Aedes aegypti mosquito movements analysis and sex classification using computer vision and deep learning
Author:
Affiliation:
1. MSA University,Faculty of Computer Science,Giza,Egypt
2. Natural Resources Institute, University of Greenwich,United Kingdom
3. Helwan University,HCI-LAB, Faculty of Computers and Artificial Intelligence
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10483564/10483566/10485146.pdf?arnumber=10485146
Reference26 articles.
1. A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences
2. Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species
3. Visual and thermal stimuli modulate mosquito-host contact with implications for improving malaria vector control tools;Carnaghi;iScience,2024
4. An Attention Based YOLOv5 Network for Small Traffic Sign Recognition
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1. Hybrid Machine Learning Approach for Mosquito Species Classification Using Wingbeat Analysis;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-05-10
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