Levenberg-Marquardt Neural Network Research Articles

Heat transfer analysis for magnetohydrodynamic peristalsis of Reiner–Philippoff fluid: Application of an artificial neural network

Present communication explores a novel application of the computational intelligence technique, namely, the Levenberg–Marquardt scheme under a Backpropagated Neural Network (LM-BNN) to solve the mathematical model for the magnetohydrodynamic peristaltic transport of Reiner–Philippoff (R–Ph) pseudoplastic fluid considering the influences of Ohmic heating, mixed convection, and viscous dissipation through a symmetric channel. The R–Ph fluid model is used in this investigation to elucidate the non-Newtonian behavior of the fluid under consideration. The Reiner–Philippoff fluid model delineates the intricate relationship between stress and deformation rate within the fluid. There are a few studies available on the peristaltic transport of the Reiner–Philippoff fluid that do not incorporate Joule heating, mixed convection, and magnetic field effects. Therefore, a novel mathematical model is developed to employ an artificial neural network technique with a different approach that has not been examined before. The governing equations of the problem are simplified using long wavelength and low Reynolds number approximations, and the resulting system is numerically solved using the BVP4c scheme in MATLAB based on the shooting algorithm. Furthermore, a dataset is constructed through the BVP4c technique for the proposed LM-BNN, considering eight scenarios of peristaltic motion of the Reiner–Philippoff fluid model by varying the Bingham number, the Brinkman number, the Grashof number, the R–Ph fluid parameter, and the Hartmann number. The numerical dataset is divided into 15% for testing, 15% for training, and 70% for validation, which are utilized in LM-BNN to analyze the numerical solutions and Levenberg–Marquardt neural networks (LM-NNs) predicted results. The consistency and effectiveness of LM-BNN are validated through regression analysis, stresses at the wall, error histogram, correlation index, heat transfer, and mean squared error based fitness curves, which vary from 10−3→10−11. Variations in several flow parameters affecting temperature and velocity profiles are explained physically through graphs. Additionally, an analysis of heat transfer and stresses at the wall, including absolute errors, is provided through tables. The outcomes reveal that the improving Grashof number and the Hartmann number tend to increase the temperature profile. Tabular results indicate that rates of heat transfer improve when assigning higher values to the Hartmann number, the Brinkman number, and the Grashof number, whereas stresses at the wall decrease for the Reiner–Philippoff fluid parameter and the Bingham number. The error analysis of numerical simulations is a valuable step in determining whether the data obtained are reliable and accurate. In terms of absolute error, the disagreement between numerical solutions and those predicted by LM-NNs is approximately 10−5→10−11. It is clear from this error analysis that LM-NNs predicted results are consistent and reliable.

Neural network architecture to optimize the nanoscale thermal transport of ternary magnetized Carreau nanofluid over 3D wedge

SignificanceIncorporation of nanoparticles in base fluid water is significant for analysis of thermal behavior of nanofluid mixtures, which has various applications in materials science and thermal engineering, and supervised neural scheme predicts the thermal behavior by solving Carreau nanofluid model. MotiveThis article brings the investigation related to prediction of thermal transport of a ternary magnetized hybrid nanofluid [(Al2O3, CuO, TiO2)/H2O] with a three-dimensional Carreau nanofluid model over a wedge. Three nanoparticles dispersed in water (H2O). Inclined magnetic field is considered for judgement of velocity profile and thermal radiation is utilized to scrutinize the temperature distribution of nanofluid. The Carreau mathematical model is chosen to depict the rheological characteristics of non-Newtonian fluids at very high and very low shear rate. MethodologyPhysical assumptions creates the system of Partial differential equations (PDEs) and these are converted into ordinary differential equations (ODEs) by similarity tool. Further ODEs are dealt with bvp4c scheme and further prediction of solution is made by Levenberg-Marquardt neural network (LM-NN) supervised neural scheme. FindingsIncreased volume friction coefficients of nanoparticles increases the transport of heat. High inclined magnetic effect, thermal radiation, pressure gradient and shear strain parameter predict higher thermal transport.

