Effectiveness Of Artificial Neural Networks Research Articles

Effectiveness of artificial neural network for forecasting of fracture toughness of concrete specimens

Effectiveness of artificial neural network for forecasting of fracture toughness of concrete specimens

Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin

This research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANN-MLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology.

Artificial intelligence based succored power prediction in stand alone PV system

ABSTRACT Accurate forecasting of solar power generation is crucial for effective management of renewable energy resources. This literature review emphasises the effectiveness of Artificial Neural Networks (ANNs) in predicting solar energy potential. While traditional ANNs with single hidden layers and predefined neuron configurations have shown success in various applications, they are not suitable for predicting nonlinear time-series databases in the context of this research. The limitations of traditional ANNs in predicting solar power potential necessitate the exploration of alternative approaches. Configuring the structure of traditional ANNs for solar power prediction is a time-consuming process that often requires manual or trial-and-error methods. To address this issue, optimisation techniques are incorporated into the ANN model. Various traditional methods have been explored in this research, but none has achieved satisfactory performance. Drawing from experiences with traditional techniques, a novel Evolution of Cub to Predator (ECP) technique is proposed. The investigation results demonstrate that the incorporation of optimisation techniques yields superior performance compared to traditional ANNs. The proposed ECP technique achieves an impressive prediction accuracy of 97.2%, surpassing existing optimisation techniques. These findings highlight the potential of the ECP technique for accurate solar power prediction and its contribution to optimising renewable energy resource management.

A machine-learning based framework for design and characterization of honeycombs with partial self-similar hierarchical architectures

Honeycomb structures with hierarchical topology are widely used in engineering fields for their excellent mechanical properties. Multiphase topology is proved to possess a larger design space than the traditional hierarchical topology. In this study, a partial self-similar hierarchical honeycomb (PSSHH), comprising of zero-order cell and first-order cell, was designed. Numerical simulations, validating against the experimental results, were carried out to investigate the in-plane crushing performance of PSSHHs with different configurations. An artificial neural network model was trained using the results of simulations. The ability and effectiveness of artificial neural networks in predicting and characterizing the crushing responses of PSSHH were evaluated through comparison with simulations. The model was primarily employed for optimizing the design of PSSHH to enhance the crushing performance. The results indicate that the optimized PSSHH outperforms traditional hexagonal honeycombs and fully self-similar hierarchical honeycombs in terms of initial peak force, mean crushing force, and energy absorption. Furthermore, the effect of number and organized patterns of the first-order cells along the loading direction on the mechanical properties of PSSHH is examined. The findings reveal that the formation and development of localized collapse bands are significantly affected by the number and distribution of hierarchical first-order cells. The deformation, which involves a sequential crushing process with the initial collapse of specific zero-order cells followed by the subsequent collapse of first-order cells, contributes to enhancing the crashworthiness performance of PSSHH.

Hyperspectral imaging predicts free fatty acid levels, peroxide values, and linoleic acid and oleic acid concentrations in tree nut kernels

Imaging technologies are advancing rapidly in food processing systems to reduce food waste. However, modelling techniques, sample sizes and dataset proportioning methods significantly affect the performance of models in predicting food quality variables. This study examined the potential of hyperspectral imaging to predict peroxide values (PV), free fatty acid (FFA) levels and fatty acid concentrations, including oleic and linoleic acid, in two tree nuts, canarium and macadamia. The effectiveness of artificial neural network (ANN) regression and partial least squares regression (PLSR) techniques to make these predictions was examined. Additionally, the importance of the dataset size on prediction accuracy and the dataset-proportioning method for developing predictive models were assessed. Both ANN and PLSR models predicted FFA levels and oleic acid concentrations with high accuracy, but PV and linoleic acid concentrations were predicted poorly. Changing the test-dataset proportioning method for the small dataset led to comparable R2test values by both ANN and PLSR in predicting FFA levels. Successful prediction of FFA levels could be explained partly by high variability, even in the dataset with the small number of samples. The study highlights the significance of factors like dataset size, test-dataset proportioning method, and model selection when predicting the quality attributes.

