Back Propagation Neural Network Approach Research Articles

Determinants influencing ecological footprints of China; An empirical evidence from factor analysis

China has advanced significantly in the last decades, but ecological concerns raise questions about the nation's long-term affluence. The hunt for possible determinants in explaining the quality of the environment (ecological footprint) is still in progress. Therefore, the current study attempts to investigate the influencing factors from various explanatory variables (eco-innovation, economic complexity, renewable energy consumption, economic growth, natural resource rent, foreign direct investment, the energy intensity level of primary energy, economic development, Public-private partnerships investment in energy, electric power consumption, and population growth) through factor analysis augmented with Back Propagation Neural Network (BPNN) approach. The annual data from 1995 to 2021 for China is retrieved for empirical analysis. The data set comprises the factors classified into training, validation, and testing divisions with a ratio of 2:1:1. The outcomes revealed that extracted factors are robust in explaining China's ecological footprint variation. Outcomes of the BPNN algorithm suggest that renewable energy consumption, eco-innovation, economic complexity, natural resource rent, and electric power consumption are the most useful determinants in explaining the ecological footprints of China. The study furnishes the possible policy implications of the research.

Freeway merging trajectory prediction for automated vehicles using naturalistic driving data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compares merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and that 2) the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

Development and Management of E-Commerce Information Systems Using Edge Computing and Neural Networks

E-commerce refers to implementing e-commerce on mobile devices, cell phones, and mobile Internet connections. The fast advancement of mobile electronic gadgets and mobile Internet connections has facilitated the emergence of this new e-commerce market. The e-commerce systems contain several concealed client behavior data and forthcoming growth patterns. Data mining technologies extract valuable data and facilitate the growth of e-commerce. This article focuses on studying mobile e-commerce systems within the framework of edge computing, specifically in the setting of industrial clusters. This study examines the importance and benefits of data mining techniques in implementing e-commerce management platforms using neural networks. It also evaluates the corresponding methods of data mining and predicts purchasing trends. The study has used the benefits of grouping and back propagation neural network approaches in data analytics to categorize goods details, buying tastes, and related data. The advantage of neural networks in processing nonlinear patterns is employed to forecast forthcoming buying power. The findings demonstrate that data analytical methods and neural networks exhibit higher precision in predicting the buying pattern. The correlating factor between actual and projected usage information was 0.98, with the highest relative average inaccuracy of 2.4%. Data mining technologies effectively extract previously unrecognized relevant data and anticipated buying patterns in e-commerce platforms. Neural networks have a solid ability to predict future consumption capacity and consumption behaviors accurately.

Machine learning approach for prediction of ionospheric irregularities on ROTI index over the Northern anomaly crest in Egypt during solar cycle 24

This paper reports the prediction results of the ionospheric irregularities represented by the ROTI index close to the equatorial ionization anomaly's Northern peak. A feedforward backpropagation Neural Network (NN) approach is implemented using a time series Nonlinear Autoregressive approach with Exogenous inputs (NARX). We used the data from a dual frequency GPS-SCINDA station located at Helwan (geographic coordinates of 29.86°N, 31.32°E, and MLAT of 29.94°N) in Egypt. To allow the model to be independently tested during varying levels of solar activities, we collected a 5-minute resolution of ROTI data for the solar cycle 24 from 2009 to 2017. The factors that influence the development of ionospheric irregularities are involved in the established model representing the diurnal and seasonal variations as well as solar and geomagnetic activity parameters. To support learning, we included IRI-foF2 and IRI-hmF2 parameters in the input layer neurons to improve the model learning about the behavior of the ionospheric F layer. The results show that the NN-ROTI values precisely match the GPS-ROTI values with an RMSE of 0.106 TECU/min and a prediction efficiency of 95%. The predicted values are highly correlated with the originals, with a regression of 0.89. Furthermore, the irregularities were more prevalent during the equinox months than during the solstice months. It is also observed that predicted NN-ROTI values in all seasons have RMSEs less than 0.03 and 0.05 TECU/min for the low and high solar activity, respectively. The results also showed a clear correlation between solar activity and the occurrence percentage of ionospheric irregularities indicating a solar cycle dependency. The prediction values of the NN-ROTI occurrence % demonstrated a remarkable correlation with the observed GPS-ROTI occurrence % across SC24.

