bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-objective Optimization Algorithm Research on Tobacco Leaf Blend Formulation Design
Authors: Li Xingliang , Guo Lei , Du Xiaozhou 
Published: August 2024

Abstract
The optimal design of formulated products is a complex and comprehensive technology. In order to obtain formulations with excellent performance and meet the requirements, it is necessary to reasonably select raw materials and determine the dosage ratio relationship of various raw materials through testing, optimization, and identification. Combining the BP neural network and NSGA-II algorithm was used to study the design optimization of tobacco leaf group formulation in this paper. Firstly, the chemical composition and sensory quality data of single-ingredient tobacco samples were collected and normalized, and then correlation analysis was performed to obtain the single ingredient tobacco proportion-chemical composition relationship model and chemical composition-sensory score relationship model. Then the two relationship models were imported into the BP-NSGA-II model to calculate the chemical composition of the leaf group formulation, and the sensory quality score was predicted based on the chemical composition to obtain the optimization results of the proportion of the tobacco leaf group formulation. There was a strong correlation between the chemical composition and sensory quality of tobacco at the significant level of α = 0.01. Except for the aroma index, the mean error of prediction of the methods in this paper for the rest of the sensory quality indicators was smaller than those of SVR and multiple linear regression methods. The absolute error of prediction for all indicators was less than the traditional BP and SVR models. The sensory quality scores of the optimized formulations in this paper are basically consistent with the results of expert evaluation, and the absolute values of relative errors are less than 4%, which is of strong practical significance for the optimization of formulation design of tobacco leaf groups.
Interactivity Analysis and Modeling of the Reversal Effect of Internet Technology and English Teaching in Colleges and Universities
Authors: Li Yanping , Tang Diandi 
Published: August 2024

Abstract
With the in-depth application of Internet technology in the field of English education, the connection between Internet technology and the reverse dialing effect of English teaching in colleges and universities has become increasingly close. The theory of the reverse dialing effect is introduced in this paper, and an Internet technology framework is proposed that incorporates an online English learning platform and a personalized recommendation model of English resources. The viscous binary particle swarm algorithm with nonlinear factors is used as the algorithm for recommending learning paths to build the online learning platform. At the same time, the online learning platform is also used as the background, and the S-CNN model is constructed by relying on the convolutional neural network model framework to propose a personalized course resource recommendation algorithm based on deep learning. University Q in Changchun City, Jilin Province, China, was used as the research site for practice. The English scores of the students in the experimental class were significantly improved, and the mean values of cooperative communication ability, problem-solving ability, and information literacy in English learning ability were higher than those of the control class by 4.427, 4.394 and 3.918, respectively, which showed significant differences (P<0.05). The average value for all learning status dimensions after the experiment was greater than 3.5, which was higher than before the experiment. The mean value of English teachers’ total teaching beliefs reached 163.76, and the mean values of all classroom behaviors except feedback and guidance behaviors were all higher than 3.5. The positive effects of students’ learning and teachers’ teaching were significantly increased.
Research on the Method of Improving the Efficiency of Dynamic Allocation of Educational Resources in the Environment of Internet of Things Based on MLP Network Modeling
Authors: Li Zongrui , Hou Yan , Wei Jianghua 
Published: August 2024

Abstract
The effective allocation of educational resources has significant practical value and importance in ensuring the fairness of education. The study examines the efficiency of educational resource allocation for 30 schools in City Z from 2020 to 2023 using the DEA model. Reasonable data are screened out based on the DEA model, and then the MLP model is used to construct a model of educational resource allocation efficiency, and the sample data are comprehensively evaluated, so as to screen out the optimal program. Then, according to the results of the MLP model, we determine the importance of each input parameter to obtain the reference basis for adjusting each investment factor to improve the efficiency of educational resources. The method for improving the dynamic allocation efficiency of educational resources is summarized at the end. The allocation efficiency of educational resources in the sample schools is above the medium level, and the comprehensive technical efficiency is 0.61~0.67, which fails to maximize the use of educational resources. The MLP model has a good predictive effect on output variables, and the teaching area (0.319), paper books (0.281), and fixed investment (0.269) are the most important input factors. It is recommended that the DEA-MLP model be used to optimize and optimize the resource management system, improve the scientific evaluation and supervision system, and enhance the management capacity of teachers, so as to further promote the efficiency of the dynamic allocation of educational resources.
Analysis of the Impact of AI Editing on Film and Television Post-Production in Digital Transformation
Authors: Liu Jiaoyan 
Published: August 2024