Neural network prediction of cooling heat transfer characteristics of supercritical R1234ze (E) in horizontal tube

<sec>The prediction of heat transfer coefficients or wall temperatures of heat exchanger tubes is an important research topic in supercritical heat transfer, which is extremely significant for the application of supercritical fluids in industrial production and the design of the entire thermal system. At present, the empirical correlation method is the most widely adopted prediction method, but its predicted heat transfer coefficient still has significant difference from the actual data near the pseudo-critical temperature. Therefore, some scholars proposed using artificial neural networks to predict the heat transfer performance of supercritical fluids in tubes. On the basis of previous researches, this work further explores the effectiveness of artificial neural network in predicting supercritical heat transfer, focusing on the influence of input parameters on neural network prediction results and the influence of genetic algorithm optimization on the prediction results.</sec><sec>In this research, a neural network prediction model for supercritical R1234ze(E) cooled in horizontal straight tubes is established and compared with the modified D-B heat transfer correlation. The result shows that the input parameter has great influence on the prediction accuracy of BPNN, and not all BPNN input parameter combinations can bring better prediction results than heat transfer correlation. The combination of <inline-formula><tex-math id="M7">\begin{document}$ {{Re} _{\text{b}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M7.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M7.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M8">\begin{document}$ {Pr _{\text{b}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M8.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M8.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M9">\begin{document}$ {{{\rho _{\text{b}}}} \mathord{\left/ {\vphantom {{{\rho _{\text{b}}}} {{\rho _{\text{w}}}}}} \right. } {{\rho _{\text{w}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M9.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M9.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M10">\begin{document}$ {{{{\overline C }_{\text{p}}}} \mathord{\left/ {\vphantom {{{{\overline C }_{\text{p}}}} {{C_{{\text{pw}}}}}}} \right. } {{C_{{\text{pw}}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M10.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M10.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M11">\begin{document}$ {{{\lambda _{\text{b}}}} \mathord{\left/ {\vphantom {{{\lambda _{\text{b}}}} {{\lambda _{\text{w}}}}}} \right. } {{\lambda _{\text{w}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M11.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M11.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M12">\begin{document}$ {{{\mu _{\text{b}}}} \mathord{\left/ {\vphantom {{{\mu _{\text{b}}}} {{\mu _{\text{w}}}}}} \right. } {{\mu _{\text{w}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M12.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M12.png"/></alternatives></inline-formula> features the best prediction performance. The AAD and Error<sub>max</sub> of the prediction result for the trial set are only 2.02 % and 9.34 %, which are far lower than the prediction deviation of the heat transfer correlation, and the predictions of the trend of <i>h</i> in the high temperature region, the maximum value of <i>h</i> and the position of the peak value of <i>h</i> are more precise than heat transfer correlation. Moreover, this research compares GA-BP model with BP model under two different fitness value calculation methods to reveal the effectiveness of GA-BP in enhancing the prediction accuracy of supercritical heat transfer, concluding that when the same dataset is adopted for network training and fitness value calculation, over-fitting will occur and the GA-BP cannot further improve the prediction accuracy; when different datasets are used to train the network and calculate the fitness value, the generalization ability of the network will be strengthened, and the root mean square deviation and the maximum deviation of the prediction result can be further reduced.</sec><sec>This work will provide a practical tool for predicting the cooling convection heat transfer of supercritical R1234ze (E) in horizontal tubes, laying the foundation for its application in trans-critical heat pump systems, and providing inspiration for potential research directions of ANN in supercritical heat transfer prediction.</sec>

The Evaluation and Development of University English Teaching Quality Based on Wireless Network Artificial Intelligence

This study introduces a novel approach to address deficiencies in prior teaching quality assessment systems by establishing a mathematical model for evaluation. Utilizing a neural network trained via a particle swarm optimization algorithm (PSO), the method develops a BP (Backpropagation) model fine-tuned by PSO to capture the intricate relationships among diverse indicators influencing teachers’ teaching quality assessment and resulting evaluations. Empirical findings highlight the effectiveness of artificial neural networks in constructing a comprehensive evaluation framework accommodating a wide spectrum of systematic assessments. This approach not only optimizes teaching methodologies but also augments overall teaching efficacy and the quality of educational delivery in a holistic manner. Moreover, it fosters the cultivation of multifaceted individuals proficient in English application skills, contributing to the development of high-quality talent in practical and complex domains. The convergence of advanced mathematical modeling techniques and computational methods, alongside the utilization of numerous indicators, aligns with combinatorial principles, exploring the permutations and relationships of diverse factors impacting teaching quality assessment.

