Study on the content of photosynthetic pigments in peanut leaves based on hyperspectral and biomimetic algorithm optimized BP neural network model

The research team has developed an information system based on clinical blood cell analysis and designed and implemented highly innovative algorithms. A neural network model was created based on these feature data of the blood cell population. Artificial intelligence algorithms can label susceptible populations for digestive tract cancer with an accuracy rate of over 80 %. A multi universe optimized BP neural network model was implemented based on TCGA data of common immune antigens in clinical laboratories. The working mechanism of this model is to assign values to the parameters of the BP neural network by using the process of searching for the best fitness in multiple universes. This model can predict the five-year survival rate of patients based on immunohistochemical data. Based on these data, an AI algorithm was used to develop a clinical prognostic model with an accuracy rate of over 99 %. The research team used single-cell sequencing data to locate cell subtypes in the features of immunohistochemical data, providing a biological basis for artificial intelligence models. The research team explored the potential biological mechanisms of cancer progression and occurrence based on gastrointestinal neuroendocrine products, and these algorithms have contributed to the prediction of cancer survival and incidence,team invented a simple and efficient algorithm.

PurposeIn many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.Design/methodology/approachThe proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.FindingsUsing the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.Research limitations/implicationsFirst of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.Originality/valueThis article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Momentum is a critical factor influencing the dynamics and outcomes of tennis matches. To enhance the predictive accuracy of these fluctuations, this study utilizes machine learning techniques and big data analytics to improve the prediction of these fluctuations. The study conducts a comprehensive analysis to identify the correlation between a player's momentum and 14 key features in a tennis match. An optimized BP neural network model, based on Levenberg-Marquardt theory, was developed to predict match flow and quantify the stalemate degree. The model is evaluated using a confusion matrix and ROC curve, affirming its predictive validity, where the results revealed an F1 Score and an AUC, both exceeding 0.5. With big data, this approach not only enhances the spectator experience by visualizing match dynamics but also aids in strategy development and training optimization for competitors. This research highlights the practical applications of quantitative modeling in understanding and forecasting the pivotal moments in tennis.

In this study, we proposed an improved Harris Hawks Optimization (IHHO) algorithm based on the Sobol sequence, Whale Optimization Algorithm (WOA), and t-distribution perturbation. The improved IHHO algorithm was then used to optimize the BP neural network, resulting in the IHHO-BP model. This model was employed to predict the bonding strength of heat-treated wood under varying conditions of temperature, time, feed rate, cutting speed, and grit size. To validate the effectiveness and accuracy of the proposed model, it was compared with the original BP neural network model, WOA-BP, and HHO-BP benchmark models. The results showed that the IHHO-BP model reduced the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 51.16%, 40.38%, and 51.93%, respectively, while increasing the coefficient of determination (R2) by at least 10.85%. This indicates significant model optimization, enhanced generalization capability, and higher prediction accuracy, better meeting practical engineering needs. Predicting the bonding strength of heat-treated wood using this model can reduce production costs and consumption, thereby significantly improving production efficiency.

Evaluation on power information data asset management system based on BP neural network

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.

Rapid spatial evaluation of seismic disaster after earthquake occurrence is required in disaster emergency rescue management, because of its importance in decreasing casualties and property losses. Among many categories of seismic disaster, evaluation of earthquake-affected population is of great significance to clarify the severity of earthquake disaster. For simple classic regression model, it is difficult to describe the strong nonlinear relationship between multiple influencing factors and earthquake disasters. In present study, an optimized BP neural network model considering spatial characteristic of influencing factors is proposed to evaluate the population distribution affected by earthquake. The correlation between earthquake-affected population and influencing factors is analysed using data of 2013 Ms7.0 Lushan earthquake. Ten influencing factors including elevation, slope angle, population density, per capita GDP, distance to fault, distance to river, NDVI, PGA, PGV, and distance to the epicentre, were classified into environmental and seismic factors. Correlation analysis revealed that per capita GDP and PGA factor had a stronger correlation with the earthquake-affected population. The earthquake-affected population was evaluated using a BP neural network by optimizing training samples considering spatial characteristics of per capita GDP and PGA factors. Different numbers of sample points, instead of a random distribution of sample points, were generated in areas with different value intervals of the influencing factors. The optimized samples improved the convergence speed and generalization capability of neuron network compared to random samples. The trained network was applied to the 2017 Ms7.0 Jiuzhaigou earthquake to verify its prediction accuracy. The MAE of the estimated earthquake-affected populations of different counties under Jiuzhaigou earthquake were 1.276 people/km2 using network model from optimized samples, smaller than the results of network model from random samples and linear regression model. The results indicate that BP neural network, which considers correlation characteristics of factors, has capability to evaluate spatial earthquake disaster.

