Basis Function Neural Network Model Research Articles

A Secure Intrusion Detection Platform Using Blockchain and Radial Basis Function Neural Networks for Internet of Drones

The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing "Things" with "Drones" while retaining incomparable features. Because of its vital applications, IoD technologies have attracted much attention in recent years. Nevertheless, gaining the necessary degree of public acceptability of IoDs without demonstrating safety and security for human life is exceedingly difficult. In addition, Intrusion Detection Systems (IDSs) in IoD confront several obstacles because of the dynamic network architecture, particularly in balancing detection accuracy and efficiency. To increase the performance of the IoD network, we proposed a blockchain-based Radial Basis Function Neural Networks (RBFNN) model in this paper. The proposed method can improve data integrity and storage for smart decision-making across different IoDs. We discussed the usage of blockchain to create decentralized predictive analytics and a model for effectively applying and sharing Deep Learning (DL) methods in a decentralized fashion. We also assessed the model using a variety of datasets to demonstrate the viability and efficacy of implementing the blockchain-based DL technique in IoD contexts. The findings showed that the suggested model is an excellent option for developing classifiers while adhering to the constraints placed by network intrusion detection. Furthermore, the proposed model can outperform the cutting-edge methods in terms of specificity, F1, recall, precision, and accuracy.

Application of computer vision in optimal design of assembled buildings

The shape data of prefabricated building components are closely related to their safety and reliability. To solve the problem of shape energy-saving optimisation, a radial basis function neural network model based on particle swarm optimisation (PSO) considering temperature compensation is studied and designed, and building information modelling technology is introduced as an auxiliary technology for effective management of visual information, which finally realises the energy-saving calculation of building shape dimensions. The results show that the maximum expansion deformation measured by the proposed model appears at the 28th minute, the maximum expansion deformation is 0.11 mm, the error between the model and the actual value is only 0.02 mm and the difference between the monitoring time points is only 3 min. The total energy consumption values of this model are 36.92, 42.15 and 33.58 kWh/m2 less than those of the PSO model for three types of buildings. In terms of the total contribution rate of energy conservation, the former are 0.76, 0.88 and 2.94% higher than the latter, respectively. Therefore, this research has effectively improved monocular machine vision technology. At the same time, the energy-saving model of shape with temperature compensation for innovative design has also been effectively verified.

Sous-vide processing of silver carp: Effect of processing temperature and cold storage duration on the microbial quality of the product as well as modeling by artificial neural networks.

Silver carp (Hypophthalmichthys molitrixi) was processed by sous-vide method at different temperatures (60, 65, 70, and 75°C). Then, the microbiological quality of the processed samples was monitored during cold storage (4°C) for 21 days. The target microorganisms were Enterobacteriaceae, Lactic Acid bacteria (LAB), Pseudomonas, Psychrotrophs, and total viable count (TVC). In samples processed at 75°C, the presence of Enterobacteriaceae, Pseudomonas and Psychrotrophs were not detectable up to 15 days of storage and lactic acid bacteria were not detectable even at the end of the storage period. A radial basis function neural network (RBFNN) model was established to predict the changes in the microbial content of silver carp. In this step, the relationship between processing temperature and storage duration on microbial growth was modeled by ANNs (artificial neural networks). The optimal ANN topology for modeling Enterobacteriaceae, Pseudomonas, and Psychrotroph contained 9 neurons in the hidden layer, but it contained 15 and 14 neurons for TVC and LAB, respectively. By experimenting with the temperature of -80°C, it was revealed that the obtained ANN model has a high potential for prediction.

Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone

Millions of people are dying, and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. Forecasting road traffic accident and predicting the severity of road traffic accident contributes a role indirectly in reducing road traffic accidents. This study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using deep artificial neural network models. Around 6170 road traffic accidents data are collected from Oromia Police Commission Excel data and Oromia Special Zone Traffic Police Department; the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model, and 1482 or (20%) of the dataset was used for the testing model. This study proposed six different neural network architectures such as backpropagation neural network (BPNN), feedforward neural network (FFNN), multilayer perceptron neural network (MLPNN), recurrent neural networks (RNN), radial basis function neural network (RBFNN) and long short-term memory (LSTM) models for accident severity prediction and the LSTM model for a time serious forecasting of number accidents within specified years. The models will take input data, classify accidents, and predict the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy accident severity prediction. According to the model performance results, RNN model showed the best prediction accuracy of 97.18%, whereas MLP, LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, and 77.26%, respectively. LTSM model forecasted accident for three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road traffic accidents.

