Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Information about customers, products, and suppliers has become an essential resource for firms. Information and communication technologies (ICTs) contribute in different ways to the operation of firms, in terms of access to information, improvements to the business model, adequate knowledge management, and reduction of risks and costs, all of which can affect economic performance. The present study focuses on new technology-based firms (NTBFs), using a Mamdani fuzzy inference system with five inputs and five outputs, to assess the impact that ICTs have on economic performance, applying the reasoning of fuzzy logic. For the construction of the fuzzy inference system, a database of NTBFs founder-promoters was used. Information about ICTs (use of internet technologies, collaboration technologies, management technologies, support for decision-making and, search and data management) and measures of economic performance (sales, net profit, profitability, productivity and improvement of production costs) were extracted from the database for use in developing the model, which begins with definitions of the relationships between the input and output variables. The analyses performed indicated a slight change in economic performance through the interaction between ICTs measures. These findings will help firms make better decisions regarding the implementation of ICT infrastructure, allowing them to improve their economic performance.

Although the number of women entrepreneurs has increased in recent years, it is still lower than that of men. In addition, although the sports sector has been characterized by its growth in recent years and contributes to the GDP of the countries by generating employment, the role that this has within female entrepreneurship has never been analysed. Therefore, the objective of this study is to know the combinations of conditions (female employment in sports, government support, financing for entrepreneurs, perception of entrepreneurial opportunities and capacities, glass ceiling index and masculine values in society) that generate high levels of female entrepreneurship in the countries of the European Union, as these values are closer to the male TEA (Total Early-Stage Entrepreneurial Activity). A total of 13 European countries were analysed using the fsQCA methodology. The results show that, for high levels of female TEA, the main combination is high levels of government support*high levels of glass ceiling index*low levels of masculinity*low levels of opportunities perception and high levels of female employment in sport, explaining 45% of the cases. The results present a series of implications for improving female entrepreneurship in the European Union countries.

In actuarial science related to pension systems, it is widely assumed that the rate at which the reserves cover the payment of annuities (calculated for a given number of lives) is equal to the expected rate of return of the portfolios in which such reserves are invested. Given this assumption, pension fund managers may take greater risks to realize higher returns and subsequently reduce their pension liabilities. This study demonstrates that the discount rate used to calculate a two-life annuity and the expected return on the portfolio are not necessarily equal. A stochastic-based model is used to determine the proper discount rate for calculating the two-life annuity. The model includes fluctuations of both the interest rate and the payments made by the annuity. In general, this study contributes to the stability of pension systems by determining the appropriate discount rate when computing required actuarial reserves or the portfolio’s required rate of return given a reserve.

SMEs have a very important performance in any current economy because they contribute both to the generation of wealth and to the creation of jobs. In this article we analyze a sample of 12,658 Catalan SMEs and show the level of association that exists between some of their financial ratios to construct a synthetic measure that explains their business size. For the study of financial ratios, we used three data analysis techniques first, and to find the optimal number of clusters, we used the Two-Phase Clusters algorithm. Subsequently, and once the optimal number of groups was known, the most probable business size (cluster) for each company was calculated through a Fuzzy Clustering analysis. Finally, the optimal cluster, estimated for each company, was validated with a Probit model. The results allowed knowing that the size reported by each company is not necessarily equal to the synthetic measure proposed in this article. It is suggested to validate our results with an accounting analysis, other equally robust methodologies or algorithms, such as neural networks.

Companies need to know customer preferences for decision-making. For this reason, the companies take into account the Customer Relationship Management (CRM). These information systems have the objective to give support and allow the management of customer data. Nevertheless, it is possible to forget causal relationships that are not always explicit, obvious, or observables. The aim of this study on new methodologies for finding causal relationships. This research used a data analysis methodology of a CRM. The traditional analysis method is the Theory of Forgotten Effects (TFE), which is considered in this work. The new approach proposed in this article is to use Data Mining Algorithms (DMA) like Association Rules (AR) to discover causal relationships. This study analyzed 5,000 users’ comments and opinions about a Chilean foods industry company. The results show that the DMA used in this work obtains the same values as the TFE. Consequently, DMA can be used to identify non-obvious comments about products and services.

In the past two decades, research on tourism or destination competitiveness has increased exponentially. The concept of improving the performance of a destination to deliver goods and services considered important for tourists is highly appealing for policy and decision-makers. Therefore, analyzing the relation between some identified causes of destination competitiveness and the effects they exert on touristic variables of a specific territory may have relevant results. The present work applies the theory of forgotten effects to identify the direct, and indirect cause-effect relationships of the identified variables. Results show that the highest indirect effect is given by the variables hospitality and sustainable development, some other interesting results are those found in causes, destination management, and accessibility; in effects, economic growth, and profitability. This work tries to shed light on the identification and initial measurement of the relevance that competitive variables have on touristic destinations.

Supplier selection is a crucial activity in the supply chain management, and fundamentally it is a multi-criteria decision-making problem. However, the interaction of the criteria, the uncertain regarding to subjectivity and the imprecise information generally present in the provider selection process is still an open issue. The main purpose of this paper is to arise a new method to support the provider selection process. In this sense, this paper introduces the intuitionistic fuzzy version of dimensional analysis under a group decision making and multi-criteria environment applied to suppliers selection. Likewise, a set of decision makers reveal their preferences about the criteria and options in evaluation by mean of a linguistic grade that are mapped via intuitionistic numbers. Consequently, the importance of each decision maker and criteria is considered in the method. Hence, the methodology assumes that exist a choice better than the rest which is called ideal alternative. Each choice in evaluation, is compared with that ideal to induce an index of similarity. Additionally, the alternative with highest index of similarity is proposed as solution to the supplier selection problem. The method offer a systematic mode to deal with the interrelationship between the criteria attached into the decision making sphere. Finally, a numerical example is addressed to supplier selection to facilitate the understanding of the proposed method.

The main motivation of this research is to develop an innovative multidimensional model through multi attribute decision making (MADM) methods for strategic plans selection process in the Balanced Scorecard (BSC). The current study adopted MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW to rank the initiatives / strategic plans in BSC. Then the results of those methods were compared against each other in order to find a robust model for selecting strategic plans. The correlation coefficient between methods indicated that multidimensional and ELECTRE methods with 0.944 are the best performing and AHP with negative correlation (–0.455) is the worst performing method for selecting strategic plans in BSC. The high correlation demonstrates that the model can be a useful and effective tool to finding the critical aspects of evaluation criteria as well as the gaps to improve company performance for achieving desired level. Developing multidimensional model is the core model for the selection of strategic plans. This study addresses the problem and issues of group decision making process for selecting strategic plans in BSC. It has numerous contributions that particularly includes; 1) Determination of the explicit criteria sub-criteria and criteria to improve ranking strategic plans in BSC, 2) Adopting MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW for the selection of strategic plans decision problem in BSC, 3) Developing multidimensional model to address the selection of strategic plans problems in BSC. The proposed model will provide an approach to facilitate strategic plans decision problem in BSC.

The objective of the paper is to present an extension of the ordered weighted average (OWA) operator, probability OWA (POWA) operator, distance measures and Pythagorean membership grades. These new operators are called the Pythagorean membership grade OWA distance (PMGOD) operator and the probabilistic Pythagorean membership grade OWA distance (PPMGOD) operator. This extension includes in one formulation the ability to measure the ideal vs. real situation of the distance operators, the weighting vector and reordering step of the OWA operator and the way in which it includes uncertainty based on the satisfaction of the criteria of the Pythagorean membership degree. These propositions are applied to new business ventures in the city of Colombia, where the decision maker can express his/her concerns about the uncertainty and probabilities of the current business environment and, thus, generate a better decision-making process.

The main aim of this paper is to propose a new aggregation operator to improve the evaluation of the transparency index. This new operator is called the prioritized induced ordered weighted average weighted average (PIOWAWA) operator. The main characteristics of the PIOWAWA operator are that it allows considering the degree of importance, reordering and weight factors given to the information in the same formulation by the decision maker. A mathematical application is performed using a Colombia transparency case. The findings highlight that according to the operator used, there are significant changes in the ranking. The main implications are given by using these aggregation operators for the generation of scenarios by considering the changes in the allocation of weights, the level of importance and the ordering of information simultaneously.

Society is increasingly concerned about environmental, social and economic issues. According to the World Tourism Organization, over the past six decades, tourism has experienced a continuous expansion and diversification to become one of the fastest-growing economic sectors in the world. Furthermore, studies affirm the complexity of the tourism sector and the fact that sustainable development depends on various topics that are not correctly identified by managers and policymakers. For these reasons, this paper aims to reflect on the effects of tourism and to propose alternatives that can be sustainably managed. In terms of results, knowledge gaps have been identified and, through a case analysis in Brazil, the forgotten effects of tourism activity that can have an impact on sustainable development have been exposed. Also, an algorithm has been presented to manage uncertainty and facilitate decision-making.

Bibliometrics is a scientific discipline that studies quantitatively the bibliographic material of a particular topic. This study analyzes management research published by Latin American countries between 1990 and 2019. The work uses the Web of Science database and provides several country-level bibliometric indicators including the total number of publications and citations, and the h-index. The results indicate that Brazil, Chile and Mexico have constantly led the region’s scientific publications. The temporal evolution shows a significant increase on the number of publications during the last years that seems to continue in the future. The results also show that operations research and finance are the most significant topics in the region.

The purpose of this study is to guide pricing policies of Airbnb accommodation rentals to reduce inefficient pricing strategies through a novel application of topic modelling and a fuzzy clustering. In particular, the method proposes the application of Structural Topic Modelling, which explains a set of observations from latent topics. The associations between topics by Fuzzy C-Means Clustering are analysed to obtain new, more compact representations of topics (i.e., metatopics). This research identifies 15-metatopics related to Airbnb accommodations based on location and connectivity, enjoyment of domestic and everyday services, and the possibility of more authentic local experiences, among others. The influence of key metatopics on the price of Airbnb accommodations is determined by applying Extreme Gradient Boosting (an efficient and scalable implementation of gradient boosting framework) and Shapley Additive Explanations values. To sum up, our research provides an explicit contribution of user-generated content to promote the development of mutually beneficial relationships between guests and hosts, and detects future lines of research and practical and conceptual implications of the findings.

The world’s population has increased exponentially in recent decades, and the rising demand for resources presents crucial challenges that need to be addressed to ensure humanity’s current pace of development without compromising the means of future generations. The purpose of the present study is to quantify the first- and second-order cause-effect degree of incidence that drives consumer behavior when selecting a sustainable product based on the opinions of academic experts in the field. The forgotten effects theory is employed for the treatment of information. The main advantage of this methodology is the combination of expert opinions with a robust mathematical procedure that allows obtaining not only the direct but also the indirect or hidden degree of incidence. The selected experts are academic leaders in the field of sustainability in Mexico. The results show a high direct incidence of variables such as education, income and culture and a strong indirect incidence of sustainable knowledge, environmental awareness and recommendations. The present study attempts to shed light on quantifying the direct and indirect elements that encourage consumers’ choice of sustainable food products and to understand the in-depth reasons for the discrepancy between the will and actions of consumers.

The empirical evidence suggests that stock returns in the emerging technology environment exhibit high stock return volatility. The fundamental aim of the article is to investigate the dynamic, time series properties of the correlations between daily log returns and magnitude of the volatility transmissions from the emerging technologies environment to the Spanish banking sector, the Spanish market portfolio and the finance industry in the EU area. Using daily log returns for the performance variables and an equally weighted index was constructed as proxy to represent the emerging technology phenomena covering a period from the 7^th of July of 2015 to the 20^th of September of 2019. The study applies generalized autoregressive conditional heteroskedasticity GARCH followed by the diagonal BEKK approach. One key finding is that the emerging technology environment is important in capturing volatility of Spanish banking sector, the Spanish market portfolio and the finance industry in the EU area through significant volatility clustering, volatility spillover and volatility persistence. Results exhibit very large GARCH and relatively low ARCH effects indicating a long persistence of resulting shocks over volatility. Broadly, the Spanish banking sector seems to be the most exposed to volatility spillover. Nevertheless, it is the finance industry across the EU which is more affected by the volatility persistence from emerging technology shocks in terms of volatility and cross – volatility point of view. Additionally, high volatility periods provide insights about an increased integration and volatility spillover. From an investor perspective, one important implication is that adding stocks from different emerging technologies to a portfolio does not necessarily lead to risk reduction.

This study compares the efficiency of conventional and Islamic banks in Malaysia by engaging in a dynamic three-step (production, intermediation, and profitability) network data envelopment analysis (DEA). The inputs and outputs for the DEA model are selected based on the CAMELS rating. The major contributions of this study are threefold. First, this study investigates the efficiency of Malaysian banks using a novel dynamic network DEA model. Second, the Malaysian banking industry is found to be efficient in creating earning assets rather than in creating loans or profit. The results reveal that only a few banks in Malaysia have been efficient in converting deposits and equities into profit. Third, Islamic banks, in general, have been performing efficiently in the production and profitability approaches. Conventional banks, in contrast, are found to have been efficient in the intermediation approach. Policy implications are derived from the main conclusions.

