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Fusion: Practice and Applications
Volume 9 , Issue 2, PP: 62-73 , 2022 | Cite this article as | XML | Html |PDF

Title

Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment

  Reem Atassi 1 * ,   Fuad Alhosban 2

1  Higher Colleges of Technology, UAE
    (ratassi@hct.ac.ae)

2  Higher Colleges of Technology, UAE
    (falhosban@hct.ac.ae)


Doi   :   https://doi.org/10.54216/FPA.090205

Received: June 15, 2022 Accepted: September 03, 2022

Abstract :

Healthcare transformation is becoming one of the highest priorities in a world whereby remarkable advances in technology are taking place. Recent healthcare data fusion management systems are centralized, which possess the probability of failure in case of a natural disaster. Blockchain has expanded fast to be the most widely spoken innovation that could address a large number of present data management problems in the health care sector. The usage of blockchain technology for the distribution of secure and safe health care datasets has received all the attention. This article presents a Bat Optimization Algorithm with Fuzzy Neural Network Based Classification (BOA-FNNC) Model for Blockchain Assisted Healthcare Data Fusion Environment. The presented BOA-FNNC technique mainly focuses on achieving security in the healthcare sector using BC technology. For accomplishing this, the BOA-FNNC technique performs BC assisted data transmission in the medical sector. Besides, the VGG-16 model is exploited for the creation of feature vectors. To classify healthcare data, the BOA with FNN model is utilized in this study, where the BOA fine tune the parameters related to the FNN model which in turn boosts the classifier efficiency. For illustrating the betterment of the BOA-FNNC technique, a series of experiments were performed. The comparison study reported the enhancements of the BOA-FNNC technique over other recent approaches.

Keywords :

Blockchain; Healthcare; Fusion Optimization; Fuzzy neural network; Bat algorithm; Security; Classification

References :

[1] Imran, M., Zaman, U., Imtiaz, J., Fayaz, M. and Gwak, J., 2021. Comprehensive survey of iot, machine

learning, and blockchain for health care applications: A topical assessment for pandemic preparedness,

challenges, and solutions. Electronics, 10(20), p.2501.

[2] Kamruzzaman, M.M., Yan, B., Sarker, M.N.I., Alruwaili, O., Wu, M. and Alrashdi, I., 2022.

Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities. Journal of

Healthcare Engineering, 2022.

[3] Kelli, V., Sarigiannidis, P., Argyriou, V., Lagkas, T. and Vitsas, V., 2021, June. A cyber resilience

framework for NG-IoT healthcare using machine learning and blockchain. In ICC 2021-IEEE

International Conference on Communications (pp. 1-6). IEEE.

[4] Li, Y., Shan, B., Li, B., Liu, X. and Pu, Y., 2021. Literature review on the applications of machine

learning and blockchain technology in smart healthcare industry: a bibliometric analysis. Journal of

Healthcare Engineering, 2021.

[5] Gunanidhi, G.S. and Krishnaveni, R., 2021. An experimental analysis of blockchain enabled intelligent

healthcare monitoring system using IoT based deep learning principles. Annals of the Romanian

Society for Cell Biology, pp.353-362.

[6] Otoum, S., Al Ridhawi, I. and Mouftah, H.T., 2021. Preventing and controlling epidemics through

blockchain-assisted ai-enabled networks. Ieee Network, 35(3), pp.34-41.

[7] Singh, S., Rathore, S., Alfarraj, O., Tolba, A. and Yoon, B., 2022. A framework for privacypreservation

of IoT healthcare data using Federated Learning and blockchain technology. Future

Generation Computer Systems, 129, pp.380-388.

[8] Singh, P.D., Kaur, R., Dhiman, G. and Bojja, G.R., 2021. BOSS: A new QoS aware blockchain assisted

framework for secure and smart healthcare as a service. Expert Systems, p.e12838.

[9] Abou-Nassar, E.M., Iliyasu, A.M., El-Kafrawy, P.M., Song, O.Y., Bashir, A.K. and Abd El-Latif,

A.A., 2020. DITrust chain: towards blockchain-based trust models for sustainable healthcare IoT

systems. IEEE Access, 8, pp.111223-111238.