Development process and height of the mining-induced water fractured zone over the longwall goaf

Abstract Providing accurate prediction methods for mining-induced water fractured zones over the longwall goaf has become increasingly significant because of the grand economic and technological challenges of deeper extraction. Taking the No. 839 coalface in the Qingdong mining area as a case study, a comprehensive study of the water-flowing fractured zone (WFFZ) formation process and peak height were carried out by the radial basis function (RBF) neural network model, parallel electrical monitoring (PEM) technology, numerical simulation model and the empirical formula method. The results show that the destruction does not occur simply layer-by-layer from the bottom up. Rather, the fractured areas appear first in soft layers above the goaf. With the advance of footage, the destruction gradually extends from soft layers to hard layers, and the WFFZ is formed when the fractured areas fit closely together. Also, the change process of the WFFZ shape is closely related to rock mass heterogeneity. In addition, the results of the comparison suggest that the RBF neural network model had more accuracy in predicting the height of the WFFZ than the numerical simulation model and empirical formula method.

A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model

A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model

Prediction of the hydrodynamic disturbance characteristics for two projectiles launched successively underwater based on the radial basis function neural network

When two projectiles are successively launched under different launch parameters, the motion of the first projectile affects the hydrodynamic characteristics of the second projectile. To predict and study such disturbances, a radial basis function (RBF) neural network model is established in this paper. Compared with the underwater launch of a single projectile, the hydrodynamic loads for two projectiles successively launched are more complex and severe. When the first projectile is launched, it will affect the forces and moments of subsequent projectiles, leading to launch failure. Thus, we apply a numerical simulation method that is verified through experiments to simulate two projectiles successively launched underwater. Then, we use the generated data to train the RBF neural network. The results show that vortices will form at the tail of the first projectile after launch due to viscous effects, which is the main reason for the hydrodynamic disturbance that affects the second projectile. Compared with numerical simulations and experimental methods, the RBF neural network model can more effectively predict the disturbance of the hydrodynamic characteristic variables of the first projectile to the second projectile. This disturbance can be reduced by increasing the spatial distance of the two projectiles, increasing the time interval between launches, and reducing the platform velocity. However, the launch time interval is the most sensitive factor affecting the hydrodynamic characteristics of projectiles.

Analysis and prediction of the temporal and spatial evolution of carbon emissions in China's eight economic regions.

Facing increasingly severe environmental problems, as the largest developing country, achieving regional carbon emission reduction is the performance of China's fulfillment of the responsibility of a big government and the key to the smooth realization of the global carbon emission reduction goal. Since China's carbon emission data is updated slowly, in order to better formulate the corresponding emission reduction strategy, it is necessary to propose a more accurate carbon emission prediction model on the basis of fully analyzing the characteristics of carbon emissions at the provincial and regional levels. Given this, this paper first calculated the carbon emissions of eight economic regions in China from 2005 to 2019 according to relevant statistical data. Secondly, with the help of kernel density function, Theil index and decoupling index, the dynamic evolution characteristics of regional carbon emissions are discussed. Finally, an improved particle swarm optimization radial basis function (IPSO-RBF) neural network model is established to compare the simulation and prediction models of China's carbon emissions. The results show significant differences in carbon emissions in different regions, and the differences between high-value and low-value areas show an apparent expansion trend. The inter-regional carbon emission difference is the main factor in the overall carbon emission difference. The economic region in the middle Yellow River (ERMRYR) has the most considerable contribution to the national carbon emission difference, and the main contributors affecting the overall carbon emission difference in different regions are different. The number of regions with strong decoupling between carbon emissions and economic development is increasing in time series. The results of the carbon emission prediction model can be seen that IPSO-RBF neural network model optimizes the radial basis function (RBF) neural network, making the prediction result in a minor error and higher accuracy. Therefore, when exploring the path of carbon emission reduction in different regions in the future, the IPSO-RBF neural network model is more suitable for predicting carbon emissions and other relevant indicators, laying a foundation for putting forward more scientific and practical emission reduction plans.

An evolving feature weighting framework for radial basis function neural network models

AbstractVia Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural‐Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W‐GrC), there is no work in the literature to demonstrate effectiveness and impact on Type‐2 Fuzzy Logic systems (T2‐FLS). The main contribution of this paper is to extend W‐GrC, for the first time, to both Type‐1 and Type‐2 models known as Radial Basis Function Neural Network (RBFNN) and General Type‐2 Radial Basis Function Neural Network (GT2‐RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained.

