Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This research paper explores the metamorphic role of deep learning models in the detection of exoplanets in order for us to enhance our knowledge and understanding of this vast universe and perhaps even more and advance in astronomical discoveries. Deep Learning models display an evolutionary ability to distinguish planetary signals, an advancement I found quite favorable while comparing traditionally used methods with modern AI techniques. These findings provide us with a robust structure and framework for future explorations in planetary science.
This study delves deep into the role of deep learning in changing the field, leveraging sophisticated neural networks to analyze the intense amount of data from telescopes.
With this comparative analysis of classical methods, the findings emphasize the potential of AI in astronomical sciences, in this case, helping to discover exoplanets.
Keywords:
Deep learning, exoplanet detection, astronomical discoveries, AI in space exploration, neural networks, telescope datasets, planetary signals, data analysis in astronomy.
Cite Article:
"Deep Learning in Exoplanet Detection: Enhancing Astronomical Discoveries", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 1, page no.a928-a930, January-2025, Available :http://www.ijrti.org/papers/IJRTI2501112.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator