Back-propagation Artificial Neural Network Research Articles

Discussion on the use of fashion elements in ceramic design in the context of big data

Abstract In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and traditional craftsmanship, it realizes the innovation of ceramic design and promotes the development of ceramic industry. A neural network-based assessment model, especially BP artificial neural network, is used to quantitatively analyze and apply fashion elements in ceramic design. First, the basic theory of neural network, including MP model and BP algorithm, is introduced. The study established a network model with a 5-30-5 structure by training 800 sets of color samples. The experimental results show that the network reaches the mean square error target (MSE=1e-7) after 1516 iterations, proving the model’s effectiveness in dealing with nonlinear color semantic mapping. The analysis of the prediction accuracy of the color scheme shows that the maximum errors are 2.22, 1.93, and 1.36 in the L, A, and B components, respectively. These results are lower than the national standard color difference range, indicating that the proposed model has practical application in ceramic design. The neural network model can effectively integrate fashion elements into ceramic design, providing a new perspective and technical support for traditional ceramic design.

Research on visualization monitoring technology of vulnerable high-voltage electrical equipment in substation based on BP artificial neural network

Abstract This study develops a visualization monitoring system for substation equipment operational status, utilizing mobile monitoring technologies. The system architecture integrates a core functional module aligned with comprehensive system requirements, enhanced by a BP neural network to optimize the server’s data mining capabilities. The research focuses on the analysis of typical faults in crucial substation electrical equipment, applying a Fourier algorithm for preprocessing the fault data. Employing the diagnostic principles of the BP neural network, the study designs a thermal fault diagnosis process for the substation apparatus. Experimental scenarios were established to evaluate the BP neural network’s performance by comparing three linear regression sample values. The practical application of the BP neural network model was assessed through integration with substation field data. Cross-validation of the field data indicates that the fault location algorithm accurately identifies 11 types of faults from 85 alarm signals in the secondary condition monitoring of substations, achieving a reliability of 98% or higher, which underscores its high applicability and operational feasibility.

AN APPLICATION OF ARTIFICIAL NEURAL NETWORK TOWARD THE MATHEMATICAL MODELING OF MHD TANGENT HYPERBOLIC NANOFLUID ACROSS A VERTICAL STRETCHING SURFACE

The Levenberg-Marquardt (LM) back propagation (BP) artificial neural networks (ANNs) (LM-BP-ANNs) procedure is used in this analysis to show the computational strategy of neural networks for the simulation of magnetohydrodynamics tangent hyperbolic nanofluid flow comprised of motile microorganism across a vertical slender stretching surface. The fluid flow were examined under the significance of chemical reaction, magnetic field, activation energy, and heat source. The modeled equations were simplified to the ordinary system of differential equations using similarity variables substitution. The Lobatto IIIA formula based on the finite difference method was employed for the nano-liquid flow problem with an accuracy up to five decimal points. The robustness of Lobatto IIIA is its straightforward execution of very nonlinear coupled differential equations. Several operations involving testing, authentication, and training were carried out by developing a scheme for different fluid problem elements using reference datasets. The accuracy of LM-BP-ANNs was tested through mean-square error, error histogram, curve fitting figures, and regression plot. Moreover, the examination of flow model factors for concentration, mass, and momentum outlines are expressed through graphs. It was perceived that the velocity field declines with the flourishing influence of the magnetic field and lessens with the upshot of Weissenberg number and power law index.

