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Classification and identification of mosquito species using artificial neural networks ☆

Paper ID Volume ID Publish Year Pages File Format Full-Text
15369 1407 2008 6 PDF Available
Title
Classification and identification of mosquito species using artificial neural networks ☆
Abstract

An artificial neural network method is presented for classification and identification of Anopheles mosquito species based on the internal transcribed spacer2 (ITS2) data of ribosomal DNA string. The method is implemented in two different multi-layered feed-forward neural network model forms, namely, multi-input single-output neural network (MISONN) and multi-input multi-output neural network (MIMONN). A number of data sequences in varying sizes of different Anopheline malarial vectors and their corresponding species coding are employed to develop the neural network models. The classification efficiency of the network models for untrained data sequences is evaluated in terms of quantitative performance criteria. The results demonstrate the efficiency of the neural network models to extract the genetic information in ITS2 sequences and to adapt to new data. The method of MISONN is found to exhibit superior performance over MIMONN in distinguishing and identification of the mosquito vectors.

Keywords
Artificial neural network; Anopheles; Internal transcribed spacer 2; Mosquitoes; Malaria
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Classification and identification of mosquito species using artificial neural networks ☆
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 32, Issue 6, December 2008, Pages 442–447
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
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Any Questions? feel free to contact us