A machine learning neural network architecture for the accelerating universe based modified gravity
The current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. The classification of the mathematical MAUMGM is presented into...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850074720266878976 |
|---|---|
| author | Zulqurnain Sabir Basma Souayeh Zahraa Zaiour Alyn Nazal Mir Waqas Alam Huda Alfannakh |
| author_facet | Zulqurnain Sabir Basma Souayeh Zahraa Zaiour Alyn Nazal Mir Waqas Alam Huda Alfannakh |
| author_sort | Zulqurnain Sabir |
| collection | DOAJ |
| description | The current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. The classification of the mathematical MAUMGM is presented into five different nonlinear classes. A dataset is designed using the explicit Runge-Kutta scheme, which is divided into training as 82% and 9%, 9% for testing and verification. The designed stochastic process for solving the MAUMGM contains log-sigmoid activation function, thirty neurons in the hidden layer, dataset based explicit Runge-Kutta, and Bayesian regularization for the optimization. The correctness of the stochastic solver is perceived by comparing the outcomes along with absolute error 10-06 to 10-09. The best training values are reported around 10-13 to 10-14, which also signify the solver’s perfection. To authenticate the accuracy, and competence of the solver, some tests have been taken using the parameters of regression, state transition, and error histogram. |
| format | Article |
| id | doaj-art-37cf6cec814e4a8ba002180d1be254f2 |
| institution | DOAJ |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-37cf6cec814e4a8ba002180d1be254f22025-08-20T02:46:29ZengElsevierEgyptian Informatics Journal1110-86652025-03-012910063510.1016/j.eij.2025.100635A machine learning neural network architecture for the accelerating universe based modified gravityZulqurnain Sabir0Basma Souayeh1Zahraa Zaiour2Alyn Nazal3Mir Waqas Alam4Huda Alfannakh5Department of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonDepartment of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa 31982, Saudi Arabia; Corresponding author.Department of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonDepartment of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonDepartment of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa 31982, Saudi ArabiaDepartment of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa 31982, Saudi ArabiaThe current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. The classification of the mathematical MAUMGM is presented into five different nonlinear classes. A dataset is designed using the explicit Runge-Kutta scheme, which is divided into training as 82% and 9%, 9% for testing and verification. The designed stochastic process for solving the MAUMGM contains log-sigmoid activation function, thirty neurons in the hidden layer, dataset based explicit Runge-Kutta, and Bayesian regularization for the optimization. The correctness of the stochastic solver is perceived by comparing the outcomes along with absolute error 10-06 to 10-09. The best training values are reported around 10-13 to 10-14, which also signify the solver’s perfection. To authenticate the accuracy, and competence of the solver, some tests have been taken using the parameters of regression, state transition, and error histogram.http://www.sciencedirect.com/science/article/pii/S1110866525000283Modified gravityBayesian regularizationNeural networkAccelerating universeSimulation |
| spellingShingle | Zulqurnain Sabir Basma Souayeh Zahraa Zaiour Alyn Nazal Mir Waqas Alam Huda Alfannakh A machine learning neural network architecture for the accelerating universe based modified gravity Egyptian Informatics Journal Modified gravity Bayesian regularization Neural network Accelerating universe Simulation |
| title | A machine learning neural network architecture for the accelerating universe based modified gravity |
| title_full | A machine learning neural network architecture for the accelerating universe based modified gravity |
| title_fullStr | A machine learning neural network architecture for the accelerating universe based modified gravity |
| title_full_unstemmed | A machine learning neural network architecture for the accelerating universe based modified gravity |
| title_short | A machine learning neural network architecture for the accelerating universe based modified gravity |
| title_sort | machine learning neural network architecture for the accelerating universe based modified gravity |
| topic | Modified gravity Bayesian regularization Neural network Accelerating universe Simulation |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000283 |
| work_keys_str_mv | AT zulqurnainsabir amachinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT basmasouayeh amachinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT zahraazaiour amachinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT alynnazal amachinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT mirwaqasalam amachinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT hudaalfannakh amachinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT zulqurnainsabir machinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT basmasouayeh machinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT zahraazaiour machinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT alynnazal machinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT mirwaqasalam machinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity AT hudaalfannakh machinelearningneuralnetworkarchitecturefortheacceleratinguniversebasedmodifiedgravity |