Anjuman Ara Rashid
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Research Areas
Biography and Research Information
OverviewAI-generated summary
Anjuman Ara Rashid's research focuses on the application of artificial intelligence and deep learning techniques to medical diagnostics. Her recent publications explore novel frameworks for disease prediction, including a hybrid deep learning and handcrafted feature fusion model for tea leaf disease classification, and a deep learning model for lung disease prediction utilizing attention mechanisms. Additionally, she has investigated an interpretable deep learning approach for cervical cancer prediction. Her work aims to enhance the accuracy and explainability of AI-driven diagnostic tools. Rashid's scholarship metrics include an h-index of 1, with three total publications and one citation.
Metrics
- h-index: 1
- Publications: 3
- Citations: 1
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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