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Öğe Analyzing activation energy and binary chemical reaction effects with artificial intelligence approach in axisymmetric flow of third grade nanofluid subject to soret and dufour effects(Begell House Inc, 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazThe use of nanotechnology has led to the design of many modern and more cost-effective implementation, such as solar power generation, the redevelopment of heat exchangers, and the modernization of the medical and pharmaceutical industries. In this study, the combined effects of activation energy with binary chemical reactant in a steady magnetohydrodynamic mixed convective third-grade nanofluid flow by radially radiative stretching plate has been analyzed with an artificial intelligence approach. Heat transfer analysis was conducted with heat generation, Joule heating, and Soret and Dufour effects. By incorporating appropriate transformations, the initial nonlinear coupled partial differential equations expressing the fluid model were formed as a comparable nonlinear ordinary differential equations system. Three different artificial neural network models were proposed in order to predict the skin friction, Nusselt number, and Sherwood number values of the fluid model by the Shooting Runge-Kutta Fehlberg 4, technique using the data set created by taking various values of the relevant parameters. It is worthy of noting that the average deviation values for each output parameter remained less than 5%. Furthermore it is also observed that mean square error values for skin friction coefficient, local Nusselt number, and local Sherwood number values were attained as 3.63 x 10(-3), 4.03 x 10(-4), and 8.62 x 10(-3), respectively. The obtained results show that artificial neural networks are an engineering tool that can be used with high accuracy to estimate the combined effects of activation energy and binary chemical reaction in a fixed magnetohydrodynamic mixed convective third-grade nanofluid flow with a radial radiative stretched plate.Öğe A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability(John Wiley and Sons, 2022) Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Akhtar, Md Tanwir; Shafiq, AnumThis study focuses on accurately predicting the behavior of new power functiondistribution using neural network and optimizing it using maximum likelihoodestimation. The main motivation of this study is that there is no study inthe literature that optimizes and predicts the reliability analysis of lifetimemodels by combining artificial neural networks and maximum likelihoodestimation methods. The numerical findings of the reliability investigationsand the values got from maximum likelihood estimation and artificial neuralnetwork modeling have been examined and investigated carefully. For theartificial neural network models, the R value was 0.99999 and the deviationratios were lower than 0.08%. The findings reveal that artificial neural networksare a powerful and useful mathematical tool for analyzing the reliabilityof lifetime models and numerical study findings via maximum likelihoodestimation are completely in accord with artificial neural network predictionresults.Öğe Comparative analysis to study the Darcy–Forchheimer Tangent hyperbolic flow towards cylindrical surface using artificial neural network: An application to Parabolic Trough Solar Collector(Elsevier, 2024) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazSolar thermal collectors convert sunlight into useful thermal energy by absorbing its incoming radiation. Concentrated solar power technologies use the parabolic trough solar collector to collect solar energy with temperatures ranging from 325– 700 K. The tangent hyperbolic fluid model is one of the most important non-Newtonian fluid models. Laboratory studies demonstrate that this model accurately predicts the shear thinning phenomenon. In addition, tangent hyperbolic fluid has a better heat transfer performance due to its rheological bearing at various shear rates. The current study investigates the heat transmission performance of Darcy–Forchheimer tangent hyperbolic radiative inclined cylindrical film movement in parabolic trough solar collector with an irregular heat sink/source utilizing the Levenberg–Marquardt technique and backpropagated neural networks. Through the implementation of required transformations, this system is turned into an equivalent nonlinear ordinary differential system. The findings are investigated for Newtonian and tangent hyperbolic fluid cases to understand the rheological characteristics. The outcomes are considered using graphical and mathematical evaluations. Fluids featuring tangent hyperbolic rheological conductivity are obligatory for active rate of heat diffusion. As a consequence, these fluids may be employed in Parabolic Trough Solar Collector for increased heat transmission rate and operational usage of solar energy. Furthermore, We create a dataset using the Runge–Kutta fourth-order shooting technique to create the proposed multilayer perceptron artificial neural network. The data points representing the MoD values are observed to be closely clustered around the zero deviation line. Additionally, it is important to highlight that these data points have relatively small numerical values. Moreover, when calculating the average MoD values for each output, it becomes evident that they are consistently very low.