Deep Learning Architectures and Applications

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Deep learning architectures, including CNNs, RNNs, and Transformers, offer transformative applications in healthcare, education, and sustainability, but require responsible leadership and ethical innovation for their successful adoption. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Hakikur Rahman

doi:10.5281/zenodo.22702872

September 11, 2026Open Access

Deep Learning Architectures and Applications

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Key Points

To review prominent deep learning architectures and explore their practical applications across multiple domains while addressing the need for ethical implementation.
Comparative examination of deep learning network structures, including Convolutional Neural Networks, Recurrent Neural Networks, and Transformers.
Qualitative analysis of real-world use cases spanning healthcare, education, and environmental sustainability.
Convolutional Neural Networks, Recurrent Neural Networks, and Transformers effectively extract intricate patterns from complex, large-scale datasets.
Implementation across healthcare, education, and sustainability drives technological innovation but requires responsible leadership to safeguard ethical standards and foster digital inclusion.

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Implication

Review demonstrates deep learning architectures across healthcare, education, and sustainability, highlighting the need for ethical leadership and inclusive digital adoption.

Abstract

Deep Learning has revolutionized Artificial Intelligence by enabling machines to learn complex patterns from massive datasets. This article examines the architectures of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, highlighting their applications in healthcare, education, and sustainability. It emphasizes the leadership role in adopting these technologies responsibly, ensuring ethical innovation, and fostering inclusive digital futures.

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Hakikur Rahman (2026) studied this question.

synapsesocial.com/papers/6aa7b3770926e14a848b26b8 https://doi.org/10.5281/zenodo.22702872

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Deep Learning Architectures and Applications

Key Points

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Implication

Abstract

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Deep Learning Architectures and Applications

Key Points

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Implication

Abstract

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