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August 22, 2025ElectronicsOpen Access

Correction: Badri et al. An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing. Electronics 2023, 12, 1441

Sahar Badri Daniyal Alghazzawi Syed Hamid Hasan Fayez Alfayez Syed Hamid Hasan Monawar Rahman Surbhi Bhatia

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Key Points

The model enhances task scheduling efficiency, improving the overall performance within cloud computing environments.
Key evidence shows significant improvements in efficiency and security when implementing adaptive optimal deep learning algorithms.
This approach utilizes adaptive optimal deep learning to optimize task scheduling, integrating advanced computational techniques.
The findings indicate potential for improved cloud resource management, calling for further exploration in diverse applications.

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Implication

Model improves task scheduling efficiency and security in cloud computing, indicating a promising adaptive approach.

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Cite This Study

Badri et al. (2025) studied this question.

synapsesocial.com/papers/68af55c6ad7bf08b1eadbb1c https://doi.org/10.3390/electronics14173339

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