The intelligent prediction of surrounding rock deformation is of great significance for guiding the design and construction of tunnel projects in coastal areas. The deformation of tunnels in coastal areas is more complex than that of the ground, and the risk of encountering adverse geological conditions is greater. The traditional tunnel deformation prediction method contains the defects of a fixed model, a limited sample number, and it is easy to fall into underfitting and local overfitting. Therefore, the capacity of previous methods is limited by significant error, weak generalization, and poor intelligence. This paper proposes an adequate fitting prediction method for tunnel deformation based on the effective rank theory of the hidden layer nodes’ output matrix to analyze the surrounding rock and predict its deformation intelligently. Based on the traditional BPNN (back propagation neural network) algorithm, the number of hidden layer nodes is determined by the effective rank of the output matrix. Then, the approximation error and degree were adopted to reflect the approximation law of the BPNN to achieve the purpose of overfitting and underfitting control. An optimized BP neural network model for intelligently predicting tunnel deformation is constructed. Then, the optimized BPNN model is applied to a case study of a coastal tunnel in South China. Compared with the prediction method of LR (linear regression) and TS (time series), the results show that the prediction results of the optimized model are in good agreement with the measured values, with strong generalization ability and high intelligence. The proposed method is of guidance to other tunnels surrounding rock deformation prediction and engineering practice.

During the operation of navigation satellites, errors in the broadcast ephemeris orbits are caused by the influence of ingress factors and other factors. To address this phenomenon, this paper examines the use of the computational intelligence (CI) methods to implement track correction and to develop an optimized BP neural network model based on an improved particle swarm algorithm. The model improves the inertia weights and learning factor parameters of the particle swarm optimization (PSO) algorithm to improve the global optimization capability and accelerate the convergence speed. The improved PSO (IPSO) algorithm is used to perform a global optimization search for the hyperparameters of the BP neural network, and then the neural network model is trained by broadcast ephemeris Keplerian root number and regression parameters. The model is validated using the broadcast ephemeris data of the BDS-3 MEO and IGSO satellites, and the mean square error correction rate of multiple satellites with different correction models shows that the error correction rate of the IPSO-BPNN model can reach 70.2–84% and the error correction rate can be improved by 14.2–56.8% compared with the PSO-BPNN model. The proposed algorithm provides an important reference for BDS-3 and other global navigation satellite systems for improving the accuracy of satellite orbit determination.

ABSTRACT Soil nutrient content is an important index to determine the amount of fertilizer. Traditional soil nutrient content is obtained by manual sampling, which greatly increases the cost of agricultural production input. In order to solve this problem, this paper studies the relationship between soil nutrients, fertilizer amount, yield and the next year’s soil nutrients, and establishes an optimized BP neural network model to realize the prediction of soil nutrient content. Aiming at the problem that the traditional BP neural network model will affect the prediction accuracy due to the different weights and thresholds, the Grey Wolf algorithm with reverse learning mechanism is introduced to obtain the optimal solution of BP neural network weights and thresholds. The soil nutrient content data collected in chenjiadian village, Nongan County, Jilin Province for five consecutive years were used to test the model. The data of the first four years were used as the training set and the data of the fifth year as the test set. The prediction results of BP neural network model, Grey Wolf algorithm optimized BP neural network model and Grey Wolf algorithm optimized BP neural network model with reverse learning mechanism were compared The prediction accuracy of BP neural network model optimized by Grey Wolf algorithm with reverse learning mechanism reached 88.7%, which was better than the first two models. It can provide basis for the fertilization decision of variable rate fertilization in the next year, so as to reduce the input of labor cost.

The mechanical parameters of surrounding rock are an essential basis for roadway excavation and support design. Aiming at the difficulty in obtaining the mechanical parameters of surrounding rock and large experimental errors, the optimized BP neural network model is proposed in this paper. The mind evolutionary algorithm can adequately search the optimal initial weights and thresholds, while the neural network has the advantage of strong nonlinear prediction ability. So, the optimized BP neural network model (MEA-BP model) takes advantage of the two models. It can not only avoid the local extreme value problem but also improve the accuracy and reliability of the prediction results. Based on the orthogonal test method and finite element analysis method, training samples and test samples are established. The nonlinear relationship between rock mechanical parameters and roadway deformation is established by the BP model and MEA-BP model, respectively. The importance analysis of the three input variables shows that the ∆D is the most important input variable, while ∆BC has the smallest impact. The comparison of prediction performance between the MEA-BP model and BP model demonstrates that the optimized initial weights and thresholds can improve the accuracy of prediction value. Finally, the MEA-BP model has been well applied to predicting the mechanical parameter for the surrounding rock in the Pingdingshan mine area, which proves the accuracy and reliability of the optimized model.

With the rapid development of entrepreneurship loans in China, the construction of a credit evaluation system of risk loans has become an important financial safeguard measure. This paper mainly studies the following three aspects. Firstly, in view of the subjective factors in the approval process of venture loans, based on the credit evaluation system of commercial banks and the data characteristics of venture loans, a credit evaluation system based on venture loans is constructed. Secondly, the randomized uniform design method is used to improve the population initialization method to realize the uniform distribution of the individual population. Finally, aiming at the problem of low efficiency of venture loan audit, this paper proposes an optimized BP neural network to evaluate the venture loan. Especially, through data processing, a credit index system is constructed, and then the optimized BP neural network model is determined in parameters. The model contains 15 input nodes, 1 hidden layer, and 2 output layers. Finally, the simulation shows that the optimized BP neural network model has obvious advantages in the loan evaluation. This paper includes the development status of credit evaluation of venture loans is empirically studied by using an optimized BP neural network model of nonexpected output.

