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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21305
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Total Authors : 22301
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| Paper Title: | Predicitive Modelling of Stellar Properties |
| Authors Name: | Abishek Xavier A , Ragavendran A , Deva Darshan A S |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205111
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| Published Paper Id: | IJRTI2509072 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i9.205111 |
| Abstract: | Machine learning methods have proven increasingly effective in astrophysics for predicting stellar attributes. This work focuses on predictive modeling of stellar properties, including spectral classification, stellar age estimation, evolutionary fate prediction, and exoplanet potential assessment. The study employs advanced algorithms, such as Random Forest and XGBoost, to analyze stellar features—temperature, radius, metallicity, luminosity, and mass—to derive meaningful predictions. A preprocessed dataset with labeled attributes allows for the training and evaluation of classification and regression models. The models classify stars into spectral types (O, B, A, F, G, K, M) and predict evolutionary outcomes such as white dwarfs, neutron stars, or black holes. For age estimation, regression models provide predictions of stellar lifetimes in Gigayears (Gyr), while binary classifiers assess the exoplanet-hosting potential. Model performance is evaluated using metrics like accuracy, precision, recall, mean squared error (MSE), and R² scores. Results demonstrate that machine learning methods, particularly ensemble-based models, enhance the accuracy and efficiency of stellar property predictions, contributing to further advancements in astrophysics and planetary science. |
| Keywords: | Stellar classification, machine learning, spectral types, stellar age prediction, evolutionary fate, exoplanet potential, Random Forest. |
| Cite Article: | "Predicitive Modelling of Stellar Properties", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a649-a654, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509072.pdf |
| Downloads: | 0001478 |
| 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 |
| Publication Details: |
Published Paper ID: IJRTI2509072
Registration ID:205111
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i9.205111
Page No: a649-a654 Country: Tuticorin, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509072 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509072 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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