This paper presents a system that allows for the identification of two values: arousal and valence, which represent the degree of stimulation in a subject, using Russell’s model of affect as a reference. To identify emotions, a step-by-step structure is used, which, based on statistical data from physiological signal metrics, generates the representative arousal value (direct correlation); from the PANAS questionnaire, the system generates the valence value (inverse correlation), as a first approximation to the techniques of emotion recognition without the use of artificial intelligence. The system gathers information concerning arousal activity from a subject using the following metrics: beats per minute (BPM), heart rate variability (HRV), the number of galvanic skin response (GSR) peaks in the skin conductance response (SCR) and forearm contraction time, using three physiological signals (Electrocardiogram - ECG, Galvanic Skin Response - GSR, Electromyography - EMG).

The objective of the paper is to present a multiple criteria hierarchical process (MCHP) approach for portfolio selection in a stock exchange. One of the problems that investors usually face is which stock should be included in the portfolio. This paper helps investors answer that question, and the paper presents an MCHP approach using different criteria based on financial ratios that the decision maker (in this case, the investor) will give different weights to make a portfolio based on her preferences; different importance is given to each criterion. An example using the Mexican Stock Exchange is presented.

This paper presents a current overview of the main productive and influential countries around the world in the computer science field. Research in the computer science field has experienced significant growth in recent years. This study develops a bibliometric overview of all journals that have been indexed in the Web of Science (WoS) database over the past 25 years (1995–2019), according to several bibliometric indicators in the seven categories of computer science research. The study shows that United States is the leading country in the computer science field. Other countries, such as the United Kingdom, China, Canada and Germany, also obtain high positions in the ranking. The average country that performs research in computer science is European, has English-speaking researchers, is highly developed and has a high income. However, there is a wide range of countries that perform research in computer science, including South American and Arabic countries, meaning that computer science traverses many countries and cultures.

This main aim of this paper is to propose a methodology for the prediction of the future price of the brown pastusa potato in Colombia, taking into consideration the variables of interest rate, as measured by fixed term deposits (FTDs), and inflation rate, as measured by the consumer price index (CPI). The methodology conducts linear regression analysis and assesses the results using the significance test, the Durbin-Watson statistic, analysis of the variance inflation factor (VIF) and the coefficient of determination. After that, the forecast of the independent variables has been conducted with the ordered weighted moving average (OWMA) operator and new proposed OWA operators using probabilities that are presented in the paper. Using these new methods and the proposed econometric model, it is possible to establish future prices. The results show a greater impact of the interest rate than inflation, as well as the need to include supply and demand variables that have not been included due to the absence of systematic information.

The aims of this study is to propose a model for managing customer experience analytics focused on value generated in an online market, this study to explore touch points experience, measured with conventional indicators and fuzzy indicators, using to a structural equation model analysis and Mamdani inference method. The investigation has delved deeper into the nature of the value of the experience construct, the results revealed of the empirical study confirm regarding how the experience value is related with the key touch points of the customer/company relationship. Very few studies in the reviewed literature about the conceptualization on customer interactive experience focused on value generated in an online environment. This study becomes more relevant today, where, after the pandemic, the value of the online customer experience becomes more important.

Price volatility is a matter of importance for making decisions in the finance world. The growing studies regarding volatility have focused on minimizing the risks through modeling, estimating and forecasting. This paper presents a bibliometric overview of the most important authors, institutions and countries that work on the topic. Additionally, a historical analysis of how the agents have interrelated is presented. For the purposes of the analysis and the design of tables and graphics, tools from the Web of Science Core Collection and the VOSviewer software were used. The results show the importance of volatility in the study of business economics and decision making.

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of complex evolutionary artificial intelligence in cognitive digital twinning.

The difficulty of sports gesture recognition is the effective cooperation of hardware and software. Moreover, there are few studies on machine learning in the capture of the details of sports athletes’ gesture recognition. Therefore, based on the learning technology, this study uses the sensor with gesture recognition algorithm to analyze the detailed motion capture of sports athletes. At the same time, this study selects inertial sensor technology as the gesture recognition hardware through comparative analysis. In addition, by analyzing the actual needs of athletes’ gesture recognition, the Kalman filter algorithm is used to solve the athlete’s posture, construct a virtual human body model, and perform sub-regional processing, so as to facilitate the effective identification of different limbs. Finally, in order to verify the validity of the algorithm model, the basketball exercise is taken as an example for experimental analysis. The research results show that the basketball gesture recognition method used in this paper is quite satisfactory.

How to apply artificial intelligence technology to help education reform is a problem that teaching researchers need to solve urgently. Using artificial intelligence technology to improve the key competences of English subjects is the new direction of current English teaching development. This research combines machine learning technology to analyze the key competences assessment of English teaching disciplines and builds an evaluation model corresponding to the threshold. Moreover, on the basis of orderly mutual information, this study combines the maximum correlation and minimum redundancy theory to select the attribute algorithm to optimize the key competences assessment function of English subjects. In addition, in this study, the performance of the research model is analyzed through a comparative test, and the results are analyzed through actual numerical comparison and error comparison. The research results show that the recognition accuracy of this research model is closer than that of the real score, has higher accuracy, and has certain practical effects.

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.

In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions.

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

The reasons for consumers’ resale behavior are complex and sometimes diverse, and the investigation of consumer resale behavior is not a simple matter. Therefore, only through a lot of investigation and inquiry can we reach relevant conclusions. Based on machine learning and BP neural network, this paper constructs a consumer online resale behavior measurement model. The contraction-expansion factor can balance the global search and local search capabilities in different iteration periods, and the differential evolution operator is introduced to solve the problem of lack of population diversity. After building the model, this study collects data through questionnaires, and combines neural network training models to take data training and data prediction. In addition, this study compares and analyzes real data with predicted data, and visually displays the comparison results through statistical graphs. The results show that the method proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

Educational information system is a hot topic in education today, and informatization is not only reflected in teaching methods. With the development of computer vision and deep learning technologies and the gradual maturity of related hardware, the application of computer algorithms and intelligent identification in distance education has become a norm. This research studies the entrepreneurial model of distance intelligent classrooms, uses machine learning technology as the basis, and combines intelligent image recognition technology to identify the status and expression of students in distance education classrooms. Moreover, this paper has carried out a more detailed study of face detection and expression recognition technology and tried to apply it to classroom teaching evaluation, which has shown certain feasibility in experiments. At the end of this article, the system was tested and analyzed with the collected data, which verified the feasibility and accuracy of the system.

At present, the recognition method based on character segmentation is not effective in recognizing English text, and the traditional methods are based on the structural features and statistical characteristics of strokes. In order to improve the recognition effect of in English text, from the perspective of machine learning, this study introduces multi-features to improve the lack of information caused by the small Chinese data set. Moreover, this study disassembles the character recognition problem into a text matching problem of question and answer, and the textual entailment problem of answer and standard answer and continues training on the data set of short text score. The final result has a certain improvement, which proves the usability of the mechanism designed in this paper. In order to study the performance of the model proposed in this paper, the model proposed in this paper and the neural network recognition model are compared in terms of recognition accuracy and recognition speed. The research results show that the algorithm proposed in this paper has a certain effect.

In remote intelligent teaching, the facial expression features can be recorded in time through facial recognition, which is convenient for teachers to judge the learning status of students in time and helps teachers to change teaching strategies in a timely manner. Based on this, this study applies machine learning and virtual reality technology to distance classroom teaching. Moreover, this study uses different channels to automatically learn global and local features related to facial expression recognition tasks. In addition, this study integrates the soft attention mechanism into the proposed model so that the model automatically learns the feature maps that are more important for facial expression recognition and the salient regions within the feature maps. At the same time, this study performs weighted fusion on the features extracted from different branches, and uses the fused features to re-recognize student features. Finally, this study analyzes the results of this paper through control experiments. The research results show that the algorithm proposed in this paper has good performance and can be applied to the distance teaching system.

The diagnostic evaluation model of English learning is difficult to judge the subjective factors in student learning, so some diagnostic evaluation models of English learning are difficult to apply to English learning practice. In order to improve the effect of English learning, based on machine learning technology, this study combines the needs of English evaluation to build a diagnostic evaluation model of English learning based on machine learning. Moreover, this study compares the methods of random forest, Bayesian network, decision tree, perceptron, K-nearest neighbor and multi-model fusion, and selects the best algorithm for diagnostic analysis. The diagnostic evaluation model of English studies constructed in this paper mainly evaluates and judges the errors in students’ English learning. In addition, this study validates the methods proposed in this study through controlled experiments. The research results show that the method proposed in this study has a certain effect.

The feature recognition of spoken Japanese is an effective carrier for Sino-Japanese communication. At present, most of the existing intelligent translation equipment only have equipment that converts English into other languages, and some Japanese translation systems have problems with accuracy and real-time translation. Based on this, based on support vector machines, this research studies and recognizes the input features of spoken Japanese, and improves traditional algorithms to adapt to the needs of spoken language recognition. Moreover, this study uses improved spectral subtraction based on spectral entropy for enhancement processing, modifies Mel filter bank, and introduces several improved MFCC feature parameters. In addition, this study selects an improved feature recognition algorithm suitable for this research system and conducts experimental analysis of input feature recognition of spoken Japanese on the basis of this research model. The research results show that this research model has improved the recognition speed and recognition accuracy, and this research model meets the system requirements, which can provide a reference for subsequent related research.

Although the data trading platform has accelerated the flow of data, the current data trading platform still has many problems. According to the characteristics of the blockchain technology, from the aspects of the attack behavior in the blockchain and the security application of the blockchain technology in power transactions, this paper studies the security of the blockchain. Moreover, this article focuses on the privacy protection of the ciphertext strategy in the CP-ABE scheme, and protects the privacy information of the access strategy by designing appropriate ciphertext and key structures and access structure forms. In addition, the system efficiency is improved by computing outsourcing, and solutions to problems in outsourcing computing are proposed. Meanwhile, two efficient and flexible support policy hidden multi-authorization center access control schemes are constructed. Finally, this study analyzes the performance of the model through controlled experiments. The research results show that this scheme has excellent performance.

At present, online education evaluation models are insufficient when dealing with small-scale evaluation data sets. In order to discriminate the learner’s learning state, this paper further studies online teaching machine learning methods, and introduces adaptive learning rate and momentum terms to improve the gradient descent method of BP neural network to improve the convergence rate of the model. Moreover, this study proposes a deep neural network model to deal with complex high-dimensional large-scale data set problems. In the process of supervised prediction, this study uses support vector regression as a predictor for supervised prediction, and this study maps complex non-linear relationships into high-dimensional space to achieve a linear relationship similar to low-dimensional space. In addition, in this study, small-scale teaching quality evaluation data sets and large-scale data sets are input into the model to perform experiments. Finally, the model proposed in this study is compared with other shallow models. The results show that the model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets.

With the deepening of people’s research on event anaphora, a large number of methods will be used in the identification and resolution of event anaphora. Although there has been some progress in the resolution of the current event, the difficult problems have not yet been completely resolved. This study analyzes the English information anaphora resolution based on SVM and machine learning algorithms and uses the CNN three-layer network as the basis to model the structure. Moreover, this study improves the semantic features by adding semantic roles and analyzes and compares the performance of the improved semantic features with those before the improvement. In addition, this study combines semantic features to compare and analyze each feature combination and uses a dual candidate model to improve the system. Finally, this study analyzes the experimental results. The results show that the performance of the system using the dual candidate model is better than that of the single candidate model system.

EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.

Due to the difficulty of athletes’ motion recognition, there are few studies on athletes’ specific motion recognition. Based on this, this study uses the acceleration sensor as the carrier, and uses human-computer interaction to transform the action of the athlete into a machine-identifiable action unit. At the same time, this paper combines the actual situation of human body motion to construct a human body motion model and builds a corresponding computer hardware and software platform. Moreover, this paper designs a classification recognition algorithm that can recognize the movement of athletes and builds SVM model based on machine learning for classification and recognition. In addition, in this study, the effectiveness of the algorithm was studied through experimental comparison. Finally, the simulation analysis was carried out to obtain the corresponding research results, and the results were analyzed by combing statistics. The research shows that the proposed algorithm can classify and recognize the collected motion data, and it has certain effects on the theoretical analysis of athletes’ motion recognition. Moreover, the algorithm can perform motion quality analysis and provide theoretical reference for subsequent related research.

