bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Market Positioning and Suitable Cargo Value Range of “Yiwu-Xinjiang-Europe” Train Based on Data Model
Authors: Cao Jingjing , Ye Lifang 
Published: September 2023

Abstract
A new globalization initiative for strengthening economic integration and policy coordination provides new opportunities for trade cooperation between China and countries along the route. In this paper, the normalization method is used to assign values, determine weights based on the coefficient of variation of indicators, and carry out dimensionless processing of each indicator. The degree of data dispersion is determined, the equivalent time distribution is assigned, and the time cost coefficient ratio is used as the corresponding time cost. The weighted average is used to calculate the value of goods, considering the annual holding cost coefficient. And combined with relying on indirect measures to measure the overall trade cost, randomly perturbed error proxy terms, and alternative utility function elasticity invariant. The residual variance is predicted by replacing the effect of multilateral resistance, and the data-based model of “Yiwu-Xinjiang-Europe” is constructed. To verify the practicality of the model, the cargo trade results show that the total export trade of the “Yiwu-Xinjiang-Europe” train has increased significantly, and the value of foreign trade exports has increased by 18.72%. The volume of China’s trade decreased by 56.1% year-on-year. The volume of “Yiwu-Xinjiang-Europe” trains increased by 15.467% year-on-year. From this, we can see the strategy to promote the high-quality development of the “Yiwu-Xinjiang-Europe” China-Europe train in terms of market positioning, expanding the value of import and export cargo sources and improving the service level of the train.
Innovation of fresh agricultural products e-commerce operation mode based on artificial intelligence and machine learning model
Authors: Cheng Jiamei 
Published: September 2023

Abstract
In recent years, there has been an increasing interest in emotional interaction models based on artificial intelligence technology, and researchers want to deal with real-life problems in daily life through artificial intelligence and emotional interaction technology. In this paper, based on artificial intelligence and the emotional interaction model, we analyze the model architecture of emotional interaction technology and the theory related to the e-commerce operation platform. Combining the two, we propose a collaborative filtering recommendation algorithm, define system performance indexes, and recommend fresh produce to target customers with similar users’ evaluation of fresh produce through artificial intelligence technology. Using the experiments of three different recommendation systems and user behavior data of different months in the past three years as reference, it is concluded that the e-commerce operation of fresh produce based on AI and emotional interaction model can effectively improve the browsing and transaction volume of the e-commerce platform. This shows that artificial intelligence and emotional interaction model can be applied to create a new e-commerce operation mode, which provides research directions and methods for the diversified development of future e-commerce operation modes.
Exploring the implementation path of teaching Civics in online courses in colleges and universities under the background of big data
Authors: Zhu Liyuan 
Published: September 2023

Abstract
In this paper, a web-based teaching platform is constructed based on the background of big data, which is modeled by using a neural network algorithm. First, the neural network structure is output, and the error function is minimized by dynamic iterations using the activation function as neurons. Then, the error values were trained with implicit nodes, the activation function was modeled nonlinearly, and the sample set was extracted to define the cost function. Finally, the optimal particle is trained iteratively using the gradient descent method to derive the optimal solution, thus completing the construction of a web-based teaching platform based on the big data background. The experimental results show that after using this platform for online ideological education teaching, the percentage of students visiting online courses every day is 35%, which is 29% higher than that before using it. Therefore, to improve the construction level of ideological and political education online courses, it is necessary to strengthen the construction of ideological and political education online course platform resources, improve the informatization level of ideological and political education teacher teams, and promote the integration of ideological and political education online courses and classroom teaching.
Research on face emotion recognition algorithm based on deep learning neural network
Authors: Chen Yan , Zhang Mengzi 
Published: September 2023

Abstract
This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.
Denoising complex background radar signals based on wavelet decomposition thresholding
Authors: Qiu Feng , Yuan Kee 
Published: September 2023

