AIMC Topic: Aedes
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Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification.
Acta tropica
Dec 1, 2023
Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) em...
AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot.
Sensors (Basel, Switzerland)
Jun 29, 2022
Mosquito-borne diseases can pose serious risks to human health. Therefore, mosquito surveillance and control programs are essential for the wellbeing of the community. Further, human-assisted mosquito surveillance and population mapping methods are t...
Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control.
PloS one
Dec 9, 2021
Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in ...
Aedes-AI: Neural network models of mosquito abundance.
PLoS computational biology
Nov 19, 2021
We present artificial neural networks as a feasible replacement for a mechanistic model of mosquito abundance. We develop a feed-forward neural network, a long short-term memory recurrent neural network, and a gated recurrent unit network. We evaluat...
Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time.
Scientific reports
May 10, 2021
Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using har...
Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
PLoS neglected tropical diseases
Sep 24, 2020
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were dev...
Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks.
Scientific reports
Jan 23, 2020
Image-based automatic classification of vector mosquitoes has been investigated for decades for its practical applications such as early detection of potential mosquitoes-borne diseases. However, the classification accuracy of previous approaches has...
Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania.
PloS one
Oct 17, 2019
Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing...
AI- modelling of molecular identification and feminization of wolbachia infected Aedes aegypti.
Progress in biophysics and molecular biology
Jul 11, 2019
BACKGROUND: The genetic control strategies of vector borne diseases includes the replacement of a vector population by "disease-refractory" mosquitoes and the release of mosquitoes with a gene to control the vector's reproduction rates. Wolbachia are...
Application of convolutional neural networks for classification of adult mosquitoes in the field.
PloS one
Jan 14, 2019
Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of ...
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