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Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) by their extraordinary capabilities, like contextual understanding, few-shot/zero-shot learning, code generation, ability to reason and problem-solving. Recent studies have shown immense work on LLMs. Yet, there is limited work on the thoroug...
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.
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...
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...
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.
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.
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