Journal of Intelligent & Fuzzy Systems - Volume 40, issue 2

Authors: Tian, Jie | Wang, Yaoqiang | Cui, Wenjing | Zhao, Kun

Article Type: Research Article

Abstract: With the rapid development of the world’s financial industry, the complexity and relevance of risks are gradually increasing. At present, there are still some deficiencies in the model for measuring financial risk. In view of this, this study analyzes the financial stock market and combines VAR model and GARCH model to conduct financial analysis. Moreover, this study uses the standard deviation in the statistical characteristics of the data to characterize the fluctuation of futures, and then uses the univariate GARCH model to measure the fluctuation. In addition, this study combines the examples to analyze the effectiveness of the model, and …compares the predicted data with the actual data to verify the model performance. The results show that the algorithm proposed in this paper has certain effectiveness, and through this research algorithm, investors, speculators or macro decision makers in the futures market can obtain some inspiration. Show more

Keywords: VAR model, GARCH model, financial stocks, market, simulation analysis

DOI: 10.3233/JIFS-189225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2277-2287, 2021

Authors: Wei, Guohua | Jin, Yi

Article Type: Research Article

Abstract: At present, data is in a state of explosive growth. The rapid growth of data collected by enterprises has exceeded the processing capacity of traditional human resource management systems, resulting in their inability to perform data management and data analysis. In order to improve the practicality of the human resource management system, this paper applies machine learning technology to the human resource management system, selects dimensions according to the prediction method, and builds a combined model consisting of an optimized GM (1,1) model and a BP neural network model. The model is implemented by a three-layer BP neural network. In …order to verify the performance of the research model, this article conducts research using an entity as an example. The research results show that the method proposed in this paper has certain practical effects and can improve the reference for subsequent related research. Show more

Keywords: Neural network, machine learning, improved model, human resources, predictive model

DOI: 10.3233/JIFS-189226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2289-2300, 2021

Authors: Yu, Hailong | Ji, Yannan | Li, Qinglin

Article Type: Research Article

Abstract: Due to the diversity of text expressions, the text sentiment classification algorithm based on semantic understanding is difficult to establish a perfect sentiment dictionary and sentence matching template, which leads to strong limitations of the algorithm. In particular, it has certain difficulties in the classification of student sentiments. Based on this, this paper analyzes the student sentiment classification model by neural network algorithm and uses the student group as an example to explore the application of neural network model in sentiment classification. Moreover, the regularization method is added to the loss function of LSTM so that the output at any …time is related to the output at the previous time. In addition, the sentimental drift distribution of sentimental words on each sentimental label is added to the regularizer, and the sentimental information is merged with the two-way LSTM to allow the model to choose forward or reverse. Finally, in order to verify the research model, the performance of the model proposed in this paper is studied through experimental research. The research shows that the model proposed in this paper has better comprehensive performance than the traditional model and can meet the actual needs of students’ sentiment classification. Show more

Keywords: GRU neural network, improved algorithm, student sentiment, sentiment classification, sentiment recognition

DOI: 10.3233/JIFS-189227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2301-2311, 2021

Authors: Zhang, Ling | Liang, Faze

Article Type: Research Article

Abstract: At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this …paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory. Show more

Keywords: BP neural network, athletes, limb recognition, motion capture

DOI: 10.3233/JIFS-189229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2325-2335, 2021

Authors: Xie, Wangsong

Article Type: Research Article

Abstract: In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates …statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper. Show more

Keywords: Support vector machine, regression analysis, financial market risk prediction, model

DOI: 10.3233/JIFS-189230

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2337-2347, 2021

Authors: Chen, Xuehua

Article Type: Research Article

Abstract: The difference between English and Chinese expressions is that English emphasizes the stress of syllables, so the recognition of English speech emotions plays an important role in learning English. This study uses transfer learning as the technical support to study English speech emotion recognition. The acoustic model based on weight transfer has two different training strategies: single-stage training and two-stage training strategy. By comparing the performance of the English speech emotion recognition model based on CNN neural network and the model proposed in this paper, the statistical comparison data is drawn into a statistical graph. The research results show that …transfer learning has certain advantages over other algorithms in English speech emotion recognition. In the subsequent teaching and real-time translation equipment research, transfer learning can be applied to English models. Show more

Keywords: Transfer learning, English, speech, emotion recognition, features

DOI: 10.3233/JIFS-189231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2349-2360, 2021

Authors: Jingchao, Hu | Zhang, Haiying

Article Type: Research Article

Abstract: The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete …the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features. Show more

Keywords: Deep learning, machine learning, student state, online recognition, feature recognition

DOI: 10.3233/JIFS-189232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2361-2372, 2021

Authors: Wenming, Huang

Article Type: Research Article

Abstract: The efficiency of traditional English teaching quality evaluation is relatively low, and evaluation statistics are very troublesome. Traditional evaluation method makes teaching evaluation a difficult project, and traditional evaluation method takes a long time and has low efficiency, which seriously affects the school’s efficiency. In order to improve the quality of English teaching, based on machine learning technology, this study combines Gaussian process to improve the algorithm, use mixed Gaussian to explore the distribution characteristics of samples, and improve the classic relevance vector machine model. Moreover, this study proposes an active learning algorithm that combines sparse Bayesian learning and mixed …Gaussian, strategically selects and labels samples, and constructs a classifier that combines the distribution characteristics of the samples. In addition, this study designed a control experiment to analyze the performance of the model proposed in this study. It can be seen from the comparison that this research model has a good performance in the evaluation of the English teaching quality of traditional models and online models. This shows that the algorithm proposed in this paper has certain advantages, and it can be applied to the practice of English intelligent teaching system. Show more

Keywords: Gaussian process, machine learning, English, teaching quality, evaluation model

DOI: 10.3233/JIFS-189233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2373-2383, 2021

Authors: Lin, Lin | Liu, Jie | Zhang, Xuebing | Liang, Xiufang

Article Type: Research Article

Abstract: Due to the complexity of English machine translation technology and its broad application prospects, many experts and scholars have invested more energy to analyze it. In view of the complex and changeable English forms, the large difference between Chinese and English word order, and insufficient Chinese-English parallel corpus resources, this paper uses deep learning to complete the conversion between Chinese and English. The research focus of this paper is how to use language pairs with rich parallel corpus resources to improve the performance of Chinese-English neural machine translation, that is, to use multi-task learning to train neural machine translation models. …Moreover, this research proposes a low-resource neural machine translation method based on weight sharing, which uses the weight-sharing method to improve the performance of Chinese-English low-resource neural machine translation. In addition, this study designs a control experiment to analyze the effectiveness of this study model. The research results show that the model proposed in this paper has a certain effect. Show more

Keywords: Machine learning, improved algorithm, spoken English, automatic translation

DOI: 10.3233/JIFS-189234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2385-2395, 2021

Authors: Liu, Jie | Lin, Lin | Liang, Xiufang

Article Type: Research Article

Abstract: The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the …group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system. Show more

Keywords: Improve machine learning, English, composition scoring, scoring model

DOI: 10.3233/JIFS-189235

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2397-2407, 2021