Öğe CONSTRUCTION OF NEURAL NETWORK BASED INTELLIGENT COMPUTING FOR TREATMENT OF DARCY-FORCHHEIMER SISKO NANOFLUID FLOW WITH ROSSELAND'S RADIATIVE PROCESS(Begell House Inc, 2023) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum NazA generalization of Newtonian and power-law fluids is the Sisko model. It foretells dilatants and fluid pseudoplas-ticity. It was first suggested to use the Sisko fluid model to gauge high shear rates in lubricating greases. Three constants in this model are easily selectable for certain fluids, and it is demonstrated that the model is a good predictor of shear thickening and thinning. The study of nanofluids is gaining popularity quickly because of unique thermal, mechanical, and chemical characteristics of nanomaterials. Sisko nanofluids are also required for the production of nanoscale materials because of the superb wetting and dispersing capabilities they possess. In the present investigation, the Levenberg-Marquardt method with backpropagated neural networks is used to evaluate the nanomaterial flow of Darcy-Forchheimer Sisko fluid model. Thermophoresis and Brownian motion effects are considered when develop-ing the nanofluid model. By applying the necessary transformations, the original nonlinear coupled partial differential system representing fluidic model are converted to an analogous nonlinear ordinary differential system. For different fluid model scenarios, a dataset for the proposed multilayer perceptron artificial neural network is produced by altering the necessary variables via the Galerkin weighted residual approach. An artificial neural network called a multilayer perceptron has been created in order to forecast the multilayer perceptron values.Öğe Construction of neural network based intelligent computing for treatment of darcy–forchheimer sisko nanofluid flow with rosseland’s radiative process(Begell House Inc., 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazA generalization of Newtonian and power-law fluids is the Sisko model. It foretells dilatants and fluid pseudoplasticity. It was first suggested to use the Sisko fluid model to gauge high shear rates in lubricating greases. Three constants in this model are easily selectable for certain fluids, and it is demonstrated that the model is a good predictor of shear thickening and thinning. The study of nanofluids is gaining popularity quickly because of unique thermal, mechanical, and chemical characteristics of nanomaterials. Sisko nanofluids are also required for the production of nanoscale materials because of the superb wetting and dispersing capabilities they possess. In the present investigation, the Levenberg-Marquardt method with backpropagated neural networks is used to evaluate the nanomaterial flow of Darcy–Forchheimer Sisko fluid model. Thermophoresis and Brownian motion effects are considered when developing the nanofluid model. By applying the necessary transformations, the original nonlinear coupled partial differential system representing fluidic model are converted to an analogous nonlinear ordinary differential system. For different fluid model scenarios, a dataset for the proposed multilayer perceptron artificial neural network is produced by altering the necessary variables via the Galerkin weighted residual approach. An artificial neural network called a multilayer perceptron has been created in order to forecast the multilayer perceptron values.Öğe A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis(Elsevier, 2024) Sindhu, Tabassum Naz; Çolak, Andaç Batur; Lone, Showkat Ahmad; Shafiq, Anum; Abushal, Tahani A.The study focuses on key metrics used to examine the characteristics of a lifetime random variable distribution in reliability and survival theory research. In this analysis, metrics including the probability density function time, mean residual lifespan, mean time between failures, hazard rate, and reliability function are essential. The focus of the inquiry is these important parameters in relation to the Burr-Hatke exponential model specifically. The study focuses on key metrics used to examine the characteristics of a lifetime random variable distribution in reliability and survival theory research. In this analysis, metrics including the probability density function time, mean residual lifespan, mean time between failures, hazard rate, and reliability function are essential The focus of the inquiry is these important parameters in relation to the Burr-Hatke exponential model specifically. A key component of the research is a comparison of the outcomes from the artificial intelligence approach and those from conventional literature-based methodologies. This comparison study sheds light on how well the artificial neural network framework performs while evaluating the Burr-Hatke exponential model’s technical features. The study allows a comprehensive analysis of the training and prediction capabilities of the growing neural network by calculating multiple performance measures. This comprehensive strategy improves our comprehension of the model’s survival traits and reliability, offering significant contributions to the larger field of study. The network structure’s mean square error was estimated to be 5.19E-04, and its coefficient of determination value was 0.99987 for the first neural network model. For the second neural network model, the coefficient of determi nation value was 0.99999 and the mean square error value was 4.58E-06. The outcomes amply revealed the neural network structure’s extraordinarily high prediction accuracy and the degree to which the prediction outputs agree with those of the Maximum Likelihood Estimation technique.Öğe Development of an intelligent computing system using neural networks for modeling bioconvection flow of second-grade nanofluid with gyrotactic microorganisms(Taylor and Francis Ltd., 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazNanoparticles are carried in bioconvective fluid flow by convective motion caused by living tissues. This flow has important applications in cell and tissue engineering because it demonstrates the mechanics of particle trans fer between cells and fluids. This type of flow is used in medicine delivery systems that particularly target cancer cells in real life. Nanofluids are cru cial suspensions that allow nanomaterials to disperse and behave in a homogeneous and stable environment. The bioconvective second-grade nanofluid flow, on the other hand, is distinguished by a more complex process that permits nanoparticle motion to be controlled by external fields and pressures. This type of flow has numerous applications, including