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Malaria Diagnosis using Swarm Intelligence

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Nathaniel A. Ojekudo, Eva N. Obinna,
10.5120/ijca2018917602

Nathaniel A Ojekudo and Eva Obinna N and. Malaria Diagnosis using Swarm Intelligence. International Journal of Computer Applications 181(9):24-28, August 2018. BibTeX

@article{10.5120/ijca2018917602,
	author = {Nathaniel A. Ojekudo and Eva N. Obinna and},
	title = {Malaria Diagnosis using Swarm Intelligence},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {9},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {24-28},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume181/number9/29800-2018917602},
	doi = {10.5120/ijca2018917602},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Malaria is a life-threatening disease that is increasing in an uncontrolled way throughout Nigeria and the entire world at large. In attempt to eradicate this deadly disease early detection needs to be done. If proper diagnosis can be achieved early enough, proper prescription can be given to the individual. In attempt to eradicate this deadly disease, there is need for an efficient malaria diagnosis system. This work designed an efficient malaria diagnosis system with capacity to diagnose malaria efficiently and produce result base on symptoms, using a swarm intelligence model known as Artificial Bee Colony (ABC) Algorithm. This work when implemented, if the result obtained is given an immediate attention, proper prescriptions can be administered to the patient.

References

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Keywords

Swarm Intelligence, ABC Algorithm, Malaria Diagnosis, Garlic Crop, Swarm Intelligence Models.