Pathway and driving forces to complete forest transition in inner Mongolia of China

China has experienced an extensive forest transition. However, the biophysical conditions and socio-economic effects in different regions are quite different in such a large country. Few studies on the forest transition of China have focused on how forest transitions occur and what process of forest transition in quality experience at the regional level. This study conducts a regional analysis on forest transition in Inner Mongolia from the dual perspectives of forest quantity and quality using data from 1980 to 2018. Radial Basis Function Neural Network (RBFNN) modeling combining the sensitivity analysis method is used to comprehensively analyze potential socio-economic factors that may drive the forest transition observed in Inner Mongolia. The results show that the changing trend of the forest area in Inner Mongolia shaped a “N" curve, while the standing forest stock showed a general slow-growing trend. It is important to note that Inner Mongolia achieved a complete forest transition around 1993. The forest transition witnessed in Inner Mongolia mainly followed two pathways: the “economic development pathway” and the “national forest policy pathway.” The results suggest that the forest area in Inner Mongolia is changing into an “N" shaped curve with the number of live standing trees in the forests generally growing slowly. The results further suggest that, rather than only analyzing the spatial expansion of forest area, ecological construction is also an important determinant of future forest quality.

Multi-objective optimization of seeding performance of a pneumatic precision seed metering device using integrated ANN-MOPSO approach

Uniform seed spacing within the row is the most desirable prerequisite for better crop yield. The seeding uniformity of a pneumatic seed metering device is significantly affected by its key design and operational parameters, i.e., shape and size of the suction hole, vacuum pressure, and forward speed of operation. Therefore, these parameters need to be optimized to achieve better seeding performance. In this study, A novel intelligent multi-objective optimization methodology based on artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO), named as integrated ANN-MOPSO approach, was employed to accomplish the set goal. Maximizing the quality feed index (QFI) and minimizing the precision index (PI) were chosen as two objectives. The multilayer perceptron (MLP) and radial basis function (RBF) neural network models were developed for predicting the QFI and PI. The results revealed that the RBFNN (4-15-2) model outperformed the MLPNN (4-7-2) model; hence it was further coupled with the MOPSO algorithm for retrieving the Pareto-optimal set of the design and operational parameters corresponding to the maximum QFI and minimum PI.The most appropriate optimum entry shape and size of the suction hole, vacuum pressure, and operating speed were found to be chamfered holes with 3 mm diameter, 3.5 kPa, and 2.84 km/h, respectively. The validation results showed a variation of -2.11 % and +5.398 % between the observed and predicted values of the QFI and PI, respectively, thus confirming the adequacy of the proposed approach.

Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression.

This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.

The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks

In this study, we examine the impact of COVID-19 on the relationship between non-renewable energy and Saudi stock market sectors for the period 11 January 2017–22 January 2022. We apply wavelet coherence and Radial Basis Function Neural Network (RBFNN) models. Our results provide evidence that COVID-19 led to an increase in the strength of the relationship between oil as a main non-renewable energy source and Saudi stock market sectors and affected the nature and direction of this relationship. The relationships between oil and commercial and professional services, materials, banks, energy, and transportation sectors are the most affected. Our results will help hedge funds, mutual funds, and individual investors, forecast the direction of Saudi stock market sectors and the use of oil for hedging or diversification during periods of uncertainty and crisis. It will also help decision and policymakers in Saudi Arabia to make the necessary decisions and actions to maintain the stability of the stock market sectors during these periods.

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model.

The Yellow River basin (YRB) plays an important role in China’s economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River’s nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai’s industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach

Crop seed yield modeling and prediction can act as a key approach in the precision agriculture industry, enabling the reliable assessment of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are employed to predict sesame (Sesamum indicum L.) seed yields (SSY) using agro-morphological features. Various ML models were applied, coupled with the PCA (principal component analysis) method to compare them with the original ML models, in order to evaluate the prediction efficiency. The Gaussian process regression (GPR) and radial basis function neural network (RBF-NN) models exhibited the most accurate SSY predictions, with determination coefficients, or R2 values, of 0.99 and 0.91, respectfully. The root-mean-square error (RMSE) obtained using the ML models ranged between 0 and 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. The estimation of the sesame seed yield with the coupled PCA-ML models improved the performance accuracy. According to the k-fold process, we utilized the datasets with the lowest error rates to ensure the continued accuracy of the GPR and RBF models. The sensitivity analysis revealed that the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.

