Match tier
Listed
Presence
Current · Arkansas
Last published
2025
Sources
OpenAlex · ORCID
Refreshed
2026-08-06
A
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Afsana Ahamed's research employs machine learning techniques to address societal challenges across diverse fields. Her work includes the prediction of diabetes using interpretable machine learning models and identifying food-insecure households through machine learning analysis. Ahamed also investigates public perception of gene-edited and genetically modified foods, drawing on theories such as the Spiral of Silence. Her research extends to the educational sector, exploring the potential of machine learning to improve student success prediction. Additionally, she has contributed to research on the structural assessment of reinforced concrete columns and iris recognition using advanced computational methods. Her scholarship includes 13 publications and a citation count of 78, with an h-index of 5.
Metrics
- h-index: 5
- Publications: 13
- Citations: 78
Selected Publications
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Exploring Public Perception Towards Gene‐Edited and Genetically Modified Foods in the United States: An Application of the Spiral of Silence Theory (2025)
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Do Machine Learning Algorithms, with their Potential to Significantly Improve the Accuracy of Predicting Student Success, Offer a Promising Future for Education? (2025)
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Risk Propensity and Acceptance of Gene-edited and Genetically Modified Food among US Consumers: A Comparison between Plants and Animal Products (2024)
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Prediction of Diabetes at Early Stage using Interpretable Machine Learning (2023)
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Iris recognition using curvelet transform and accuracy maximization by particle swarm optimization (2022)
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Impact of Direction Parameter in Performance of Modified AODV in VANET (2020)
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Traffic Analysis of a Congested and Uncongested Network in a Sequential Server Model (2020)
Collaboration Network
Top Collaborators
Syed Imran Ali Meerza
Arkansas Tech University
3 shared publications
In database
- Prediction of Diabetes at Early Stage using Interpretable Machine Learning
- Risk Propensity and Acceptance of Gene-edited and Genetically Modified Food among US Consumers: A Comparison between Plants and Animal Products
- Exploring Public Perception Towards Gene‐Edited and Genetically Modified Foods in the United States: An Application of the Spiral of Silence Theory
Alwin Dsouza
New Mexico State University (US)
2 shared publications
- Risk Propensity and Acceptance of Gene-edited and Genetically Modified Food among US Consumers: A Comparison between Plants and Animal Products
- Exploring Public Perception Towards Gene‐Edited and Genetically Modified Foods in the United States: An Application of the Spiral of Silence Theory
Syed Irfan Ali Meerza
University of Tennessee at Knoxville (US)
1 shared publication
- Iris recognition using curvelet transform and accuracy maximization by particle swarm optimization
Mohammad Sajidul Islam
Arkansas Tech University
1 shared publication
In database
- Prediction of Diabetes at Early Stage using Interpretable Machine Learning
Md Minul Alam
Arkansas Tech University (US)
1 shared publication
- Prediction of Diabetes at Early Stage using Interpretable Machine Learning
Khondoker Abdul Mottaleb
Texas Tech University (US)
1 shared publication
- Risk Propensity and Acceptance of Gene-edited and Genetically Modified Food among US Consumers: A Comparison between Plants and Animal Products
Rezwanul Parvez
Colorado State University (US)
1 shared publication
- Do Machine Learning Algorithms, with their Potential to Significantly Improve the Accuracy of Predicting Student Success, Offer a Promising Future for Education?
Mostofa Ahsan
North Dakota State University (US)
1 shared publication
- Do Machine Learning Algorithms, with their Potential to Significantly Improve the Accuracy of Predicting Student Success, Offer a Promising Future for Education?
M R Rahul
Institute of Economic Growth (IN)
1 shared publication
- Exploring Public Perception Towards Gene‐Edited and Genetically Modified Foods in the United States: An Application of the Spiral of Silence Theory
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