Real-time automatic identification methods for downhole whirling based on mechanical specific energy model of drill bit

In the drilling exploitation of hot dry rock for geothermal energy, whirling is one of the main low-efficiency conditions that affect the efficiency of Polycrystalline Diamond Compact (PDC) bit in horizontal well drilling. Realizing automatic and real-time identification with whirling is of great significance to save non-productive time and ensure drilling safety and benefit. In this paper, we first constructed a real-time drilling mechanical specific energy (MSE) model combined with while-drilling testing to reflect the real-time drilling conditions. The MSE model is used to normalize multi-source parameters. Secondly, we constructed a Back Propagation (BP) artificial neural network (ANN), and then the normalization effect verification, optimization of network parameters, and identification effect verification of whirling were carried out. The final results show that the established MSE model has a favourable effect on data normalization, which could also reduce the complexity of the required network model, and shorten the training time by 20–30 s/step. The optimal algorithm is Trainscg, whose optimal number of hidden layer nodes is 5, and the optimal maximum number of iteration steps is 1000. The established ANN model can accurately identify whirling based on MSE, the accuracy is about 0.94, and the average relative error is 1.3%. The method established in this paper provides a reference for the automatic identification of various low-efficiency conditions based on MSE.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.

Machine learning approach of Casson hybrid nanofluid flow over a heated stretching surface

<abstract> <p>The present investigation focused on the influence of magnetohydrodynamic Gold-Fe<sub>3</sub>O<sub>4</sub> hybrid nanofluid flow over a stretching surface in the presence of a porous medium and linear thermal radiation. This article demonstrates a novel method for implementing an intelligent computational solution by using a multilayer perception (MLP) feed-forward back-propagation artificial neural network (ANN) controlled by the Levenberg-Marquard algorithm. We trained, tested, and validated the ANN model using the obtained data. In this model, we used blood as the base fluid along with Gold-Fe<sub>3</sub>O<sub>4</sub> nanoparticles. By using the suitable self-similarity variables, the partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs). After that, the dimensionless equations were solved by using the MATLAB solver in the Fehlberg method, such as those involving velocity, energy, skin friction coefficient, heat transfer rates and other variables. The goals of the ANN model included data selection, network construction, network training, and performance assessment using the mean square error indicator. The influence of key factors on fluid transport properties is presented via tables and graphs. The velocity profile decreased for higher values of the magnetic field parameter and we noticed an increasing tendency in the temperature profile. This type of theoretical investigation is a necessary aspect of the biomedical field and many engineering sectors.</p> </abstract>

Predicting Stock Market Trends Based on Macroeconomic Indicators through Machine Learning Approach: A Case Study of KSE 100 INDEX

The purpose of our research is to model the monthly price of the KSE 100 index based on Pakistan's macroeconomic indicators using a Machine Learning (ML) approach. The novelty of the study is forecasting the future value of the stock market using ML. Monthly data was collected for the period from Feb 2004 to Dec 2020. The output layer of our study is the closing price of the KSE 100 index, and the input layer consists of 16 macroeconomic variables of the Pakistan economy, which are the industrial production index (IPI), the exchange rate (EX-RATE), money supply (M2), consumer price index (CPI), foreign direct investment (FDI), Treasury bill on 3-months treasury, interest rate as KIBOR – Month Average (1 Month), Foreign Exchange reserves (FXR), Consumer Financing for house building (house financing), Balance of Trade (BOT), crude oil, Gold, Labor force participation rate, GDP growth (annual %), Households and NPISHs Final consumption expenditure (household consumption) (current US$), and Domestic savings. The prediction uses the Artificial Neural Network (ANN) Backpropagation algorithm. The model developed in this research achieved 99% accuracy using macroeconomic indicators. The accuracy level indicates that the model of the KSE 100 index can predict future trends. This study also forecasts the future monthly values of the KSE 100 index from Jan 21 to Jun 23 and daily future values from Oct 1, 2022, to Dec 31, 2020.

A NOVEL SYSTEM FOR PREDICTING THE DAMAGE OF RICE DISEASES IN AN GIANG PROVINCE, VIETNAM

Many researchers have recently considered information technology applications to support smart agriculture. Numerous solutions and information technology systems have been implemented to benefit farmers, such as an e-commerce website for exchanging agricultural products, an agricultural information system, a livestock information management system, and an agricultural environment monitoring system. This paper presents a novel system that combines artificial back-propagation neural networks with genetic algorithms for predicting the damage of rice diseases in An Giang province. The system predicts rice disease damage in the coming weeks in 11 districts in An Giang based on input parameters such as climate conditions and previous rice disease damage. The approach was evaluated on a dataset collected by the An Giang Plant Protection Department over 22 years. The rice diseases forecasting system produced promising results and received positive feedback from forecast department specialists, with an RMSE of 19.31 on the test dataset (ha).

