Neural Network Evaluation Model Research Articles

Retracted: Characteristics Analysis of Mental Health Data of College Students Based on Convolutional Neural Network and TOPSIS Evaluation Model

Retracted: Characteristics Analysis of Mental Health Data of College Students Based on Convolutional Neural Network and TOPSIS Evaluation Model

A model for predicting the similarity between real and virtual T-shirts based on sensory evaluation and image quantitative index evaluation

Reported herein is a model for evaluating and predicting the similarity between real clothing and clothing simulated by the virtual fitting software Vidya, the aim being to estimate accurately the similarity between real and virtual clothing. Firstly, we make real T-shirts and virtual T-shirts. According to Kawabata Evaluation System test parameters, 24 kinds of digital fabrics were constructed, and 96 virtual T-shirts were modeled from these digital fabrics. Then, 24 real T-shirts were made from the corresponding fabric. After that, the similarity evaluation indexes of virtual T-shirts and real T-shirts were extracted. Expert evaluation and digital image processing were used to evaluate the visual sense of the images and extract the indices of the evaluation-site images. Finally, the principal component analysis (PCA)-backpropagation (BP) evaluation model was established. Based on PCA, effective evaluation indices for real and virtual T-shirts were selected to construct an evaluation index system of clothing modeling and a BP neural network evaluation model. After repeatedly debugging the number of neurons, the model was optimized with four hidden-layer neurons, and the overall correlation coefficient R of the model reached 0.91075. In this paper, an evaluation index system for real and virtual T-shirts was constructed and, based on the indices, a rapid method for evaluating the virtual fitting modeling effect based on the PCA-BP model was established, which also provides a reference for the whole process of modeling evaluation of other virtual fitting software.

Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining.

Due to improper operation of the shield construction process and unknown geological surveys, shield construction faces many risks in passing through complex strata, among which the excavation face instability is the most serious, potentially leading to disastrous accidents. To address these issues, this research focuses on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, an analytical formula for the limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is used to assess the excavation face stability quantitatively. Then the rough set algorithm is used to analyse the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The back propagation (BP) neural network is used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10-4 is established. The prediction performance of BP is verified by comparison with the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Research on Evaluation of University Emergency Management Ability Based on BP Neural Network.

University emergency management ability is an important part of university safety management. To evaluate university emergency management ability scientifically, objectively, and accurately, this study constructs three first-level indexes, namely, pre-prevention ability, in-process control ability, and post-recovery ability, and 15 s-level indexes, including the establishment of emergency management institutions; the construction of emergency plans; the allocation of emergency personnel, equipment, and materials; and the training and exercise of emergency plans. On the basis of the backpropagation (BP) neural network method and MATLAB platform, an evaluation model of university emergency management ability is constructed. The neural network evaluation model is trained with sample data, and a university in Beijing is adopted as an example to verify the good prediction effect of the model. The results show that applying the evaluation model based on the BP neural network to the emergency management ability of colleges and universities is feasible. The model provides a new method to evaluate the emergency management ability of colleges and universities.

Applicable Framework for Evaluating Urban Vitality with Multiple-Source Data: Empirical Research of the Pearl River Delta Urban Agglomeration Using BPNN

Urban vitality is a mirror reflection of ‘urban disease’ in cities. The research on urban vitality has made great progress in evaluation frameworks; however, these frameworks cannot jointly account for the macro and micro performance of urban vitality. It is better to establish an integrated evaluation framework for this topic. This paper defines urban vitality as the comprehensive strength to support dense and diverse activities based on urban development and the urban environment, and subsequently develops an integrated framework including economic, social, cultural, and spatial dimensions. With the nonlinear evaluation model of a back propagation neural network, we further presented the result of an application on the Pearl River Delta urban agglomeration. Our profiling results illustrate the core-edge structure of urban vitality. There are differences in vitality performance within built-up areas, which shows that areas with urban landscapes and excellent infrastructure are more vibrant. The integrated framework with good applicability improves the evaluation of urban vitality that is crucial to city examination and urban planning. Hence, this study provides a comprehensive reference for optimizing resource allocation and promoting sustainable development.