Research on strip crown by uncertain sampling strategy modified particle swarm optimization with RBF neural network

The strip crown directly affects the quality of strip. The prediction of strip crown by general machine learning models usually focuses on the production of a single category of strip steel, and the models lack good prediction ability for complex industrial data environments, which will lead to an increase in production costs and a decrease in product quality. Therefore, this paper proposes a new Radial Basis Function (RBF) neural network model, which is optimized by a new uncertain sampling strategy modified PSO algorithm (US-MPSO algorithm). The main feature of the algorithm is to add a new clustering dimension to the particle swarm algorithm (PSO). In this paper, clustering optimization is combined with uncertain sampling strategy to help particle swarm optimization iteration. On the one hand, the algorithm collects the population information of the strip crown as much as possible to help the neural network to converge. On the other hand, it adopts the uncertain sampling strategy to realize the optimal iteration of the samples. Finally, it realizes the improvement of the running speed and accuracy of the algorithm. This paper uses a specific model initialization strategy and combination strategy to combine the optimization algorithm with the RBF neural network. In this paper, four different types of data sets are tested to prove the adaptability of the model for complex industrial environments. The results show that the RBF neural network optimized by the US-MPSO algorithm has better prediction accuracy than the previous RBF neural network. The optimization effect of the algorithm is very significant.

AI-enabled talent training for the cross-cultural news communication talent

In the context of economic globalization, the demand for cross-cultural news communication talents is increasing. This study aims to analyze and optimize the training strategy of composite international talents (CITs) in news communication in the context of artificial intelligence (AI) to comprehensively improve the quality of talents. To organize the relevant research on AI and cross-cultural second language acquisition, a literature review is conducted. The advantages and disadvantages of the present training mechanism were then systematically summarized by evaluating the current situation of CIT training in news communication. Subsequently, the radial basis function neural network (RBFNN) model was applied to ameliorate the CIT training strategy for news dissemination. The analysis of the CIT talent training strategy for news communication demonstrates that the number of talents predicted by the model remained optimal through the model's iteration over six data lists. The RBFNN model proposed herein also outperforms other models with two experimental data sets. The model registers a recognition accuracy of 70 % and precision of 80 % at the 100th iteration. The recognition accuracy and precision improves significantly to 82.3 % and 83.6 %, respectively, after the 600th iteration. This study can further enhance the effects of CIT training in news communication and can serve as a reference for the digital intelligence of the news communication industry.

Predicting Surface Residual Stress for Multi-Axis Milling of Ti-6Al-4V Titanium Alloy in Combined Simulation and Experiments

As one essential indicator of surface integrity, residual stress has an important influence on the fatigue performance of aero engines’ thin-walled parts. Larger compressive or smaller tensile residual stress is more prone to causing fatigue cracks. To optimize the state of residual stress, the relationship between the surface residual stress and the machining conditions is studied in this work. A radial basis function (RBF) neural network model based on simulated and experimental data is developed to predict the surface residual stress for multi-axis milling of Ti-6Al-4V titanium alloy. Firstly, a 3D numerical model is established and verified through a cutting experiment. These results are found to be in good agreement with average absolute errors of 11.6% and 15.2% in the σx and σy directions, respectively. Then, the RBF neural network is introduced to relate the machining parameters with the surface residual stress using simulated and experimental samples. A good correlation is observed between the experimental and the predicted results. The verification shows that the average prediction error rate is 14.4% in the σx direction and 17.2% in the σy direction. The effects of the inclination angle, cutting speed, and feed rate on the surface residual stress are investigated. The results show that the influence of machining parameters on surface residual stress is nonlinear. The proposed model provides guidance for the control of residual stress in the precision machining of complex thin-walled structures.

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming.

A new approach for saturation height modelling in a clastic reservoir using response surface methodology and artificial neural network

• Brook-Corey's function was the best conventional water saturation prediction model. • The RSM model gave a better prediction fit than the conventional methods. • The independent variables had an influence on water saturation via 3D response surface plots. • Mathematical links improved the RSM model's performance in design space. • The RBFNN model was superior compared to the RSM model with mathematical links. Saturation Height Modelling (SHM) is an important reservoir characterization method for estimating the water saturation component which is a fraction of the total reservoir fluids in porous media. Although several methods have been used in calculating water saturation, little or no research has employed Response Surface Methodology (RSM) in predicting water saturation in field cores. In this paper, well datasets were tested with four conventional curve fitting models including Lambda, Thomeer, Leverette J, and Brooks-Corey of which Brook-Corey gave the best fit with an R 2 of 0.485. The RSM with the Central Composite Design (CCD) was used to obtain the mathematical and statistical relationship between the predictors and target as well as the variables’ interactions optimization. The model was analysed using Analysis of Variance (ANOVA) which validated the correlations with an F-value of 4.96, a p-value less than 0.05, and a Lack of Fit-value of 0.76 implying that the model developed was statistically significant. The RSM model gave a better fit with an R 2 value of 0.817 with mathematical links, almost twice that predicted by the conventional methods. Then, the testing performance of the RSM model was compared to the standard radial basis function neural network model ( R 2 of 0.9779). The results proved that both RSM and RBFNN models’ performance was accurate and reliable and could give a precise prediction of water saturation without any conventional curve fitting parameters.