Optimization of mix proportion and strength prediction of magnesium phosphate cement-based composites based on machine learning

In order to optimize the mix ratio design of magnesium phosphate cementitious composites (MPCC) to obtain excellent workability and strength, this study investigated the effects of MgO-phosphate ratio, water-binder ratio, aggregate-binder ratio, the mass ratio of coarse and fine aggregates, mineral admixture content and steel fiber content on the flowability, setting time and cubic compressive strength (CCS) of MPCC using orthogonal design of experiments with a total of 32 sets of mix ratios. The experimental results were also analyzed utilizing range and variance analysis to determine the main influencing parameters and the optimal dosage of each factor, and the optimal MPCC mix ratio was obtained. In addition, the prediction of CCS for MPCC using five machine learning models was first introduced, and the predicted results were interpreted and analyzed regarding the SHAP (Shapley Additive exPlanations) features importance. The results showed that the effect of steel fiber volume content and water-binder ratio on the flowability was significant, and M/P significantly affected the setting time of MPCC. The CCS of MPCC increased with the age of curing, and MPCC exhibited high early strength, with 1-day CCS accounting for 55%～85% of the 28-day CCS. The effect of M/P on the 1-day, 3-day and 28-day CCS was highly significant, with M/P contribution rate as high as 76.3%, 80.3% and 83.6% of the CCS at 1, 3 and 28 days, respectively. Besides, the effect of steel fiber volume content on 28-day CCS was also significant. In summary, the optimal mix ratio of MPCC was A4B1C4D1E4F4G1 (M/P = 3, w/b=0.11, a/b=1.4, c/f=3, FA content =15%, MK content= 15%, steel fiber volume content= 0%). Furthermore, random forest (RF) and particle swarm optimization of back propagation artificial neural network (PSO-BP-ANN) models have a higher prediction accuracy, and the results of SHAP feature significance further confirmed the above test results.

Food safety testing by negentropy-sorted kernel independent component analysis based on infrared spectroscopy

In the field of food safety testing, variety, brand, origin, and adulteration are four important factors. In this study, a novel food safety testing method based on infrared spectroscopy is proposed to investigate these factors. Fourier transform infrared spectroscopy data are analyzed using negentropy-sorted kernel independent component analysis (NS-kICA) as the feature optimization method. To rank the components, negentropy is performed to measure the non-Gaussian independent components. In our experiment, the proposed method was run on four datasets to comprehensively investigate the variety, brand, origin, and adulteration of agricultural products. The experimental results show that NS-kICA outperforms conventional feature selection methods. The support vector machine model outperforms the backpropagation artificial neural network and partial least squares models. The combination of NS-kICA and support vector machine (SVM) is the best method for achieving high, stable, and efficient recognition performance. These findings are of great importance for food safety testing.

Artificial Neural Network-Based Prediction Model Back Propagation on Blood Demand and Blood Supply

The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition must be maintained to reduce unused or expired blood supplies. Despite the situation in UTD-PMI, where the blood supply exceeds demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying the forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the mean square error (MSE). Ultimately, the research results present the prediction model for the four types of blood with respect to the demand and supply. These findings can serve as a reference to regulate future blood donation activities carried out by the UTD-PMI.