Athletes’ sports detection has a heavy pressure on athletes’ training and post-injury rehabilitation. In the traditional mobilization test, there is no effective combination of exercise and rehabilitation, which directly leads to the athlete’s physical health cannot be guaranteed. Based on this, this study combines the current situation of the athletes’ field and the training ground, and uses monocular vision as the video input interface, and combines the monocular vision technology in the research. Moreover, in the research, this paper combines the human body model to construct an athlete’s human body model that adapts to monocular vision. At the same time, this paper combines the image processing technology to transform the image of the monocular visual athlete into a skeleton model, so as to realize the modeling of the athlete’s movement. In addition, this paper combines the model to explore the indoor and outdoor athlete recovery techniques and validates the model by experiment. The research shows that the research model has certain effects, which can meet the actual needs, and can provide theoretical reference for subsequent related research.

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

English part-of-speech intelligent recognition is the scientific and technological basis for the development of intelligent speech systems. The difficulty in the current English speech recognition system lies in the recognition of English parts of speech. In order to improve the effect of English part-of-speech recognition, this study builds the language rules and morphological models of English morphological forms based on machine learning algorithms. Moreover, this study proposes a stemming extraction algorithm and a syllable division algorithm based on English characteristic rules. By studying basic phrases in English, this study analyzes the compositional structure of phrases, and determines the basic phrase structure and composition rules of English such as noun, verb, and adjective. In addition, this research studies the basic English phrase recognition algorithm based on the rule method and the analysis of basic phrase ambiguity resolution. Finally, this study designs a control experiment to analyze the performance of the algorithm proposed in this paper model and confirm the classification algorithm. The research results show that the algorithm proposed in this paper has a certain practical effect.

Classroom student behavior recognition has important guiding significance for the development of distance education strategies. At present, the accuracy of students’ classroom behavior recognition algorithms has problems. In order to improve the effect of distance education student status analysis, this study combines the traditional clustering analysis algorithm and the random forest algorithm to improve the traditional algorithm and combines the human skeleton model to identify students’ classroom behavior in real time. Moreover, this research combines with the needs of students’ classroom behavior recognition to build a network topology model. The error rate of feature reconstruction using spatio-temporal features is lower than that of a single feature. Through experiments, this study verifies the effectiveness of the extracted spatial angle features based on the human skeleton model. The results of algorithm performance test show that the proposed algorithm network structure is superior to the network structure of single feature extraction algorithm.

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

The difficulty of knowledge point recommendation based on the learning diagnosis model lies in how to perform feature recognition and selection of recommended knowledge points. At present, the recommendation system has certain problems in the accuracy of recommended knowledge points. Based on this, this study mainly studies the personalized problem recommendation of middle school students in the field of education. Moreover, this study takes the answer records of students’ exercises as data, and combines the characteristics of the field of education to propose an exercise recommendation algorithm based on hidden knowledge points and an exercise recommendation method based on the decomposition of student exercise weight matrix. In addition, in order to verify the effectiveness of this research algorithm, this paper selects the accuracy rate and recall rate as evaluation indicators to analyze the recommendation results of this algorithm and the current more advanced CF algorithm, and the statistical experiment results are drawn into charts. The research results show that the method proposed in this paper has certain advantages and can be used as one of the subsystems of the learning system.

The intelligent evaluation of classroom teaching quality is one of the development directions of modern education. At present, some teaching quality evaluation models have accuracy problems, and the evaluation process is affected by a variety of interference factors, which leads to inaccurate model results, and it is impossible to find out the specific factors that affect teaching. In order to improve the accuracy of classroom teaching quality evaluation, this study improves RVM based on the method of feature extraction and empirical modal decomposition of ACLLMD method, and establishes classroom theoretical teaching quality evaluation model and experimental teaching quality evaluation model based on RVM algorithm. Moreover, this study uses test data to analyze the accuracy and reliability of the evaluation results to verify the feasibility and reliability of the new method. In addition, this study verifies the reliability of this algorithm by comparing with the manual scoring results. The research results show that RVM can be used to construct classroom theory teaching quality evaluation models and experimental teaching quality evaluation models with high accuracy and good reliability.

The traditional English examination and the current examination system have been unable to meet the needs of the education industry for English examinations. In view of this, based on the neural network algorithm, this study proposes a hierarchical network management model from the user’s perspective. Based on the in-depth study of the neural network, this study combined with the network performance characteristics of large data volume, complex data to propose a new BP neural network algorithm. By dynamically changing the momentum factor and learning rate, the algorithm has greatly improved the accuracy and stability of the error. In addition, this study proposes a user perception prediction model, and the model is continuously trained on the model based on the improved BP neural network algorithm and the monitored network performance. In order to study the performance of the research model, a control experiment is designed to analyze the performance of the model. The research results show that the intelligent model and algorithm proposed in this paper are completely feasible and effective.

The increasing complexity of the financial system has increased the uncertainty of the market, which has led to the complexity of the evolution of limited rational investor behavior decisions. Moreover, it also has a negative effect on the market and affects the development of the real economy and social stability. In view of the interconnected characteristics of various elements presented in financial complexity, based on complex network theory, Bayesian learning theory and social learning theory, this study systematically describes the behavioral decision-making mechanism of individual investors and institutional investors from the perspective of network learning. In addition, this study builds an evolutionary model of investor behavior based on Bayesian learning strategies. According to the results of the horizontal and vertical bidirectional studies simulated by experiments, we can see that the method proposed in this study has a certain effect on the evaluation and decision support of stock market investment.

Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.

In the era of the Internet of Things, smart logistics has become an important means to improve people’s life rhythm and quality of life. At present, some problems in logistics engineering have caused logistics efficiency to fail to meet people’s expected goals. Based on this, this paper proposes a logistics engineering optimization system based on machine learning and artificial intelligence technology. Moreover, based on the classifier chain and the combined classifier chain, this paper proposes an improved multi-label chain learning method for high-dimensional data. In addition, this study combines the actual needs of logistics transportation and the constraints of the logistics transportation process to use multi-objective optimization to optimize logistics engineering and output the optimal solution through an artificial intelligence model. In order to verify the effectiveness of the model, the performance of the method proposed in this paper is verified by designing a control experiment. The research results show that the logistics engineering optimization based on machine learning and artificial intelligence technology proposed in this paper has a certain practical effect.

Innovation and entrepreneurship are an important support for social and economic development in the new era, and it is also the key to the cultivation of practical talents in universities. In order to mine the effective information of innovation and entrepreneurship data, based on the neural network algorithm, this paper combines the bat algorithm to construct a data processing model to obtain an artificial intelligence innovation and entrepreneurship system with data analysis capabilities. Moreover, this study combines with actual needs to improve the algorithm, effectively eliminate the noise existing in the data, eliminate the interference of invalid data on the judgment ability of the system model, and choose the best denoising algorithm through comparison and verification of various algorithms. In order to verify the model proposed in this paper, the data is input into this research model by collecting data in a college survey, so as to verify and analyze the performance of the model. The research results show that the artificial intelligence system proposed in this paper has good performance and has certain practical value.

At present, there is a certain lag in the construction of the service platform of the smart home pension system in my country, which does not reflect the use characteristics of the elderly. In order to improve the reliability of the smart service system for the elderly, this research builds a smart home care service platform based on machine learning and wireless sensor networks around the state of the elderly’s home life, disease stage, physical state, and intellectual state. Moreover, after comparing the advantages and disadvantages of several wireless sensor communication network technologies and in-depth understanding of communication principles and network topology, the overall design of the system is proposed. In addition, this study combines the design requirements of the system to optimize and improve the wearable physiological parameter collection system and focuses on the design and implementation of the hardware and software of the physiological parameter collection module in the construction of the new system platform. Finally, this study analyzes the performance of the model in this study through controlled trials. The results of the study show that the platform constructed in this paper is effective.

The fundamental solution to the problems of college students’ employment is to encourage college students to start their own businesses. Only by using entrepreneurship to promote employment can the real solution of China’s higher education employment problems be truly solved. Aiming at the current situation of college students’ entrepreneurship and employment, this paper builds a model system suitable for college students’ employment and entrepreneurship forecast and guidance through artificial intelligence algorithms and fuzzy logic models. The diversity-enhanced employment recommendation system developed in this paper uses the MVC three-tier architecture. Moreover, the diversity-enhanced employment recommendation system designed in this paper provides two recommendation methods: individual diversity optimization and overall diversity optimization, which takes into account the relationship between students’ personal interests and employment work. In addition, the system uses the basic idea of user-based collaborative filtering. Finally, this paper designs a control experiment to analyze the performance of this research model. The research shows that the entrepreneurship employment forecast and guidance model constructed in this paper has a certain effect.

Government subsidies have an important impact on the development of high-interest technology companies and technological innovation. In order to study the relationship between government investment and the development of high-tech enterprises and technological innovation, based on artificial intelligence and fuzzy neural network, this paper builds an analysis model based on artificial intelligence and fuzzy neural network. According to the operation of each loop, this study designs a scheduling strategy that dynamically allocates network utilization according to the dynamic weight of the loop, and periodically changes the sampling period of the system, so that the system can not only run stably but also maximize the use of limited bandwidth. The network resource allocation module allocates the available network bandwidth of each control loop according to the dynamic weight of each loop, and the sampling period calculation module calculates a new sampling period based on the allocated network utilization rate. In addition, in this study, the performance of the model constructed in this paper is analyzed through empirical analysis. The results of the study show that the model constructed in this paper is effective.

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Aiming at the actual problems encountered in the specific poverty alleviation work, this article designs a management system specifically designed for poverty alleviation workers to solve poverty alleviation data sharing and online editing and uploading of poverty alleviation logs. Based on the neural network and network characteristics, a system model is constructed, and the application of structural disturbance theory in dynamic networks is studied. Moreover, in this study, the dynamic change information between time-series networks is taken into account for structural disturbances. By combining structural disturbances and local topology, a new similarity measurement method suitable for dynamic networks is proposed. In addition, this study proposes an algorithm based on evolutionary clustering and density clustering to detect the structure of dynamic communities. Finally, this study compares the proposed method with the classic method in the artificial network and the real network and analyzes the performance of the research model through data analysis. The research results show that the model constructed in this paper has good performance.

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

For industrial production, the traditional manual on-site monitoring method is far from meeting production needs, so it is imperative to establish a remote monitoring system for equipment. Based on machine learning algorithms, this paper combines artificial intelligence technology and Internet of Things technology to build an efficient, fast, and accurate industrial equipment monitoring system. Moreover, in view of the characteristics of the diverse types of equipment, scattered layout, and many parameters in the manufacturing equipment as well as the complexity of the high temperature, high pressure, and chemical environment in which the equipment is located, this study designs and implements a remote monitoring and data analysis system for industrial equipment based on the Internet of Things. In addition, based on the application scenarios of the actual aeronautical weather floating platform test platform, this study combines the platform prototype system to design and implement a set of strong real-time communication test platform based on the Windows operating system. The test results show that the industrial Internet of Things system based on machine learning and artificial intelligence technology constructed in this paper has certain practicality.

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.

The paper presents integration of Discrete Wavelet Cosine Transform technique and Bacterial Foraging Algorithm (BFO) for the development and optimization of speech coder. It is depicted how by filtering the limited number of high energy components of transformed coefficients with parallel programming can maintain the speech signal quality in coding over wide range of bit rates. The performance of existing and proposed speech coding techniqueattributes such as compression ratio, coding delay, computational complexity and quality of reconstructed speech is examined for multiple bit rates and compared with other existing speech coding techniques in Matlab environment. The result showsimprovement in performancewith respect to all attributes at the cost of increase in complexity.

DC-DC converters are widely used in many consumer electronic devices such as computers, medical equipment, battery chargers, cellular phones and many Industrial drives. These electronic devices require different voltage levels which is supplied from battery or some external supply. In multiple battery mission voltage decays as its stored energy is drained and requires large saving space. The switched DC-DC converters overcome these drawbacks and also regulate the output voltage for different power levels efficiently. This paper elaborates the structure of Luo converter with optimized PI controller. Positive Output Elementary Luo Converter (POELC) is designed for boost operation by choosing the appropriate duty cycle. The PI controller parameters are optimized using Cuckoo and Crow search algorithms. The proposed control methods are investigated for the transient and steady state region. The sensitivity of these controllers to supply load and line disturbances are also studied along with the servo response are presented. The controller incorporates a Luo converter is evaluated in terms of Integral Time Square Error (ITSE) and Integral Time Absolute Error. Dynamic modelling of the power converter is derived by using state space averaging method. The simulation model of the Luo converter with its control circuit is implemented in MATLAB/SIMULINK. Experimental result shows that Cuckoo PI controller has significantly performance improvement in comparison with both the conventional and Crow PI controller.

Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. This work can handle any number of sensitive attributes. The major security breaches are membership, identity and attribute disclosure. In this paper, a novel approach based on slicing that adheres to the principle of k-anonymity and l-diversity is introduced. The proposed work withstands all the privacy threats by the incorporation of k-means and cuckoo-search algorithm. The experimental results with respect to suppression ratio, execution time and information loss are satisfactory, when compared with the existing approaches.

It is difficult for the intelligent teaching system in colleges to effectively predict student grade, which makes it difficult to formulate follow-up teaching strategies. In order to improve the effect of student grade prediction, this study improves the neural network algorithm, combines support vector machines to build a student grade prediction model, and uses PCA to reduce the dimensionality of the sample data. The specific operation is realized by SPSS software. Moreover, this study removes redundant information inside the input vector and compresses multiple features into a few typical features as much as possible. In addition, the research set a control experiment to analyze the performance of the research model and compare the advantages and disadvantages of the classification prediction effect of traditional machine learning algorithms and neural network algorithms. Through experimental comparison, we can see that the model constructed in this paper has certain advantages in all aspects of parameter performance, and the prediction model proposed in this study has certain effects.

University legal education is of great significance to the personal development and social stability of college students. At present, there are certain problems in the traditional teaching system, which has led to inefficient university legal education. In order to improve the legal teaching effect of the university, based on machine learning and neural networks, this paper integrates and optimizes the original hardware and software and operation process, and further highlights the functions of interconnection and sharing, automatic sensing, real-time recording, interactive feedback, dynamic supervision, and intelligent analysis, which greatly facilitates the evaluation of teaching at all levels. In particular, this study uses big data technology to conduct an intelligent analysis of data completeness, multimedia application rate, system execution, and average test scores, and scientifically evaluates the implementation of basic-level education systems and the effectiveness of education, which can effectively solve the problems of quantitative formalization and qualitative subjectivity of current education evaluation from a technical level. In addition, this study designs a control experiment to analyze the system performance. The research results show that the model proposed in this paper has a certain effect.

Sports competition characteristics play an important role in judging the fairness of the game and improving the skills of the athletes. At present, the feature recognition of sports competition is affected by the environmental background, which causes problems in feature recognition. In order to improve the effect of feature recognition of sports competition, this study improves the TLD algorithm, and uses machine learning to build a feature recognition model of sports competition based on the improved TLD algorithm. Moreover, this study applies the TLD algorithm to the long-term pedestrian tracking of PTZ cameras. In view of the shortcomings of the TLD algorithm, this study improves the TLD algorithm. In addition, the improved TLD algorithm is experimentally analyzed on a standard data set, and the improved TLD algorithm is experimentally verified. Finally, the experimental results are visually represented by mathematical statistics methods. The research shows that the method proposed by this paper has certain effects.

English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.

English feature recognition has a certain influence on the development of English intelligent technology. In particular, the speech recognition technology has the problem of accuracy when performing English feature recognition. In order to improve the English feature recognition effect, this study takes the intelligent learning algorithm as the system algorithm and combines support vector machines to construct an English feature recognition system and uses linear classifiers and nonlinear classifiers to complete the relevant work of subjective recognition. Moreover, spectral subtraction is introduced in the front end of feature extraction, and the spectral amplitude of the noise-free signal is subtracted from the spectral amplitude of the noise to obtain the spectral amplitude of the pure signal. By taking advantage of the insensitivity of speech to the phase, the phase angle information before spectral subtraction is directly used to reconstruct the signal after spectral subtraction to obtain the denoised speech. In addition, this study uses a nonlinear power function that simulates the hearing characteristics of the human ear to extract the features of the denoised speech signal and combines the English features to expand the recognition. Finally, this study analyzes the performance of the algorithm proposed in this study through comparative experiments. The research results show that the algorithm in this paper has a certain effect.

The results of data mining can be used to predict the physical health status of sports athletes and college sports students and provide physical fitness warnings, so that students can pay attention to physical health status and adjust their physical exercise status. Discrete Morse theory, as a powerful optimization theory, plays a big role in algorithm optimization. This paper combines data mining and discrete Morse theory to propose a grid clustering algorithm based on discrete Morse theory. Moreover, according to the theorem that the cell complex reaches the optimum when it has the smallest possible critical point, this study applies the concept of critical points in the discrete Morse theory to optimize the grid clustering process to obtain clustering results. In addition, this study uses the improved C4.5 algorithm to analyze the physical fitness assessment results and obtains a valuable analysis of the physical fitness assessment results.

Text-to-voice conversion is the core technology of intelligent translation system and intelligent teaching system, which is of great significance to English teaching and expansion. However, there are certain problems with the characteristics of factors in the current text-to- voice conversion. In order to improve the efficiency of text-to- voice conversion, this study improves the traditional machine learning algorithm and proposes an improved model that combines statistical language, factor analysis, and support vector machines. Moreover, the model is constructed as a training module and a testing module. The model combines statistical methods and rule methods in a unified framework to make full use of English language features to achieve automatic conversion of letter strings and phonetic features. In addition, in order to meet the needs of English text-to- voice conversion, this study builds a framework model, this study analyzes the performance of the model, and designs a control experiment to compare the performance of the model. The research results show that the method proposed in this paper has a certain effect.

The eco-economic activity modeling is an effective method to analyze the eco-economic system. From the existing models, it can be seen that the disadvantages of eco-economic activity modeling are that the model evaluation accuracy is not high, and the system stability is poor. In order to improve the evaluation effect of the ecological economic activity, based on the machine learning algorithm, this study establishes a PNN evaluation model based on the probabilistic neural network classification principle. Moreover, in this study, a certain number of learning samples are generated by random interpolation of evaluation index standards, and then Matlab software is used to simulate the training and test of the model, and the feasibility and effectiveness of the model are verified by statistical indicators. In addition, this study combines the actual case to analyze the performance of the model and analyze the test results by statistical analysis methods. The research results show that the model proposed in this study has certain effects and high stability.

At present, experts and scholars have conducted more research on the ability of colleges and universities to transform scientific and technological achievements. However, they pay more attention to the holistic research on the transformation of scientific and technological achievements in colleges and universities across the country, while rarely divide the research objects in detail. In order to improve the evaluation effect of scientific and technological achievements in colleges and universities, this paper builds a university science and technology achievement evaluation system based on machine learning and image feature retrieval on the basis of analyzing the needs of high-tech achievement evaluation. The system has certain flexibility. Moreover, this study selects the appropriate network architecture based on the actual data and mission objectives of the high-tech achievement evaluation. In addition, this paper proposes a FT-GRU model of a gated recurrent unit network incorporating N nearest neighbor text, and a more stable model structure is obtained through system optimization. Finally, this study designs experiments to verify the performance of the model. The research results show that the university science and technology achievement evaluation system based on machine learning and image feature retrieval constructed in this study meets the expected goals and has certain practical significance.

There is a certain subjectivity in the teaching evaluation process, which leads to a low accuracy of the intelligent scoring system. In order to promote the intelligent development of teaching evaluation, based on machine learning, this study briefly introduces the background and current status of teaching evaluation, and describes in detail the relevant algorithm principles of data analysis and modeling using data mining technology and machine learning methods. Moreover, this study describes the establishment process of the traditional classroom teaching evaluation system and uses the classification algorithm in machine learning in the construction of evaluation models to further improve the scientificity and feasibility of teaching evaluation. In addition, in this study, empirical algorithm is used as the basic algorithm to evaluate teaching quality, and the topic word distribution obtained by joint model training is used as the original knowledge. Finally, this research analyzes the performance of this research system through a control experiment. The research results show that the scores of the research model are close to the standard manual scores and can provide a theoretical reference for subsequent related research.

Online classroom teaching is difficult to identify students’ learning status in real time. Therefore, we need to combine intelligent image recognition technology to analyze student status through eye movement features. This study solves the problem of inaccurate positioning of the initial position of the shape model in the process of eyelid matching through machine learning. Moreover, this study improves the algorithm and uses the AK-EYE model based on the combination of ASM algorithm and Kalman filtering to establish a local feature model for each feature point. According to the gray information in the normal direction of the feature point, the local gray information is modeled. After training through the sample set to obtain the state model, the target eye can be searched, and the pose parameters can be determined. Finally, this study designs a control experiment to analyze the performance of the model proposed in this study. The research shows that the algorithm proposed in this paper has a high recognition accuracy and has a practical basis, which can be used as one of the subsequent classroom teaching system algorithms.

Agricultural IoT technology realizes the technology of precise, intelligent, and scientific management of agricultural production. Accurate perception and efficient transmission of farmland data is the basis for precision and smart agriculture. Based on the consideration of WSN distributed monitoring sensor nodes, this paper designs a multi-core sensing agricultural Internet of Things monitoring system based on the low efficiency of existing single-core computing and the inability to adapt to massive sensing data node operations. Multi-core data fusion was simulated and analyzed. Firstly, a method for constructing key value subspaces based on logical landmarks is proposed. The node set maintained by the subspace adds local physical location features to coordinate node discovery and routing. Compared with the traditional key value space, the subspace has a higher system priority, which makes the route local priority, thus realizing traffic localization. The simulation results show that the distributed agricultural network data aggregation algorithm based on multi-core perception can significantly reduce the energy consumption of sensor nodes in WSN, prolong the service life of WSN, and greatly improve the computational efficiency and data accuracy.

In order to meet the rapid growth of educational data, to automate the processing of educational data business, improve operational efficiency and scientific decision-making, a statistical analysis platform for educational data is designed, and Hadoop-based education is designed from the conceptual model, logical model, and physical model. Data warehouse; designed and researched the storage of educational multidimensional data model; and then compared and tested the query efficiency and storage space of HBase and Hive in the Hadoop ecosystem based on educational big data, and used HBase+Hive integrated architecture to complete the education data The statistical analysis tasks and the function of the educational data statistical analysis platform are transplanted to the educational big data platform based on Hadoop; the performance test of the conversion efficiency of educational big data in the ETL link is performed, which illustrates the effectiveness of the educational big data platform based on Hadoop. An object-oriented analysis and design method used to analyze and design the business requirements of teaching resource sharing services. From the perspective of managers and teachers, use case diagrams and use case description tables to define system business requirements. The role of teachers is further refined as the theme of teaching and research. Participants, participants in the subject teaching and research, initiators of simulation teaching research and development, participants, famous teachers, high-quality course judges and experts. The recording, accumulation, statistics and analysis of students’ learning behaviors will provide more valuable applications for school education.

Medical image recognition is affected by characteristics such as blur and noise, which cause medical image features that cannot be effectively identified and directly affects clinical diagnostics. In order to improve the diagnostic effect of medical MR image features, based on the FRFCM clustering segmentation method, this study combines the medical MR image feature reality, collects data for traditional clustering method analysis, and sorts out the shortcomings of traditional clustering methods. Simultaneously, this study improves the traditional clustering method by combining medical image feature diagnosis requirements. In addition, this study carried out image data processing through simulation, and designed comparative experiments to analyze the performance of the algorithm. The research shows that the FRFCM combined with the intuitionistic fuzzy set proposed in this paper has greatly improved the noise immunity and segmentation performance compared with the FCM based fuzzy set.

First, the recommendation system and its advantages are introduced in detail, and based on the characteristics of the intelligent topic logical interest set resource and user behavior in the existing intelligent topic logical interest set resource platform, a personalized fuzzy logic model of the intelligent topic logical interest set resource is established and adapted to it. The personalized fuzzy logic user personalized fuzzy logic interest model of personalized fuzzy logic is designed, and the user personalized fuzzy logic interest transfer method is designed to simulate the user learning process. Secondly, on the basis of the established model, according to the idea of collaborative filtering, the personalized fuzzy logic user’s personalized fuzzy logic interest value and the user’s rating of resources are respectively predicted, and the two prediction results are combined to recommend resources to the user. Finally, the ontology is applied to user interest description, and a method based on personalized fuzzy logic user rough interest vector and nearest neighbor concept aggregation is proposed to find fine-grained user interest and recommend interest resources. Experimental tests show that this method can better describe the composition and development of user interests, making the recommendation effect of interest resources for specific users more accurate and reliable. The problem of collaborative recommendation in personalized fuzzy logic systems is further studied, the basic principles and typical technologies of collaborative recommendation are analyzed, and the collaborative recommendation method based on users with similar interests and the collaborative recommendation method based on weighted association rules are proposed.

