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Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimens...
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As the technology nodes of semiconductor devices have become finer and more complex, progressive scaling down has been implemented to achieve higher densities for electronic devices. Thus, three-dimensional (3D) channel field-effect transistors (FETs), such as fin-shaped FETs (FinFETs) and gate-all-around FETs (GAAFETs), have become popular as they...
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... Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: ...
... Zhang [18] summarized three methods for the calculation of VAR : variance-covariance methods, conventional simulation methods and Monte Carlo simulation methods; and, taking the regional market financial comprehensive index as the research object, he calculated VaR at different confidence levels to measure the risk tolerance of investors, which proved the feasibility of using a VaR method to analyze investment risk in the regional financial market. Bohušova [19] took the regional stock market as the research object to compare the risk ratio, made assumptions on the model with different distributions and calculated the VaR. ...
The error measurement of fiscal accounting data can effectively slow down the change of financial assets. Based on deep neural network theory, we constructed an error measurement model for fiscal and tax accounting data, and we analyzed the relevant theories of fiscal and tax performance evaluation. By establishing a batch evaluation index of finance and tax accounting, the model can monitor the changing trend of the error of urban finance and tax benchmark data scientifically and accurately, as well as solve the problem of high cost and delay in predicting the error of finance and tax benchmark data. In the simulation process, based on the panel data of credit unions, the entropy method and a deep neural network were used to measure the fiscal and tax performance of regional credit unions. In the example application, the model, combined with MATLAB programming, calculated the contribution rate of regional higher fiscal and tax accounting input to economic growth. The data show that the contribution rates of some fiscal and tax accounting input, commodity and service expenditure, other capital expenditure and capital construction expenditure to regional economic growth are 0.0060, 0.0924, 0.1696 and -0.0822, respectively. The results show that the proposed method can effectively map the relationships between variables.
... In terms of prediction of irregularities an overall accuracy of 80.87% was reported. In another recent study, Zhang [7] proposed the evaluation of a financial audit model based on a Convolutional Neural Network (CNN). The study compared classification results of financial data based on CNN (accuracy of 93.4%) and ANN (accuracy of 90.0%). ...
Abstract: Background: Inefficient use of public funds can have a negative impact on the lives
of citizens. The development of machine learning-based technologies for data visualisation and
prediction has opened the possibility of evaluating the accountability of publicly funded projects.
Methods: This study describes the conception and evaluation of the architecture of a system that can
be utilised for project profile definition and prediction. The system was used to analyse data from
20,942 System of Management of Agreements and Transfer Contracts (SICONV) projects in Brazil,
which are government-funded projects. SICONV is a Brazilian Government initiative that records the
entire life cycle of agreements, transfer contracts, and partnership terms, from proposal formalisation
to final accountability. The projects were represented by seven variables, all of which were related to
the timeline and budget of the project. Data statistics and clustering in a lower-dimensional space
calculated using t-SNE were used to generate project profiles. Performance measures were used
to test and compare several project-profile prediction models based on classifiers. Results: Data
clustering was achieved, and ten project profiles were defined as a result. Among 25 prediction
models, k-Nearest-Neighbor (kknn) was the one that yielded the highest accuracy (0.991 � 0.002).
Conclusions: The system predicted SICONV project profiles accurately. This system can help auditors
and citizens evaluate new and ongoing project profiles, identifying inappropriate public funding.
... Using the global search ability of the genetic algorithm, more reasonable hyperparameters can be obtained. The optimization objects of the genetic algorithm are mainly the learning rate, the number of training rounds, and the number of hidden layers, which make the neural network have the ability of self-adaptation and self-evolution [29,30]. Using the loss function obtained in each round of training as the reference object, the hyperparameters are modified. ...
Hyperparameters involved in neural networks (NNs) have a significant impact on the accuracy of model predictions. However, the values of the hyperparameters need to be manually preset, and finding the best hyperparameters has always puzzled researchers. In order to improve the accuracy and speed of target recognition by a neural network, an improved genetic algorithm is proposed to optimize the hyperparameters of the network by taking the loss function as the research object. Firstly, the role of all loss functions in object detection is analyzed, and a mathematical model is established according to the relationship between loss functions and hyperparameters. Secondly, an improved genetic algorithm is proposed, and the feasibility of the improved algorithm is verified by using complex fractal function and fractional calculus. Finally, the improved genetic algorithm is used to optimize the hyperparameters of the neural network, and the prediction accuracy of the model before and after the improvement is comprehensively analyzed. By comparing with state-of-the-art object detectors, our proposed method achieves the highest prediction accuracy in object detection. Based on an average accuracy rate of 95%, the detection speed is 20 frames per second, which shows the rationality and feasibility of the optimized model.
... e overall objective of an audit is to obtain reasonable assurance that the financial statements are free from material misstatement due to fraud or error. It aims to issue an audit report in accordance with auditing standards and to communicate with the client's management [1][2][3]. To achieve these goals, under the modern risk-oriented audit model, the main line of audit work requires auditors to identify, evaluate, and respond to the risks of material misstatement. ...
The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper.
... After learning the initial inputs and their relationships, it can infer invisible relationships between invisible data, thereby generalizing and predicting unknown data. Artificial neural networks can be applied in many fields, including the control field, the psychological field, the medical field, the educational field, the financial field [13], the traffic field, and the information field. Figure 1 shows some common applications of the artificial neural networks. ...
In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.