Is it Feasible to Use BP Neural Network for Academic Journal Evaluation: an Attempt Based on 6 Single Objective Weighting Methods

ABSTRACTIn the new era, the analysis of academic journal evaluation methods and the comprehensive comparison of the advantages, disadvantages, and stability of different methods can help to provide some reference for academic journal evaluation. In this study, single model evaluation was carried out for 6 weighting methods without comprehensive evaluation value, and fuzzy comprehensive evaluation is performed on the results passing the nonparametric test. Based on the evaluation, BP neural network is introduced, and BP neural network evaluation model is established. The results show that the fuzzy Borda evaluation can integrate the evaluation value and evaluation order of single models, and has higher accuracy compared with single evaluation models. The prediction rate of the network model based on the gradient descent optimization algorithm can reach more than 80%, and the weights obtained from the continuous self‐learning of the neural network training set can reduce the subjectivity and mutual interference between indicators.

An Evaluation Model of Urban-Rural Exchange Teachers in Elementary School Based on Optimal Control Neural Network

In this paper, an optimal control neural network algorithm is used to conduct an in-depth study and analysis of the evaluation of elementary school urban-rural exchange teachers, and an optimal control neural network evaluation model is designed and applied to the actual elementary school urban-rural exchange process. A tracking controller is designed to track the target trajectory for a typical second-order nonlinear system where the system model is partially unknown and the internal state of the system is unpredictable. The neural network observer is first designed using the input and output information of the system to approximate the drift dynamics of the system on the one hand and estimate the internal state information of the system on the other hand; then, based on the estimated system state, the sliding mode tracking controller is designed to achieve tracking of the established target trajectory. It is found that the policy implementation is not in place, teachers are not motivated to exchange and most of them aim at position promotion, their sense of responsibility is weak, and they cannot treat the matter of exchange seriously and correctly, thus cannot play their role in the exchange school. The ways to prevent and alleviate the conflict of interest among policy subjects are mainly divided into subjective and objective aspects: first, from the subjective level to arouse the value recognition of multiple interest subjects, including the government, should play the function of policy guidance and exhortation and clarify the value orientation of the policy; schools should cultivate the moral quality and professionalism of teachers and strengthen ideological mobilization; and teachers should improve their ideological awareness and consciously change their willingness to exchange. Establishing teachers’ status as civil servants, paying attention to teachers’ professional development and safeguarding the interests of rotating teachers to increase teachers’ motivation to exchange and rotate, creating a good social opinion environment, providing a good inflow school support environment for rotating teachers, and building a cloud platform for sharing high-quality educational resources create a good policy implementation environment to ensure that the teacher exchange and rotation policy is effectively implemented to achieve its goals of balancing teacher deployment. Although gradient-based methods can achieve high training accuracy, the performance of the backpropagation algorithm may be unsatisfactory when applied to test data, that is, if the size of the training data is not large enough.

Discrimination of raw and sulfur-fumigated ginseng based on Fourier transform infrared spectroscopy coupled with chemometrics

