Achievement prediction and analysis based on neural network for smart education

Sound quality evaluation may be conducted with multiple different objective evaluation models, but reliability of evaluation results is different.Noise samples of two ears of the driver were gathered when the car operated at the speed of 60km/h and different music was played in the car.After equal loudness, frequency band and filtering treatment of sound samples, subjective evaluation of sound quality was carried out.Based on calculation of objective parameters, MLR (Multiple Linear Regression) objective evaluation model for sound quality, objective evaluation model for sound quality based on BP neural network and objective evaluation model for sound quality based on RBF (radial basis function) neural network were established.The test samples were substitute into the three models, and their sound quality prediction effects at each frequency band were compared.The results show that MLR model is simple and has fast calculation speed, so it is suitable for linear problem analysis; BP neural network model is suitable for solving large sample problem with complex internal mechanism; RBF neural network model has high precision and small relative error, so it is suitable for analysis of nonlinear problem of small sample.Under experimental conditions, prediction effect of RBF model is superior to MLR model and BP neural network model.

The accurate calculation of the height of water-flowing fractured zone (WFFZ) in coal mine is a critical factor in ensuring mine safety and protecting surficial eco-environment. In view of inapplicability of traditional empirical formula for predicting the height of WFFZ, the correlation of the height of WFFZ and influence factors was analyzed firstly based on 82 collected groups of coalfield measured data in China. Results show that the mining thickness and mining depth have a significant effect on the height of WFFZ. Subsequently, the measured data were divided into two parts: 80% for training models and the remaining 20% for validation. Two prediction models, i.e., the multiple regression (MR) model and BP neural network (BPNN) model, were established and trained. A new merging model, multiple regression-BP neural network (MR-BPNN) model, was proposed by combining the multiple regression and BP neural network model. The prediction accuracy and generalization ability of the three models were verified by the recorded testing samples. Results of comparison suggest that three models all had better applicability for predicting the height of WFFZ in the coalmine, compared with the existing empirical prediction methods. More importantly, the MR-BPNN merging model combined the nonlinear mapping ability of neural network and empiric of multiple regression model, which could provide high-accurate, strong-generalized, and practical application for predicting the height of WFFZ of coalfield. In addition, the reason for the inapplicability of traditional empirical formula and the practicability of the proposed neural network-based prediction models were discussed.

myStudentStatus is a student course status tracking system that has been developed to address the challenges academic advisors face in managing and monitoring student course status. This system provides academic advisors with comprehensive information about students’ final exam results and their performance in each course they have taken in quickly and accurately monitoring students’ academic progress. This system has also been able to assist academic advisors in identifying students who may be at risk of falling behind or dropping out of a course. This includes the student’s course status, including grades, completion of prerequisites and overall academic performance. This quantitative study involved 57 respondents consisting of lecturers and students of Polytechnic Kuching Sarawak. Overall, 100% of respondents acknowledged the effectiveness of the myStudentStatus application where the response for each evaluation item received the approval of respondents above 80%. This user-friendly myStudentStatus application is instrumental in increasing the productivity of academic advisors, reducing errors and indirectly improving student support services. By leveraging technology to streamline administrative processes, myStudentStatus contributes to a more efficient and effective education system that benefits both academic advisors and students.

Az értékesnek számító tudás változásával, a gondolkodási és tanulási képességek szerepének felértékelődésével megjelent az igény olyan felsőoktatásban is könnyen alkalmazható tesztek, tesztrendszerek kidolgozására, amelyek túllépnek a hagyományos tudásszintmérő teszteken és szélesebb skálán jellemzik a diákok kognitív fejlődését. Miután a dinamikus problémamegoldó képesség jól jellemzi a diákok tanulási potenciálját és a frissen tanultak alkalmazási képességét, egy potenciális mérési területévé vált egy, a fenti igényeket kielégítő, a Szegedi Tudományegyetemen 2015 óta működtetett mérési rendszernek. A négy évet átfogó kutatási eredmények alapján megállapítható, hogy stabilan és megbízhatóan működtethető egy olyan értékelési rendszer, amely túllép a tudásszintmérő tesztek világán és azonnali visszacsatolás mellett alkalmas annak megállapítására, hogy a hallgatók milyen tanulási potenciállal és problémamegoldó képességgel kezdik meg egyetemi tanulmányaikat. A közel 7000 diák részvételével végzett mérés alapján megállapítható, hogy 2015 óta jelentős mértékben nőtt az egyetemre frissen felvett diákok tanulási potenciálja és problémamegoldó képessége. Mindemellett a diákok közötti különbség meglehetősen nagy, több éves fejlődésnek felel meg. Vannak olyan hallgatók, akik átlagos képességszintje egy általános iskolás diákénak felel meg. Az egyetemi bemeneti kompetenciamérés eredményei 2018-ban már negyedik alkalommal bizonyították, hogy az érettségi eredmények nem jelzik kellő biztonsággal az egyetemi tanulmányokra való alkalmasságot. A közép és emelt szintű vizsgák és a teszteredmények közötti összefüggés tantárgyanként és évenként is különböző erősségű. A különböző szinteken nyújtott teljesítmények nem hasonlíthatóak össze egymással és az érettségi által jellemzett tudásszint évenként változik.

