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Multi-agent Architecture for Internet of Medical Things

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Abstract

The technological advancements in recent years has enabled the creation of the Internet of Medical Things, i.e. solutions where medical devices can communicate with each other and exchange data. The guiding idea is to model solutions that can reduce the amount of expected time for analysis of examination results, quick response in the case of diseases as well as assist doctors. In this paper, we propose a solution based on a multi-agent system, where agents are adapted to use classifiers based on artificial intelligence techniques. In the proposed model, we also analyze patient data security and describe the solution so that it is possible to use data in training classifiers without affecting their patient identity. Our approach has been tested on solution simulations based on the most popular technique of artificial intelligence – convolutional neural network.

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References

  1. Abdulghafor, R., Turaev, S., Zeki, A., Abubaker, A.: Nonlinear convergence algorithm: structural properties with doubly stochastic quadratic operators for multi-agent systems. J. Artif. Intell. Soft Comput. Res. 8(1), 49–61 (2018)

    Article  Google Scholar 

  2. Albahar, M.A.: Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access 7, 38306–38313 (2019)

    Article  Google Scholar 

  3. Alkhazaleh, S., Hazaymeh, A.A.: N-valued refined neutrosophic soft sets and their applications in decision making problems and medical diagnosis. J. Artif. Intell. Soft Comput. Res. 8(1), 79–86 (2018)

    Article  Google Scholar 

  4. Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: Abc-svm: artificial bee colony and svm method for microarray gene selection and multi class cancer classification. Int. J. Mach. Learn. Comput 6(3), 184 (2016)

    Article  Google Scholar 

  5. Alsubaei, F., Abuhussein, A., Shiva, S.: Security and privacy in the Internet of medical things: taxonomy and risk assessment. In: 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops), pp. 112–120. IEEE (2017)

    Google Scholar 

  6. Balestrieri, E., et al.: The architecture of an innovative smart t-shirt based on the Internet of medical things paradigm. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2019)

    Google Scholar 

  7. Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172. IEEE (2018)

    Google Scholar 

  8. Dilawar, N., Rizwan, M., Ahmad, F., Akram, S.: Blockchain: securing Internet of medical things (iomt). Int. J. Adv. Compu.t Sci. Appl. 10(1), 82–89 (2019)

    Google Scholar 

  9. Dinh, T.T.A., Liu, R., Zhang, M., Chen, G., Ooi, B.C., Wang, J.: Untangling blockchain: a data processing view of blockchain systems. IEEE Trans. Knowl. Data Eng. 30(7), 1366–1385 (2018)

    Article  Google Scholar 

  10. Dubey, H., et al.: Fog computing in medical Internet-of-Things: architecture, implementation, and applications. In: Khan, S.U., Zomaya, A.Y., Abbas, A. (eds.) Handbook of Large-Scale Distributed Computing in Smart Healthcare. SCC, pp. 281–321. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58280-1_11

    Chapter  Google Scholar 

  11. El-Zeheiry, H., Elmogy, M., Elaraby, N., Barakat, S.: Fuzzy c-mean and density-based spatial clustering for internet of things data processing. In: Hassanien, A.E., Dey, N., Borra, S. (eds.) Med. Big Data Internet Med. Things, pp. 161–187. CRC Press, Boca Raton (2018)

    Chapter  Google Scholar 

  12. Elhoseny, M., Bian, G.B., Lakshmanaprabu, S., Shankar, K., Singh, A.K., Wu, W.: Effective features to classify ovarian cancer data in Internet of medical things. Comput. Netw. 159, 147–156 (2019)

    Article  Google Scholar 

  13. Gordon, W.J., Catalini, C.: Blockchain technology for healthcare: facilitating the transition to patient-driven interoperability. Comput. Struct. Biotechn. J. 16, 224–230 (2018)

    Article  Google Scholar 

  14. Lamonaca, F., et al.: An overview on Internet of medical things in blood pressure monitoring. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2019)

    Google Scholar 

  15. Magsi, H., Sodhro, A.H., Chachar, F.A., Abro, S.A.K., Sodhro, G.H., Pirbhulal, S.: Evolution of 5g in Internet of medical things. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1–7. IEEE (2018)

    Google Scholar 

  16. Mizera, M., Nowotarski, P., Byrski, A., Kisiel-Dorohinicki, M.: Fine tuning of agent-based evolutionary computing. J. Artif. Intell. Soft Comput. Res. 9(2), 81–97 (2019)

    Article  Google Scholar 

  17. Suganthi, M.V., Elavarasi, M.K., Jayachitra, M.J.: Tele-health monitoring system in a rural community through primary health center using Internet of medical things. Int. J. Pure Appl. Math. 119(14), 695–703 (2018)

    Google Scholar 

  18. Tang, H., Shi, Y., Dong, P.: Public blockchain evaluation using entropy and TOPSIS. Expert Syst. Appl. 117, 204–210 (2019)

    Article  Google Scholar 

  19. Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)

    Article  Google Scholar 

  20. Walczak, S., Velanovich, V.: Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks. Decis. Support Syst. 106, 110–118 (2018)

    Article  Google Scholar 

  21. Winnicka, A., Kesik, K.: Idea of using blockchain technique for choosing the best configuration of weights in neural networks. Algorithms 12(8), 163 (2019)

    Article  Google Scholar 

  22. Xu, G., Zhang, M., Zhu, H., Xu, J.: A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM. Gene 604, 33–40 (2017)

    Article  Google Scholar 

  23. Xu, J., Wang, S., Bhargava, B., Yang, F.: A blockchain-enabled trustless crowd-intelligence ecosystem on mobile edge computing. IEEE Trans. Industr. Inf. 15, 3538–3547 (2019)

    Article  Google Scholar 

  24. Zhang, W., Yang, J., Su, H., Kumar, M., Mao, Y.: Medical data fusion algorithm based on Internet of Things. Pers. Ubiquit. Comput. 22(5–6), 895–902 (2018). https://doi.org/10.1007/s00779-018-1173-y

    Article  Google Scholar 

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Correspondence to Dawid Połap .

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Połap, D., Srivastava, G., Woźniak, M. (2020). Multi-agent Architecture for Internet of Medical Things. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-61534-5_5

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  • Online ISBN: 978-3-030-61534-5

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