The optical fiber network has the characteristics of providing users with wider bandwidth and supporting the changing needs of more users. At the same time, the optical fiber network can also reduce the network infrastructure investment. The traditional Ad Hoc On-demand Distance Vector (AODV) algorithm does not consider the impact of node movement on the network, and the link disconnection frequently occurs during the routing process. The ant colony algorithm based on swarm intelligence only considers the unique factor of pheromone concentration to find the optimal path through multiple iterations, which will increase the complexity of the algorithm and affect the route establishment delay. In response to the above problems, this paper proposes a routing algorithm based on fuzzy logic. The algorithm can comprehensively consider the three factors of node location, mobility and signal strength, and greatly reduces the complexity of the algorithm. This paper gives a detailed definition of profust reliability of the Ethernet Passive Optical Network (EPON) system for distribution network communication and obtains the profust reliability parameters based on Monte Carlo simulation. After that, the reliability of profust under different networking modes was simulated, and the influence of network scale, component failure rate, component repair time and other parameters on the reliability of EPON networking profust was analyzed. The fuzzy probist reliability analysis method uses analytical methods, which are commonly used to deal with end-to-end reliability analysis problems and has certain limitations. Profust reliability analysis treats the system as a whole, which is more suitable for the reliability of complex end-to-multi-end systems.

With the rapid development of science and technology, positioning technology has been widely used in people’s daily lives and related scientific research activities. However, the traditional positioning system mostly uses GPS for positioning, and then transmits the positioning information to the remote server through GPRS / GSM, but it is not applicable in remote mountain areas where some base station signals cannot reach. Moreover, the accuracy of single GPS positioning is difficult to be guaranteed. This paper mainly studies the design of Beidou-GPS dual-mode positioning system based on Android platform mobile communication equipment. First, analyse the composition of the satellite positioning system and design the overall architecture of the Beidou-GPS dual-mode positioning system for mobile communication equipment. Then, it analyses the most important star selection algorithm in dual-mode positioning technology, and proposes an improved star selection algorithm based on azimuth. Second, build the overall architecture of the Android platform for mobile communication devices based on dual-mode positioning. Finally, by comparing with the traditional star selection algorithm, the proposed improved positioning algorithm is experimentally verified. Simulation experiment results show that the proposed dual-mode positioning algorithm has high accuracy and can meet the real-time requirements of the system.

Fuzzy knowledge graph system is a semantic network that reveals the relationships between entities, and a tool or methodology that can formally describe things in the real world and their relationships. Smart education is an educational concept or model that uses advanced information technology to build a smart environment, integrates theory and practice to build an educational framework for information age, and provides paths to practice it. Artificial intelligence (AI) is a comprehensive discipline developed by the interpenetration of computer science, cybernetics, information theory, linguistics, neurophysiology and other disciplines, which is a direction for the development of information technology in the future. On the basis of summarizing and analyzing of previous research works, this paper expounded the research status and significance of AI technology, elaborated the development background, current status and future challenges of the construction and application of fuzzy knowledge graph system for smart education, introduced the methods and principles of data acquisition methods and digitalized apprenticeship, realized the process design, information extraction, entity recognition and relationship mining of smart education, constructed a systematic framework for fuzzy knowledge graph, and analyzed the high-quality resources sharing and personalized service of AI-assisted smart education, discussed automatic knowledge acquisition and fusion of fuzzy knowledge graph, performed co-occurrence relationship analysis, and finally conducted application case analysis. The results show that the smart education knowledge graph for AI-assisted smart education can integrate teaching experience and domain knowledge of discipline experts, enhance explainable and robust machine intelligence for AI-assisted smart education, and provide data-driven and knowledge-driven information processing methods; it can also discover the analysis hotspots and main content of research objects through clustering of high-frequency topic words, reveal the corresponding research structure in depth, and then systematically explore its research dimensions, subject background and theoretical basis.

In this paper, through the edge computing application path, the educational evaluation system was optimized using the adaptive entropy theory polymerization method which based on applying the path. By adding multiple constraints to filter out nodes and educational evaluation edges that do not meet the requirements, the improved algorithm is used to optimize the redundant paths to avoid loops and node detour problem. To improve the accuracy of education evaluation and evaluation, ensure the load balance in the domain, and solve the problems of single evaluation attribute and high overlap of education evaluation paths. This paper proposes a multi-attribute education evaluation model that refines the evaluation attributes of education evaluation and uses analytic hierarchy process perform weight distribution. The algorithm can improve the accuracy of the evaluation of the education evaluation system while ensuring the computational efficiency, and can ensure the load balance within the domain, and improve the network survival time.

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.

The current application of intelligent algorithms has achieved certain applications in smart medical, but its application in the automatic grading of admitted patients is in a blank, which makes it difficult to allocate hospital resources effectively. In order to improve the efficiency and accuracy of automatic classification of patients admitted to hospital, this study builds the corresponding genetic algorithm operator based on genetic algorithm. At the same time, this paper uses the random method to generate the initial population and uses the inversion mutation operator to perform the mutation operation. In addition, this article combines image processing to automatically classify patient types and patient levels. Finally, this paper uses the data collection method to verify the model and input the data into the research model. The research shows that the model proposed in this paper has certain effects, which can realize the automatic grading of patients admitted, and can provide theoretical reference for subsequent related research.

Wearable-devices have developed rapidly. Meanwhile, the security and privacy protection of user data has also occurred frequently. Aiming at the process of privacy protection of wearable-device data release, based on the conventional V-MDAV algorithm, this paper proposes a WSV-MDAV micro accumulation method based on weight W and susceptible attribute value sensitivity parameter S and introduces differential-privacy after micro accumulation operating. By simulating the Starlog dataset and the Adult dataset, the results show that, compared with the conventional multi-variable variable-length algorithm, the privacy protection method proposed in this paper has improved the privacy protection level of related devices, and the information distortion has been properly resolved. The construction of the release model can prevent susceptible data with identity tags from being tampered with, stolen, and leaked by criminals. It can avoid causing great spiritual and property losses to individuals, and avoid harming public safety caused by information leakage.

The unintentional electromagnetic (EM) emission of computer monitors may cause the leakage of image information displayed on the monitor. Detection of EM information leakage risk is significant for the information security of the monitor. The traditional detection method is to verify EM information leakage by reconstructing an image from EM emission. The detection method based on image reconstruction has limitations: adequate signal sampling rate, accurate synchronization signal, and dependence on operational experience. In this paper, we analyze the principle of image information leakage and propose an innovative detection method based on Convolutional Neural Network (CNN). This method can identify the image information in EM emission to verify the EM information leakage risk of the monitor. It overcomes the limitations of the traditional method with machine learning. This is a new attempt in the field of EM information leakage detection. Experimental results show that it is more adaptable and reliable in complex detection environment.

With the wide application of electric drive equipment and variable frequency load, the power supply system based on the DC grid has attracted much attention because of its high energy density and simple control, and the operation mode of rectifier AC synchronous generators operating in parallel is often adopted. The available topology structures of the rectifier permanent magnet (PM) generator sets are analyzed in this paper, the parallel operation principle of uncontrolled rectifier PM generator sets is analyzed in theory. The parallel operation characteristics of the generator sets are summarized when the voltage-stabilizing and power balanced measures are not taken, and the influence factors of power balancing among parallel operation generators are analyzed. The power-balanced method of rectifier generator sets operating in parallel based on a master-slave control strategy is proposed, which can realize power balanced with the closed-loop control of the DC side output current. The simulation and experiment results show that the proposed method can realize the power balanced control of rectifier generator sets operating in parallel well. The output power of each generator set can be distributed according to capacity. The rationalization proposal of how to matching generators’ parameters in the power supply system of rectifier PM generator sets operating in parallel is given.

The tailings dam safety monitoring system is a system that plays an important role in disaster prevention and reduction. This paper divides the whole network of mine dam safety monitoring systems into two parts, that is, the basic wireless network and GPRS network from the gateway to the monitoring center. First, it’s the hardware of the design module, the network is divided into uplink communication and downlink communication, uplink communication is to upload the dam data collected by the terminal node to the gateway through the network. Downlink communication is the instruction sent by the gateway to the monitoring center, send to terminal nodes through data conversion between protocols. Secondly, the gateway also needs to solve the data conversion between the network and GPRS network protocol, so that the entire security monitoring system can communicate accurately. The communication between gateway and monitoring center is realized through the GPRS network, this requires adding the GPRS communication module to the gateway module. To increase the diversity of communications, communication between GSM short message communication methods and monitoring centers has also increased. The experimental results prove that the system in this paper can be applied to the mine dam safety monitoring system, meet the design requirements.

With the continuous implementation of the Belt and Road Initiative of China, cross-border e-commerce is gradually becoming one of the main ways of trade. The smart operation of cross-border logistics has become an important factor affecting the quality of cross-border e-commerce transactions by its characteristics of high-efficiency, high-quality, and low-cost. As China’s first smart city in China with the theme of the aviation economy, the top priority for developing cross-border e-commerce in the Zhengzhou Airport Economic Zone is to construct cross-border e-commerce smart logistics. This paper expounds on the significance of applying big data technology on the construction of the intelligent logistics, analyzing its important roles not only in further promoting the cross-border e-commerce development in the Zhengzhou Airport Economy Zone but also during the process of the entire national economic transformation and escalation. The smart logistics constructing strategy in the Zhengzhou Airport Economy Zone is expected to provide ideas and support for the Zhengzhou Airport Economy Zone making continuous improvement in leading and pushing the Henan regional economy to achieve sustainable development in the future.

The actuator is an important component of missiles and other aircraft to maintain the flight attitude. A method to calculate the power of the electric servo motor was proposed according to the load characteristics’ of both the servo motor and actuator. An optimization method for the transmission reduction ratio was obtained by considering the load torque equation. Dynamics simulations of the actuator were conducted under a variety of conditions. The simulation results show that the clearance and the friction between the ball screw and the fork, which consist of the transmission mechanism, induce the torque fluctuations, as a source of noise in the motor load. According to the optimization design of the Electric, the Actuator prototype has passed the test, the performance meets the design requirements.

In product sales with network externalities, a Stackelberg game model is established with a new product and an existing product in the market, investigating the influence of first-mover strategy and non-strategic pricing model on the pricing, market share, and profit of an enterprise. Furthermore, the influence of network externalities and transfer costs on the strategies of latecomers is studied. Finally, the market equilibrium is analyzed. The results show that under the strategic pricing, the first entrant grabs the market share with low price and low profit in the first stage, to obtain greater network externalities in the second stage, enhance the competitiveness with latecomer, and make the total revenue greater. Given network externalities and transfer costs, the strategic behavior of the first entrant makes it harder for the later entrant to enter the market.

Smart home products and equipment are relatively expensive while using specific physical objects to prove functional characteristics, the cost is high, and it is difficult to meet the personal needs of customers. Based on the above background, the purpose of this research is the application and design of a smart home R&D system based on virtual reality. This study proposes the concept of introducing virtual reality methods into the control scene given the shortcomings of the existing smart home control interface interaction methods. From the perspective of being more suitable for the user’s needs, the virtual reality method is used to optimize the smart home interaction methods. Through the analysis of the user’s lifestyle and needs, the functional module model of applying virtual reality to the smart home control scheme is established. Then, by collecting data, use Sketchup software to build and optimize the model of the simulation system to build a realistic family scene model. Finally, through the integrated use of the Unity 3D rendering engine and the virtual simulation system technology, the intelligent simulation of the interior functions of the house is realized. Experimental results show that using virtual reality to optimize the interaction of smart homes, the control method is relatively simple, and the cost can be reduced by about 20%.

With the development of science and technology, the intelligent robot has become an important tool in our production and life. It not only improves people’s living standards but also promotes economic development. At present, the related technology in the field of the intelligent robot has been developed rapidly, but at the same time, many technical problems have been exposed. The single path planning problem can be well solved, but the dynamic path planning of a robot is one of the current technical difficulties. At present, the genetic algorithm is the mainstream scheme, but its control accuracy is still lacking in practical application. To solve this problem, this paper proposes a dynamic path planning scheme for intelligent robots based on a fuzzy neural network. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of existing technologies. The second part is the research of related basic theory, which deeply studies the core theory of intelligent robot and dynamic path planning, which provides a theoretical basis for the later model implementation. The third part is the design and implementation of dynamic path planning based on a fuzzy neural network. This paper gives the design principle and specific improvement method in detail. At the end of the paper, that is, the fourth part, through comparative analysis experiments, further proves the superiority of the fuzzy neural network algorithm. Compared with the traditional particle swarm optimization algorithm, it can significantly improve the control accuracy and robustness of the model.

Action is the key to sports and the core factor of standardization, quantification, and comprehensive evaluation. However, in the actual competition training, the occurrence of sports activities is often fleeting, and it is difficult for human eyes to identify quickly and accurately. There are many existing quantitative analysis methods of sports movements, but because there are many complex factors in the actual scene, the effect is not ideal. How to improve the accuracy of the model is the key to current research, but also the core problem to be solved. To solve this problem, this paper puts forward an intelligent system of sports movement quantitative analysis based on deep learning method. The method in this paper is firstly to construct the fuzzy theory human body feature method, through which the influencing factors in the quantitative analysis of movement can be distinguished, and the effective classification can be carried out to eliminate irrelevantly and simplify the core elements. Through the method of human body characteristics based on fuzzy theory, an intelligent system of deep learning quantitative analysis is established, which optimizes the algorithm and combines many modern technologies including DBN architecture. Finally, the accuracy of the method is improved by sports action detection, figure contour extraction, DBN architecture setting, and normalized sports action recognition and quantification. To verify the effect of this model, this paper established a performance comparison experiment based on the traditional method and this method. The experimental results show that compared with the traditional three methods, the accuracy of the in-depth learning sports movement quantitative analysis method in this paper has greatly improved and its performance is better.