The Shanghai and Shenzhen 300 Index covers the epitome of 60% of the Shanghai and Shenzhen stock markets, and is a high-level summary of the Shanghai and Shenzhen stock markets. If we can predict the closing price of the Shanghai and Shenzhen 300 Index, it will guide the investment of the Shanghai and Shenzhen stock markets, promote the sound development of the stock market, and improve the overall economic strength of the country. For the study of nonlinear systems, neural networks have unique advantages, especially BP neural networks, which can train, learn and predict complex data. In this paper, BP neural network is used to establish the BP neural network model of the Shanghai and Shenzhen 300 Index. The opening price, the highest price and the lowest price of the Shanghai and Shenzhen 300 Index are used as input variables, and the closing price is taken as the output variable to study and predict the closing of the Shanghai and Shenzhen 300 Index. Through the model results, it is found that the BP neural network model can better simulate the Shanghai and Shenzhen 300 Index and achieve the effect of predicting the Shanghai and Shenzhen 300 Index. Applying BP neural network model to nonlinear estimation of the Shanghai and Shenzhen 300 Index, solving nonlinear problems, can promote the healthy development of China’s stock market.

Objective A BP neural network model for diagnosing type 2 diabetic nephropathy based on laboratory tests was developed and evaluated. Methods Patients with type 2 diabetic nephropathy from 5 hospitals of Chongqing, Guizhou and Sichuan Provinces from January 2016 to December 2016 were collected in the study. Totally 89 parameters were analyzed by univariate analysis to identify significant variables by SPSS 19.0 and MATLAB 2014a. The diagnostic performance of the two methods were compared. Results A total of 477 patients with type 2 diabetic nephropathy and 449 patients of control group were included. Univariate analysis showed that 42 variables had significant difference. Logistic regression analysis showed that 12 variables were included in the optimal regression equation. This BP neural network had 42 input layer nodes, 15 hidden layer nodes and 1 output layer nodes. The Youden index of logistic regression analysis and BP neural network(training set and test set) were 0.76, 0.89 and 0.83. The accurately diagnosed were 88.12%, 94.24%, and 91.34%, the AUC were 0.95, 0.98, and 0.96. Conclusion A BP neural network model was developed, which has important accessory diagnostic value for diagnosis of type 2 diabetic nephropathy. But all these conclusions need further validation in clinic. (Chin J Endocrinol Metab, 2017, 33: 943-949) Key words: BP neural network; Diagnostic model; Diabetic nephropathies; Cognitive map

Long-term food choices influence dietary patterns, contribute to the nutritional status of adolescents, affect cognitive function, and affect academic achievement. This study provides a novel perspective by examining food choices across urban and rural settings, an area seldom explored in the Indonesian context, and their association with adolescents' nutritional status and academic performance. This study employed a cross-sectional design to capture the relationship among food choice, nutritional status, and academic achievement at a single point in time. in November–December at two schools, Senior High School 1 Dramaga as an urban area and Senior High School 1 Nanggung as a rural area, involving 377 subjects. The collected data included nutritional status using anthropometric measurements, food choice analysis using FCQ, and academic achievement using final exam results. Data analysis included univariate methods, bivariate Spearman's rank correlation, and the Mann–Whitney U test to assess differences between the two study areas. This study showed that there is a correlation between food choice and nutritional status. In urban areas, the results were significant (p=<0,001) for health (r=0,732) mood (r= -0,592), convenience (r= -0,509), sensory appeal (r= -0,223), familiarity (r= 0,570), and weight control (r= 0,708). In rural areas, there were significant differences (p<0,001) in (r= 0,610), mood (r= -0,473), sensory appeal (r= -0,409), natural content (r= 0,577), price (r= 0,571), familiarity (r= 0,582), and weight control (r= 0,690).. Moreover, nutritional status was positively correlated with academic achievement (p < 0,001) in urban (r= 0,741) and rural areas (r=0,525). In conclusion, food choice significantly affects adolescents’ nutritional status and academic achievement in urban and rural areas. Therefore, structured education on the importance of making food choices based on health considerations and nutritional content is essential as a strategic effort to improve long-term nutritional status and academic performance.