Abstract
The echo signals of the radar in complex backgrounds are often very unstable and thus require effective noise cancellation. In this paper, according to the characteristics of continuous wavelet variation and discrete wavelet variation, the decomposition effect of multi-resolution analysis and orthogonal Mallat algorithm on low-frequency and high-frequency non-smooth signals is studied, and the selection method of wavelet bases is explored. Then, the noise characteristics affecting the pulsed LIDAR system are analyzed, and the LIDAR pulse signal is simulated by MATLAB, while Gaussian white noise is introduced to obtain the noise-added echo signal, and then multiple wavelet threshold denoising methods are applied to denoise the echo signal. For the input signal-to-noise ratio of −10.57 dB, the output signal-to-noise ratios of db8, db9, db10, and bior3.5 wavelet bases under forced thresholding are −1.971, −2.178, −2.173, and −1.032, respectively. For different input signal-to-noise ratios, the average root mean square error of db8, db9, db10, and bior3.5 wavelet bases under default thresholding is 1.51. The denoising methods for radar signals using the properties of wavelet decomposition have obvious superiority compared to traditional filters, and the wavelet transforms threshold denoising methods have wide adaptability.
The use of color elements in graphic design based on convolutional neural network model
Authors: Song Shuhan 
Published: September 2023

Abstract
Exploring the effective application of color elements in graphic design based on a convolutional neural network model is beneficial to promote the innovative development of the graphic design. Starting from the convolutional neural network, this paper defines the quantification of geometric features of graphic images by convolutional operation and pooling operation and uses pixel and spatial distribution for color element extraction. The global color features and local color features are used for color matching perception feature quantification, and then a graphic image design feature model is constructed, and the application analysis of color elements is carried out for this model. From the primary design color, the average selection rate of this paper’s model is 41.58%, which is 13.4% and 9.6% higher than that of the DCGAN and PLS-MLP models, respectively. From the keyword color quality generation, the color design results of graphic images have significant differences (p < 0.05). This indicates that the use of CNN-based graphic image design feature models can realize the effective application of color elements and also provides a new research direction for intelligent graphic design.
Analysis of the Evolution Trend of Segmentation of Online Public Opinion Based on Dynamic Tracking Technology
Authors: Gong Zhen 
Published: September 2023

Abstract
In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a basic database of sensitive information. The test of statistical characteristics of the evolution process of public opinion is realized by using the prediction of time series by the ANOVA algorithm, and on this basis, the smoothness of the model is realized by testing the parameters. In the process of establishing the quantitative index system, the seasonal difference algorithm is introduced to predict the development trend of public opinion on hot topics to realize the visualization model design. Finally, in order to verify the validity of the model, an experiment was conducted with Event A as an example. The results show that users can set a total of 4 keywords with an average word frequency of 0.03 Hz. The prediction accuracy in different periods can reach 88.73% on average, and the adaptation value can reach 0.8 percentage points on average. Thus, it can be seen that the trend analysis model of online public opinion evolution constructed in this paper can quantitatively analyze the mechanism of public opinion segmentation evolution and the trend prediction problem and provide a theoretical reference for the government to govern public opinion.
The evolution of Chinese traditional ethnic clothing design style based on interactive dichroism algorithm
Authors: Zhao Jianzhang 
Published: September 2023

Abstract
In this paper, a traditional ethnic clothing design style model is proposed, aiming to promote the good development of clothing in artificial intelligence design. The model uses an interactive dichotomous meter algorithm for top-down information gain, differentiates sample attributes by selecting entropy values, and defines an average categorical fuzzy subset based on discrete attribute leaf node division for deep, circular arithmetic decision-making. After satisfying the clothing design evolution condition, the minimum prediction entropy value genus block is extended, and the feedback matrix random input variables are used to induce the regularized likelihood frequency classification value distribution. The results of the analysis of traditional ethnic clothing design show that there are {0, 0.5, 1} mathematical expressions on clothing design hues, the demand of clothing design value subject shows a positive state, the change of consumption wave that festival and style will bring, the knowledge value in the mean difference of clothing design orientation is as high as 0.9173 with a significant value of 0.00<0.05. Therefore, the design style of traditional ethnic clothing is not only the inheritance and promotion of national culture but also the enhancement and development of aesthetic consciousness.
First principles study of the electronic structure of antimony (Sb) and samarium (Sm) doped tin dioxide (SnO2) based on big data analysis
Authors: Liu Danjuan , He Wei , Fu Siyong 
Published: September 2023

