Eclipse Assessment Using Distributed Gradient Boosted Decision Tree-Specific Machine Learning Model

Prasoon Modi, Anushree Sinha, Tanisha Verma, Sushruta Mishra*, Charu Arora, Rajkumar Singh Rathore

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad mewn cynhadleddadolygiad gan gymheiriaid

Crynodeb

In the observable universe, we have mainly two types of eclipses on Earth: solar eclipse and lunar eclipse. Solar eclipse is defined as the alignment of Moon between Sun and Earth. Lunar eclipse is defined as the alignment of Earth between Sun and moon. A very popular method of predicting the eclipses is using Saros series which is approximately a cycle of 18 years and 11 days. A solar eclipse is estimated after 9 years and 5.5 days after the occurrence of lunar eclipse and vise versa. Eclipses impact the movement of organisms on earth. Also these are great astronomical events which is needed to be studied. In this paper, we have used machine learning models like XGBoost, gradient boosting, decision tree, and random forest classifier. This paper includes training of these algorithms and there prediction accuracy.

Iaith wreiddiolSaesneg
TeitlProceedings of 5th Doctoral Symposium on Computational Intelligence - DoSCI 2024
GolygyddionAbhishek Swaroop, Vineet Kansal, Giancarlo Fortino, Aboul Ella Hassanien
CyhoeddwrSpringer Science and Business Media Deutschland GmbH
Tudalennau57-67
Nifer y tudalennau11
ISBN (Argraffiad)9789819763177
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 30 Tach 2024
Digwyddiad5th Doctoral Symposium on Computational Intelligence, DoSCI 2024 - Lucknow, India
Hyd: 10 Mai 202410 Mai 2024

Cyfres gyhoeddiadau

EnwLecture Notes in Networks and Systems
Cyfrol1095 LNNS
ISSN (Argraffiad)2367-3370
ISSN (Electronig)2367-3389

Cynhadledd

Cynhadledd5th Doctoral Symposium on Computational Intelligence, DoSCI 2024
Gwlad/TiriogaethIndia
DinasLucknow
Cyfnod10/05/2410/05/24

Dyfynnu hyn