In view of the fact that the material thickness of grate cooler system is difficult to measure, we present a new method of modeling that takes advantage of genetic algorithm to optimize the BP neural network and has a contrast with the pure BP neural network. Simulation shows that the BP neural network model which is optimized by genetic algorithm has a small error. At the same time, it is more stable than the BP neural network model. It can also make a foundation of controlling grate cooler system in future.

The polynomial model and wood model have been extensively applied to predict the milk yield of cows, which aims to measure and partially explain the uncertainty under single factor condition. However, different sample data would affect the goodness of fit of the models. To investigate the milk yield regularities of the Chinese Holstein cows in Yunnan Standardized Pasture (YSP), data of cows with 1–3 parities using different observation periods were derived from the 401,497 records of 1,826 cows collected from YSP, and fitted in the BP neural network (BPNN) model, the Quadrinomial model and the Wood model. Prediction results of three models were compared. The results show that, for group data (mean), the Quadrinomial model was likely to over-fit under the single factor condition with small sample data, while the Wood model had a better goodness of fit; the BPNN model was more suitable for multi-factor and large sample data analysis.

Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level.

The estimation of state of charge(SOC) is among the key technologies in battery management system. Compared with many estimation methods, neural network has obvious advantages in the estimation accuracy. But requiring great amount of data is its main limitation. In order to solve the above problems, a method for SOC estimation based on the cross validation of artificial neural network and k-fold was proposed. An 18650 power lithium-ion battery is used as the experimental object, and the data are obtained by collecting the battery voltage, current and temperature data. High SOC estimation accuracy on different verification sets is pursued under circumstance of low data amount. The BP artificial neural network model is established based on the Keras platform with tensorflow, and K-fold cross validation method is added to the network. After training, the model is used to predict the experimental data. The estimation of SOC is realized by function fitting. Compared with the traditional BP neural network prediction, the prediction accuracy is 99%. Finally, it is proved that the method combining BP neural network and K-flow cross validation is effective and the validity of the neural network model for SOC estimation is verified.

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

Madrasah Aliyah Negeri (MAN) 3 Palembang is unique enough to be studied because since inception of Islamic religion teacher education institutions. The results of this study found that type of leadership visoner is owned by head of Man 3 Palembang oriented management of change and continues over time. Visionary leadership is the key. Head of MAN 3 Palembang has been able to create, formulate, communicate, socialize, transform, and implement the ideal thoughts coming from him and the result of social interaction between members of organization and stakeholders that are believed to achieve ideals of organization in the future. In addition, management of change has been initiated through an internal analysis of the organization by identifying four areas, namely services, finance, human resources and facilities in order to discover the strengths and weaknesses internal to the organization. All of this has been the commitment of all personnel MAN 3 Palembang to achieve my goals and success. Lastly, head of MAN 3 Palembang has been able to implement management changes, so that the organization built quality improvement-oriented leadership, teacher quality, school programs, students' final exam results, student achievement and strong school culture.

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision‐making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.

Due to the influence of internal and external factors, stock prices often fluctuate greatly, which is also a big difficulty in the field of data mining and machine learning. This paper uses linear regression model, BP neural network model, and hard voting model to forecast Google stock. The results and accuracy of these three prediction models were observed respectively. Then a more accurate and practical model was selected to make a reasonable prediction of the closing price of Google stock in the following 15 days. The data set used is divided into two subsets, with some data used to fit the model and the rest used to evaluate the model. The results prove that the BP neural network model has the best prediction effect. Therefore, this model was selected for the next step of prediction, and the conclusive prediction result proved that the closing price of Google stock would decline in the following 15 days.