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Title: A Neuro-Fussy Based Model for Diagnosis of Monkeypox Diseases
Authors: Tom, Joshua J.
Anebo, Nlerum P.
Keywords: Monkeypox
Fuzzy logic
Issue Date: 2018
Publisher: International Journal of Computer Science Trends and Technology (IJCST)
Citation: Tom, Joshua Joshua, Dr. Anebo, Nlerum P. (2018). A Neuro-Fussy Based Model for Diagnosis of Monkeypox Diseases. International Journal of Computer Science Trends and Technology (IJCST).
Abstract: The largest vertebrate viruses known, infecting humans, and other vertebrates are poxviruses including cowpox, vaccinia, variola (smallpox), and monkeypox viruses. Monkeypox was limited to the rain forests of central and western Africa until 2003. A smallpox-like viral infection caused by a virus of zoonotic origin, monkeypox belongs to the genus Orthopoxvirus, family Poxviridae, and sub-family Chordopoxvirinae. Monkeypox has a clinical presentation like ordinary forms of smallpox, including flulike symptoms, fever, malaise, back pain, headache, and characteristic rash. In view of the eradication of smallpox, such symptoms in a monkepox endemic region should be carefully diagnosed. The problem in diagnosing monkeypox lies in the fact that it is clinically indistinguishable from other pox-like illnesses making virus differentiation difficult. In this paper, we present a neuro-fuzzy based model for early diagnosis of monkeypox virus with a differentiation from other pox families.
ISSN: 2347-8578
Appears in Collections:Research Articles

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