biology, the environment, and energy. It is particularly useful in medical imaging, cancer hyperthermia treatment, and nanodrug delivery systems. The primary purpose of this research is to use an artificial neural network to examine the rate of heat, mass, and motile microbe movement in the convective flow of magnetohydrodynamic second-grade nanofluid toward vertical surface. Suspended nanoparticles are effectively stabilized by the action of microorganisms, facilitated through bioconvection. This process is influenced by both nanoparticle attributes and buoyancy forces. In add ition to thermophoretic dynamics and Brownian motion, the model consid ers radiation and Newtonian heating effects. Nonlinear equation systems are obtained using appropriate transformations. The non-linear simplified equations underwent numerical calculations utilizing the fourth-order Runge-Kutta shooting method. The Sherwood number, Nusselt number, and density of motile microorganism coefficient were determined using various parameters, and three distinct artificial neural networks were built employing the findings.Öğe Modeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspective(Elsevier, 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Abushal, Tahani A.The core objective of this research is to describe the behavior of the distribution using the MLE method to es timate its parameters, as well as to determine the optimal Artificial Neural Network method by comparing it to the maximum likelihood estimation method and applying it to real data for breast cancer patients to determine survival, risk, and other survival study functions of the log-logistic distribution. The parameters were defined in the input layer of the artificial neural network developed for the purpose of survival analysis and reliability function, hazard rate function, probability density function, reserved hazard rate function, Mills ratio, Odd function and CHR values were obtained in the output layer. The findings show that risk function increases with the increase in the time of infection and then decreases for a group of breast cancer patients under study, which corresponds to the theoretical properties of this according to the practical conclusions. The examination of survival analysis reveals that practical conclusions correspond to the theoretical properties of log-logistic dis tribution. Artificial neural networks have proven to be one of the ideal tools that can be used to predict various vital parameters, especially survival of cancer patients, with their high predictive capabilities.Öğe Modeling of Darcy-Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network(John Wiley and Sons Ltd, 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazModern industries face a new challenge in cooling processes. Traditional cooling lubricants have limited heatconducting capacity. The development of nanofluids possessing superior properties such as high thermal conductivity, homogeneity, and long-term stability has revolutionized the cooling lubrication industry. The literature reports a wide range of applications of nanofluid, such as cooling devices, peristaltic pumps for diabetic treatments, accelerators, reactors, petroleum industry applications, solar collectors and so forth. Nanofluids like Williamson nanofluid are very important non-Newtonian fluids that have pseudoplastic properties. Williamson nanofluid has a number of applications in the medical and engineering sciences. It is used in food processing, inkjet printing, adhesives and emulsions, coated photographic films, and many other applications. In the current study, the nanomaterial flow of the Darcy-Forchheimer Williamson nanofluid model is evaluated using the Levenberg–Marquardt approach with backpropagated neural networks. Thermalphoresis and Brownian motion are incorporated into the nanofluid model. This system is converted into an analogous nonlinear ordinary differential system through the application of necessary transformations. A dataset for the proposed multilayer perceptron artificial neural network is generated by altering the necessary variables through a Runge–Kutta fourth-order shooting procedure. It has been created an artificial neural network called a multiple-layer perceptron in order to forecast the values of the multiple-layer perceptron. It is discovered that the (Formula presented.) parameter had the highest mean deviation of (Formula presented.) and the (Formula presented.) parameter had the lowest mean deviation of (Formula presented.). Furthermore, MSE value of ANN model developed to estimate the skin friction coefficient value as (Formula presented.) and R value as 0.99954 whereas MSE and R values of the ANN model developed for the estimation of the LNN value were obtained as (Formula presented.) and 0.99999, respectively.Öğe Optimization of micro-rotation effect on magnetohydrodynamic nanofluid flow with artificial neural network(John Wiley and Sons Inc, 2024) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazIt is a major research area in mathematics, physics, engineering, and computer science to study the heat and mass transfer properties of flow. Suspensions containing multiple nanoparticles or nanocomposites have recently gained a wide range of applications in biological research and clinical trials under certain conditions. Nanofluids are important suspensions that allow nanoparticles to disseminate and behave in a homogeneous and stable environment. Therefore, here magnetohydrodynamic micropolar nanofluid flow towards the stretching surface with artificial neural network has been considered. In this study, radiation and heat source phenomena have been presented in heat convection. Brownian and thermophoresis effects and micro-rotational particles are also taking into account. The non-linear simplified equations have been calculated numerically via Runge-Kutta fourth-order shooting process. The calculation of the Sherwood number, Nusselt number, couple stress coefficient, and skin friction coefficient has been conducted utilizing diverse parameters. Furthermore, the outcomes have been employed to create four distinct artificial neural networks. Our observation indicates that an increase in the heat source quantity (Formula presented.) leads to a rise in heat generation, resulting in a greater total heat output and an increase in the temperature field. Coefficient of determination “R” values higher than 0.99 have been obtained for the artificial neural network models. The obtained findings have shown that artificial neural networks can predict thermal parameters with high accuracy.