Syeda Fahima Nazreen
Papers
1
Total Citations
5
H-Index
1
About
Syeda Fahima Nazreen is a researcher whose work sits at the intersection of artificial intelligence and public health, with a particular focus on leveraging deep learning for vector-borne disease control. Her most cited paper, "Detection of Mosquito Larvae Using Convolutional Neural Network" (2021), addresses a critical gap in mosquito population management. Rather than targeting adult mosquitoes—an approach that has proven largely ineffective—Nazreen’s work proposes an innovative, preemptive strategy: using convolutional neural networks to automatically detect mosquito larvae in their breeding habitats. This early-intervention framework has the potential to significantly reduce the transmission of deadly diseases such as dengue, malaria, and Zika. Though her citation count is still growing, with 5 citations on this key paper, the work signals a promising direction at the crossroads of computer vision and epidemiology. Nazreen’s contribution is notable for its practical, scalable approach to a global health challenge, and her research stands as a compelling example of how AI can be harnessed for environmental monitoring and disease prevention.
Research Focus
Key Achievements
Top Papers
- 1Detection of Mosquito Larvae Using Convolutional Neural Network5 citations · 2021