Performance Evaluation of Artificial Neural Network Modelling to a Ploughing Unit in Various Soil Conditions

Abstract The specific objective of this study is to find a suitable artificial neural network model for estimating the operation indicators (disturbed soil volume, effective field capacity, draft force, and energy requirement) of ploughing units (tractor disc) in various soil conditions. The experiment involved two different factors, i.e., (Ι) soil texture index and (ΙΙ) field work index, and included soil moisture content, tractor engine power, soil bulk density, tillage speed, tillage depth, and tillage width, which were linked to one dimensionless index. We assessed the effectiveness of artificial neural network and multiple linear regression models between the values predicted and the actual values using the mean absolute error criterion to test data points. When the artificial neural network model was applied, the mean absolute error values for disturbed soil volume, effective field capacity, draft force, and energy requirement were 69.41 m3·hr−1, 0.04 ha·hr−1, 1.24 kN, and 1.95 kw·hr·ha−1, respectively. In order to evaluate the behaviour of new models, the coefficient R 2 was used as a criterion, where R 2 values in artificial neural network were 0.9872, 0.9553, 0.9948, and 0.9718, respectively, for the aforementioned testing dataset. Simultaneously, R 2 values in multiple linear regression were 0.7623, 0.696, 0.492, and 0.5572, respectively, for the same testing dataset. Based on these comparisons, it was clear that predictions using the artificial neural network models proposed are very satisfactory.

Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle

The CO2 combined cooling and power cycle (CCP) is a promising alternative for waste heat recovery due to its environmental friendliness and excellent performance. However, the transient dynamic behavior analysis and control of CCP systems are challenged by the instability of waste heat sources. In transient dynamic modeling, artificial neural networks, with their nonlinear mapping capabilities and relatively low computational requirements, prove advantageous over dynamic simulation models. In this study, six commonly used artificial neural network architectures are employed for approximating and predicting the transient dynamic behavior of CCP systems and subjected to preliminary applications. Results show that the multilayer feedforward neural network is the most suitable among the six networks for predicting and approximating the CCP system's transient dynamic behavior. Based on this model, a trajectory optimization control strategy is designed, leading to a 5.3 % improvement in CCP net power. This research underscores the effectiveness of artificial neural networks in the field of CCP dynamic modeling, offering valuable guidance for its application.

Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forcasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran)

Air pollution is one of the major concerns for the population and the environment due to its hazardous effects. PM10 has affected significant scientific and regulatory interest because of its strong correlation with chronic health such as respiratory illnesses, lung cancer, and asthma. Forcasting air quality and assessing the health impacts of the air pollutants like particulate matter is crucial for protecting public health.This study incorporated weather, traffic, green space information, and time parameters, to forcst the AQI and PM10. Traffic data plays a critical role in predicting air pollution, as it significantly influences them. Therefore, including traffic data in the ANN model is necessary and valuable. Green spaces also affect air quality, and their inclusion in neural network models can improve predictive accuracy. The key factors influencing the AQI are the two-day lag time, the proximity of a park to the AQI monitoring station, the average distance between each park and AQI monitoring stations, and the air temperature. In addition, the average distance between each park, the number of parks, seasonal variations, and the total number of vehicles are the primary determinants affecting PM10.The straightforward effective Multilayer Perceptron Artificial Neural Network (MLP-ANN) demonstrated correlation coefficients (R) of 0.82 and 0.93 when forcasting AQI and PM10, respectively. This study also used the forcasted PM10 values from the ANN model to assess the health effects of elevated air pollution. The results indicate that elevated levels of PM10 can increase the likelihood of respiratory symptoms. Among children, there is a higher prevalence of bronchitis, while among adults, the incidence of chronic bronchitis is higher. It was estimated that the attributable proportions for children and adults were 6.87% and 9.72%, respectively. These results underscore the importance of monitoring air quality and taking action to reduce pollution to safeguard public health.

Retracted: Evaluation of the Effectiveness of Artificial Neural Network Based on Correcting Scoliosis and Improving Spinal Health in University Students.

[This retracts the article DOI: 10.1155/2022/2092034.].