Öğe Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with Bayesian regularization(John Wiley and Sons Ltd, 2023) Sindhu, Tabassum Naz; Çolak, Andaç Batur; Lone, Showkat Ahmad; Shafiq, AnumThe investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering the issue of modeling the metrics of model reliability using artificial neural networks (ANNs). This study addresses this vacuum by discussing the multilayer ANN with Bayesian regularization modeling for reliability metrics of generalized exponential model based on inverse power law (IPL). The numerical findings of the reliability investigations and the values obtained from the ANN have been examined and analyzed carefully. The findings show that ANNs are a powerful and useful mathematical tool for analyzing the reliability of lifetime model based on IPL. Finally, a real life framework is implemented that support the theory of a research study.Öğe Reliability study of generalized Rayleigh distribution based on inverse power law using artificial neural network with Bayesian regularization(Elsevier, 2023) Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Shafiq, Anum; Abushal, Tahani A.Using the generalized Rayleigh distribution and the inverse power law, this paper proposes a new reliability model and investigates the effect of the key parameters on reliability measurements. This proposed new model offers a more accurate way to model the performance of electronic components over their lifetimes. In order to analyze the reliability parameters, a multi-layer artificial neural network model has been developed by using the datasets generated by numerical methods and obtained in four different scenarios. Using the artificial neural network model with 5 neurons in the hidden layer, the reliability parameters Hazard Rate Function, Odds function, Reversed Hazard Rate Function, Mean Time to Failure and Mean Time Between Failures have been estimated. The results obtained have been analyzed comprehensively and explained with graphics. The study findings showed that there was a direct relationship between the reliability parameters examined in all scenarios and an increase in the Mean Time Between Failures value appeared for each scenario. However, it has also been seen that the developed artificial neural network can make predictions with very high accuracy and is a powerful engineering tool that can be utilized in reliability analysis.Öğe Significance of bioconvective flow of MHD thixotropic nanofluid passing through a vertical surface by machine learning algorithm(Elsevier, 2022) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazScientists have made significant contributions in the current decade due to the importance of bioconvection in biotechnology and a variety of biological systems. In this study, a theoretical bioconvective model is constructed in the current framework to investigate the thermally developed thixotropic nanoparticles flow by incorporating narrative flow features such as thermophoresis, Brownian motion, convective condition, and radiation features, along with artificial neural network models. The non-linear complex equations were numerically calculated using the Runge–Kutta fourth-order shooting procedure. The Sherwood number, motile density of micro-organism, skin friction coefficient and Nusselt number were calculated utilizing different parameters, and four different artificial neural networks were established depending on the outcomes. R values for the developed neural network models were obtained as higher than 0.99. The results showed that artificial neural networks can give high accuracy results in the analysis of thermally developed thixotropic nanoparticles flow. Theoretical findings gained reveal industrial applications, engineering, and thermal procedures involving heat transfer. The claimed outcomes can be used to enhance cooling and heating procedures, thermal devices, energy generation, solar systems, and manufacturing procedures, among other things.Öğe Significance of EMHD graphene oxide (GO) water ethylene glycol nanofluid flow in a Darcy–Forchheimer medium by machine learning algorithm(Springer Science and Business Media Deutschland GmbH, 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum NazThe low heat efficiency of base fluids is a key problem among investigators. To address this issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to boost thermal performance of base solvents. A numerical investigation on the thermal application functioning of graphene oxide water/ethylene glycol-based nanofluids under the influence of the electromagnetohydrodynamic and Darcy–Forchheimer medium has been compiled in the present study via a machine learning algorithm. In the study of nanofluid flow, thermal radiation and a convective boundary condition are also used. The Runge–Kutta fourth-order shooting method was utilized to calculate the system of equations. The skin friction coefficient and Nusselt parameter were simulated with various variables, and two distinct artificial neural networks have been developed based on the findings. It is beneficial to estimate the fluid temperature with a large Biot number. R value above 0.99 was obtained for the developed artificial neural networks. The deviation rate was also calculated at very low values. The outcomes show that the proposed artificial neural network models can accurately predict the skin friction coefficient and Nusselt number.
