With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing... Show More

With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing industry, becoming the commanding point of a new round of global manufacturing competition. Based on the literature review of intelligent manufacturing and intelligent supply chain, a total factor production cost model for intelligent manufacturing and its formal expression are proposed. Based on the analysis of the model, 12 first-level indicators and 29 second-level indicators of production line, workshop/factory, enterprise and enterprise collaboration are proposed to evaluate the intelligent manufacturing capability of supply chain. This article also further studies the layout superiority and spatial agglomeration characteristics of intelligent manufacturing supply chain, providing useful reference and support for enterprises and policy makers in the decision-making.

BACKGROUND: With the gradual improvement of market economy, people' s consumption level is constantly improving, and the quality requirements are getting higher and higher. OBJECTIVES: In order to... Show More

BACKGROUND: With the gradual improvement of market economy, people' s consumption level is constantly improving, and the quality requirements are getting higher and higher. OBJECTIVES: In order to study the management accounting information analysis platform based on Artificial Intelligence (AI) and realize the goal of accounting computerization, the application of AI in expert system is applied to the field of accounting information analysis. METHODS: The combination of subsystems is applied to the construction of AI accounting information Web system, and the feasibility analysis of its theory and technology is carried out. RESULTS: The results show that its effect is obvious: accelerating the flow of all information and promoting the change of enterprise management mode. Moreover, compared with the traditional system algorithm, the accuracy of the system model is improved by 6% and the time delay is reduced by 9ms, which makes the overall management level of the enterprise further improved, the scope of enterprise competition further expanded, the cost of enterprise saved

Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining... Show More

Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.

Public opinion surveys over the past thirty years show that public opinion is split on the issue of global warming. One of the problems with “solicited” opinion polls is that the findings may be... Show More

Public opinion surveys over the past thirty years show that public opinion is split on the issue of global warming. One of the problems with “solicited” opinion polls is that the findings may be selectively interpreted in favour of the political goals of a particular interest group. To gain a better understanding of the general public’s unsolicited responses to climate change news, the current study examined Twitter messages containing the words “global warming” spanning 16 months. Using a framework combining a sentiment analysis technique, Hedonometer from the perspective of natural language processing, and Appraisal Theory from a discourse analysis perspective, the study shows that the demonstrated happiness level in tweets containing the words “global warming” is consistently lower than the general level on Twitter, due to increased use of negative words and decreased use of positive words. The Appraisal analysis shows that Appreciation is used most frequently and Affect least.

Purpose- The aim of this study is to analyze the effect of corporate social responsibility (CSR) and social preference on quality improvement of the agricultural products supply chain composed of... Show More

Purpose- The aim of this study is to analyze the effect of corporate social responsibility (CSR) and social preference on quality improvement of the agricultural products supply chain composed of agricultural products producer and processor (A3P) and supermarket by theoretical analysis and empirical evidence. Methodology- This paper sets Stackelberg game model under A3P’s CSR by considering supermarket’s altruistic reciprocity and A3P’s fairness conern, respectively. By comparative analysis, we study the effect of CSR, altruistic reciprocity and fairness conern on the quality improvement of the agricultural products supply chain. Then, we adopt the empirical evidence to analyze the correlation between CSR, altruistic reciprocity (fairness concern) and quality improvement and the mediating effect of altruistic reciprocity (fairness concern) by investigating the agricultural enterprises.

This paper aims to expand the acceptance of the AI Virtual Assistant model from the perspective of user’s cognition. Based on the 240 samples, we used multi-layer regression analysis to investigate... Show More

This paper aims to expand the acceptance of the AI Virtual Assistant model from the perspective of user’s cognition. Based on the 240 samples, we used multi-layer regression analysis to investigate the influencing factors and differential effects of users' acceptance of AI Virtual Assistant. The results show that functional cognition and emotional cognition of users are important influencing factors for an artificial intelligence virtual assistant. This provides a new perspective for user acceptance processes of the AI Virtual Assistant. We also examined the moderating effect of social norms between user cognition and AI Virtual Assistant. At last, a new AI acceptance model of AI Virtual Assistant was established.

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data... Show More

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

To solve the dilemma between the increasing demand for cross-border e-commerce talents and incompatible students’ skill level, Industry-University-Research cooperation, as an essential pillar for... Show More

To solve the dilemma between the increasing demand for cross-border e-commerce talents and incompatible students’ skill level, Industry-University-Research cooperation, as an essential pillar for inter-disciplinary talent cultivation model adopted by colleges and universities, brings out the synergy from relevant parties and builds the bridge between the knowledge and practice. Nevertheless, industry-university-research cooperation developed lately in the cross-border e-commerce field with several problems such as unstable collaboration relationships and vague training plans.

The work used the current mature computer technology, machine learning technology, and other high-tech to explore the comprehensive application of educational information management under the... Show More

The work used the current mature computer technology, machine learning technology, and other high-tech to explore the comprehensive application of educational information management under the Internet to provide educational scientific researchers with a retrieval platform for educational statistical information. Deep learning was used to extract useful network features more effectively and make the machine learning model fully consider the constraints of satisfying the constraints and optimization objectives in the problem. Based on the classification of the restricted Boltzmann machine, the Gauss-binary conditional classification of the restricted Boltzmann machine model was proposed as the routing decision unit, with the given specific training algorithm of the model.