Crunching Numbers, Making Decisions: Artificial Intelligence and Statistics for Financial Distress Forecasting in Algeria and Saudi Arabia

Predicting financial distress has been a significant concern for both researchers and practitioners for long period. This topic has garnered substantial interest due to the potential benefits of using predictive models to anticipate financial troubles and help companies steer clear of financial risks that could lead to bankruptcy and liquidation. The primary aim of this research is to forecast financial distress, comparing the effectiveness of Artificial Neural Network (ANN) with Logistic Regression (LR). This evaluation is based on data from 12 Algerian companies and 12 Saudi companies during the period from 2015 to 2019. The study's findings indicate that the LR model outperformed the Wide Neural Network (WNN) model in accurately predicting financial distress, achieving optimal classification accuracy for both Algerian and Saudi companies. Consequently, the LR model emerges as the preferred choice for forecasting financial distress in both countries.

Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression

This study investigates the surface parameters and environmental factors affecting the energy production of a 500 kWp photovoltaic (PV) solar power plant in Igdir province. Using both the PV panel characteristics and the weather conditions specific to the power plant location, a total of 7 detailed features were included. The estimation of the power plant efficiency, a novel contribution not found in previous studies, is also a major focus. The performance evaluation of different models, including feed-forward neural networks and multiple linear regression, demonstrates the effectiveness of artificial neural networks in capturing the complex relationships between features and efficiency despite limited data availability. Principal Component Analysis (PCA) was used to reduce feature dimensions, showing that even with a reduced feature set, accurate efficiency prediction is still achievable. Prediction using PCA is one of the novelties of the paper. The effects of solar irradiation, module power, and module temperature on power plant efficiency are revealed. The results provide valuable insights for optimizing energy investments in the Igdir region and highlight the potential of artificial neural networks in energy forecasting, demonstrating their suitability for capturing complex patterns in solar power plant efficiency.

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides.

Predicting behavior of photonic crystal fiber lasers using artificial neural networks

Simulating pulse propagation within fiber lasers is a difficult and computationally demanding task, which limits the ability to accurately capture their complex nonlinear dynamics. This paper leverages artificial intelligence (AI) and artificial neural networks (ANN) to predict the output pulse shape parameters for a ring cavity fiber laser, thus avoiding the need for traditional complex numerical calculations and reducing the overall computational cost. This study focuses on a dissipative soliton resonance (DSR) ring cavity fiber laser, incorporating a photonic crystal fiber (PCF) and mode locked by a real saturable absorber. The integration of the PCF into the laser cavity design enhances the control of both dispersion and nonlinearity, allowing for a greater range of output pulse shapes and parameters. The proposed approach demonstrates the effectiveness of artificial neural networks in predicting pulse parameters, and could pave the way for more efficient design and optimization of fiber lasers. The results of the study show that the ANN model can accurately predict the output pulse shape parameters for the DSR ring cavity fiber laser, and thus offers a promising tool for future research in the field.

Comparison of an effectiveness of artificial neural networks for various activation functions

Activation functions play an important role in artificial neural networks (ANNs) because they break the linearity in the data transformations that are performed by models. Thanks to the recent spike in interest around the topic of ANNs, new improvements to activation functions are emerging. The paper presents the results of research on the effectiveness of ANNs for ReLU, Leaky ReLU, ELU, and Swish activation functions. Four different data sets, and three different network architectures were used. Results show that Leaky ReLU, ELU and Swish functions work better in deep and more complex architectures which are to alleviate vanishing gradient and dead neurons problems but at the cost of prediction speed. Swish function seems to speed up training process considerably but neither of the three aforementioned functions comes ahead in accuracy in all used datasets.

RESEARCH ON THE PRINCIPLES OF ARTIFICIAL NEURAL NETWORK CONSTRUCTION AND PARAMETER MODELING

Studies show that neural networks with multilayer direct propagation have be-come more widespread, which is due to the simplicity of their implementation, the availability of advanced learning methods, and the parallel execution of calculations. In these networks, the computational complexity increases proportionally to the square of the data size, which leads to a decrease in performance and an increase in hardware costs. The main problems of synthesis of artificial neural networks, criteria for evaluating the effectiveness of artificial neural networks, issues of choosing layers and the number of neurons in each layer are investigated. In the work, the mean square error, mean absolute error and mean absolute percentage error criteria are considered for evaluating the efficiency of neural networks. In general, one hidden layer of a neu-ral network is sufficient for most problems, two hidden layers can represent a function of any shape, and adding a third and more layers results in very little improvement in network performance. Expressions are given for the total number of parameters de-pending on the number, and the number of inputs from the binary sequence, as well as the minimum and maximum values of the training examples depending on the total number of parameters and the number of outputs. The studies have shown that the se-lection of the statistical model efficiency evaluation criteria depends on the problem to be solved. The results of the simulations performed using the MATLAB package are presented.

