Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance

Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance

This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector-borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors a...

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Bibliographic Details
Main Authors: Komi Mensah Agboka, Elfatih M. Abdel-Rahman, Daisy Salifu, Brian Kanji, Frank T. Ndjomatchoua, Ritter A.Y. Guimapi, Sunday Ekesi, Landmann Tobias
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:MethodsX
Subjects:
Combining Self-Organizing Maps (SOM) and Convolutional Neural Network (CNN) for improving model accuracy
Online Access:http://www.sciencedirect.com/science/article/pii/S2215016125000469
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