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001-es BibID:	BIBFORM126393
Első szerző:	Ali, Ali Hasan (matematikus)
Cím:	Design of Morlet Wavelet Neural Networks for Solving the Nonlinear Van der Pol-Mathieu-Duffing Oscillator Model / Ali Hasan Ali, Muhammad Amir, Jamshaid Ul Rahman, Ali Raza, Ghassan Ezzulddin Arif
Dátum:	2025
ISSN:	2073-431X
Megjegyzések:	The motivation behind this study is to simplify the complex mathematical formulations and reduce the time-consuming processes involved in traditional numerical methods for solving differential equations. This study develops a computational intelligence approach with a Morlet wavelet neural network (MWNN) to solve the nonlinear Van der Pol-Mathieu-Duffing oscillator (Vd-PM-DO), including parameter excitation and dusty plasma studies. The proposed technique utilizes artificial neural networks to model equations and optimize error functions using global search with a genetic algorithm (GA) and fast local convergence with an interior-point algorithm (IPA). We develop an MWNNbased fitness function to predict the dynamic behavior of nonlinear Vd-PM-DO differential equations. Then, we apply a novel hybrid approach combining WCA and ABC to optimize this fitness function, and determine the optimal weight and biases for MWNN. Three different variants of the Vd-PM-DO model were numerically evaluated and compared with the reference solution to demonstrate the correctness of the designed technique. Moreover, statistical analyses using twenty trials were conducted to determine the reliability and accuracy of the suggested MWNN-GA-IPA by utilizing mean absolute deviation (MAD), Theil`s inequality coefficient (TIC), and mean square error (MSE).
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Computers. - 14 : 1 (2025), p. 1-12. -
További szerzők:	Amir, Muhammad Rahman, Jamshaid Ul Raza, Ali Arif, Ghassan Ezzulddin
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM129621
035-os BibID:	(Scopus)105003916863
Első szerző:	Amir, Muhammad
Cím:	Deep Learning-Based Technique for Investigating the Behavior of MEMS Systems with Multiwalled Carbon Nanotubes and Electrically Actuated Microbeams / Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, Husam A. Neamah
Dátum:	2025
ISSN:	2215-0161
Megjegyzések:	This paper proposes a model of a doubly clamped electrically actuated microbeam, a structure frequently utilized in microelectromechanical systems (MEMS). The model investigates the effect of electrostatic forces on the deflection of the beam, based on the Euler-Bernoulli beam theory. The Galerkin technique is employed to calculate the beam's deflection, while the parallel plate capacitor model simulates the electric field. We also evaluate the performance of multi-walled carbon nanotubes (MWCNTs) in MEMS. MWCNTs are promising for MEMS applications due to their significant thermal, mechanical, and electrical properties. However, predicting the behavior of these systems is challenging due to their stiffness, parametric sensitivity, and non-linearity. Deep learning strategies for handling dynamical systems are a rapidly emerging field of research. In this paper, we employ a machine learning method, called deep neural networks (DNN), to solve the non-linear systems that arise in MEMS. The primary aim of this study is to investigate the nonlinear vibration properties of MEMS oscillators, specifically those related to nanotubes and electrically actuated microbeams, using DNN algorithms. Different optimizers are used to analyze the performance and capability of these non-linear dynamical models. Numerical simulations and graphical demonstrations are presented to verify the accuracy and efficiency of the algorithm.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok magyar nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Neural network Optimizers Microelectromechanical systems Multiwalled carbon nanotubes Rlectrically actuated microbeam
Megjelenés:	Methodsx. - 14 (2025), p. 1-14. -
További szerzők:	Rahman, Jamshaid Ul Ali, Ali Hasan (1989-) (matematikus) Raza, Ali Abduljabbar, Zaid Ameen Neamah, Husam A. (1990-) (mérnök)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI
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001-es BibID:	BIBFORM116516
035-os BibID:	(WoS)001128609500001 (Scopus)85178204153
Első szerző:	Amir, Muhammad
Cím:	Computational results of convective heat transfer for fractionalized Brinkman type tri-hybrid nanofluid with ramped temperature and non-local kernel / Muhammad Amir, Qasim Ali, Ali Raza, M.Y. Almusawa, Waleed Hamali, Ali Hasan Ali
Dátum:	2024
ISSN:	2090-4479
Megjegyzések:	Engineers have recently become attracted to electrically conducting nanofluids (NFs) due to various applications in several applied science and engineering disciplines. They have been employed in magnetic refrigeration, cancer treatment (hyperthermia), medicine, and magnetic resonance imaging, among other things. Considering the importance of electrically conducting NFs, in this article, we have proposed a fractionalized MHD (Magnetohydrodynamics) and thermal transmission of a Brinkman-type tri-hybrid nanofluid over an infinite plate saturated through a porous medium with generalized velocity and ramped conditions. For the solution of the governed fractional model, we have utilized a recent definition of fractional derivatives, known as AtanganaBaleanu (AB) fractional derivative and Laplace transformation. The computational results are exhibited for trihybrid (TiO2-Al2O3-CuO/H2O) NF and described through graphical diagrams and tables to discover the physics of numerous relevant flow parameters on temperature and velocity profiles. It is detected that both thermal transmission and momentum profile for tri-hybrid NF is a better technique as compared to hybrid NF and NF for both graphical and numerical comparisons.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Ain Shams Engineering Journal. - 15 : 3 (2024), p. 1-11. -
További szerzők:	Ali, Qasim Raza, Ali Almusawa, M.Y. Hamali, Waleed Ali, Ali Hasan (1989-) (matematikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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