Double layered combined convective heated flow of Eyring-Powell fluid across an elevated stretched cylinder using intelligent computing approach

This study investigates the possessions of a dual stratified common on the diverse convection barrier layer discharge of an Eyring-Powell fluid (CBLFEPL) induced by an prone extensive barrel. The temperature and concentration at the exterior of the barrel are assumed to be larger than the moving fluid. To solve the resulting flow equations, an brilliant numerical established aggregating solver is employed using the Levenberg-Marquardt neural network scheme (LMNNS). The governing equations of partial differential are converted into interconnected nonlinear ordinary differential equations using appropriate transformations. First, a dataset is generated for two distinct cases, one with a zero curvature parameter (plate) and the other with a non-zero curvature parameter (cylinder). The behaviors of skin-friction coefficient, Sherwood number and Nusselt number are presented over chart obtained using the BVP4C technique. Subsequently, an intelligent computing algorithm nftool, is utilized for training, validation, and testing steps to approximate solutions for various cases. The designed solver, LMNNS, is applied to solve the CBLFEPL problem through regression, mean squared error (MSE), histogram studies, and gradient analysis. The study of double-layered combined convection flow of Eyring-Powell fluid around an elevated stretched cylinder provides insights into heat and mass transfer characteristics, crucial for applications in engineering and fluid dynamics. This study aims to contribute insights that can be applied to engineering and fluid dynamics, aiding in the optimization of relevant processes and applications. The research methodology involves employing numerical technique, three-stage Lobatto IIIa formula, to solve the governing equations of the double-layered combined convection flow of Eyring-Powell fluid around an elevated stretched cylinder, while systematically varying key parameters to analyze their impact on heat and mass transfer phenomena. The speed of the fluid experiences a notable rise with higher values of curvature parameter K, fluid parameter M, mixed convection parameter λm, and the ratio of buoyancy forces N. Conversely, the velocity profile exhibits a contrasting behavior concerning the thermal stratification parameter ϵ1, solutal stratification parameter ϵ2, and an inclination angle α.

Numerical treatment of singular functional systems in quantum calculus: adaptive backpropagated Levenberg–Marquardt neural networks

Numerical treatment of singular functional systems in quantum calculus: adaptive backpropagated Levenberg–Marquardt neural networks

Intelligent Levenberg–Marquardt neural network solution to flow of carbon nanotubes in a nozzle of liquid rocket engine

In this research, we utilized artificial neural networks along with the Levenberg–Marquardt algorithm (ANN-LMA) to interpret numerical computations related to the efficiency of heat transfer in a regenerative cooling channel of a rocket engine. We used a mixture of Kerosene and carbon nanotubes (CNTs) for this purpose, examining both single-wall carbon nanotubes and multi-wall carbon nanotubes. The primary equations were converted into a dimensionless form using a similarity transformation technique. To establish a reference dataset for ANN- LMA and to analyze the movement and heat transfer properties of CNTs, we employed a numerical computation method called bvp4c, which is a solver for boundary value problems in ordinary differential equations using finite difference schemes combined with the Lobatto IIIA algorithm in MATLAB mathematical software. The ANN- LMA method was trained, tested and validated using these reference datasets to approximate the solutions of the flow model under different scenarios involving various significant physical parameters. We evaluated the accuracy of the proposed ANN- LMA model by comparing its results with the reference outcomes. We validated the performance of ANN- LMA in solving the Kerosene-based flow with CNTs in a rocket engine through regression analysis, histogram studies, and the calculation of the mean square error. The comprehensive examination of parameters undertaken in this research endeavor is poised to provide invaluable support to aerospace engineers as they endeavor to craft regenerative equipment with optimal efficiency. The pragmatic implications of our study are wide-ranging, encompassing domains as diverse as aerospace technology, materials science, and artificial intelligence. This research holds the potential to catalyze progress across multiple sectors and foster the evolution of increasingly efficient and sustainable systems.

