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As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper. [ABSTRACT FROM AUTHOR]

Neural network refers to an algorithmic mathematical model that imitates the behavioral characteristics of animal neural networks and performs distributed information processing. This kind of network depends on the complexity of the system and needs to adjust the internal node relationship, so as to achieve the purpose of processing information. With the continuous development of the economy, the transportation problem needs to be solved urgently, and the urban subway has emerged at the historic moment. The subway is a fast, large-capacity, electric-driven rail transit built in the city. The advantages of the subway provide conditions for the mitigation of urban traffic, due to the large number of cars, traffic jams, frequent accidents, and serious environmental pollution. In the city center, there are more cars and less space, and the parking lot is not commensurate with the number of cars, making parking difficult. This paper aims to study the intelligent performance evaluation of urban subway PPP projects based on deep neural network models. The subway project has a large investment, a long period, and a wide range, but the development time of the subway in China is relatively short. In order to promote the stable progress of subway projects, it is very necessary to conduct intelligent performance evaluation on subway projects. This paper compares and analyzes the basic characteristics of the PPP model and verifies the applicability and necessity of the PPP model in urban subway transportation projects. Through the investigation of relevant literature, this article puts forward the research content of the social impact assessment of subway projects. The experimental results of this paper show that, from the perspective of whether it is necessary to evaluate the performance of PPP projects, 65% of people think it is very necessary, and 22% think it is more necessary. 3% of people think it is unnecessary, and 10% of people hold an indifferent attitude. These data show that the intelligent performance evaluation of urban subway PPP projects has exploratory significance for urban infrastructure design and construction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we have first selected 28 indicators based on the selection principle of financial indicators adopted in relevant studies both at home and abroad from seven aspects, which include profitability, long-term and short-term solvency, company development capacity, operating capacity, cash flow generation capability, equity characteristics, and board characteristics. Subsequently, we have conducted the comparative analysis and comprehensive study on the logistic early warning model and BP neural network to provide reference for managers and stakeholders to select the optimal model. In addition, through our study on the dynamic early warning of BP neural network, we intend to convey the concept of constantly updating the model to both managers and stakeholders. Therefore, this paper provides ideas for the research on the model of financial crisis early warning for China's manufacturing industry. The study is of significance for guiding the research of related issues in the manufacturing sector and can also provide reference for the early warning of other industries. [ABSTRACT FROM AUTHOR]

In recent years, China's engineering construction management level has been greatly improved, but compared with other industries, the construction industry still has low production efficiency, serious waste, and low level of information problems, and especially in the process of engineering management practice, schedule delay has become the focus of engineering management problems. With the continuous development of science and technology, computer and information technology have been continuously applied in engineering, among which deep neural network (DNN) technology, lean management, information visualization technology, and other technologies have become the hot spot of industry research, and the application of these emerging technologies to improve the level of project schedule control has become an urgent demand of the industry. Therefore, on the basis of deep learning, this paper analyzes the principle and application of object detection and feature extraction constructed by neural network and combines text feature extraction and image feature extraction methods. This application provides a new idea for the development of the construction industry. [ABSTRACT FROM AUTHOR]

In 2014, the relevant research data from the Ministry of Environmental Protection and the Ministry of Land and Resources showed that the total exceedance rate of soil heavy metal pollution in China had reached 16.1%, and in the construction of ecological civilization in the 13th Five-Year Plan, China has made the prevention and control of soil heavy metal pollution as the focus of prevention and control. Therefore, in this paper, four neural optimization network models, that is, radial basis neural network (RBFNN), generalized regression neural network (GRNN), wavelet neural network (WNN), and fuzzy neural network (FNN), are simulated and created to measure and correlate the soil heavy metal content in a city in northwest China and a city in central China from the actual situation in China. The simulations were conducted. Finally, by analyzing the comparison of predicted and true values of these four models on the test data of two sets of experimental data, the distribution of predicted differences to true values, and the calculation results of three error indicators, we found that WNN has the best prediction performance when using RBFNN, GRNN, WNN, and FNN for soil heavy metal content prediction. [ABSTRACT FROM AUTHOR]

Based on the development background of the interweaving and integration of computer technology and Internet technology, China's artificial intelligence industry is quietly rising. In the social life of the information age, the artificial intelligence industry represented by machine deep learning is playing a very important role. This study in combination with the background of the new health reform, in view of the reform of the medical industry, analyzes the connotation of financial wisdom based on the important role of the hospital financial development problems, puts forward the development direction of artificial intelligence hospital financial wisdom development measures, designed to meet the changing external environment demand, reduces human costs, and improves the overall efficiency of hospital financial fund management. Based on the evaluation results, this paper proposes the correct direction for the development of hospital intelligence finance by using the BP neural network model. After the analysis, it is found that the development of artificial intelligence is an important measure to promote the development of hospital intelligent finance. In other words, hospital intelligent financial management is the product of the continuous progress of artificial intelligence technology. At the present stage, the intelligent financial management problems of hospitals are mainly as follows: (1) lack of financial informatization, (2) lack of perfect financial risk early warning system, and (3) the phenomenon of "information island" in the financial system. After analyzing the above problems, the research believes that the development of hospital intelligent finance based on artificial intelligence needs to solve the above thorny problems, so as to improve the outcome of hospital intelligent finance development. The following work should be done: (1) strengthen the design of information sharing module, (2) intelligent control of the cost of hospitals, and (3) intelligent treatment of hospital accounting. Combining the development of artificial intelligence and hospital intelligent finance theory, combined with the actual trend of financial intelligence development under the background of artificial intelligence development in the new era, it provides scientific basis for the development of hospital intelligent finance. [ABSTRACT FROM AUTHOR]

Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world's population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability. [ABSTRACT FROM AUTHOR]

Teachers in traditional English education devote a great deal of time and effort to developing excellent English teaching courseware and other teaching materials. However, because the network's powerful sharing capability was not fully utilized, poor resource sharing hampered the school's overall teaching efficiency. The goal of this paper is to investigate how to use a ubiquitous learning resource sharing platform and a neural network to analyze the method of English major education resource sharing. Because the BP neural network algorithm has better classification and prediction functions, it was proposed because it can classify and predict information in the sharing of teaching resources for English majors. This allows for seamless sharing of teaching materials. According to the findings of this study, there were 562.13 million people learning English in China in 2015, accounting for 37% of the population. By 2018, 945.61 million people were learning English, accounting for 85 percent of the population. Almost every student is learning English, demonstrating the language's importance. As a result, the importance of English instruction is clear, and the sharing of teaching resources should be prioritized. [ABSTRACT FROM AUTHOR]

Physical education curriculum has been paid more and more attention by teachers and parents, and having a healthy body is the foundation. School sports competition is also more and more concerned by major researchers, and scholars have produced in-depth research and analysis of sports competition results prediction because prediction results can better let teachers carry out appropriate sports training for students, so as to achieve the best learning effect. The construction of the prediction model and whether the performance and universality of the model after construction are suitable for predicting sports competitions have also become a major research point. Deep neural network is a complex network method to analyze the structure of the human brain, which plays a core role in the field of sports planning and performance prediction and can know the future performance of athletes or students in advance in sports competitions. This paper establishes the autoregressive summation model prediction model, the complex neural network prediction model, and the improved complex neural network prediction model. It is concluded that only the improved BP neural network model has a remarkable effect on performance prediction, and the prediction value obtained by this prediction model can reduce the systematic error of prediction, so that it can better infer the performance prediction of sports competitions in China and plan which sports events are suitable for which prediction model. [ABSTRACT FROM AUTHOR]

The relationship between financial development and economic growth has become a hot topic in recent years and for China, which is undergoing financial liberalisation and policy reform, the efficiency of the use of digital finance and the deepening of the balance between quality and quantity in financial development are particularly important for economic growth. This paper investigates the utility of digital finance and financial development on total factor productivity in China using interprovincial panel data decomposing financial development into financial scale and financial efficiency; an interprovincial panel data model is used to explore the utility of digital finance on total factor productivity. This involves the collection and preprocessing of financial data, including feature engineering, and the development of an optimised predictive model. We preprocess the original dataset to remove anomalous information and improve data quality. This work uses feature engineering to select relevant features for fitting and training the model. In this process, the random forest algorithm is used to effectively avoid overfitting problems and to facilitate the dimensionality reduction of the relevant features. In determining the model to be used, the random forest regression model was chosen for training. The empirical results show that digital finance has contributed to productivity growth but is not efficiently utilised; China should give high priority to improving financial efficiency while promoting financial expansion; rapid expansion of finance without a focus on financial efficiency will not be conducive to productivity growth. [ABSTRACT FROM AUTHOR]

At present, the number of enterprises in financial crisis in China is rising sharply, and the ability of enterprises to resist risks is generally weak. Therefore, it is necessary to establish a corporate financial crisis early warning system, to detect the signs of corporate financial crisis before it arrives and to inform managers in advance, so that effective measures can be taken as soon as possible to eliminate hidden dangers. This paper selects the two-year data of 40 companies from 2017 to 2019 as training samples and the data of 20 companies as prediction samples. After testing, 12 index variables that can reflect the financial problems of energy companies are finally selected as the basis for modeling. Then, we use Logistic and BP neural network modeling, respectively, to study and compare the data from 2017 to 2019 to predict the financial risk in the following year. The results show that the BP neural network model in the two models is better than the Logistic model in terms of fitting degree or prediction accuracy for enterprise financial early warning. Therefore, the BP neural network model has a better effect and is more suitable for the practical application of enterprises in China. [ABSTRACT FROM AUTHOR]