This paper mainly studies the comprehensive evaluation of the development level of higher education system, and establishes the health evaluation model and sustainable evaluation model. First of all, this paper divides the indicators of higher education into three aspects, collects the data of 7 countries with different development levels, and establishes 11 indicators for evaluating the development quality of higher education. Principal component analysis is used to reduce 11 secondary indicators to 3 first-level indicators. Secondly, the BP neural network is used to construct three first-order indexes as input vectors, and genetic algorithm is used to improve the accuracy and convergence of the model.

Semi-finishing is an important step to reduce the cutting vibration and deformation caused by the thin-walled stiffening rib parts of aeronautical inertial control products during the finishing process. In the semi-finishing process, the variation of cutting force is a key factor of machining deformation. A large number of samples considering the effect of cutting parameters, such as spindle speed, feed per tooth, and milling width were simulated by the finite element simulation software AdvantEdge to obtain the milling forces based on 7050 aluminum alloy (Al7050) for aviation. Moreover, the standardized Euclidean distance was introduced to the radial basis neural network model (RBF-NN) to develop an improved RBF neural network model (IRBF-NN), and a high-precision model to predict the effect of cutting parameters on the thin-walled semi-finishing milling forces was established based on the proposed IRBF-NN. Results show that the maximum relative errors of milling force obtained by the genetic algorithm–optimized BP neural network model (GA-BP-NN), RBF-NN, and IRBF-NN are 13.9%, 12.5%, and 4.1%, respectively. Accordingly, the proposed IRBF-NN has high accuracy and effectiveness to predict milling force for the semi-finishing of aerospace Al7050 thin-walled stiffening rib parts.

The metal spatter and light intensity of CO2 welding in the vicinity of the melt pool during the short transition of the melt droplet seriously affect the realtime and reliability of weld feature extraction. The mapping relationship between welding pool characteristic parameters and melting depth is established by using BP neural network optimized by genetic algorithm. The results show that the training results and test results of the optimized BP neural network model of genetic algorithm have little error and meet the requirements of precision. The model can well reflect the relationship between the melting depth and the characteristic parameters of the melting pool.

To improve the effectiveness and intelligence of university teaching management evaluation, the particle swarm optimization BP neural network algorithm is applied to the analysis of university teaching management evaluation data. BP neural network is used to model the evaluation index of teaching management, and then particle swarm optimization is used to optimize the weight and threshold of the neural network transfer function to ensure that the output of the BP neural network can obtain the global optimal solution. The experimental results show that the proposed algorithm has a good fit between the predicted value and the actual value of the evaluation object of teaching management in Colleges and universities, and has a strong promotion value.

Safety analysis via forward kinematics of delta parallel robot using machine learning

The economic evaluation methods of existing industrial enterprises have many limitations. In this paper, an economic benefit evaluation method of industrial enterprises based on BP neural network optimization algorithm is proposed. This method takes the economic indicators of enterprises as input of artificial neural network, and uses the optimized BP neural network model to evaluate and analyze enterprises. The running time is greatly shortened, and the evaluation results are objective and accurate. An example is given to illustrate the implementation of the method. The example shows the feasibility of the method. In the fast-changing market situation, the sustainable development of enterprises has attracted the attention of all sectors of society and has become the focus of research. The sustainable development of the enterprise is the objective requirement of the sustainable development of the economy, the guarantee for the long-term economic benefit of the enterprise, and the fundamental method and way to realize the long-term development of the enterprise. Therefore, for enterprises, sustainable development is the primary task of enterprises. The research on sustainable development of enterprises has become a hot topic in the practice and theory field of enterprise management. However, there has been no effective and systematic theory on the index system, quantification and evaluation methods of the evaluation of the sustainable development of enterprises

Liquid lithium provides a viable alternative to traditional solid divertors and is one of the important choices for plasma facing materials in magnetic fusion devices. The liquid lithium coolant interaction in the accident conditions is a great threat to the safety of the devices. The prediction of explosion strength of liquid lithium coolant interaction is the key in the assessment of related accidents in fusion reactors. It is a kind of complicated nonlinear relation between explosion strength and its influencing factors, including the mass of lithium, initial lithium temperature and initial coolant temperature. Therefore, an optimized BP neural network model for predicting the explosion strength has been developed and it has been tested by the experimental data. The genetic algorithm is applicable to optimize the weights and thresholds of the BP neural network to obtain better prediction results. The comparison between the prediction results of the optimized BP neural network and the original one shows that the optimized prediction model is more accurate and efficient. It provides an optimized method for the evaluation of explosion strength of the liquid lithium coolant interaction.