Exergy assessment of infrared assisted air impingement dryer using response surface methodology, Back Propagation-Artificial Neural Network, and multi-objective genetic algorithm

This study deals with the exergy analysis of the thin-layer drying process of apple fruit via an infrared-assisted air impingement dryer. In the study, process conditions, namely, drying temperature (50–70 °C), slice thickness (2–6 mm), and recirculation ratio (10–90 %) were considered as independent parameters. The impacts of process parameters were studied over the responses, namely, exergy efficiency, exergy loss, improvement potential, and sustainability index. A comparative study was conducted between a Back-Propagation Artificial Neural Network (BP-ANN) coupled with a multi-objective genetic algorithm (MOGA) and Response Surface Methodology (RSM). It was found that both BP-ANN and RSM had good prediction ability, but BP-ANN performed slightly better with higher R2, lower RMSE, and MAE values. The optimized conditions for BP-ANN-MOGA were found to be a temperature of 50 °C, slice thickness of 3.9 mm, and recirculation ratio of 76.38 %, which yielded a response of exergy efficiency of 62.23 %, exergy loss of 221 kJ, an improvement potential of 105 kJ, and a sustainability index of 2.65. This study showed a better exergy assessment of the developed hybrid dryer from a thermodynamic point of view.

BPANN modified constitutive descriptions for flow behavior and softening mechanism in the α+β phase region of Ti-55511 alloy with equiaxed microstructure

The deformation behavior of Ti-55511 alloy was investigated by hot compression tests conducted at deformation temperatures of 993∼1113 K and strain rates of 0.001–1 s−1. To eliminate the effects of friction and adiabatic temperature rise on the flow stresses, the flow curves were corrected. Subsequently, a strain compensated Arrhenius (SCA) model was developed and further modified using the back propagation artificial neural network (BPANN) method. The modified model exhibits significant improvements compared to the SCA model. The average absolute relative error (AARE) decreases from 4.895 % to 0.756 %, while the relative error (δ) range decreases from −14.75 %–17.99 % to −4.78 %–8.01 %. Moreover, cross-validation also confirms the modified model has higher generalization ability and prediction accuracy than the SCA model. Microstructural characterization reveals dynamic recrystallization (DRX) of the α phase and dynamic recovery (DRV) of the β phase are the main mechanisms responsible for softening, and their volume fractions increase with decreasing strain rate and increasing deformation temperature. The equiaxed α phase undergoes elongation first, followed by gradual migration and transformation of grain boundaries. Finally, the β phase wedges into the α phase until the α phase separates. The continuous dynamic recrystallization (CDRX) of β phase accompanies its DRV, which is mainly characterised by progressive sub-grain rotation. The α grains aggregated by dislocations promote CDRX of the β phase.

Identification of Bioethanol Quality for Motorcycle Fuel

The availability of crude oil as a raw material for vehicle fuel is dwindling and limited in nature. One of the renewable energies worth developing is bioethanol, which is one of the alternative fuels that can be used as a biofuel and can be processed from plants containing starch and glucose. In this research, the entire bioethanol identification system in a sugar cane drip distillation apparatus was examined. The distillation process using MQ3 and MQ135 sensors resulted in an alcohol percentage of 42% and 46%. The maximum temperature measured by a thermocouple during distillation was 88°C, while the minimum temperature recorded was 87°C. This study utilized a backpropagation artificial neural network method to identify the detected bioethanol. The architecture of the artificial neural network included 2 input nodes, 4 neuron nodes, and 2 output nodes. The training and testing results showed that the formed backpropagation was able to identify and differentiate the detection of bioethanol according to the given inputs with a success rate of 88.86% for detected bioethanol and 94.86% for undetected bioethanol..