The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.

In order to explore the impact of the system-driven supply chain, collaborative operations, and organizational characteristics on supply chain operational performance, this paper based on the system dynamics method to simulate the established information collaborative supply chain model, analyze market demand data, inventory before and after the supply chain sharing The changes of inventory fluctuations in the supply chain and related calculations are compared with the simulation results under the current model to prove the importance of implementing information collaboration in the supply chain of a large retailer-led supply chain. The research in this paper shows that with the supply chain information collaboration model, the average value of the manufacturer’s order quantity has dropped by 30.4%. Affected by this, the dispersion coefficient has also dropped from 0.76 to 0.6, and the average number of orders in the distribution center has also dropped by 12.2%; With the supply chain information synergy model, the average value of the raw material inventory of manufacturers has dropped significantly, from 3400 in the current model to 2500 in the information synergy model, a decrease of 27%, the standard deviation has also decreased by 57%, and the dispersion coefficient has dropped from 0.98 to 0.50; The standard deviation rate of the inventory of the distribution center is 30%; from the perspective of the overall retail supply chain, the inventory has fallen by 14%, the standard deviation has fallen by 34%, and the dispersion coefficient has dropped from 0.76 in the current model to the information collaboration model. 0.6, it can be seen that the mode of supply chain information coordination has a great effect on reducing supply chain costs and improving supply chain efficiency.

The paper first analyzes the correlation between text sentiment values and personality traits, proves that text sentiment can have a good support effect on user personality prediction, then on this basis, a method based on CNN-LSTM is proposed, which can be used to deeply analyze the sentiment analysis capability of the model, hoping to improve the precision of sentiment classification and lay a solid foundation for the next experiment. This experiment proves that the CNN-LSTM constructed in this paper can better predict the emotional tendency of the short text of microblog, has good generalization ability, and has higher precision than other methods.

At present, the majority of sports games video adopts MPEG image technology, and MPEG video compression is the current more mainstream approach. After compression, the quality of the video will decline, and other practical problems. However, the existing detection methods of sports video scene conversion, when dealing with MPEG compressed video, are not ideal, often appear the phenomenon of missing detection and wrong detection. In order to solve this problem, this paper proposes a detection method of sports scene conversion on MPEG compressed video based on fuzzy logic. Introducing fuzzy logic into the detection method of video scene conversion is the highlight of this method. Firstly, this paper preprocessed the video image according to the Convention. In this paper, the recognition of image features and specific extraction methods are introduced in detail, and the extraction algorithm of image color features is further optimized. For the design of the detection method, the main innovation is to fully combine the fuzzy logic and macroblock information. In the existing detection methods, different detection schemes are given for the abrupt change of video scene and the gradual change of scene. Finally, in order to verify the actual effect of the detection method in this paper, an experimental analysis based on the keyframe complexity detection method is established. After a number of experiments including the experimental results of scene transition, analysis, and processing time, through the analysis of data, a step-by-step proof of this method has good accuracy and recall.

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

This research mainly discusses the characteristics of BIM architecture design and its application in traditional residential design from the perspective of smart cities. Given the topics that people are more concerned about, this research mainly uses BIM modeling technology to initially build a virtualized building model. It discusses the convenience of intelligent automation technology in terms of resource consumption and house security. In terms of safety, different levels of wind blowing strength are mainly used to measure the distance moved by the house to evaluate the safety factor. Divide the wind blowing intensity into A, B, C, D, E, F, and 6 levels to test the strength of the house. When the wind intensity level is F, the safety factor is the weakest, which is 20%. When conducting a house consumption test, directly measure the house’s electricity consumption within a specified time to conduct a resource rate consumption test. Divide the time period into 1 h, 2 h, 3 h, 4 h, 5 h, 6 h, 6 different time periods to measure power consumption. The resource consumption rate reaches a maximum value of 96% when the length of time is 6 h. The experimental results show that the safety characteristics of BIM technology are the weakest when the wind strength level is F, and the safety performance is different when the wind strength level is different. In terms of resource consumption, the resource consumption rate reaches the maximum value when the time is 6 h, and the length of time directly determines the housing resource consumption rate. From the perspective of a smart city, BIM building design has the advantages of low resource consumption and high safety factor.

Nowadays, with the development of science and technology, the progress of society, and the fierce competition among enterprises in the market, the current market competition has gradually turned into the competition of talents, and the excellent talent reserve of enterprises is a competitive advantage. However, there are many enterprises and many places where human resource management is not in place. At the same time, many imperceptible problems in human resource management, most of which are hidden and uncertain, lead to business problems and related phenomena and threaten the further development of enterprises. Although there are many research methods for these problems, it is difficult to analyze the current situation with this method because of its strong subjectivity. In order to better solve the above problems, this paper studies the standard system of human resource management under the background of the fuzzy system and uses the new structure of human resource fuzzy theory decision-making which has strong theoretical and practical value in human resource system. In the research of this paper, human resource management indicators are divided into comprehensive and professional. Aiming at these two categories of indicators, this paper uses human resource management theory to analyze them systematically and designs a more reasonable indicator system. Then, taking an enterprise as an example, it uses a fuzzy comprehensive evaluation method to combine qualitative and quantitative research to analyze the enterprise. In the analysis, this paper finds that there are some problems in human resource management, such as performance management is not in place, employees’ sense of belonging is not strong, and through the fuzzy comprehensive evaluation of the enterprise situation, it is found that the enterprise human resource management system is good, but still needs to further improve the enterprise management system.

Ensuring the stable and safe operation of the power system is an important work of the national power grid companies. The power grid company has established a special power inspection department to troubleshoot transmission line components and replace faulty components in a timely manner. At present, assisted manual inspection by drone inspection has become a trend of power line inspection. Automatically identifying component failures from images of UAV aerial transmission lines is a cutting-edge cross-cutting issue. Based on the above problems, the purpose of this article is to study the component identification and defect detection of transmission lines based on deep learning. This paper expands the dataset by adjusting the size of the convolution kernel of the CNN model and the rotation transformation of the image. The experimental results show that both methods can effectively improve the effectiveness and reliability of component identification and defect detection in transmission line inspection. The recognition and classification experiments were performed using the images collected by the drone. The experimental results show that the effectiveness and reliability of the deep learning method in the identification and defect detection of high-voltage transmission line components are very high. Faster R-CNN performs component identification and defect detection. The detection can reach a recognition speed of nearly 0.17 s per sheet, the recognition rate of the pressure-equalizing ring can reach 96.8%, and the mAP can reach 93.72%.

With the vigorous promotion of the construction of smart campus by the ministry of education, the development concept of smart campus will have broad application prospects. However, colleges and universities are still at the stage of digital campus and there are many problems left. It is difficult to complete the transition from digital campus to smart campus. The main problem is that the campus data has only been digitized but not informational. The purpose of this article is to study a smart campus management system based on the Internet of Things technology. This research uses the unified data collection source of face recognition terminal hardware products based on the Internet of Things technology, unified management in the background of the system, and calculates and analyzes the data to obtain valuable campus big data. This study designed and implemented a complete smart campus management system by analyzing the system design principles and design goals. This system is mainly divided into the face recognition terminal hardware and smart campus software system based on the Internet of Things. By analyzing the data generated by students and faculty and staff, it can provide a reference for campus managers to improve management quality, and help teachers and students to formulate more efficient learning and teaching and research plans. This article tests the practicability of the system and obtains the user’s satisfaction as 8.0.

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.

Aiming at the problem of low accuracy of the current English interpretation teaching quality evaluation, a teaching quality evaluation method based on a genetic algorithm (GA) optimized RBF neural network is proposed. First, the principal component analysis is used to select the teaching quality evaluation index, and then design The RBF neural network teaching evaluation model is used, and GA is used to optimize the initial weights of the RBF neural network. Experimental results show that this method can effectively evaluate the quality of English interpretation teaching, and has high accuracy and real-time performance.

Wearable robots must adjust the assist mode/intensity according to human motion during the motion assistance process. By decoding the surface electromyography (sEMG) signal, the standard deviation of the fractal dimension is used as a characteristic index of muscle contraction-relaxation ability, and explore the feasibility of using the standard deviation of the fractal dimension to estimate the human motor function and thus provide a basis for decision-making for the flexible control of wearable robots. First, the sEMG signals of several subjects with different motor functions were collected and their time-domain and frequency-domain features were extracted. The experimental results for one hour of walking showed that the time-domain and frequency-domain feature values increased with muscle fatigue. The trend has little to do with the inherent motor function of the human body; Second, due to the strong nonlinearity, time-varying, and strong complexity of the sEMG signal, the fractal dimension nonlinear method is used to characterize the complexity of the EMG signal that is closely related to muscle function. Besides, theoretical and experimental studies have been conducted to clarify the feasibility of the complexity of fractal dimension representation and to provide theoretical support for the further use of the standard deviation of fractal dimension to estimate human motor function. The experimental results of continuous walking for one hour show that, macroscopically, the fractal dimension of each muscle of the individual subject does not change significantly with walking time, which shows that the fractal dimension has nothing to do with exercise time and muscle fatigue; On the microscopic level, the value of the fractal dimension changes when the subject’s muscles contract and relax. Subjects with strong motor function have smaller fractal dimensions when their muscles contract than subjects with weaker motor function, and the opposite happens when their muscles relax, and it can be seen that there is a positive correlation between the difference in the fractal dimension during muscle contraction and relaxation and the muscle contraction-relaxation ability and the human body’s inherent motor function. The test results verify the feasibility of using the standard deviation of fractal dimension to estimate the intrinsic motor function of the human body.

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219219.

To improve the effectiveness and intelligence of university teaching management evaluation, the particle swarm optimization BP neural network algorithm is applied to the analysis of university teaching management evaluation data. BP neural network is used to model the evaluation index of teaching management, and then particle swarm optimization is used to optimize the weight and threshold of the neural network transfer function to ensure that the output of the BP neural network can obtain the global optimal solution. The experimental results show that the proposed algorithm has a good fit between the predicted value and the actual value of the evaluation object of teaching management in Colleges and universities, and has a strong promotion value.

Taking the rapid development of electronic science and technology as an opportunity, MCU which undertakes the equipment control task is developing towards the direction of intelligence, self-learning and multi-function integration. They have been applied to all aspects of human production and life. Driven by computer network technology, the development of Internet of Things technology is promoted. In this era, only by strengthening the research and development and improvement of MCU control system can we promote the development of the entire society and economy. This article mainly studies the application of MCU Technology in IoT electronics. This article first briefly explains the definition of MCU, and then summarizes the entire development process of MCU. On this basis, it is effectively combined with the actual situation, and puts forward the practical application of the MCU Technology in the Internet of Things electronic products. On the basis of ensuring that the personalized needs of modern people are met, it can lay a good foundation for the future development of electronic products. The research experiments in this paper found that up to 70 meters, and found that a large number of packet loss has affected the basic communication. It is believed that communication can be performed at 70 meters but the communication quality is poor. It is not recommended to use, and the test is terminated. It can be seen from the results that the communication distance of the terminal node is finally within 30 meters, which can ensure that the data is almost 100% received. The packet loss rate within 60 meters is within 2%, and the communication quality is good. Guarantee basic communication functions.

The problems and disadvantages of the traditional teaching mode of Taekwondo in colleges and universities are obvious, which is not conducive to cultivating the interest of contemporary college students in learning Taekwondo. In order to improve the teaching effect of Taekwondo, based on the intelligent algorithm of human body feature recognition, this study uses support vector machine to construct a Taekwondo teaching effect evaluation model based on artificial intelligence algorithm. The model corrects the movement of the students by recognizing the movement characteristics of the students’ Taekwondo and can conduct the movement guidance and exercises through the simulation method. In order to verify the performance of the model in this study, this study set up control experiments and mathematical statistical methods to verify the performance of the model. The research results show that the model proposed in this paper has a certain effect and can be applied to teaching practice

The teaching of linguistics is limited by the influence of various factors, which leads to poor teaching effect, and the teaching process is difficult to evaluate. In order to improve the efficiency of linguistics teaching, this paper uses improved machine learning algorithms to construct a linguistics artificial intelligence teaching model. According to the teaching needs of linguistics, the efficiency of the teaching process is improved, and the teaching evaluation is performed, and the root cause analysis algorithm based on MCTS is optimized. Moreover, according to the frequent item set algorithm in data mining, a layered pruning strategy is proposed to further reduce the search space and improve the efficiency of the model. In addition, this study combines with the comparative teaching experiment to study the efficiency of artificial intelligence models in linguistics teaching. The statistical results show that the model proposed in this paper has a certain effect.

