ArticleSaudi journal of biological sciences2022
Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology.
Article in Saudi journal of biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
- Article
- TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model.PloS one · 2025Article
- Enhance fashion classification of mosquito vector species via self-supervised vision transformer.Scientific reports · 2024Article
- Advanced vision transformers and open-set learning for robust mosquito classification: A novel approach to entomological studies.PLoS computational biology · 2024Article
- Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis.Biomedicines · 2023Review
- Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.Sensors (Basel, Switzerland) · 2023Article
- Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system.Scientific reports · 2023Article
- White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.Healthcare (Basel, Switzerland) · 2022Article
- AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot.Sensors (Basel, Switzerland) · 2022Article
- Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study.Social network analysis and mining · 2022Article
Corrections and comments
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Authors and funding
11 authors.
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Abstract
Every year about one million people die due to diseases transmitted by mosquitoes. The infection is transmitted to a person when an infected mosquito stings, injecting the saliva into the human body. The best possible way to prevent a mosquito-borne infection till date is to save the humans from exposure to mosquito bites. This study proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes such as the Aedes and Culex. The proposed system will effectively aid in epidemiology to design evidence-based policies and decisions by analyzing the risks and transmission. The study proposes an effective methodology for the classification of mosquitoes using ML and CNN models. The novel RIFS has been introduced which integrates two types of feature selection techniques - the ROI-based image filtering and the wrappers-based FFS technique. Comparative analysis of various ML and deep learning models has been performed to determine the most appropriate model applicable based on their performance metrics as well as computational needs. Results prove that ETC outperformed among the all applied ML model by providing 0.992 accuracy while VVG16 has outperformed other CNN models by giving 0.986 of accuracy.
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