Analysis of influencing factors of aircraft fuel tank flammability

PurposeIt is an indispensable part of airworthiness certification to evaluate the fuel tank flammability exposure time for transport aircraft. There are many factors and complex coupling relationships affecting the fuel tank flammability exposure time. The current work not only lacks a comprehensive analysis of these factors but also lacks the significance of each factor, the interaction relationship and the prediction method of flammability exposure time. The lack of research in these aspects seriously restricts the smooth development of the airworthiness forensics work of domestic large aircraft. This paper aims to clarify the internal relationship between user input parameters and predict the flammability exposure time of fuel tanks for transport aircraft.Design/methodology/approachBased on the requirements of airworthiness certification for large aircraft, an in-depth analysis of the Monte Carlo flammability evaluation source procedures specified in China Civil Aviation Regulation/FAR25 airworthiness regulations was made, the internal relationship between factors affecting the fuel tank flammability exposure time was clarified and the significant effects and interactions of input parameters in the Monte Carlo evaluation model were studied using the response surface method. And the BP artificial neural network training samples with high significance factors were used to establish the prediction model of flammability exposure time.FindingsThe input parameters in the Monte Carlo program directly or indirectly affect the fuel tank flammability exposure time by means of the influence on the flammability limit or fuel temperature. Among the factors affecting flammability exposure time, the cruising Mach number, balance temperature difference and maximum range are the most significant, and they are all positively correlated with flammability exposure time. Although there are interactions among all factors, the degree of influence on flammability exposure time is not the same. The interaction between maximum range and equilibrium temperature difference is more significant than other factors. The prediction model of flammability exposure time based on multifactor interaction and BP neural network has good accuracy and can be applied to the prediction of fuel tank flammability exposure time.Originality/valueThe flammability exposure time prediction model was established based on multifactor interaction and BP neural network. The limited test results were combined with intelligent algorithm to achieve rapid prediction, which saved the test cost and time.

A hybrid optimization approach for intelligent manufacturing in plastic injection molding by using artificial neural network and genetic algorithm

This study presents a novel hybrid optimization approach for intelligent manufacturing in plastic injection molding (PIM). It focuses on globally optimizing process parameters to ensure high-quality products while reducing cycle time, material waste, and energy consumption. The method combines a backpropagation neural network (BPNN) with a genetic algorithm (GA) and employs a multi-objective optimization model based on design of experiments (DoE). A BP artificial neural network captures the relationship between optimization goals and process parameters. Leveraging the genetic algorithm, it effectively optimizes process parameters for achieving global optimization goals. The case study involves a polypropylene product, considering dimensional deviation, weight, cycle time, and energy consumption during the PIM cycle. Design variables include melt temperature, injection velocity, injection pressure, commutation position, holding pressure, holding time, and cooling time. The results demonstrate that this approach efficiently adjusts process parameters to meet quality standards, significantly reducing raw material consumption (2%), cycle time (12%), and energy consumption (16%). This offers substantial benefits for companies in highly competitive markets demanding swift adoption of smart production methods.

Design and Development of a Coffe Blending Device with Carbon Monoxide (CO) Level Identification Based on Artificial Neural Networks

Coffee is categorized into three types Robusta, Arabica, and Liberica. The roasting process is the most crucial step in developing the aroma, flavor, and underlying color that determine coffee quality. The coffee roasting process produces complex aroma compounds that impart the desired taste and aroma characteristics of coffee. This research aims to design a coffee content detection tool by determining the carbon monoxide (CO) level in Arabica, Robusta, and Liberica coffee. The research uses the Backpropagation artificial neural network method with 2 hidden layers, including 4 input layers and 3 output layers, to identify the tested coffee varieties. The highest carbon monoxide (CO) levels were found in Arabica Special coffee, with an ADC level of 662 carbon monoxide gas. The lowest carbon monoxide (CO) levels were detected in Liberica coffee, with an ADC level of 105 carbon monoxide gas. Coffee identification was carried out using an artificial neural network method with a success rate of 98% for Liberica coffee, 100% for Arabica coffee, and 98% for Robusta coffee.