“College English teaching guide” puts in forth unique challenges and needs for College teaching skills of English. It is pressing to cultivate innovative talents with quality writing in English. Teaching in English, as a subject to check the mastery of students’ English knowledge. The successful instructional project of English writing is an assurance and support smooth growth of English writing. It can make education get double the efforts, and allow the students development.’ writing ability y, but in fact, the current situation of structural design of writing English in college is not optimistic. With the rise of “Web in addition to”, varying backgrounds have experienced emotional changes. “Web in addition to training” has become a pattern of advancement, which has brought new chances and difficulties for the educating and learning of College English composition. This research first describes the research status of data mining and College English writing at places of abroad and input the future the content of research and methods of research. Taking college English writing teaching as the research object, the association rules algorithm in data mining is applied to analyze the correlation factors of students’ writing performance and provide decision-making suggestions for teachers’ teaching.

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.

The modernization of our country has opened a new era, and the “Internet +” national strategy has gone deep into various areas of people’s livelihood. Faced with many changes in the new situation, a variety of emerging information technologies continue to integrate into the classroom, such as Baidu teaching, Whiteboard, IPAD, flipping classroom, and learning space and so on, which makes the teacher’s teaching method and organization form have very big changes. There is a certain “flip” in the classroom indeed. This study explores the connotation and characteristics of music intelligent teaching and the main factors that affect the formation of music teachers’ intelligent teaching under the background of “Internet +” education. In this paper, the related strategies of the formation of wisdom teaching are applied through the design of the auxiliary system, in order to optimize the traditional teaching mode by making full use of the network technology, improve the teaching efficiency, and train more and more music talents for the country.

Based on the analysis of the characteristics of artificial intelligence and agent, this paper discusses the feasibility of introducing Web services and intelligent agent technology into online teaching and learning and proposes a modern distance education system model based on artificial intelligence agent technology web. The architecture integrates the advantages of Agent technology and Web services. Starting from improving the shortcomings of the traditional Web-based distance teaching system, it strives to increase learners’ self-directed learning interest, monitor students’ emotions, and exchange knowledge between teaching agents. To realize students’ on-demand learning according to their aptitude, teachers’ teaching ultimately improve the system’s flexibility, personalization, and artificial intelligence. Under the guidance of learning communities and other theories, construct learner model ontology inference rules. Based on the learner’s relationship characteristics, the knowledge domain that the learner is interested in is inferred, thus constructing an intelligent information retrieval system. Knowledge retrieval is realized quickly and accurately, thereby verifying the application of learner relationship characteristics in digital learning.

Traditional physical education in colleges and universities is difficult to arouse students’ interest in sports, resulting in low activity participation rate and inability to exercise the body. How to effectively improve the effectiveness of physical education in colleges and universities has become one of the hot topics of most concern from all walks of life. In physical education, innovative teaching concepts and methods, teaching methods and processes, and teaching evaluation methods are all conducive to improving the classroom atmosphere of physical education and successfully improve the effectiveness of physical education. This article focuses on analyzing the current status of physical education in colleges and universities. Based on the rapid development of artificial intelligence technology, how to improve the effectiveness of physical education is studied, and an experimental method is used to compare and analyze physical education in a college. The analysis results show that artificial intelligence-based physical education can obviously improve students’ strength quality, speed quality, endurance quality, and agility quality, which provides a more important reference and reference for improving the effectiveness of college physical education.

The focus of this article is to explore the application of artificial intelligence in university sports information services based on the development trend of artificial intelligence technology. Research and analyze the characteristics and functions of new intelligent information service tools, and explore the effects of artificial intelligence in optimizing university sports information services from the three aspects of intelligent evolution information service, intelligent push information, and intelligent retrieval information, and the connotation of intelligent environment Analyze characteristics and technical support to promote the optimization and upgrading of university sports information services, research on the transformation of evaluation methods from manual evaluation to intelligent evaluation, and from standardized evaluation to differential evaluation, and specifically analyze the connotation of intelligent evaluation and differential evaluation, Features and key technologies, analyze the general process of intelligent evaluation, and summarize the implementation suggestions for intelligent evaluation. And discuss the application of artificial intelligence in university sports information services from scientific decision-making and automated management.

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

The physical health test of college students is an important part of the school physical education work and an important part of the school education evaluation system. It is an educational method that promotes the healthy development of students’ physical fitness and encourages students to actively take physical exercises. It is an individual evaluation standard for students’ physical fitness. It is also one of the necessary conditions for students to graduate. In order to improve the physique and health of college students, this article first introduces functional exercise tests to comprehensively measure the exercise capacity of the main muscle groups and joints of the human body, and integrate flexibility and strength qualities. Secondly, this article quantitatively studies the interaction law between the natural light environment comfort of sports training facilities and architectural design elements, and adopts appropriate dynamic optimization methods to improve the light environment quality of the sports space, thereby enhancing the visual comfort of the sports crowd in the stadium. Finally, the artificial intelligence technology is introduced, through the design of artificial intelligence system, intelligent data collection, and analysis. From the perspective of physical education, the functional exercise test based on artificial intelligence conforms to the essential meaning of the physical fitness test and helps to enhance the awareness of college students’ physical exercise. And the intelligent remote multimedia physical education system based on artificial intelligence makes the physical education process flexible, free from time and place restrictions, and can adopt different teaching strategies according to the different situations of students to implement personalized teaching.

This article is based on artificial intelligence technology to recognize and identify risks in college sport. The application of motion recognition technology first need to collect the source data, store the collected data in the server database, collect the learner’s real-time data and return it to the database to achieve the purpose of real-time monitoring. It is found that in the identification of risk sources of sports courses, there are a total of 4 first-level risk factors, namely teacher factors, student factors, environmental factors, and school management factors, and a total of 15 second-level risk factors, which are teaching preparation, teaching process, and teaching effect. When the frequency of teaching risks is low, the consequence loss is small. When the frequency of teaching risks is low, the consequences are very serious. Risk mitigation is the main measure to reduce the occurrence of teaching risks and reduce the consequences of losses.

Recently, Intense training in specialized sport among colleges and universities has steadily improved. Although most sportspeople accept that some degree of expertise in sport is required to achieve the elite stage, there is controversy over risk recognition and identification of sports activities in colleges and universities to optimize future performance. There is concern that a young athlete can suffer from sports specialization before adolescence due to various risk factors such as injuries, social tension, and hypertension etc..,. Furthermore, PubMed and OVID are looking to discuss sports specialization and athlete experience-based consensus opinions and position statements based on Universities and Colleges. Risk recognition and identification tools are developed to identify the locations of athletes within the specialization spectrum using Linear Structural Modeling (LSM). Here, a degree of sports expertise is needed to create elite skill levels to overcome risk factors that have been suggested in the linear model. In most sports, though, such accelerated preparation should be deferred until late adolescence to reduce risk factors. The psychological burden has been analyzed for recognition, and classification based on the case study has been firmly researched in this paper.

This study utilizes empirical research method to compile the questionnaire for the motivation of Chinese medical students coming to China. The survey was conducted on 653 medical students from 55 origin countries and 8 universities in China. In addition to motivation research, we also analyzes the relationship between the factors such as university, educational background, gender and origin of students and the motivation. The results show that safe and stable social environment, profound history and culture, and a large number of universities are the most important motives for foreign students to study medicine in China. There are significant differences in the motivations of international medical students from different genders, universities, and educational background to study abroad, and but there were no significant differences among students from different countries of origin.

The quality of Physical Education (PE) education in high schools is closely related to interactive educational efficiency in classrooms. Teachers and students can improve their interest in learning through classroom interaction. Teachers can adjust educational programs according to the existing shortcomings of physical education, stimulate students’ interests in sports, and reduce student tensions and learning pressures. Students can increase their enthusiasm and creativity in sports, thereby enhancing students’ sports skills. Therefore, in a practical teaching process, it’s important to emphasize enhancing the effectiveness of interactive instruction in the classroom. This makes it possible to develop sports instruction. This paper analyzes how to effectively improve the effects of classroom interactions in a lower secondary school, and proposes a concrete teaching method for physical education. First, this paper explains the importance of improving the effectiveness of classroom education for junior high school students, and analyzes the present state of PE classroom education, and proposes an improvement strategy including physical education, and rationalizes students’ physical and mental development to stimulate students’ interest in sports. The classroom is innovative education and means that students improve their classroom enthusiasm.

With the development and application of Web Semantic, users are no longer satisfied with basic metadata descriptions such as titles and link texts and string-matching search results. They hope that the resource description can provide the theme ideas, topics, and topics involved in the resource. Potential semantic information contains documents such as teaching methods and knowledge-concept relationships. This research starts from the demand for semantic annotation of resources in the process of resource library construction and sharing, and uses the LDA model to semantically model the document resources in the resource library to mine potential topics in the document. From “document-topic-keyword” scheme, the semantic description of teaching resources at different levels enriches the metadata attributes and content of resources, and adds more related topics and corresponding keyword descriptions related to disciplines, teaching content, teaching methods, etc., providing resource retrieval and sharing. The experimental results show that the LDA model can catalogues teaching resources from a macro perspective, and model LDA on teaching subject resources of the same teaching content. It can mine the inherent semantic topic features and detailed differences of resources. The final performance analysis verifies LDA’s advantages of the model in parallel computing in the big data environment.

This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavior, test behavior, cooperation behavior and resource search behavior and other human-computer interaction behavior data, also includes demographic background information, learning ability, learning attitude, and other characteristic data that affect the learning effect. We tried to collect relevant indicators to the maximum extent. An audiovisual fusion method based on Convolutional Neural Network (CNN) is proposed. The independent CNN structure is used to realize independent modeling of audiovisual perception and asynchronous information transmission and obtain the description of audiovisual parallel data in the high-dimensional feature space. Following the shared fully connected structure, it is possible to model the long-term dependence of audiovisual parallel data in a higher dimension. Experiments show that the AVSR system built using a CNN-based audiovisual fusion method can achieve a significant performance improvement, and its recognition error rate is relatively reduced by about 15%. The speech recognition system trained with the cross-domain adaptive method can obtain a significant performance improvement, and its recognition error rate is more than 10% lower than that of the baseline system..

With the continuous development of society, work pressure and study pressure are increasingly becoming the key factors affecting people’s physical and mental health, especially for college students, good knowledge and skills as well as superior psychological quality are the important factors for them to become excellent talents. As an important factor affecting people’s psychological quality, mental health has been paid more and more attention by people. The effect of traditional mental health education in daily education activities is not very obvious, which cannot achieve a profound impact on people’s mental health training and development. In this paper, based on the artificial intelligence online technology, an expert system of mental health education and daily consultation is designed. The system mainly integrates four application modules: psychological knowledge learning, psychological daily evaluation, psychological expert consultation and personal personalized management. The experiment shows that the artificial intelligence online expert consultation system designed in this paper can query daily psychological problems conveniently and quickly. At the same time, the treatment of human psychological problems can be realized through the intelligent online psychological counselling and the relief of psychological symptoms. The artificial intelligence online mental health education system has obvious practical value and social significance.

As an inevitable trend in the development of English teaching, English distance education needs to use artificial intelligence to control the classroom, so as to improve the degree of control of teacher over the classroom. Based on the machine learning algorithm, according to the needs of English distance education classroom management, this paper builds an English distance education classroom management system based on improved machine learning artificial intelligence algorithms. Moreover, this research constructs the system function module through requirement analysis, and combines the positioning algorithm to locate students in real time. In addition, this study analyzes the students’ status through intelligent database processing to grasp the students’ learning status in a timely and effective manner. In order to verify the performance of this system, this study verifies the performance of the model by means of comparative experiments. The research results show that the system constructed in this paper has a certain effect.

This article first studies and designs the college English test framework and performance analysis system. The author analyzes a large number of data collected by the system in three dimensions: using data mining title association models, using machine learning to merge college English score prediction models, and finally diagnosing on the basis of the sexual evaluation model, the author designed and implemented a test paper algorithm based on the association rules of the question type, and carried out relevant verification from the three aspects of test paper time, test question recommendation and improvement according to scores. Finally, according to the needs analysis, the author uses the diagnostic evaluation model and related test paper algorithm to design and implement the diagnostic evaluation model, which is added to the college English diagnostic practice system. It can be obtained through comparative experiments that the paper-based algorithm based on the diagnostic evaluation model proposed in this paper can effectively give better practice guidance and test question recommendation to the learner’s learning status and knowledge point problem obstacles, and can effectively improve learning. The achievements of the authors have broad application prospects and research value.

