An effective going concern prediction model for the sustainability of enterprises and capital market development

Chen, Suduan

doi:10.1080/00036846.2019.1578855

Cited by 15 publications

(27 citation statements)

References 27 publications

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“…Competition in sports is an enjoyable recreational activity based on sports regulations and is a significant component of physical exercise in general. Sports tournaments have drawn a large number of competitors and viewers since their inception because of their distinctive charm [ 1 ]. Sports tournaments are frequently scrutinized from a technical standpoint in an effort to improve the level of competitiveness within the sport in question.…”

Section: Introductionmentioning

confidence: 99%

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training

Hui

Wang

2022

Computational Intelligence and Neuroscience

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Nowadays, China’s sports industry has attained effective development, but the athlete’s efficiency in the training process is too complex to have a scientific guarantee. Machine learning technology’s help in guiding the sports training process has become a hot spot. In this work, we investigate the use of deep learning in real-time analysis of basketball sports data, utilizing research approaches such as scientific reporting, audio/video analysis, experimental research, and mathematical statistics. The suggested basketball stance action recognition and analysis system are made up of two pieces that are sequentially connected. The bottom-up stance estimate approach is utilized to locate the joint locations in the first segment, which is then used to extract the target’s posture sequence from the video. The analyses are needed for a Support Vector Machine (SVM) algorithm based on the deep learning method of the space-time graph. The basketball activity of the set classification is recognized and extracted from the segmented stance sequence. The study used an auxiliary method, which is contrasted to standard training, in order to get higher accuracy and also correct player errors in a timely manner. The approach can help players rectify technical errors, develop muscle memory, and increase their abilities. The results revealed that the algorithm generated 97.7% accuracy in evaluating data from basketball training.

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Section: Introductionmentioning

confidence: 99%

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training

Hui

Wang

2022

Computational Intelligence and Neuroscience

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“…Both models produce an accuracy rate higher than 85%. These are great results in social science [10,11]. As far as error rates are concerned, Type I error rate and Type II error rate of the LSTM model are both 2.56%, or lower than Type I error rate of 5.13% and Type II error rate of 7.69% for the RNN model.…”

Section: Discussionmentioning

confidence: 96%

“…Some studies in the literature indicate that financial statement fraud is increasingly serious [8][9][10][11][26][27][28]. What is worse is that criminals are getting better at circumventing regulatory schemes, and fraud are evolving into greater complexity [29].…”

Section: Related Workmentioning

confidence: 99%

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

Jan

2021

Sustainability

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Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator.

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“…Assume that the dataset D has m different class attributes with different values, which are labeled as B i (i = l, 2 ⋯ n). Therefore, the amount of classification information can be expressed by equation (1), where the weight of class attribute B i is expressed by b i , which can be calculated by b i /d. The logarithmic function with a base of 10 is used here because the information is encoded in binary.…”

Section: Online Data Migration Study Of Sportsmentioning

confidence: 99%

“…The viewing of sports competitions plays a unique role in meeting the exercise needs of people. Since their inception, sports competitions have attracted many participants and spectators with their unique charm [1]. However, when people study sports competitions, they tend to pay more attention to the technical factors and how to promote the level of competition, but few people study sports competitions from the aesthetic level, to explore the deep-seated reasons why sports competitions are so attractive to people, and to develop a certain ideal space for sports competitions.…”

Section: Introductionmentioning

confidence: 99%

Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application

Huang

Tsai

2021

Wireless Communications and Mobile Computing

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The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a k -nearest neighbor-based ID3 optimization algorithm, which selects values similar to k -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the k -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining.

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An effective going concern prediction model for the sustainability of enterprises and capital market development

Cited by 15 publications

References 27 publications

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training

Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application

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