The Effect of Artificial Neural Network Towards the Number of Particles of Rao-Blackwellized Particle Filter using Laser Distance Sensor

Rao-Blackwellized particle filter (RBPF) algorithm aims to solve the Simultaneous Localization and Mapping (SLAM) problem. The performance of RBPF is based on the number of particles. The higher the number of particles, the better the performance of RBPF. However, higher number of particles required high memory and computational cost. Nevertheless, the number of particles can be reduced by using high-end sensor. By using high-end sensor, high performance of RBPF can be achieved and reduced the number of particles. But the development of the robot came at a high cost. A robot can be equipped with low-cost sensor in order to reduce the overall cost of the robot. However, low-cost sensor presented challenges of creating good map accuracy due to the low accuracy of the sensor measurement. For that reason, RBPF is integrated with artificial neural network (ANN) to interpret noisy sensor measurements and achieved better accuracy in SLAM. In this paper, RBPF integrated with ANN is experimented by using Turtlebot3 in real-world experiment. The experiment is evaluated by comparing the resulting maps estimated by RBPF with ANN and RBPF without ANN. The results show that RBPF with ANN has increased the performance of SLAM by 25.17% and achieved 10 out of 10 trials of closed loop map by using only 30 particles compared to RBPF without ANN that needs 400 particles to achieve closed loop map. In conclusion, it shows that, SLAM performance can be improved by integrating RBPF algorithm with ANN and reduces the number of particles.

Darcy-Forchheimer entropy based hybrid nanofluid flow over a stretchable surface: intelligent computing approach

The aim of this article is to explore incompressible and steady hybrid nanofluid flow in Darcy-Forchheimer porous medium (ISHN-DFPM) with entropy production along a stretchable surface via the strength of stochastic numerical computing by incorporating the applicability and effectiveness of artificial neural networks with Levenberg-Marquardt backpropagation (ANNs-LMB). The reference dataset is constructed in MATHEMATICA using the ND Solve command. Moreover, the generated dataset is further exploited after generating in the Mathematica software to export in the MATLAB environment under the functionality and performance of the Levenberg-Marquardt backpropagation technique. The results verification is presented by comparing the model with already perfumed simulations. The thermal onsets of the hybrid nanofluid model are characterized by using the molybdenum disulfide and silicon dioxide nanoparticles with water base fluid. The error analysis for the implemented algorithm is also presented. The numerical outcomes are listed for Nusselt number and wall shear forces against and nanoparticles. It is observed that the heat transfer phenomenon enhanced with the increasing Brinkman number. Moreover, the Bejan number and entropy generation phenomenon increased with the radiation parameters. The dataset is prepared in terms of training, testing, and validation processes to enterprise the suggested intelligent solver ANNs-LMB which is further authenticated through convergence graphs of the fitness-dependent mean square error, histogram error, and regression measure which also ensure high accuracy and predictive strength of the solver.

Effectiveness of Artificial Neural Networks for Solving Inverse Problems in Magnetic Field-Based Localization.

Recently, indoor localization has become an active area of research. Although there are various approaches to indoor localization, methods that utilize artificially generated magnetic fields from a target device are considered to be the best in terms of localization accuracy under non-line-of-sight conditions. In magnetic field-based localization, the target position must be calculated based on the magnetic field information detected by multiple sensors. The calculation process is equivalent to solving a nonlinear inverse problem. Recently, a machine-learning approach has been proposed to solve the inverse problem. Reportedly, adopting the k-nearest neighbor algorithm (k-NN) enabled the machine-learning approach to achieve fairly good performance in terms of both localization accuracy and computational speed. Moreover, it has been suggested that the localization accuracy can be further improved by adopting artificial neural networks (ANNs) instead of k-NN. However, the effectiveness of ANNs has not yet been demonstrated. In this study, we thoroughly investigated the effectiveness of ANNs for solving the inverse problem of magnetic field-based localization in comparison with k-NN. We demonstrate that despite taking longer to train, ANNs are superior to k-NN in terms of localization accuracy. The k-NN is still valid for predicting fairly accurate target positions within limited training times.