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A Proposed Architecture for Predicting Breast Cancer using Fog Computing

Laila A. Abd-Elmegid. Published in Data Mining.

Communications on Applied Electronics
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Laila A. Abd-Elmegid
10.5120/cae2019652815

Laila A Abd-Elmegid. A Proposed Architecture for Predicting Breast Cancer using Fog Computing. Communications on Applied Electronics 7(26):32-35, February 2019. BibTeX

@article{10.5120/cae2019652815,
	author = {Laila A. Abd-Elmegid},
	title = {A Proposed Architecture for Predicting Breast Cancer using Fog Computing},
	journal = {Communications on Applied Electronics},
	issue_date = {February 2019},
	volume = {7},
	number = {26},
	month = {Feb},
	year = {2019},
	issn = {2394-4714},
	pages = {32-35},
	numpages = {4},
	url = {http://www.caeaccess.org/archives/volume7/number26/848-2019652815},
	doi = {10.5120/cae2019652815},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A new emerged concept, known as fog computing, aims to local processing and storage of large amounts of data at its sources. Such concept differs from the centralized style of processing and storage used with the cloud computing concept. Fog computing technology improves both the performance and the efficiency. The main cause of advantages gained from fog computing is the reduced amount of transferred data to the cloud need to be analyzed, processed, and stored. little research has been dedicated to study how to efficiently apply big data analytics locally where data has been collected and stored. This study proposes a fog computing-based architecture for prediction of the breast cancer prognosis. The proposed architecture uses the BCOAP model for the prediction purpose. It addresses the problem of real time processing of large amounts of data without overwhelming the data center at the cloud. It distinguishes between the processes to be performed on the cloud and the others to be performed on the fog. Initial prototype of the proposed architecture has highlighted its efficiency and how it increases the potentials of the applied BCOAP model.

References

  1. Haffty B., Buchholz T., Perez C. 2008. Early stage breast cancer. In: Halperin EC, Perez CA, Brady LW, editors. Principles and practice of radiation oncology. 5th ed. Philadelphia: Lippincott Company
  2. Ahmed, A., Laila A., Sherif K., Ayman G.,2018. Classification Based on Clustering Model for Predicting Main Outcomes of Breast Cancer Using Hyper-Parameters Optimization. International Journal of Computer Science and Applications 9 (12), 268-273
  3. Zhu, J., Chan, D.S., Prabhu, M.S., Natarajan, P., Hu, H., Bonomi, F., 2013. Improving websites performance using edge servers in fog computing architecture. In: Proceedings of the IEEE Seventh International Symposium on Service-Oriented System Engineering, pp. 320–323.
  4. Houriyeh, E., Mitra M., Reza K., Raziyeh H., Ali N., Vahid M. 2017. Prognosis and Early Diagnosis of Ductal and Lobular Type in Breast Cancer Patient” ,Iran J Public Health,. 46 (11), pp.1563-1571.
  5. Botta, A., Donato, W.D., Persico, V., Pescap, A., 2015. Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 60 (5), 23–30.
  6. P. Hu, S. Dhelim, H. Ning, T. Qiu, Survey on Fog Computing: Architecture, Key Technologies, Applications and Open Issues, Journal of Network and Computer Applications 98 (2017) 27-42
  7. Ahmed, E., Rehmani, M.H., 2017. Mobile edge computing: opportunities, solutions, and challenges. Future Gener. Comput. Syst. 70, 59–63.
  8. Stojmenovic, I., Wen, S., 2014. The fog computing paradigm: Scenarios and security issues. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 1–8.
  9. Aazam, M., Huh, E.N., 2016. Fog computing: the cloud-iot/ioe middleware paradigm. IEEE Potentials 35 (3), 40–44.
  10. Stantchev, V., Barnawi, A., Ghulam, S., Schubert, J., Tamm, G., 2015. Smart items, fog and cloud computing as enablers of servitization in healthcare. Sens. Transducers 185 (2), 121–128.
  11. Shi, Y., Ding, G., Wang, H., Roman, H.E., 2015. The fog computing service for healthcare. In: Proceedings of the International Symposium on Future Information and Communication Technologies for Ubiquitous Healthcare, pp. 70–74.
  12. Cao, Y., Chen, S., Hou, P., Brown, D., 2015. Fast: a fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In: Proceedings of the IEEE International Conference on Networking, Architecture and Storage, pp. 2–11.
  13. Kyriazakos, S., Mihaylov, M., Anggorojati, B., Mihovska, A., Craciunescu, R., Fratu, O., Prasad, R., 2016. eWALL: an intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wirel. Personal. Commun. 87 (3), 1093–1111.
  14. Ahmad, M., Amin, M.B., Hussain, S., Kang, B.H., Cheong, T., Lee, S., 2016. Health fog: a novel framework for health and wellness applications. J. Supercomput. 72 (10), 36773695.

Keywords

Breast cancer, BCOAP, Classification, Cloud computing, Fog computing.