Abstract
This paper studies the framework of an AI intelligent editing system, explores the functional modules of intelligent editing, deeply analyses the use of AI editing in film and television post-production, and carries out an in-depth study of AI editing methods. Then, we established an impact effect analysis model using the analysis of variance, t-test, and regression analysis methods. Based on this model, we compared the audience viewing data for AI editing and ordinary editing, analyzed the audience’s emotional tendency and participation, and studied the impact of AI editing on the effects of film and television post-production. The study demonstrates that AI editing has a significant promotion effect on creative freedom, content and theme fit, cost-effectiveness, and diversity of content in film and television works, with significance coefficients of 0.046, 0.023, 0.012, and 0.006, respectively. This study holds significant value in promoting the integration and development of AI technology in film and television, as well as strengthening its application in the field of digital media.
Optimization Design of Physical Education Course Evaluation System in Colleges and Universities - Algorithmic Logic Integration Based on the Perspective of Hefei University of Technology
Authors: Liu Ping 
Published: August 2024

Abstract
In recent years, with China’s economic and social development, higher education has developed rapidly, and college physical education courses have received more and more attention, so the research on the evaluation system of college physical education courses is particularly important. Based on the perspective of Hefei University of Technology, this paper integrates a K-means clustering algorithm to optimize the evaluation system of college sports courses. By analyzing the problems existing in the current evaluation system of physical education courses at Hefei University of Technology, a method of quantitative evaluation of “course quality” by calculating the evaluation value of course attainment and course attainment rate is studied. It also proposes to use the K-means clustering mining algorithm as the mining algorithm for the evaluation of university physical education courses, improve the algorithm, and then apply it to the evaluation analysis of university physical education courses. By analyzing the quality of physical education courses at Hefei University of Technology, the total percentage of failing and poorer students is 8%, which indicates that the quality of physical education courses at Hefei University of Technology is better. The optimized evaluation system of college physical education courses is practiced and taught, and the results show that the optimized evaluation system of college physical education courses has a positive effect relative to the traditional evaluation mode from the analysis of student’s interest and attitude towards course evaluation.
The effectiveness of visual music therapy for community-based rehabilitation of mental disorders
Authors: Liu Ting , Xiao Xue , Jiang Fan 
Published: August 2024

Abstract
With the transformation of the new medical structure in the new era, the center of gravity of treatment for patients with mental disorders gradually shifted from the hospital to the community. The study randomly divided 70 community hospitalized patients with mental disorders who met the diagnostic criteria into an experimental group and a control group of 35 patients each and treated them with visualization music therapy and traditional spiritual rehabilitation therapy, respectively. At the end of the treatment, the basic characteristics, sleep quality, cognitive function, and related symptoms of the patients with community psychiatric disorders before and after the treatment were analyzed by using independent samples t-test, and the quality of life and intimate adaptability of the patients after community rehabilitation before and after the treatment were also explored. The results showed that there were significant time main effects in depression (F=234.801, P<0.05) and anxiety (F=256.714, F<0.05) symptom scores, and post hoc comparisons revealed significant intergroup differences in posttest depression and anxiety symptom scores between the two groups (P<0.05). There was a significant difference in total quality of life scores (P=0.000<0.05), psychosocial scores (P=0.041<0.05), and an increase in adaptability and intimacy after treatment, but no significant difference was reached.
Optimizing Higher Education Curriculum Design and Teaching Content Recommendation System Using Data Mining Algorithms
Authors: Li Yanping , Shao Dawei , Sun He 
Published: August 2024

Abstract
In recent years, education has been the focus of attention from all walks of life, and the education curriculum of colleges and universities is the key to cultivating students’ comprehensive quality and ability. College education needs to optimize the design of higher education courses to achieve the goal of talent training and to design a teaching content recommendation system based on students’ cognitive level. On the one hand, this study uses the support vector machine algorithm to quantitatively analyze students’ qualitative comments on the course to extract useful teaching feedback for course design optimization, and on the other hand, designs a teaching content recommendation system based on deep learning algorithm as an adjustment tool to jointly improve the teaching quality of higher education and improve the learning effect of students. The CBOW model and SVM algorithm model were used to analyze a large number of qualitative texts, and the cosine similarity of related words was calculated, such as “recommended” 0.98416, “sufficient” 0.9986 and “good” 0.99762, and the word heat map was drawn according to the word frequency. In addition, an empirical study was conducted on the teaching content recommendation system in school A. The accuracy of the recommendation results and the impact of the system on students’ academic performance were analyzed. The average accuracy of the learning resource recommendation results was 91.65%. The student’s scores were significantly improved after using the system, which proved that the design of the system was effective.
Teaching Strategies and Student Performance in Combining Mathematics Teaching and Data Mining Techniques in Higher Education
Authors: Ai Jiao 
Published: August 2024