Optimizing Cardiovascular Disease Prediction: A Synergistic Approach of Grey Wolf Levenberg Model and Neural Networks

Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases. Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity. Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy. Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness. Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model functions

Analyzing the Effects of Plasma Treatment Process Parameters on Fading of Cotton Fabrics Dyed with Two-Color Mix Dyes Using Bayesian Regulated Neural Networks (BRNNs)

ABSTRACT This study used Bayesian Regulated Neural Networks (BRNN) with 10-fold cross-validation to accurately forecast fading effects of plasma treatment on cotton fabrics for a given set of parameters. By training six independent BRNN models, a reduction in model complexity and an enhancement in generalizability to unknown datasets were achieved. The input comprises plasma treatment parameters and color measurements of the cotton fabric before fading, while the output comprises color measurements after fading. The plasma treatment parameters included color depth, air (oxygen) concentration, water content and treatment time. Color measurements included CIE L*a*b*C*h and K/S values. Furthermore, 162 datasets derived from two-color mixed-dye cotton fabrics were utilized for training and testing. The outcomes revealed superior prediction performance of the BRNN compared to the Levenberg-Marquardt Neural Networks, with R2 values approaching 1 and 82.35% to 94.12% of the sample predictions lying within the acceptable color difference range. Through global sensitivity analysis, the impact of treatment parameters on fading effects was quantified, providing a scientific basis for parameter adjustment. This study not only elucidated the mechanism of plasma treatment-induced fading but also offers effective prediction tools for the intelligent and digital development of the fashion clothing fading domain.

Double regulation Levenberg–Marquardt neural networks: an aero-engine fuel flow prediction method

Double regulation Levenberg–Marquardt neural networks: an aero-engine fuel flow prediction method

Lifetime Extension Approach Based on the Levenberg–Marquardt Neural Network and Power Routing of DC–DC Converters

The power conversion system based on the modular connection has widespread applications in various power electronic systems. To accurately estimate the state of health without recognizing the systematic mathematical model and to extend the lifetime, this paper proposes a lifetime extension approach based on the Levenberg-Marquardt back propagation neural network (LM-BPNN) and power routing of interleaved DC-DC boost conversion systems. The LM-BPNN model is constructed based on the voltage, current, and temperature data generated by the system. On the basis of the trained LM-BPNN, the real-time cumulated damage estimation of each power cell in the conversion system can be accomplished. Applying the power routing concept, the DC-DC boost conversion system allocates different power to the cells according to the cumulated damage of each cell, thereby delaying the failure of cells with higher cumulated damage. Numerical simulation results show that the proposed lifetime extension approach can extend the overall system lifetime. Furthermore, an experimental setup of the interleaved DC-DC boost conversion is constructed to verify the proposed approach, which is of great significance for predictive maintenance in the industrial system.

Decoupling Control of Six-Pole Hybrid Magnetic Bearing Based on LM Neural Network Inverse System Optimized by Improved Differential Evolution Algorithm

In order to solve the problems of strong coupling and nonlinearity of the hybrid magnetic bearings for flywheel energy storage, a novel decoupling control combining Levenberg Marquardt neural network inverse system (LMNNIS) with improved differential evolution (IDE) algorithm is proposed for the six-pole radial-axial hybrid magnetic bearing (HMB). Firstly, the structure and principle of the HMB are introduced, and the mathematical model of its radial-axial suspension forces is deduced. The IDE algorithm is used to optimize the initial model parameters of LMNNIS, and the displacement and current data are collected and normalized to input into LMNNIS for training, so as to realize the decoupling control between the suspension forces of the HMB. Then, the simulation system is constructed, and the simulation experiments of the rotor floating and anti-interference are carried out, which show the convergence speed and anti-interference performance of IDE algorithm optimized LMNNIS are better than the traditional neural network inverse system. Finally, the verification experiments are carried out on the basis of the experimental platform, which prove the feasibility and reliability of the proposed method.