[10] Islam, N., Faheem, Y., Din, I.U., Talha, M., Guizani, M. and Khalil, M., 2019. A blockchain-based fog

computing framework for activity recognition as an application to e-Healthcare services. Future

Generation Computer Systems, 100, pp.569-578.

[11] Bhattacharya, P., Tanwar, S., Bodkhe, U., Tyagi, S. and Kumar, N., 2019. Bindaas: Blockchain-based

deep-learning as-a-service in healthcare 4.0 applications. IEEE Transactions on Network Science and

Engineering, 8(2), pp.1242-1255

[12] Abbas, A., Alroobaea, R., Krichen, M., Rubaiee, S., Vimal, S. and Almansour, F.M., 2021. Blockchainassisted

secured data management framework for health information analysis based on Internet of

Medical Things. Personal and Ubiquitous Computing, pp.1-14.

[13] Lakhan, A., Mohammed, M.A., Kozlov, S. and Rodrigues, J.J., 2021. Mobile‐fog‐cloud assisted deep

reinforcement learning and blockchain‐enable IoMT system for healthcare workflows. Transactions on

Emerging Telecommunications Technologies, p.e4363

[14] Alqaralleh, B.A., Vaiyapuri, T., Parvathy, V.S., Gupta, D., Khanna, A. and Shankar, K., 2021.

Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things

Environment. Personal and ubiquitous computing, pp.1-11.

[15] Al-Qarafi, A., Alrowais, F., S. Alotaibi, S., Nemri, N., Al-Wesabi, F.N., Al Duhayyim, M., Marzouk,

R., Othman, M. and Al-Shabi, M., 2022. Optimal Machine Learning Based Privacy Preserving

Blockchain Assisted Internet of Things with Smart Cities Environment. Applied Sciences, 12(12),

p.5893

[16] Alharby, M. and van Moorsel, A., 2020. Blocksim: An extensible simulation tool for blockchain

systems. Frontiers in Blockchain, 3, p.28.

[17] Kim, B., Yuvaraj, N., Sri Preethaa, K.R., Santhosh, R. and Sabari, A., 2020. Enhanced pedestrian

detection using optimized deep convolution neural network for smart building surveillance. Soft

Computing, 24(22), pp.17081-17092.

[18] Liu, X.H., Zhang, D., Zhang, J., Zhang, T. and Zhu, H., 2021. A path planning method based on the

particle swarm optimization trained fuzzy neural network algorithm. Cluster Computing, 24(3),

pp.1901-1915.

[19] Shareh, M.B., Bargh, S.H., Hosseinabadi, A.A.R. and Slowik, A., 2021. An improved bat optimization

algorithm to solve the tasks scheduling problem in open shop. Neural Computing and

Applications, 33(5), pp.1559-1573.


Cite this Article as :
Style #
MLA Reem Atassi, Fuad Alhosban. "Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment." Fusion: Practice and Applications, Vol. 9, No. 2, 2022 ,PP. 62-73 (Doi   :  https://doi.org/10.54216/FPA.090205)
APA Reem Atassi, Fuad Alhosban. (2022). Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment. Journal of Fusion: Practice and Applications, 9 ( 2 ), 62-73 (Doi   :  https://doi.org/10.54216/FPA.090205)
Chicago Reem Atassi, Fuad Alhosban. "Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment." Journal of Fusion: Practice and Applications, 9 no. 2 (2022): 62-73 (Doi   :  https://doi.org/10.54216/FPA.090205)
Harvard Reem Atassi, Fuad Alhosban. (2022). Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment. Journal of Fusion: Practice and Applications, 9 ( 2 ), 62-73 (Doi   :  https://doi.org/10.54216/FPA.090205)
Vancouver Reem Atassi, Fuad Alhosban. Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment. Journal of Fusion: Practice and Applications, (2022); 9 ( 2 ): 62-73 (Doi   :  https://doi.org/10.54216/FPA.090205)
IEEE Reem Atassi, Fuad Alhosban, Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment, Journal of Fusion: Practice and Applications, Vol. 9 , No. 2 , (2022) : 62-73 (Doi   :  https://doi.org/10.54216/FPA.090205)