Anjuman Ara Rashid
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Researcher
Graduate Student Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Anjuman Ara Rashid's research focuses on the application of machine learning techniques, particularly deep learning models, for disease prediction and classification. Her recent publications explore hybrid frameworks that combine deep neural networks with handcrafted features to improve diagnostic accuracy. Specifically, she has investigated models for predicting cervical cancer using a deep feature fusion mechanism and for classifying tea leaf diseases with an emphasis on explainable AI. Rashid has also developed lightweight deep learning models for lung disease prediction, incorporating attention mechanisms for interpretability. Her work contributes to the development of AI-driven tools for medical diagnosis and analysis.
Metrics
- Publications: 3
Selected Publications
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DF2SPred: An Efficient Cervical Cancer Prediction Using Hybrid Deep Feature Fusion Mechanism (2025)
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A Hybrid Deep Learning and Handcrafted Feature Fusion Framework for Tea Leaf Disease Classification with Explainable AI (2025)
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An Interpretable Lightweight Squeeze-And-Excitation Block-Based Deep Learning Model for Lung Disease Prediction (2025)
Collaboration Network
Top Collaborators
- An Interpretable Lightweight Squeeze-And-Excitation Block-Based Deep Learning Model for Lung Disease Prediction
- DF2SPred: An Efficient Cervical Cancer Prediction Using Hybrid Deep Feature Fusion Mechanism
- An Interpretable Lightweight Squeeze-And-Excitation Block-Based Deep Learning Model for Lung Disease Prediction
- DF2SPred: An Efficient Cervical Cancer Prediction Using Hybrid Deep Feature Fusion Mechanism
- An Interpretable Lightweight Squeeze-And-Excitation Block-Based Deep Learning Model for Lung Disease Prediction
- DF2SPred: An Efficient Cervical Cancer Prediction Using Hybrid Deep Feature Fusion Mechanism
- An Interpretable Lightweight Squeeze-And-Excitation Block-Based Deep Learning Model for Lung Disease Prediction
- An Interpretable Lightweight Squeeze-And-Excitation Block-Based Deep Learning Model for Lung Disease Prediction
- A Hybrid Deep Learning and Handcrafted Feature Fusion Framework for Tea Leaf Disease Classification with Explainable AI
- A Hybrid Deep Learning and Handcrafted Feature Fusion Framework for Tea Leaf Disease Classification with Explainable AI
- A Hybrid Deep Learning and Handcrafted Feature Fusion Framework for Tea Leaf Disease Classification with Explainable AI
- A Hybrid Deep Learning and Handcrafted Feature Fusion Framework for Tea Leaf Disease Classification with Explainable AI
- DF2SPred: An Efficient Cervical Cancer Prediction Using Hybrid Deep Feature Fusion Mechanism
- DF2SPred: An Efficient Cervical Cancer Prediction Using Hybrid Deep Feature Fusion Mechanism
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