Stochastic Levenberg–Marquardt Neural Network Implementation for Analyzing the Convective Heat Transfer in a Wavy Fin

The present research examines the steady, one-dimensional thermal distribution and heat transfer of a wavy fin. This heat transfer analysis considers convective effects as well as temperature-dependent thermal conductivity. Furthermore, a novel implementation of a neural network with backpropagated Levenberg–Marquardt algorithm (NN-BLMA)-based machine learning intelligent strategies is provided to interpret the heat transfer analysis of a convective wavy fin. The non-linear ordinary differential equation (ODE) of the study problem is converted into its non-dimensional form using the similarity transformation technique. The dimensionless equation obtained is then numerically explored via the Runge–Kutta–Fehlberg scheme. A data set for varying the pertinent parameters is generated, and an artificial neural network model is designed to estimate the heat transfer behavior of the wavy fin. The effectiveness of the proposed NN-BLMA is subsequently endorsed by analyses using a regression model, mean square error, and histograms. The findings of comprehensive computational parametric studies illustrate that the presented technique, NN-BLMA is an effective convergent stochastic numerical solver employed for the heat transfer model of the convective wavy fin. The wavy fin’s temperature dispersion optimizes as the thermal conductivity parameter rises. Heat transfer rate is higher in wavy fin compared to rectangular fin.

Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach

Digital financial assets such as cryptocurrency are playing an increasingly crucial role in the digital economy era. Cryptocurrency is characterized by significant volatility and asset price fluctuations in the short term. Therefore, the development of an accurate and technologically reliable forecasting approach is important. For accurately predicting the closing price of the cryptocurrency, as a representative digital financial asset, we developed a reconstructed dynamic-bound Levenberg–Marquardt neural network (R-DB-LM-NN) architecture and a corresponding neural-network training algorithm with a moving-boundary mechanism for evaluating the correctness of each descent direction. We used a high-frequency blockchain information dataset for training and prediction. The dynamic bound was introduced to increase the step size so that the neural network could effectively cross the local minimum and to avoid interrupting the neural-network iteration process. Then, we built a high-frequency encrypted digital currency blockchain information dataset. Experiments confirmed that the proposed architecture and algorithm are superior to traditional neural-network machine-learning methods, such as artificial neural networks, and deep-learning methods, such as long short-term memory and convolutional neural networks, with regard to prediction performance. Finally, the implications of the study and limitations of the proposed approach are discussed, along with the extension of the approach to other time series research domains, for researchers and practitioners.

Artificial neural networks-based improved Levenberg–Marquardt neural network for energy efficiency and anomaly detection in WSN

Artificial neural networks-based improved Levenberg–Marquardt neural network for energy efficiency and anomaly detection in WSN

Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks

Abstract This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.

Comparison of neural network and response surface methodology techniques on optimization of biodiesel production from mixed waste cooking oil using heterogeneous biocatalyst

The present research aims to optimize the process parameters of biodiesel production from mixed waste cooking oil, consisting of sunflower and palm oil. The biocatalyst was synthesized from Chinese broccoli, Cauliflower and Napa cabbage vegetable wastes and potassium hydroxide-impregnated biocatalyst was synthesized through the incipient wet impregnation method. Artificial neural networks (Scaled Conjugate Gradient, Bayesian Regularization and Levenberg Marquardt Neural Network) and RSM-based central composite design are used to optimize the parameters in biodiesel production. The obtained predicted results from ANN and RSM models are compared with experimental results. The experimental results showed that biocatalyst impregnated with KOH has produced a maximum yield of 94.7 % at a reaction temperature of 60 °C, time of 120 min, catalyst loading of 7 wt%, 1:9 M ratio and 900 rpm stirrer speed. In addition, among the three algorithms of ANN, BRNN neural network had better prediction results in biodiesel production. At the same time, LMNN, and SCGNN are nearly similarly efficient on the other hand the BRNN model has lower error values. The ANN and CCD models have demonstrated lower RMSE and MAPE such as 0.001 to 0.018, and 3.22–4.37 % respectively, and high R2 values in the ranges of 0.97 to 0.99, indicating the excellent reliability of these two approaches for process optimization. Hence, the above study recommended a biocatalyst impregnated with KOH as a catalyst to achieve maximum biodiesel yield. Further, ANN and RSM techniques are suggested for biodiesel process optimization and yield prediction.