Numerical investigation of a fractional order Wolbachia invasive model using stochastic Bayesian neural network

The major goal of this research study is to solve the fractional order Wolbachia invasive model (FWIM) by developing a computational framework based on the Bayesian regularization backpropagation neural network (BRB-NN) approach. The population of mosquitoes is categorized into two classes, Wolbachia-infected mosquitoes and Wolbachia-uninfected mosquitoes. We also incorporate incomplete cytoplasmic incompatibility and imperfect maternal transmission. We investigate the effects of the fractional order derivative (α) and reproduction rate of Wolbachia-infected mosquitoes ϕw on the dynamics of mosquitoes. The proposed Bayesian regularization backpropagation scheme is applied to three distinct cases using 80% and 20% of the created dataset for training and testing, respectively, with 15 hidden neurons. Comparisons of the results are presented to verify the validity of the proposed technique for solving the model. The Bayesian regularization approach is used to lower the mean square error (MSE) for the fractional order Wolbachia invasive model. The achieved results are based on MSE, correlation, state transitions, error histograms, and regression analysis to confirm the effectiveness of the suggested approach. Additionally, the absolute error value modifies the designed approach’s accuracy.

A reliable neural network framework for the Zika system based reservoirs and human movement

The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical model is presented by using the stochastic LMB neural network process. A dataset is designed based on the Adam solver in order to reduce the mean square error by dividing the data as training, endorsement, and testing with the use of 75%, 10% and 15%. Ten numbers of neurons have been taken by using the log-sigmoid transfer in the hidden layer for solving the human movement model. The precision of the proposed solver is authenticated by using the valuation of reference and proficient outcomes, while the reducible absolute error presents the correctness of the scheme. Moreover, the statistical performances based different proportional schemes have been used to authenticate the reliability of the scheme.

Adaptive Back-Propagation Neural Network and Particle Swarm Optimization-Based Approach for Optimizing the Output Power Biogas Fueled Electric Generator

Adaptive Back-Propagation Neural Network and Particle Swarm Optimization-Based Approach for Optimizing the Output Power Biogas Fueled Electric Generator

Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach.

The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future.

Agricultural products supply chain risk assessment model construction and application in IOT environment

This paper constructs the operation model of agricultural products supply chain under an IoT (Internet of Things) environment, based on which the HHM (Hodrick-Prescott Filter) model is used to identify the risk. The ISM (Internal Supply Management) model was used to analyze risk factors. A risk index system was constructed, which was divided into three primary indexes and 18 secondary indexes. The backpropagation (BP) neural network approach was used to establish the risk assessment model. The sample data from 2017 to 2020 was employed as the test sample to test the network assessment model. There was a very small error in the risk level assessment and training results. The results showed that the risk level assessment model was highly operable and can have practical value for effective assessment of the risk level.

Combined quantitative lipidomics and back-propagation neural network approach to discriminate the breed and part source of lamb

The study successfully utilized an analytical approach that combined quantitative lipidomics with back-propagation neural networks to identify breed and part source of lamb using small-scale samples. 1230 molecules across 29 lipid classes were identified in longissimus dorsi and knuckle meat of both Tan sheep and Bahan crossbreed sheep. Applying multivariate statistical methods, 12 and 7 lipid molecules were identified as potential markers for breed and part identification, respectively. Stepwise linear discriminant analysis was applied to select 3 and 4 lipid molecules, respectively, for discriminating lamb breed and part sources, achieving correct rates of discrimination of 100 % and 95 %. Additionally, back-propagation neural network proved to be a superior method for identifying sources of lamb meat compared to other machine learning approaches. These findings indicate that integrating lipidomics with back-propagation neural network approach can provide an effective strategy to trace and certify lamb products, ensuring their quality and protecting consumer rights.

Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach

AbstractThe rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.

A back propagation neural network approach to estimate the glomerular filtration rate in an older population

BackgroundThe use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group.MethodsAdults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR).ResultsThe study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m2, which was smaller than that of LMR (4.59 ml/min/1.73 m2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m2; p = 0.31), EKFC (-1.41 ml/min/1.73 m2; p = 0.26), BIS1 (0.64 ml/min/1.73 m2; p = 0.99), and MDRD (1.11 ml/min/1.73 m2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55−2.78] and 0.24 [-2.58−1.61], respectively), smaller than any other equation.ConclusionsThe novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.

Stakeholders' purchase intention of products with recycled content: a combination of SEM and BPNN approach.

The rapid urban renewal progress has generated a large amount of construction and demolition (C&D) waste in China. According to the concept of circular economy (CE), producing products with recycled content (PwRC) is an effective solution to achieve sustainable C&D waste management. However, the low market acceptance of PwRC affects stakeholders' purchase intention, which hinders the development of the PwRC industry. In order to clarify the stakeholders' purchase intention and investigate the influencing mechanism of stakeholders' purchase intention, a novel method which combines structural equation modeling (SEM) with a back-propagation neural network (BPNN) is proposed. The method makes full use of the causal analysis ability of SEM and the nonlinear characteristics of BPNN to simulate and to verify the path of product cues, perceived value, and behavioral intention of stakeholders. The results reveal that the brand and materials of PwRC can affect stakeholders' perceived value, and in turn, affect their purchase intention. Focus on the brand and materials of PwRC will help recycled product manufacturers and governments to promote stakeholders' purchase intention and the development of the C&D waste recycled products market.