Artificial neural network (ANN) in forecasting of poverty line and economic-energetic efficiencies into the maize-based agroecosystems

ABSTRACT In Mexico, corn is cultivated in small agroecosystems by rural farmers on communal lands. These farmers are economically vulnerable, and low yields from their plots affect both their economic and food security. A Feed-Forward Back Propagation Artificial Neural Network (ANN) aids in estimating the variables with the greatest impact on the agroecosystem related to Economic Efficiency (EC ha−1), Energy Efficiency (ENE ha−1), and the ‘Poverty Coverage Line’ (PCOVER). With an R = 0.86, the ANN has identified that the variables with the most significant impact on EC ha−1 are the ‘cultivated area’, the ‘total energy consumed per hectare’, and the ‘presentation of products in the market’. For ENE ha−1 and PCOVER, the key variables are the ‘cultivated area’, the ‘planting rate’, and the ‘total energy consumed per hectare’. The ANN demonstrates its utility by predicting that the cultivated acreage and the total energy invested by the farmer in their activities are the primary factors contributing to the poverty line in the agricultural sector of a rural community in Mexico. The variables feeding into this ANN encompass the fundamental energy and economic investments in other crops, making it adaptable and replicable in various agricultural contexts.

Predicting the medium-temperature thermal stability of impact-strengthened medium-thick plate aluminum alloy using a back propagation artificial neural network

A normalized medium-thick plate of aluminum alloy (4038) was impact-strengthened using a free-fall method at room temperature (approximately 20 °C). Specimens were then aged at 450 °C, 550 °C and 650 °C for 10, 20, 30 and 40 min respectively. Micro-hardness of each sample was tested. Micro-structure of samples annealed at 650 °C for different durations was characterized. A three-layer back propagation artificial neural network (BPANN) was trained using actual state parameters of the prepared samples. Results reveal that medium-temperature thermal stability of the prepared plate can be predicted through the BPANN model. Deviation of predicted values from the experimental ones is within 6 %, with a prediction accuracy exceeding 94 %. Variation trend of the predicted and the experimental thermal stability is consistent, but the predicted values are all higher than the measurements. Prediction accuracy of BPANN can be improved by increasing convergence rate of the error function. By adding relevant parameters of the micro-structure from samples aged at 650 °C to the input layer, BPANN model further improve its output and approach the real state of samples. The findings of this study can help researchers reduce the number and cost of experiments. The aim of this work was to predict the medium-temperature thermal stability of impact-strengthened normalized medium-thick plate of aluminum alloy annealed at different temperatures, and it also can be used as reference for other similar experiments.

Comparative QSAR Modeling for Predicting Anticancer Potency of Imidazo[4,5-b]Pyridine Derivatives Using GA-MLR and BP-ANN Techniques

Background: Prediction of toxicity of imidazo[4,5-b]pyridine derivatives is carried out using GA-MLR and BPANN methods. Objective: A quantitative structure-property relationship (QSPR) was determined based on methods, including genetic algorithm-multiple linear regression (GA-MLR) and backpropagation artificial neural network (BP-ANN). These methods were employed for modeling and predicting the anticancer potency of imidazo[4,5-b]pyridine derivatives. Materials and Methods: A dataset of imidazo[4,5-b]pyridine derivatives was randomly divided into two groups, training and test sets consisting of 75% and 25% of data points, respectively. The optimized conformation of compounds was obtained using the DFT-B3LYP method and 6-31G* basis sets level with Gaussian 09 software. A large number of molecular descriptors were calculated using Dragon software. The QSAR models were optimized using multiple linear regressions (MLR). Results: The most relevant molecular descriptors were obtained using the genetic algorithm (GA) and backward stepwise regression. The predictive powers of the GA-MLR models were studied using leaveone- out (LOO) cross-validation and an external test set. Conclusion: The obtained results of statistical parameters showed the BP-ANN model to have better performance compared to the GA-MLR model. To assess the predictive ability of QSAR models, many statistical terms, such as correlation coefficient (R2), leave-one-out cross-validation (LOOCV), root mean squared error (RMSE), and external and internal validation were used. The results of validation methods demonstrate the QSAR model to be robust and with high predictivity.