Abstract
To study the electronic structure of Sb and Sm co-doped SnO2 materials, a lattice model of Sb and Sm co-doped SnO2 is designed in this paper based on a big data analysis algorithm. The physical properties of the SnO2 ground state are described by the particle density function using the density generalized function theory. The interactions between the particles are all subsumed into the exchange-correlation generalized function by the Kohn-Sham equation. A big data analysis algorithm is used to construct the electron wave function to reflect the luminescence mechanism of the spectrum produced by the electron leap between energy levels, which makes the computational effort significantly reduced. The results show that the enthalpy change of Sb and Sm co-doped SnO2 in the design model of this paper is −5.59918, and the energy interval of the density of states of s orbitals is [2.36, 31.45]. It can be seen that the co-doping of Sm and Sb can increase the electron polarization ability and electron leap probability of SnO2 in the infrared band and enhance the infrared reflectivity, and the co-doped system has the highest electron-binding ability reflectivity.
The value dimension and realization of online ideological and political education in colleges and universities based on deep learning
Authors: Gao Haiwei 
Published: September 2023

Abstract
This paper proposes a value dimension and realization way of online ideological and political education in colleges and universities based on deep learning. Firstly, a deep learning teaching platform is established, which is conducive to teacher-student communication, teachers can better grasp the focus of teaching, and students can be more clear about their learning status. The classroom teaching reform and auxiliary learning reform are carried out in three dimensions: theoretical classroom, practical classroom, and guided classroom. And a multi-dimensional response theory model is established to create conditions for the comprehensive and free development of college students so as to realize the value dimension of online ideological and political education in colleges and universities. The results show that more than 55% of students get political information through major portals and through students’ ideological and moral performance on the Internet, and then implement targeted education, which can effectively improve students’ online moral literacy.
Research on the integration path of cultural creative industry and tourism industry based on collaborative filtering recommendation algorithm
Authors: Yin Ying 
Published: September 2023

Abstract
In order to increase regional economic benefits and attract more tourists for local economic development. This paper adopts different ways to evolve industrial integration, enrich and expand the theoretical system of the industrial economy, establish new tourism strategies to meet personalized and diversified needs and make the tourism sector achieve industrial integration. Useful data are selected to filter cultural and creative, and tourism industry information by mining hidden information of coding sequences and finding support data items. Using collaborative filtering recommendation method to obtain the preference degree of fused data items, populate the data according to behavioral speculation, calculate the utility matrix similarity, and clarify the development of industry fusion path in the context of big data. The results show that the average error rate after fusion does not exceed 4%, and the fusion path of cultural and creative industries and the tourism industry improve the regional economy and the overall local living conditions.
Research on the development of literature practice teaching for college students in the context of computer technology
Authors: Liang Xueman 
Published: September 2023

Abstract
In this paper, intelligent information retrieval of relevant literary content through collaborative filtering algorithms in Internet technology is used to analyze the similarity between users and, thus, to select similar items in a weighted ranking. The Jacobian formula is used to calculate the common feature values and thus predict the user’s unrated item ratings. Word normalization is performed through information extraction of text keywords to effectively abstract the dimensionality of text features. The results showed that the influence of learning based on language literacy was 0.697, the preference for the literary genre of prose was as high as 49.4%, and students could accept infiltration teaching. It indicates that making full use of Internet technology means can break the time and space limitations in the traditional teaching of literature, provide effective support for students’ interactive communication, expand students’ cultural horizons, and improve their humanistic literacy.
Analysis of the construction and reform path of preschool education professional practice courses in the context of computer big data
Authors: Zheng Lixia 
Published: September 2023