Ginseng (Panax ginseng), as a tonic and functional food in many countries and regions for thousands of years, is often sulfur-fumigated (SF) for storage and protection. However, our previous study indicated sulfur-fumigation could transform ginsenosides, the active components of ginseng, into sulfur-containing derivatives and thus affect the quality and safety of ginseng. In this study, a rapid and efficient method in discrimination of non-fumigated (NF) and SF ginseng was developed using Fourier transform infrared (FT-IR) spectroscopy coupled with multivariate statistical analysis. A total of 240 batches of raw spectra were obtained from NF and SF ginseng by FT-IR spectroscopy. After excluding the outliers, the different performance of 3 spectral signal enhancing methods, 3 modeling evaluation methods, and 4 model evaluation indexes were compared. The results demonstrated the feasibility of using FT-IR spectroscopy between 3650 and 3200 cm−1 for the detection of sulfur-fumigation in ginseng. After sulfur fumigation, the peak areas in fingerprint and functional group area varied significantly. In addition, the parameters of back propagation artificial neural network (BP-ANN) evaluation model are the highest, its accuracy = 91.67%, precision = 89.29%, recall = 96.15%, and F1 = 92.59%. The error rates of 3 models were k-nearest neighbor algorithm (KNN) (25.00%) > logistic regression (LR) (16.67%) > BP-ANN (8.33%). It can be concluded that FT-IR spectroscopy combined with multivariate statistical analysis has great potential in rapid discrimination of NF and SF ginseng, which can provide a valuable reference for the quality and effectiveness of edible and medicinal application of ginseng.

Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network.

This paper studies the privacy risk perception of online medical community users based on deep neural network. Firstly, this paper introduces privacy protection based on deep neural network and users’ privacy risk perception in online medical community. Then, using the fuzzy neural network to deal with highly complex and nonlinear data, we can better obtain the accurate evaluation value, and use the improved gravity search optimization algorithm to optimize the fuzzy neural network evaluation model and improve the convergence puzzle of the model. Finally, using the experimental method of questionnaire survey, and the questionnaire is composed of three parts. The first part investigates the basic personal information of the subjects, including gender, age, educational background, physical condition, physical examination frequency, Internet use experience, long-term residence, etc.; The second part is the measurement items of each variable in the theoretical model, including nine variables: service quality, personalized service, reciprocal norms, result expectation, material reward, perceived risk, trust in doctors, trust in websites, and willingness to disclose health privacy information. The experimental results show that the correlation coefficient between the interaction items of personalized service and reciprocal norms on material reward is positive (β = 0.072, P < 0.01), and the correlation coefficient between sexual service and material reward was positive (β = 0.202, P < 0.01), then reciprocal norms positively regulate the relationship between personalized service and material reward.

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network.

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.

Risk Management of Prefabricated Building Construction Based on Fuzzy Neural Network

With the rapid development of society, the risk management of personal health and assets has become an important content that cannot be ignored in all walks of life. With the in-depth advancement of risk management, most of the construction risks of prefabricated buildings adopt qualitative research based on experience and intuitive judgment and quantitative research on quantitative mathematical statistics, but there are few models for risk assessment of prefabricated buildings with dynamic characteristics to adapt to the rapid development of prefabricated buildings and the lack of prefabricated construction in various stages and complex environments, with risk prediction and effective response capabilities. Based on this, this paper attempts to propose a fuzzy neural network risk research method for prefabricated building construction, making full use of the fuzzy neural network’s qualitative knowledge expression and quantitative numerical computing advantages, to establish a set of strong fault tolerance and better adaptive ability: fuzzy neural network evaluation model for extensive prefabricated building construction risk. Through the design of the fuzzy network model structure, the membership vector of the qualitative and quantitative indicators of the fuzzy comprehensive evaluation of the risk of prefabricated building construction is used as the input vector of the neural network, and the evaluation result is used as the output of the neural network. The samples were trained, programmed, and debugged, and it was found that the training results of the samples were in good agreement with the expected output results, which further verified the feasibility and applicability of the fuzzy neural network in the risk assessment process of prefabricated buildings. It is of good guiding significance to conduct continuous observation and formulate effective risk aversion and response plans.

Characteristics Analysis of Mental Health Data of College Students Based on Convolutional Neural Network and TOPSIS Evaluation Model

With the rapid development of modern society, there are many problems concerning the physical and mental health of students. This paper develops a feature analysis method of the mental health data of students in different colleges and regions and of different ages based on a convolutional neural network and TOPSIS evaluation model and studies the college students’ mental health analysis model based on convolutional neural network. First, through the data cluster summary and internal characteristics analysis of college students’ psychological questionnaire survey data in different regions and grades, we established a college students’ mental health grade system and evaluation index system. Then, the TOPSIS analysis method is used to analyze the characteristics of the data results, and the feasibility of the accuracy of the evaluation index standard is analyzed. Finally, the experimental results show that the college students’ mental health analysis model based on convolutional neural network can effectively classify and summarize various mental health data, quickly locate the mental health problems of different students and analyze the optimal solutions, and can effectively promote the process of analysis and research on the mental health problems in modern college students.