Utilizing Artificial Intelligence Approaches to Determine the Shear Strength of Steel Beams with Flat Webs

Steel beams’ shear strength is one of the most important factors that influence how quickly webs buckle. Despite extensive studies having been performed over the previous three decades, the existing procedures did not achieve the necessary reliability to predict the ultimate shear resistance of plate girders. New techniques called Learner Techniques have started to be used over the last few years; these techniques were applied to calculate the steel beam shear strength. In this study, a Regression Learner Techniques model was built using data from 100 test results from previously published research. Based on the geometric and material properties of the web and flanges available in the published tests, a model was built using Artificial Neural Networks. Based on sensitivity analysis, a Cascade Forward Backpropagation Neural Networks (CFBNN) approach was utilized to anticipate the shear strength of steel beams. The proposed models outperformed current hybrid artificial intelligence models developed using the same collected datasets and demonstrated to accurately predict the ultimate shear strength. The performance of the models was evaluated using a range of statistical assessment methods, which led to a valuable conclusion. The CFBNN model achieved the highest root mean square (R2 = 0.95). The results corresponding to each test were verified by specimen shear strength values calculated by a theoretical approach. The resultant maximum shear force obtained by the proposed modified equation was compared with the experimental results and the shear force was estimated using two different approaches proposed by the European code. Finally, two approaches were used to verify the proposed model. The first approach was the data reported from an experimental shear test program conducted by the authors, and the second was the results of the shear values acquired experimentally by other researchers. Based on the test results of the previous studies and the current work, the suggested model gives an adequate degree of accuracy for estimating the shear strength of steel beams.

Monthly streamflow prediction in Amasya, Türkiye, using an integrated approach of a feedforward backpropagation neural network and discrete wavelet transform

Monthly streamflow prediction in Amasya, Türkiye, using an integrated approach of a feedforward backpropagation neural network and discrete wavelet transform

A neural network approach for short-term water demand forecasting based on a sparse autoencoder

Abstract This paper presents a backpropagation neural network (BPNN) approach based on the sparse autoencoder (SAE) for short-term water demand forecasting. In this method, the SAE is used as a feature learning method to extract useful information from hourly water demand data in an unsupervised manner. After that, the extracted information is employed to optimize the initial weights and thresholds of the BPNN. In addition, to enhance the effectiveness of the proposed method, data reconstruction is implemented to create suitable samples for the BPNN, and the early stopping method is employed to overcome the BPNN overfitting problem. Data collected from a real-world water distribution system are used to verify the effectiveness of the proposed method, and a comparison with the BPNN and other BPNN-based methods which integrate the BPNN with particle swarm optimization (PSO) and the mind evolutionary algorithm (MEA), respectively, is conducted. The results show that the proposed method can achieve fairly accurate and stable forecasts with a 2.31% mean absolute percentage error (MAPE) and 320 m3/h root mean squared error (RMSE). Compared with the BPNN, PSO–BPNN and MEA–BPNN models, the proposed method gains MAPE improvements of 5.80, 3.33 and 3.89%, respectively. In terms of the RMSE, promising improvements (i.e., 5.27, 2.73 and 3.33%, respectively) can be obtained.

Power quality improvement by PV integrated UPQC using multi-level inverter with resilient back propagation neural network approach

To increase the life and efficiency of power electronics equipment in a utility distribution system, the power quality improvement is essential part. In this work, to improve power quality by using Robust Resilient Back Propagation Neural Network (RBPNN) scheme for a Photovoltaic (PV)-Integrated Unified Power Quality Conditioner (UPQC) with cascaded multi-level inverter configurations are described. Among the proposed methods, there is no need to use a transformer and filter when multilevel UPQC is applied, and it is one of the great advantages. The proposed UQPC offers a PV array composition with a power converter connected to a DC-link capacitor that can compensate for voltage sag, swell, voltage interruption, harmonics and reactive power. The Robust Resilient Back Propagation Neural Network controller is generate gating pulses to the UPQC. The reference currents and voltages for the controller are estimated using Synchronous Reference Frame (SRF) theory. The proposed cascaded multi-level inverter-based UPQC is designed using Matlab/Simulink Software. The simulation results confirm that the proposed method gives good results compared with existing Adaptive neural Fuzzy Inference System (ANFIS) and fuzzy logic methods. A real-time hardware system is also established to validate the simulation results. The effectiveness of the proposed system RBPN-UPQC approach is compared for both simulation and experimental results gives better low THD level 1.22%.

Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach

Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both solar power automation subsystem and transformer simultaneously or consumption unit; otherwise it works with fully or lesser efficiency. Statistically independent failures and repairs are considered. Using the elementary probabilities phenomenon incorporated with differential equations is employed to examine the system reliability, for repairable and non-repairable system, and to analyze its cost function. The accuracy and consistency of the system can be improved by feed forward- back propagation neural network (FFBPNN) approach. Its gradient descent learning mechanism can update the neural weights and hence the results up to the desired accuracy in each iteration, and aside the problem of vanishing gradient in other neural networks, that increasing the efficiency of the system in real time. MATLAB code for FFBP algorithm is built to improve the values of reliability and cost function by minimizing the error up to 0.0001 precision. Numerical illustrations are considered with their data tables and graphs, to demonstrate and analyze the results in the form of reliability and cost function, which may be helpful for system analyzers.

Distance Metric-based Segmentation and Score-Level Classification for Optimized Tumor Identification in MR Images

Tumors in the brain and pancreas are formed by abnormal cells accumulating in a tissue mass in the human body. The standard technique for MRI tumor detection and categorization would be to investigate them utilizing MR medical images. Due to a discrepancy among many brain and image characteristics, an MR picture is segmented at the early stage of diagnosis. The tumor identification framework is processed in the following order: The initial pre-processing (or) filtering is carried out using a Modified Decision-Based Coupled Windows Median Filter (MDBCWMF) technique. This stage's output is transmitted to a unique Distance Metric-based variant of the Fuzzy C-Means algorithm for segmenting MDM-FCM. This method results in a lower segmentation error. The image is then feature extracted using a Gray-Level Co-Occurrence Matrix (GLCM) algorithm and classified using a parameterized combination of Back Propagation Neural Network (BPNN) and Sparse Representation (SR) approaches known as Dynamic Score Level Integration (DSLI). Compared to other state-of-the-art methodologies, MDM-FCM segmented yields 0.9674 and 0.9481 in classification yield 97.84% and 95.62% accuracy for brain and pancreas images, experimental valuation being done with Datasets from TCIA.

Extraction of reflectance spectra features for estimation of surface, subsurface, and profile soil properties

• Accuracy of reflectance spectroscopy estimates of soil properties varied by depth. • Number and location of important wavebands varied by depth and by soil property. • Some calibrations could be applied successfully across multiple depth intervals. • Results could guide development of discrete-waveband soil property sensors. Diffuse reflectance spectroscopy in the visible and near-infrared wavelength ranges has potential to provide high-resolution, pollution-free, and nondestructive estimation of soil chemical and physical properties for use in precision agriculture. Practical implementation of this approach would be facilitated if soil property sensors using a limited number of reflectance bands could maintain accuracy similar to more expensive and complex full-spectrum sensors. Studies identifying such bands are limited, especially for subsurface soils. Thus, in this study, an existing spectral database of 697 soil samples was used to compare results for three soil categories (profile, surface, and subsurface) and multiple waveband selection methods. Soil cores were obtained to approximately 1.2 m depth from ten fields, two each in Missouri, Illinois, Michigan, South Dakota, and Iowa, USA, then sieved and air-dried. Laboratory soil spectra were obtained from 350 to 2500 nm using a commercial spectrometer and soil properties (total nitrogen, soil organic carbon, total carbon, magnesium, calcium, potassium, soil texture (clay, silt, and sand) fractions, cation exchange capacity, and pH) were measured using standard laboratory analyses. The ability of ten spectral preprocessing techniques to improve analysis results was investigated. Backward interval partial least squares was used to identify those spectral regions most predictive of soil properties. Alternatively, specific characteristic wavelengths were identified by a combination genetic algorithm (GA)-back propagation neural network (BPNN) approach. Results were compared for three soil property estimation methods: (1) partial least squares regression (PLSR) models based on the full spectrum, (2) PLSR models based on sensitive regions, and (3) BPNN models based on characteristic wavelengths. The best results for profile and subsurface soils were obtained with absorbance preprocessing, but for the surface soils, the standard normal variate transformation was best. For some soil properties, the prediction R 2 of the PLSR models based on sensitive regions was better than that of the PLSR models based on the full spectrum, demonstrating that retaining only sensitive wavebands could improve estimates. However, in some cases, the reduction in wavebands decreased accuracy. Differences in prediction accuracy across all calibration models over profile and subsurface soils were relatively small but were larger for surface soils. Furthermore, application of characteristic wavelength calibrations to other soil datasets resulted in a lower accuracy than with the full spectrum calibration developed for that dataset. In general, this study shows that there are measurable differences in prediction accuracy across all calibration models over the three soil depth categories. The experimental results of this study illustrate the potential for a set of wavelengths optimized for one depth category to still provide acceptable estimates for other depth categories. Overall, these results provide important guidance for the development of DRS soil sensors based on discrete wavebands to reduce cost and increase the speed of in-field data collection.