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C. Prabha

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Open access Sep 2026

An adaptive dual-threshold chunking framework for performance optimization in fog–cloud systems

Although a slight decrease in delivery ratio and throughput is observed compared to IDPC, the framework improves load balancing and responsiveness, offering a scalable and efficient solution for real-time IoT and fog-based distributed computing environments.

Anjuli Goel, C. Prabha · 0 citations
#edge computing Open access Sep 2026

Lightweight and explainable CNNLSTM model for accurate lung cancer detection in thoracic CT images

To have a positive impact on patient survival and death rates, lung cancer must be diagnosed early and as accurate as possible. While deep learning has shown exceptional success in medical image analysis, a large number of the current methods use computationally intensive architectures that are not very interpretable a...

Shaily Jain, Karan Bajaj, C. Prabha et al. · 0 citations
Review Open access Jul 2026

Cyber Threats in E-healthcare and an Autonomous Cybersecurity Agent: A Comprehensive Review

A seven-stage cybersecurity response framework consisting of identification, protection, detection, response, recovery, procurement, and security audit is introduced and highlights the significance of AI-assisted security mechanisms, staff awareness, and privacy-preserving practices in enhancing defenses of the electro...

Divya Sharma, C. Prabha, Amna Bamaqa et al. · 0 citations
Open access Jul 2026

Learning-driven MIMO channel estimation using a residual U-Net-BiLSTM-attention hybrid model.

This work introduces a hybrid residual U-Net-bidirectional long short-term memory with attention (ResUNet-BiLSTM-Attention) channel estimator that learns a nonlinear mapping from noisy pilot observations to MIMO channel coefficients and consistently outperforms both conventional and learning-based baselines.

Mohammad Zubair Khan, Ibrahim Aljubayri, C. Prabha et al. · 0 citations
Open access Jul 2026

A 3D residual U-Net with attention-driven spatial pyramid pooling for accurate multimodal MRI brain tumor segmentation

Experimental results demonstrate that the proposed 3D U-Net achieves superior performance, with a Dice coefficient of 0.83 and a Jaccard index of 0.82, outperforming baseline models across all evaluation metrics, indicating strong generalization capability.

Retinderdeep Singh, C. Prabha, Navita Gupta et al. · 0 citations

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