Factor Analysis and Regression Prediction Model of Swimmers' Performance Structure Based on Mixed Genetic Neural Network.

With the development of economy, people put forward higher demands on material life and spiritual life, and sports have become an indispensable part of daily life. Ten km freestyle, although it has been started late, is loved by people all over the world, and it is getting more and more attention in the world of competitive sports. Through the theoretical research of 10 km freestyle, it can be seen that most of the research on this project is focused on the training, selection of materials, and how to improve the technique of crossing obstacles or pools, while the research on the performance of 10 km freestyle is limited to the analysis of the development trend of the performance and no research on the prediction of the actual performance, which to some extent restricts the scientific development of 10 km freestyle. The efficiency of a suitable learning is a factor that influences whether the training and learning of a BP neural network is stable or not. It can also directly determine the choice of weights. When the learning rate is chosen to be large, it increases the speed of learning and makes the stability of the network change. This has limited the scientific development of 10 km freestyle to a certain extent, and it is also a good entry point for the research of 10 km freestyle. BP neural network is one of the most widely used neural network models, which can be used for classification, clustering, prediction, and other related problems. In this study, we propose a comprehensive evaluation method of 10 km freestyle performance based on BP neural network and try to establish a neural network evaluation model by combining the athletes' physiological and biochemical indexes and sports performance. In this study, the normalized index values are used as the network input, and the athletes' performance in 10 km freestyle is used as the network output to predict the athletes' performance in 10 km freestyle. This study provides a theoretical basis for adjusting the condition of 10 km freestyle athletes and improving their performance. Optimize the reasonable allocation of resources between coaches and athletes, and increase the opportunities for coaches and athletes to communicate and learn from outside.

Application of Business Intelligence Based on the Deep Neural Network in Credit Scoring

In order to solve the economic globalization and the intensification of financial market volatility, this study puts forward the judgment of credit risk of loan enterprises, so as to effectively improve the ability of commercial banks to control credit risk. At the same time, neural network technology is applied to solve nonlinear data modeling. Experiments and evaluation are carried out by using the artificial neural network (ANN) evaluation model. The experimental evaluation model data obtained from the experimental evaluation model show that the prediction effect is good, which proves the effectiveness of this method. According to the data of China Banking Regulatory Commission, at the end of the third quarter of 2016, the balance of nonperforming loans of Chinese commercial banks was 1493.9 billion yuan. By the end of the third quarter of 2021, the balance of nonperforming loans of Chinese commercial banks had reached 28153.5 trillion yuan, a month on month increase of about 42.7 billion yuan. At the end of the third quarter of 2021, the domestic and foreign currency assets of Chinese banking financial institutions were 339.4 trillion yuan, a year-on-year increase of 7.7%. This shows that the credit risk remains high, but it is still at a controllable level. Commercial banks are the center of the modern economic system. Their credit risk is very important to the stability and safe development of national economy. It is also the key factor for its own business sustainability and the protection of the interests of depositors. Therefore, effectively preventing and reducing credit risk is a key field in the financial risk management of commercial banks.

A Real-Time Effectiveness Evaluation Method for Remote Sensing Satellite Clusters on Moving Targets.