With the downward pressure of China's economy and the impact of the epidemic, the accumulated market risk has increased the liquidity pressure of the banking industry, and the mismatch between deposit maturity and loan maturity is the main cause for the increase of liquidity risk. The twenty-first century is the era of rapid and in-depth development of data management technology. The explosive growth of massive financial data makes the information data related to the liquidity risk of commercial banks present the characteristics of complexity, diversity, and heterogeneity. The traditional risk early warning model cannot deal with the influence between a large number of influencing factors and the nonlinear factors of commercial bank liquidity risk. Based on this transformation, the circular neural network model is introduced into the field of liquidity risk early warning of commercial banks from the perspective of the mismatch risk of financing maturity of commercial banks, and the driving factors and risk warning signs of liquidity risk of commercial banks are further analyzed from the institutional level, policy level, industry level, and micro commercial bank level. This paper uses network crawler technology, text analysis, and grounded analysis technology to intelligently identify the liquidity risk of commercial banks and establishes an early warning index system based on the influencing factors of commercial banks and internal liquidity risk. Also, it constructs an intelligent early warning model of commercial bank liquidity risk based on deep learning and uses the data of commercial banks from 2000 to 2020 for early warning. The results show that the constructed model has high accuracy, which can provide support for banks and relevant government departments to formulate and resolve bank liquidity risk. [ABSTRACT FROM AUTHOR]

With the intensification of global market competition and the continuous development of the information technology, competition in the apparel market has become increasingly fierce. The key to whether China's garment industry can maintain its advantage in the international market competition in the future lies in whether it can promote and realize the informatization of the garment industry or not. After all, under the context of increasingly developed information technologies and growing competition in the garment market, mass customization of garments has become a future trend in the garment industry. As custom-made clothing is more in line with consumers' individual needs in terms of style, fabric, and size, the focus of development for clothing companies is increasingly on the grasp of the fit of clothing. However, with China's large population and the wide variety of body types, traditional hand-made garments are time-consuming and cannot meet the differentiated needs of consumers in the modern market. The design of garment samples is an important part of the industrial production of garments and is highly dependent on the skills and experience of the operators. In other words, the level of technical expertise can determine the quality and shape of a garment product to a certain extent. As a result, in order to further improve the efficiency and quality of garment sample design and to reduce the dependence on operator skills and experience, this study proposes an intelligent garment paper sample design system based on BP neural networks. The system mainly utilizes the self-learning, self-organizing, and adaptive as well as nonlinear mapping functions of artificial neural networks to design clothing samples autonomously, thus improving the design efficiency. In the era of rapid development of information technology and artificial intelligence technology, the development of intelligent garment pattern design systems with independent intellectual property rights is of great significance in promoting the prosperity of the garment industry. [ABSTRACT FROM AUTHOR]

As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based algorithm is proposed that can not only check English pronunciation but also help learners distinguish between phonemic and quality phonemic while listening and differentiating, as well as correct phonemic errors, thereby increasing their language learning capacity. In order to study the application of nonlinear network identification technology in English learning, this paper evaluates the English pronunciation quality through the deep learning algorithm of deep learning combined with the related contents of neural network data model, and the experimental results of speech recognition structure are analyzed and discussed in detail. The concordance between machine and manual intonation evaluation is 80%, the concordance rate of adjacent intonation evaluation is 98.33%, and the Pearson correlation coefficient is 0.627 that shows the technique is reliable. The method of English pronunciation and speech identification model is sensible and dependable, which can give beginners a punctual, exact and impartial judgment and response guidance, assist learners to get on the differences between their phonemic and standard phonemic, and correct phonemic mistakes, in order to enhance the ability of oral English learning. [ABSTRACT FROM AUTHOR]

Shaanxi is one of China's most important cradles of civilization. The main vein of Chinese culture is rich history and culture, and brilliant red culture embodies the essence of socialist core values. It is still relatively weak to deeply analyze the related research of Shaanxi Province's cultural province construction on the basis of studying the achievements of cultural development in foreign countries and China and combining with the reality of Shaanxi Province. In this paper, a BPNN (BP neural network) model is selected to study the comprehensive evaluation of tourism competitiveness of smart tourism cities, and the software is used to realize the simulation of the comprehensive evaluation system of tourism competitiveness of smart tourism cities, which more comprehensively and objectively reflects the level of comprehensive competitiveness of each city. It is believed that there are some problems in Shaanxi regional cultural communication, such as insufficient exploration of content resources, insufficient communication channels, and low audience awareness, hoping to provide ideas and reference for further exploring the promotion of cultural communication power. [ABSTRACT FROM AUTHOR]

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) plays an irreplaceable role in the monitoring of marine oil spills. However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle noise. In this paper, the convolutional neural network AlexNet model is used to extract the oil spill information from SAR images by taking advantage of its features of local connection, weight sharing, and learning for image representation. The existing remote sensing images of the oil spills in recent years in China are used to build a dataset. These images are enhanced by translation and flip of the dataset, and so on and then sent to the established deep convolutional neural network for training. The prediction model is obtained through optimization methods such as Adam. During the prediction, the predicted image is cut into several blocks, and the error information is removed by corrosion expansion and Gaussian filtering after the image is spliced again. Experiments based on actual oil spill SAR datasets demonstrate the effectiveness of the modified AlexNet model compared with other approaches. [ABSTRACT FROM AUTHOR]