into two stages. In the first stage, a skin detection approach was developed by applying multi-agent learning based on different colour spaces. This stage aimed to create a decision matrix of various colour spaces and three groups of criteria (i.e. reliability, time complexity and error rate within a dataset) for testing and evaluating the developed skin detection approaches. In the second stage, Pearson rules were utilised to calculate the correlation between the criteria in order to make sure, either needs to use all of the criteria in decision matrix and the criteria facts that affect the behaviour of each criterion, in order to make sure that use all the criteria in evaluation as multidimensional measurements or not. In the third phase, the MCDM method was used by integrating between a technique in order of preference by similarity to the ideal solution and multi-layer analytic hierarchy process to benchmark numerous real-time IoT skin detection approaches based on the performed decision matrix from the second phase. Three groups of findings were obtained. Firstly, (1) statistically significant differences were found between the criteria that emphasise the need to use all of the criteria in evaluation. (2) The behaviour of the criteria in all scenarios was affected by the distribution of threshold values for each criterion based on the different colour spaces used. Therefore, the differences in the behaviour of criteria that highlight the use of the criteria in evaluation were included as multidimensional measurements. Secondly, an overall comparison of external and internal aggregation values in selecting the best colour space, namely the normalised RGB at the sixth threshold, was discussed. Thirdly, (1) the YIQ colour space had the lowest value and was the worst case, whereas the normalised RGB had the highest value and was the most recommended of all spaces. (2) The lowest threshold was obtained at 0.5, whereas the best value was 0.9.

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  1. Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

    A. A. Zaidan, B. B. Zaidan, O. S. Albahri & M. Y. Qahtan

  2. College of Administration and Economic, University of Mosul, Mosul, Iraq

    M. A. Alsalem

  3. Iraqi Commission for Computers & Informatics, Baghdad, Iraq

    A. S. Albahri

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  1. A. A. Zaidan
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  2. B. B. Zaidan
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  3. M. A. Alsalem
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  4. O. S. Albahri
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  5. A. S. Albahri
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  6. M. Y. Qahtan
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Correspondence to A. A. Zaidan.

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Appendix

Appendix

See Tables 17, 18, 19, 20, 21, and 22.

Table 17 First evaluator results to evaluate and benchmark for different colour space algorithms
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Table 18 Second evaluator results to evaluate and benchmark for different colour space algorithms
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Table 19 Third evaluator results to evaluate and benchmark for different colour space algorithms
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Table 20 Fourth evaluator results to evaluate and benchmark for different colour space algorithms
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Table 21 Fifth evaluator results to evaluate and benchmark for different colour space algorithms
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Table 22 Sixth evaluator results to evaluate and benchmark for different colour space algorithms
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Zaidan, A.A., Zaidan, B.B., Alsalem, M.A. et al. Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput & Applic 32, 8315–8366 (2020). https://doi.org/10.1007/s00521-019-04325-3

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