Recently, remote sensing satellites have become increasingly important in the Earth observation field as their temporal, spatial, and spectral resolutions have improved. Subsequently, the quantitative evaluation of remote sensing satellites has received considerable attention. The quantitative evaluation method is conventionally based on simulation, but it has a speed-accuracy trade-off. In this paper, a real-time evaluation model architecture for remote sensing satellite clusters is proposed. Firstly, a multi-physical field coupling simulation model of the satellite cluster to observe moving targets is established. Aside from considering the repercussions of on-board resource constraints, it also considers the consequences of the imaging’s uncertainty effects on observation results. Secondly, a moving target observation indicator system is developed, which reflects the satellite cluster’s actual effectiveness in orbit. Meanwhile, an indicator screening method using correlation analysis is proposed to improve the independence of the indicator system. Thirdly, a neural network is designed and trained for stakeholders to realize a rapid evaluation. Different network structures and parameters are comprehensively studied to determine the optimized neural network model. Finally, based on the experiments carried out, the proposed neural network evaluation model can generate real-time, high-quality evaluation results. Hence, the validity of our proposed approach is substantiated.

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model.

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine.

Ablation state assessment of SF6 circuit breaker contacts based on BP neural network and mean impact value

In order to evaluate the ablation state of the SF6 circuit breaker contacts, simulated ablation tests were carried out on the circuit breaker, and the dynamic resistance curve as a function of contact travel was measured with three levels of injected current. The results showed that the increasing of test current value can reduce the fluctuation of the dynamic resistance curve as a function of contact travel. In addition to the ablation effect of current, the loosening of the operating mechanism will affect the accuracy of opening and closing action of the contact, resulting in the continuous reduction of the main contact travel in the dynamic resistance curve as a function of contact travel. Based on the measurement results, a BP neural network evaluation model was built to evaluate the ablation state of the contact. Seven parameters were extracted from the dynamic resistance curve as a function of contact travel as model inputs, and mean impact value algorithm was used to optimize the model. After the optimization, the average relative error of the evaluation to the verification set is less than 10.4%.

An English Teaching Ability Evaluation Algorithm Based on Frequency Effect

Background. English is one of the courses offered in all colleges and universities. The quality of English teaching is directly related to the quality of talent training and the development of students themselves. “Teaching quality evaluation” specifically refers to the education evaluation with teaching as the evaluation object. It is the core and foundation of the whole education evaluation. Teaching quality evaluation is based on certain teaching objectives and teaching norms and standards, through the systematic detection and assessment of teaching and learning. Evaluate its teaching effect and the degree of realization of teaching objectives, and use scientific and feasible methods to make corresponding value judgments to improve the process of teaching. To improve the accuracy of English teaching ability evaluation, an English teaching ability evaluation algorithm based on frequency effect is proposed. Methods. The paper proposes an English teaching ability evaluation algorithm based on frequency effect. Firstly, it constructs the evaluation index system of English teaching ability, including expert evaluation system, student evaluation system, and teacher evaluation system. Then, the indexes affecting the evaluation of English teaching ability are quantified by fuzzy synthesis, and the evaluation indexes are refined. Finally, the basic principle of frequency effect is analyzed, combined with the convolutional neural network. Results. The convolutional neural network evaluation model is constructed, the teaching ability indicators are input into the model, the final evaluation results are output, and the design of the English teaching ability evaluation algorithm based on frequency effect is completed. Conclusions. The experimental results show that this method has high accuracy and efficiency.

Evaluation and Analysis of Multimedia Collaborative Building Design Relying on Particle Swarm Optimization Algorithm

This article proposes improvements to the placement, organizational structure, and adaptability of the evaluation model and proposes a comprehensive evaluation model for the multiattribute scheme of the particle swarm optimization algorithm. The structure table of the multimedia building design plan evaluation index, the plan evaluation table, and the standardized model of the plan evaluation value of the common method was proposed. It also proposes the import of “with tutor” type data of particle swarm optimization algorithm as well as the self-organizing neural network evaluation model and calculation method of “no tutor” type resource import. According to the complexity of architectural design evaluation and the nonlinear relationship between related influencing factors, we have studied the principles and methods of applying system analysis, designed comprehensive evaluation models, and comprehensively evaluated architectural design schemes.

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is5.921×10−4, which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.