Thermal performance of chemically reactive transient thin film flow of Maxwell nanofluid with activation energy: novel computational intelligent networks

The present study is based on a novel application of computational intelligent heuristics based on backpropagated Levenberg–Marquardt neural networks (BLM-NNs) to numerically investigate the transient-thin-film flow of chemically reactive Maxwell nanofluid (TTFFCRMN) with radiation (nonlinear), magnetic field and Joule heating over a horizontal disk with rotating effect, by utilizing the Buongiorno model of nanofluid. The partial differential equations of physical problem problem, are reduced to ODEs by exploiting the notion of Boussinesq approximations. The data set is generated by the Adams numerical method for proposed computational technique in case of the variants, Debora number, unsteadiness, rotation, nonlinear radiation, reaction rate and activation energy parameters. The strength of the proposed computational intelligent solver is incorporated by performing training, testing and validation, to solve the considered fluid model for various scenarios. The precision of the proposed numerical technique is verified by comparison with standard solution of the problem through graphical outcomes of error analysis, regression analysis and histograms. Moreover, higher radial velocity is observed for higher unsteadiness parameter; higher radiation parameter enhances temperature profile and decaying nanoparticles concentration profile is observed for higher reaction rate parameter.

An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches

Wireless sensor network (WSN) is widely acceptable communication network where human-intervention is less. Another prominent factors are cheap in cost and covers huge area of field for communication. WSN as name suggests sensor nodes are present which communicate to the neighboring node to form a network. These nodes are communicate via radio signals and equipped with battery which is one of most challenge in these networks. The battery consumption is depend on weather where sensors are deployed, routing protocols etc. To reduce the battery at routing level various quality of services (QoS) parameters are available to measure the performance of the network. To overcome this problem, many routing protocol has been proposed. In this paper, we considered two energy efficient protocols i.e. LEACH and Sub-cluster LEACH protocols. For provision of better performance of network Levenberg-Marquardt neural network (LMNN) and Moth-Flame optimisation both are implemented one by one. QoS parameters considered to measure the performance are energy efficiency, end-to-end delay, Throughput and Packet delivery ratio (PDR). After implementation, simulation results show that Sub-cluster LEACH with MFO is outperforms among other algorithms.Along with this, second part of paper considered to anomaly detection based on machine learning algorithms such as SVM, KNN and LR. NSLKDD dataset is considered and than proposed the anomaly detection method.Simulation results shows that proposed method with SVM provide better results among others.

MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm

Various studies have been conducted on the topic of predicting the thermal conductivity of nanofluids. Here, the thermal conductivity of nanofluids is determined using artificial neural networks since this approach is rapid and accurate, as well as cost-effective. To forecast the thermal conductivity of magnetohydrodynamic Williamson nanofluids flow through a vertical sheet, a feed-forward neural network with various numbers of neurons has been evaluated, and the best network based on the performance is selected. The fluid model incorporates the effects of Joule heating, heat generation absorption, thermal radiation, and a chemical reaction (MHD-WNF-HGA). A combination of heat radiation and reactive species improves the energy and solute profiles. The magnetic Reynolds number is assumed to be so small; therefore, the generated magnetic field has no effect. A postulate of similarity variables is used to convert the physical model in the form of nonlinear partial differential equations to an ordinary differential equation system. A supervised Levenberg–Marquardt backpropagation algorithm possesses a multilayer perceptron that is used for training the network, which is one of the top algorithms in machine learning. The bvp4c numerical technique is adopted to build the datasets for the construction of continuous neural network mapping. Flow, energy, and concentration profiles of the fluidic flow are constructed by adjusting several physical quantities such as the Williamson parameter, thermal radiation parameter, magnetic parameter, Eckert number, Darcy number, Brownian motion, and thermophoresis parameter. Analytical techniques such as error histogram graphs and regression-based statistical graphs are used to examine the accuracy of a suggested method. It has been found that the Levenberg–Marquardt backpropagation neural network mappings’ derivation, convergence, authentication, and consistency have been proven. Furthermore, thermal radiation assists the energy distribution to increase smoothly. Fluid velocity drops with the Williamson parameter, whereas thermophoresis impact enhances the strength of the nanofluid density.

