Enhanced Covid-19 Contact-Tracing System
Keywords:
covic-19, Contact-Tracing, sensor, swarm, fuzzy logicAbstract
Covid-19 is a global pandemic that has brought the world to a standstill. The virus originated from Wuhan China and has claimed the lives of over 5 million people according to World Health Organization. The Nigeria centre for Disease control is an agency that manages pandemics in Nigeria. They have created awareness son the management of Covic-19. Contact tracing of people that have come in contact with infected people poses a lot of problem. In this study, optimized system for con tact tracing of Covic-19 was carried out. Object Oriented Analysis Design Methodology (OOADM) was adopted and implementation was achieved with python programming language. The result obtained showed better and optimized performance in contact-tracing based on symptomatic (1) and asymptomatic (1+1) infection generation using fuzzy logic as an accurate decision making tool.
References
. Abebe, S. L. & Tonell, P. (2015), Extraction of Domain Concepts from the Source Code, Science of Computer Programming, 98(4), 680–706.
. Achim S., Frederick O., & P.K Yadav (2019), Distributed System & its role in Health Care System, International Journal of Computer Science & Mobile Computing (IJCSMC), 4(4), 302 - 308
. Adam R., John I, & Debora M. (2017), Deep Generative Models of Genetic Variation Capture Mutation effects, bioRxiv preprint, doi:https://doi.org/10.1101/235655
. Aderemi A., Richard O., Segun P., & Victor M. (2016), Development of Smart Assistive DTMF Home Automated System for Ageing Population, Proceedings of the World Congress on Engineering & Computer Science 2016, Vol I WCECS 2016, October 19 – 21, 2016, San Francisco, USA
. Frey I. & Osborne A. (2013), The impact of Artificial Intelligence in the Modern Century, International Journal of Engineering Technology (IJET), 4(9), 3 – 9
. Hayley R., Bruce M. & Elizabeth B. (2014), The Role of Healthcare Robots for Older People at Home: A Review, International Journal of Soc Robotics, 6: 575 – 591
. David A. & Graham P. (2012), Design Science in Decision Support Systems Research: An Assessment using the Hevner, March Park, & Ram Guidelines, Journal of the Association for Information Systems (JAIS), 13(11), 923 – 949
. Stefano F., Berardina C., Paziemza E., & Domenico R. (2015), An Agent Architecture for Adaptive Supervision & Control of Smart Environments, https://www.researchgate.net/publication/283109898
. Juan P., Marcela R., Monica T., Diana S., Angel A. & Adan E. (2010), An Agent-based Architecture for Developing Activity Aware Systems for Assisting the Elderly, Journal of Universal Computer Science, 16(12), 1500 – 1520
. Pekka R., Timo P., Saija L., Marja A., & Alan L. (2017), An In-home Advanced Robotic System to manage Elderly Home-Care Patients’ Medications: A Pilot Safety & Usability Study Clinical Therapeutics, 39(5), 2017
. Ayman S., Osamah K., & Ghaida A. (2016), An Adaptive Intelligent Alarm System for Wireless Sensor Network, Indonesian Journal of Electrical Engineering & Computer Science, 15(1), 142 – 147
. Chibroma A. (2017), Structured Generative Models using the IoT, Proceedings of the 31st International Conference on Machine Learning, Beijing, China, JMLR: W & CP, V.32, arXiv:1401.0514v2[cs.PL], 1 – 14
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Gabriel Ihuoma Lilian, Barifaa Naakorobee
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