In this paper, the mathematical model and algorithm based on knowledge forgetting curve are studied. Through the analysis of the current mathematical modeling and application of “knowledge forgetting curve”, the artificial intelligence method of fuzzy mathematics knowledge and differential modeling is adopted. This paper puts forward the mathematical model and algorithm design of the new “knowledge forgetting curve”, which aims to improve the intelligence of the software and bring a new learning experience for the teaching evaluation of the education system in colleges and universities. The fuzzy logic theory is applied to the teaching evaluation system of higher learning pedagogy, according to pedagogy and other related theories, combined with the current teaching evaluation indicators of colleges and universities, the teaching evaluation indicators of higher learning education are set according to certain requirements. The sample wood data is divided into two parts by using the fuzzy logic principle, and the training model is obtained by training the sample data in the evaluation system, and the training model is used to intelligently evaluate and analyze the prediction data.

With the development of artificial intelligence in education, online education has been recognized by the society as a new teaching method. It can make full use of the advantages of the network across regions, and make full use of the advantages of network technology to share the resources of colleges and universities, which is a promising educational method. In response to the demand of online education for learner information, this paper proposes the learner model Neighbor Mean Variation Multi-Objective Particle Swarm Optimization-Genetic Algorithm (NMVMOPSO-GA). This model includes the learner’s learning interest sub-model, the learner’s cognitive ability sub-model and the learner’s knowledge sub-model. The modelling techniques of the three sub-models are discussed separately, and their status and role in the online education system are analyzed. At the same time, for the knowledge model that reflects the learner’s learning progress and knowledge mastery, a learner knowledge sub-model constructed with Bayesian networks is proposed. The neighbor mean mutation operator is introduced to optimize the multi-objective particle swarm optimization algorithm and improve the convergence performance and stability of the multi-objective particle swarm optimization algorithm. We study the application of multi-objective particle swarm optimization algorithm in online course resource generation service. Through simulation experiments, it is verified that the multi-objective particle swarm optimization algorithm can improve the performance and stability of online course resource generation.

The traditional English online teaching model is limited by the teaching location and the difficulty of online teaching, which prevents teachers from controlling students. In order to improve the ability of the English online teaching model to supervise and recognize the status of students, this paper proposes an English online teaching model based on artificial intelligence technology, and adopts a positioning method based on an improved deep belief network for real-time position control and status recognition for students in online learning. Moreover, this study combines intelligent algorithms to build the model structure and verify the performance of the model. The results show that the performance of the model is good. In addition, on the basis of performance testing, the recognition effect of the artificial intelligence-based student online learning recognition model constructed in this paper is recognized. The results show that the model proposed in this paper has a certain effect and meets the actual needs of intelligent teaching.

The manual evaluation method to evaluate the effect of physical education teaching is tedious, and it will have a large error when the amount of data is large. In order to improve the efficiency of physical education evaluation, this article uses artificial intelligence for data analysis and uses machine vision to identify the teaching process to assist teachers in physical education. In order to reduce the calibration error of the parameters and obtain more accurate camera imaging geometric parameters, this paper adopts the method of averaging multiple sample points to determine the calibration parameters of the camera. In addition, this study builds system function modules according to actual needs and verifies system performance through experimental teaching methods. The research results show that the model proposed in this paper has a certain practical effect.

Ideological and political education plays an important role in supporting social talent input. However, the current evaluation effect of ideological and political education is difficult to quantify. Therefore, in order to improve the evaluation effect of ideological and political education, based on artificial intelligence algorithms, this study combines machine learning ideas and the current status of ideological and political education to build a fuzzy analytic hierarchy process model of the of ideological and political teaching quality based on machine learning and artificial intelligence. Moreover, this study uses a three-tier structure to build a model network structure, and based on the characteristics of fuzzy evaluation, this study uses the expert system to conduct data management, operation and control of model evaluation, and build a corresponding database to update the data in real time. In addition, in order to verify the effect of the model, this study sets simulation experiments to analyze the model performance. From the point of view of running effect and running speed, this research model meets the actual needs of the system, so it can be applied to the evaluation process of ideological and political teaching quality in colleges and universities.

In ideological and political teaching, students have more serious problem behaviors in the classroom, including distracted, dazed, inattentive, and sleeping. In order to improve the efficiency of ideological and political teaching, based on artificial intelligence technology, this paper constructs a real-time monitoring system for ideological and political classrooms based on artificial intelligence algorithms, and builds model function modules according to the actual needs of ideological and political teaching monitoring. Moreover, this study makes reasonable calculations on the information monitoring and information transmission parts and installs a different number of monitoring equipment in different fixed locations according to the needs of signal monitoring. In addition, this paper designs a control experiment to study the system performance and verify the parameters from multiple aspects. The research results show that the system model constructed in this paper is stable in ideological and political teaching and has certain effects.

There are certain disadvantages in the traditional physical education teaching model. In order to improve the advanced nature of physical education teaching methods, this paper builds a physical education evaluation system based on artificial intelligence fuzzy algorithm. The system uses fuzzy control instructions as the basis to combine human language and mechanical language, so that the machine can recognize human working language habits and execute commands according to the instructions. Moreover, in this study, the trapezoid function is selected as the membership function, and the improved particle optimization algorithm is used to capture the student’s motion process and the motion vector decomposition, and the system structure model is constructed based on the functional requirements analysis. In addition, this study conducts system performance analysis through experimental teaching methods. The research results show that this system can effectively promote the reform of teaching methods in physical education and has a certain practical effect.

The linguistic artificial intelligence teaching model can be assisted by the intelligent speech recognition model. The traditional speech recognition algorithm has certain problems, so it cannot effectively eliminate speech noise. Based on the advantages of the linguistics teaching model, this article combines the linguistics teaching model and the artificial intelligence model to build an artificial intelligence assisted teaching model that can be used for classroom teaching. Moreover, this study improves the traditional algorithm and constructs an artificial intelligence linguistics teaching model based on the improved algorithm. The filtering part of noise includes preliminary filtering of speech signals based on the short-term energy detection method, and further detection and recognition of preliminary filtering speech signals based on the artificial intelligence model detection method. After these two steps of filtering and recognition, the voice file is sent to the client for processing and control. In addition, this study set up a control experiment to analyze the performance of the model. The research results show that the algorithm in this paper has a certain effect.

Based on the cloud computing artificial intelligence model, the English interactive teaching model summarized and analyzed in an in-depth manner, the characteristics of the smart classroom explored, and the interactive teaching model reform practiced. This article has studied and analyzed the classic teaching model. Finally, based on constructivism, the advantages of the constructivism teaching model, cooperative teaching model, and mastering learning model selected to construct the teaching model of artificial intelligence courses. Through the questionnaire survey of the current teaching status of artificial intelligence courses, and the investigation of each link of the constructed model, according to the results of the survey to optimize the construction of artificial intelligence courses teaching model to make it more perfect. Based on the cloud computing technology, the system architecture and function module division of the network open class platform designed based on the overall needs, and developed and implemented on this basis. Through global and local two-level authentication, user information synchronization, and interconnection between homogeneous clouds, the identity management function realized. With the help of the e-schoolbag function, the learning results continuously and accurately evaluated, so that every learner can get a good learning experience.

Based on the existing literature review and background analysis, the article first expounds the theoretical basis, realistic basis, main principles and links of the situational inquiry method applied to the research teaching of ideological and political courses, and combines teaching practice Interviews and investigations conducted an in-depth analysis of the current situation of the application of situational inquiry methods in the research teaching of ideological and political courses. Through inquiry analysis, it summarizes a series of positive effects of the situational inquiry method in the research teaching of ideological and political courses, such as enriching the teaching methods and enhancing the fun of the classroom; improving the effectiveness of classroom teaching and enhancing the comprehensive quality of students; promoting the professionalism of teachers Development, teachers’ teaching literacy. At the same time, it also found some problems to be solved urgently, such as insufficient participation of teachers, insufficient enthusiasm of students, chaotic and disorderly classroom teaching process caused by random arrangement, and difficulty in application caused by the limitations of situational inquiry method itself. Secondly, based on the analysis of the existing problems, it is proposed to strengthen the continuing education of teachers and promote teachers ’"one specialty and multiple abilities"; create a good atmosphere and improve students’ classroom participation; optimize the teaching process and generate charming classrooms; adhere to appropriate use and other teaching The combination of optimization methods such as the combination of methods is aimed at further optimizing the effective use of situational inquiry methods in the research teaching of ideological and political courses, and further improving the effectiveness of classroom teaching.

In order to study the role of English situational teaching in higher vocational colleges, based on information technology and artificial intelligence, this research combines with the needs of English teaching to construct a English situation teaching in higher vocational colleges with the support of 5G network technology and artificial intelligence. Moreover, this research builds a data processing model based on the system architecture diagram of cache placement, uses storage space and computing resources to save more backhaul link bandwidth, and adopts the “many to many” algorithm extended by the “one to many” algorithm, and uses the on-demand method to obtain scenario teaching data from the cloud. In addition, this research constructs the intermediate link of data processing, and uses 5G network transmission to solve the problem of data transmission speed. Finally, this study uses a controlled experiment to evaluate the effectiveness of the artificial intelligence teaching model constructed in this study. The research shows that the English situation teaching method based on 5G network technology and artificial intelligence in vocational colleges has a certain effect and can effectively improve the English scores of vocational college students.

This paper investigates the cognition status of information piano education for teachers and students in a university, which mainly includes a summary of the piano teaching status in a university and make an analysis and summary of the investigation results. In addition, this paper puts forward the direction of the network information reform and construction for piano majors in Colleges and universities, mainly including three aspects, that is, taking piano “micro class” teaching to arm traditional classroom teaching, using the new media to build a networked piano learning environment, and building the piano teaching “MOOC” platform.

The teaching evaluation index system based on artificial intelligence not only evaluates and reflects the teaching situation of ideological and political theory courses in universities as a whole, but also provides specific feasible goals and direction guidance for the construction of ideological and political theory courses in universities. Based on data mining technology, this paper combines machine learning algorithms and dimensional analysis to study the ideological and political evaluation model of colleges and universities and builds an artificial intelligence teaching evaluation model based on actual needs. Moreover, this study transforms the model selection problem into a hybrid optimization algorithm optimization problem, and the algorithm attempts to find the optimal model from the model set. In addition, this study designs a control experiment to perform model performance analysis. The results of the study show that the performance of the model meets the expected goals and can be applied to practice.

The popularization of virtual reality technology has become a brand-new carrier of knowledge and information dissemination and a new tool for art creation and design teaching in Colleges and universities. In this paper, first of all, from the three aspects of the definition and characteristics of virtual reality technology, the integration of virtual reality technology into art design teaching, and the significance of using virtual reality technology in art design teaching, the reform of art creation and design teaching in Colleges and Universities under virtual reality technology is discussed. In order to carry out the application research, this paper then chooses to carry on the simulation modeling teaching of the bamboo forest in the intelligent virtual reality environment, making full use of the intelligent modeling tools, rendering tools and mapping tools to complete the teaching application examples of the art modeling design in Colleges and universities. In the specific teaching of art creation and design in Colleges and universities, the creation and design modeling of individual bamboos, the creation and design modeling of bamboos forest, and the creation and design modeling of different lighting effects and scene rendering are carried out respectively. Finally, from the advantages of intelligent virtual reality technology in art creation and design of colleges and universities, the expansion and promotion of teaching resources, and the synergistic effect of teaching results, we analyze the strategies of art creation and design teaching in Colleges and Universities under the background of intelligent virtual reality technology in detail.

With the arrival of the era of artificial intelligence, based on the problems existing in the teaching process of marketing specialty, combined with the future business development trend and the core needs of enterprise operation, this paper analyzes the system reform of the practical courses of artificial intelligence and marketing specialty. With the rapid development of computer technology, intelligence has gradually become an important means to solve problems in various industries. In this paper, the modern media as a means, marketing teaching in Colleges and universities as the research background, through the establishment of the depth of marketing in Colleges and Universities Based on artificial intelligence network research learning platform, build a post-modern media communication perspective system. Based on probabilistic neural network and from the perspective of modern media marketing application system construction, the paper proves that the artificial intelligence prediction based on probabilistic neural network has good convergence, fault tolerance and data processing ability through MATLAB. Finally, this paper takes the pricing strategy in marketing as an example, and focuses on the application of artificial intelligence technology in marketing teaching from four aspects: preparation before class, implementation in class, consolidation after class and marketing teaching examination. According to the function and application of the theory of artificial intelligences in marketing teaching, we can find out that teachers must deeply understand the situation of each student’s artificial intelligences, so as to use the theory of artificial intelligences to change the traditional view of students and talents, and teach students according to their aptitude, so as to achieve better teaching effect.