Intelligent computing for entropy generation in Jeffrey nanofluid through radiated flow

This study explores the Entropy generation in the Flow of Jeffery Nanomaterial Model (EFJNM) past a variable thicked surface by considering joule heating effect, convectively heated surface, nonlinear radiative heat flux, and convective boundary conditions via Artificial Back Propagated Levenberg Marquardt Neural Networks (ABP-LMNNs). The ODEs system is obtained by implementing suitable transformation on PDEs for EFJNM. Applying homotopy analysis method (HAM) to create dataset for ABP-LMNNs by the variation of physical parameters of EFJNM. The reference datasets are then segmented in validation/training/testing process to scrutinize the approximated solution for EFJNM and further to compare it with standard solution. The better performance is further investigated by MSE based curves, regression fitting, absolute error, error histogram analysis. Results reveals that performance in form of MSE is in ranges of 10−9, 10−11, 10−9, 10−9, 10−10, 10−9, 10−10, 10−9, and 10−10 with 494, 285, 496, 219, 71, 167, 194, 121, 179, and 738, respectively, for all scenarios. The impact of variants of EFJNM on velocity, entropy production rate, temperature, and concentration are also analyzed. As the entropy generation increase with the increment in Deborah number and Brinkman number. The velocity increases with the escalation in Deborah number and wall thickness variable whereas decreases with the increase in relaxation to retardation ratio. The absolute errors of effective parameters for velocity are 10−7-10−4, 10−7-10−3, 10−7-10−4, and 10−6-10−3, the absolute errors for temperature are 10−7-10−3, 10−7-10−3, and 10−7-10−4 whereas the absolute errors for concentration are 10−8-10−4, 10−8-10−3, and 10−8-10−4, which show the accuracy of proposed ABP-LMNNs. Abbreviations: ABP-LMNNs, Artificial back propagated Levenberg Marquardt Neural Networks; EFJNM, Entropy generation in the Flow of Jeffery Nanomaterial Model; HAM, Homotopy analysis method

Neuro-computing intelligent networks for entropy optimized MHD fully developed nanofluid flow with activation energy and slip effects

In this paper, the main focus of this research is to represent an intelligent computing model through an artificial backpropagated Levenberg-Marquardt neural network (ABP-LMNN) for entropy optimized magnetohydrodynamic fully developed nanofluid flow with slip and activation energy effects. In mathematical modeling, dimensionless non-linear ODEs represent the magnetohydrodynamic nanofluid flow model (MHD-NFM). A reference dataset of ABP-LMNN is constructed for diverse situations of MHD-NFM by discrepancy of parameters. The attained reference dataset (RD) is randomly utilized for validation, testing and training processes for ABP-LMNN are employed to examine the approximate solution of MHD-NFM is demonstrated by comparison of outcomes. The authentic performance of the ABP-LMNN is validated through accuracy in the phrase of error histogram, mean square error and regression learning. The thermal and solutal parameters upsurge both the thermal and the concentration gradients. Moreover, the velocity profiles are declined owing to an increase in the second-order slip parameter in the tangential direction of the flow.