A Comprehensive Framework for Epidemic Response in Smart cities through Machine Learning and NFA
P. Punitha Ilayarani1, M. Maria Dominic2
1P. Punitha Ilayarani, Department of Computer Science, Sacred Heart College (Autonomous), Tirupattur, India,
2M. Maria Dominic, Department of Computer Science, Sacred Heart College (Autonomous), Tirupattur, India
Manuscript received on December 14, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 2896-2901 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7379129219/2020©BEIESP | DOI: 10.35940/ijitee.B7379.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Expertise in early detection against intimidating devastation using preventive procedure is an extremely challenging mission of this modern civilization. Sudden increases in the number of cases of disease threaten public health security. Guiding the public for the period of emergency situations plays a crucial role and this procedure saves countless lives. In this paper, we have developed a predictive model. When Epidemic predicted earlier on a proper and speedy response, the losses can be mitigated with the help of learning technology in AI. We proposed a comprehensive framework for the Epidemic management system which employs cumulated knowledge base construction through Machine Learning and Non-Deterministic Finite Automata technique. In this investigation, Real-time data acquisition, sharing accurate information and precise decision making are greatly augmented. This proposed model will enhance the government agencies and responders to act upon at all outbreaks.
Keywords: Epidemic Detectors, Fog Computing, Preparedness system, IoT Devices, Machine Learning Algorithms, NFA Techniques.
Scope of the Article: Machine Learning