It is necessary to find new ideas of business transformation of traditional financial enterprises under the background of Internet finance. Based on DL (deep learning) algorithm, the BPNN (Back... Show More

It is necessary to find new ideas of business transformation of traditional financial enterprises under the background of Internet finance. Based on DL (deep learning) algorithm, the BPNN (Back Propagation neural network) model and Vector Autoregression model are used to analyze the business conflict of commercial banks among traditional financial enterprises under Internet finance. The business integration point of the two is found through the impulse response analysis of the impact of the Internet financial business on the traditional financial industry. Then, the DL algorithm based on BPNN is used to obtain the optimal solution of business integration, to promote the transformation of traditional financial services under the background of Internet finance. The results show that there is a close correlation between Internet finance and traditional financial business. The initial conflicts between the two are serious, but as time passes, they have a trend of mutual integration.

In order to reduce the risk of enterprise management, the financial risk early warning methods of listed companies are mainly studied. The financial risk characteristics of listed companies are... Show More

In order to reduce the risk of enterprise management, the financial risk early warning methods of listed companies are mainly studied. The financial risk characteristics of listed companies are analysed. With the help of rough set theory, the financial risk indicators are selected, and the financial risk early warning index system is established. The financial risk early warning model is constructed by using back propagation neural network (BPNN) algorithm based on deep learning. Finally, the accuracy and feasibility of the constructed neural network model are verified. The results show that rough set theory can be used to screen financial risk indicators and select important indicators, which can simplify the data and reduce the complexity of calculation. BPNN can calculate the simplified data and identify and evaluate the financial risk. Empirical analysis shows that the proposed method can shorten the training time of the model to a certain extent, and improve the accuracy of financial risk prediction.

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation... Show More

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

This paper aims to study the Countermeasures of big data security management in the prevention and control of computer network crime in the absence of relevant legislation and judicial practice.... Show More

This paper aims to study the Countermeasures of big data security management in the prevention and control of computer network crime in the absence of relevant legislation and judicial practice. Starting from the concepts and definitions of computer crime and network crime, this paper puts forward the comparison matrix, investigation and statistics method and characteristic measure of computer crime. Through the methods of crime scene investigation, network investigation and network tracking, this paper studies the big data security management countermeasures in the prevention and control of computer network crime from the perspective of criminology. The experimental results show that the phenomenon of low age is serious, and the number of Teenagers Participating in network crime is on the rise. In all kinds of cases, criminals under the age of 35 account for more than 50%.

Enterprise financial risks are analyzed utilizing the theory of organizational behavior, and a financial risk management system is constructed to improve the design and algorithm of the enterprise... Show More

Enterprise financial risks are analyzed utilizing the theory of organizational behavior, and a financial risk management system is constructed to improve the design and algorithm of the enterprise risk management system. Base on the CCER (China Center for Economic Research) database, the early warning model for enterprise financial risk management containing five indices is proposed for enterprises. Through Logistic regression analysis, the design principle of the financial risk management system based on AI (Artificial Intelligence) technology is explained. The proposed system innovatively introduces the AI integrated learning method, optimizes objective function through XGBoost (eXtreme Gradient Boosting) algorithm, and trains the model through BP (Backpropagation) NN (Neural Network). Finally, following comparative analysis, the effectiveness of the proposed method is verified.

The great popularity of cloud services, together with the increasingly important aim of providing Internet context-aware services, has spurred interest in developing diverse agriculture... Show More

The great popularity of cloud services, together with the increasingly important aim of providing Internet context-aware services, has spurred interest in developing diverse agriculture applications. This paper presents a cloud-based service built by incrementally integrating state-of-the-art models of deep learning, photography, object recognition and the multi-functionalities of cloud services. This study consists of an object detection phase with a convolutional neural network (CNN) model, which involves enabling simultaneous image data gathered from drones. The experimental results show 97% accurate watermelon recognition. Our results also include a two-model comparison in the cloud-based service, with the main findings demonstrating the feasibility of developing accurate object recognition using a CNN model without the need for additional hardware. Finally, this study adopted a confusion matrix to validate the result with RetinaNet for recognizing images taken on the watermelon farm with an average precision in recognizing watermelon quantity of up to 98.8%.

This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence... Show More

This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence data protection and the BP neural network prediction model to analyze the data privacy protection in news crowdfunding in the artificial intelligence era. Finally, this paper also combines the questionnaire survey method to understand the public’s awareness of privacy. The results of this paper show that artificial intelligence can promote personal data awareness and privacy, improve personal data and privacy measures and methods, and improve the effectiveness and level of privacy and privacy. In the analysis, the survey found that male college students only have 81.1% of the cognition of personal trait information, only 78.5% of network trace information, and only 78.3% of female college students’ cognition of personal credit.

At present, most risk management work mainly relies on manpower, and manpower relies on the professional knowledge of relevant skilled workers to discover hidden safety risks in production... Show More

At present, most risk management work mainly relies on manpower, and manpower relies on the professional knowledge of relevant skilled workers to discover hidden safety risks in production activities. This article combines relevant big data theories and 4V characteristics to analyze and investigate safety production and big data, study data structure, data source and data type. Using 5W1H scientific big data and applications, this analysis method analyzes the theoretical basis, applications and beneficiaries of big data related to safety production, some of which are application links and important theoretical issues. Secondly, it studies the security risk management model based on big data, proposes a risk management model based on big data, the technical basis of big data and the idea of a three-dimensional model, and applies the systematic space method, which is reflected in three aspects of risk management. In the end, a risk identification model based on big data, a risk assessment classification model, and a risk early warning and pre-control model are defined.

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the... Show More

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on... Show More

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.