Advances in Architectures for Deep Learning: A Thorough Examination of Present Trends

  • Khan M
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Abstract

This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.

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APA

Khan, Md. R. (2024). Advances in Architectures for Deep Learning: A Thorough Examination of Present Trends. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 1(1), 24–30. https://doi.org/10.60087/jaigs.v1i1.p30

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