Abstract
This paper first uses the big data adaptive learning system to facilitate students’ mastery of knowledge points, improve their learning performance, and stimulate their interest in learning. Then, the practical curriculum of preschool education is realized through curriculum facilities, curriculum objectives, curriculum content, curriculum organization and implementation, and curriculum evaluation. Next, the QFD entity model for quality assurance of preschool education personnel training is established. The realistic basis for the construction of the model is also elaborated, and the conceptual framework of the QFD model for quality assurance of higher education personnel training is proposed and comprehensively analyzed. The results show that the cooperative group learning approach is more favored by students, up to 64.44% in the practical courses. The number of graduates who chose to work in early education and other industries increased by 10%. It shows that improving students’ personal comprehensive quality can adapt to society’s comprehensive demand for professionals.
A study of algorithms for solving nonlinear two-level programming problems oriented to decision tree models
Authors: Lin Jinshan , Lin Min , Xu Hang 
Published: September 2023

Abstract
In this paper, the original two-level planning problem is transformed into a single-level optimization problem by combining the penalty function method for the large amount of data processing involved in the training process of the decision tree model, setting the output as a classification tree in the iterative process of the CART decision tree, and recursively building the CART classification tree with the training set to find the optimal solution set for the nonlinear two-level planning problem. It is verified that the proposed solution method is also stable at a convergence index of 1.0 with a maximum accuracy of 95.37%, which can provide an efficient solution method for nonlinear two-level programming problems oriented to decision tree models.
Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models
Authors: Li Yanggui 
Published: September 2023

Abstract
In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and reduce the document length to obtain keywords without destroying the original text feature information. The similarity of text features is combined with a Bayesian algorithm for label classification to facilitate data query and indexing. The results show that the running time of the system is kept around 14s, the recall and accuracy can be close to about 75% and 72% on average, and the number of keywords can reach 5971 with an F1 value of 0.9, which proves the effectiveness of the artificial intelligence legal regulation system based on big data analysis.
The main color structure setting of Chinese oil painting based on content analysis method
Authors: Chen Haiwei 
Published: September 2023

Abstract
Based on the content analysis method, this paper first uses the PCA technique to disassemble and transform the base color information to extract multiple feature values. Secondly, the sparse autoencoder is used to adjust the feature parameters by training, and the activation function pair is used to calculate the confidence to complete the effective classification. Finally, the SVM algorithm is introduced to optimize the color information parameters, and the corresponding color label values are calculated by constructing a decision function to accurately set the pixel positions. The results show that the color difference value of Chinese oil painting image keeps between 0.5nbs -1.5nbs, the standard deviation value of saturation is 165.55, and the resolution is 610ppi, which indicates that the content-based analysis method can better coordinate and integrate the relationship between various color structures.
The application of red music culture resources in the education of ecological civilization concept in colleges and universities under the background of multimedia
Authors: Wang Yijin 
Published: September 2023

Abstract
This paper uses a user-based collaborative filtering algorithm to predict the ratings of items by target users and to recommend items that target users have not interacted with. The red music and cultural resources were ranked according to their values, and the Bartholomew coefficient was introduced to calculate the similarity of items from the perspective of a probability distribution, and the content recommendation algorithm was used to construct a model of ecological civilization education concept. It is found that more than 50% of undergraduate college students pay regular attention to ecological environment information, and 30.56% of them will actively learn ecological civilization knowledge, which is not thorough and comprehensive enough for ecological civilization concept establishment and cultivation. It is suggested that universities should integrate red music culture into ecological civilization education to create an ecological civilization cultivation atmosphere.
Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data
Authors: Liu Ke 
Published: September 2023

Abstract
The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.