Yazar "Muhammad, Taseer" seçeneğine göre listele

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    OPTIMIZATION OF DARCY-FORCHHEIMER SQUEEZING FLOW IN NONLINEAR STRATIFIED FLUID UNDER CONVECTIVE CONDITIONS WITH ARTIFICIAL NEURAL NETWORK
    (Begell House Inc, 2022) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz; Muhammad, Taseer
    In cases when high velocity occurs, non-Darcy phenomena are essential for explaining fluid motion in porous media and have wide range of applications. The present study displays the magnetohydrodynamic (MHD) squeezing flow of fluid through a non-Darcian medium towards a stretched permeable surface. The heat and mass procedures are investigated using convective conditions and nonlinear stratification. The radiation and viscous dissipation phenomena are implemented to enhance the heat transfer. The nonlinear simplified equations are evaluated using a numerical Runge-Kutta fourth-order approach via the shooting process. To see the variation in the relevant fields, graphs of essential parameters have been provided. The Sherwood number, Nusselt number, and the skin friction coefficient were calculated numerically for various parameters and three different artificial neural networks (ANNs) were developed with the obtained data. The obtained results have shown that artificial neural networks can make predictions and optimizations with high accuracy.
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    Reliability modeling and analysis of mixture of exponential distributions using artificial neural network
    (Wiley, 2024) Shafiq, Anum; Colak, Andac Batur; Lone, Showkat Ahmad; Sindhu, Tabassum Naz; Muhammad, Taseer
    In recent years, statisticians have become more and more interested in the study of mixture models, especially in the last decade, without adequately considering the difficulty of modeling the reliability measures of mixture models using artificial neural networks. In this study, in which artificial neural networks and mixed model reliability criteria are analyzed, various reliability parameters are calculated considering different scenarios. In order to estimate the obtained numerical reliability parameters, a multilayer artificial neural network model has been developed. Seven different reliability parameter values have been obtained from the artificial neural network model designed with four input parameters. The prediction values obtained from the artificial neural network model developed with five neurons in the hidden layer have been compared with numerical data, and the performance of the model has been analyzed comprehensively. The mean squared error (MSE) value for the network model has been calculated as 1.98E-08 and the R value as 0.99991. The results clearly revealed that the artificial neural network model developed using data from the appropriate statistical model is an excellent tool that can be used to estimate reliability measures.