Abstract
Using data mining technology to obtain important information in massive data has gradually become an important basis for mathematics teaching strategies in colleges and universities. Combined with data mining technology, this paper proposes the analysis of students’ behavior in mathematics classrooms and personalized mathematics learning strategies and constructs the corresponding method model. The behavior of students in the mathematics classroom is analyzed by using the character behavior recognition technology, and the 7Hu moments of the motion history graph and the motion energy graph are used as the features of behavior recognition. Construct a learning model using the user-item scoring matrix, improve and standardize the null-filling method of scoring, and provide a basis for students’ personalized learning in mathematics. After applying the teaching strategy and the corresponding model to 70 mathematics majors in a university, the student’s performance in mathematics was significantly better than that of the previous semester after the weekly practice 4, and their average score in the midterm examination reached 63.8. The mean values of all dimensions of motivation increased compared with the pre-practice period, and all dimensions were significantly different except the dimension of the learning environment factors (p<0.05). The students demonstrated significant improvement in all dimensions of math performance, with significant differences.
Automatic identification of depressive symptoms in college students: an application of deep learning-based CNN (Convolutional Neural Network)
Authors: He Tianqing , Huang Wei 
Published: August 2024

Abstract
Facial behavior is the most direct and easily accessible behavioral data. In this paper, based on the face action unit based on FACS, we have conducted quantitative research on the expression behavior pattern of depressed people with digital features through the DAIC-WOZ corpus dataset and E-DAIC dataset and completed the construction of the expression behavior and the application of the automatic identification model of college students’ depressive symptom with the optimization of CNN-LSTM method. For the experimental design and result analysis of the time-frequency ratio of expression behavior and the dynamic rate of change of expression behavior in depressed patients, the digital features are obtained, and the unique expression behavior pattern of depressed patients is argued. The main findings are as follows: Compared to the normal population, depressed patients have special behavioral patterns in emotional feedback and emotional cognition. The characteristics of reduced positive emotional feedback, enhanced negative emotional feedback, easy-to-misjudge neutral stimuli as negative stimuli, and slow changes in expression behavior are mostly indicative of this. By studying the two aspects of the time-frequency ratio of expression behavior and dynamic rate of change of expression behavior, it is found that the CNN-LSTM model obtains 73.21% recognition accuracy and 85.71% recall rate when applied, which is more suitable for depression primary screening scenarios. This paper’s research results offer a methodological basis and technical support for automatically identifying depressive symptoms in college students.
Neural network-based prediction of college students’ physical fitness test scores
Authors: Hu Yunjing , Fan Ting , Wang Zihao 
Published: August 2024

Abstract
College students’ physical fitness test scores are an important criterion for evaluating students’ physical fitness, and scientific and effective prediction and analysis of physical fitness test scores can provide a theoretical basis for subsequent physical education teachers to carry out teaching. This study proposes a combined prediction model of the gray system and neural network to deal with the small sample data of college students’ physical fitness test scores, introduces the basic concepts of the GM(1,1) model and BP neural network, respectively, and explains the advantages and complementarities between the gray prediction and the neural network prediction, which provides theoretical support for the combined prediction model. By capturing 2000 college students’ physical fitness test scores from a university as the research object, 1600 of them were used as training samples and the remaining 400 as test samples, and different data sets were divided by gender. The model was applied to predict individual specific item scores and classify the total assessment. Taking the girls’ 50-meter running performance as an example to draw the comparison curve of the prediction model, it was found that the error of the gray neural network model prediction was within 0.5 seconds. In addition, the RMSE values of the prediction results of other sports performance were all below 0.06, and the MAPE values were all below 3%, which means that the model can meet the practical requirements of the prediction of the physical fitness test. The horizontal ladder plot and confusion matrix plot reflect that the model is relatively accurate in predicting the overall rating level of students’ physical fitness test scores, with an accuracy of 95.142% in the boys’ dataset and 95.425% in the girls’ dataset.
Big Data-Driven Cross-Border E-commerce Platform Operation Strategy Based on Data Mining
Authors: Kong Ting 
Published: August 2024

Abstract
In the context of the big data era, cross-border e-commerce enterprises are confronting significant challenges. The traditional marketing model finds it challenging to adapt to the evolving needs, making precision marketing for cross-border e-commerce platforms particularly crucial. This paper, based on the big data-driven path of operating cross-border e-commerce platforms, designs the operation strategy of these platforms from the perspective of precision marketing and empirically analyzes its impact. The RFM model is first used to design user value labels, and the K-means algorithm then uses the clustered labels. Combined with mining the three types of key data—user basic attributes, user value labels, and user consumption behaviors—of Company A’s Amazon store to construct user profiles, analyze them, and further design precise marketing strategies based on user profiles and analyze their effects, This paper classifies customer groups into three categories: high-value premium types, dynamic premium types, and growth types. High-value, quality customers account for most of Company A’s business, and through personalized marketing, their sales show a certain growth trend. Vitality-quality customers stimulate the desire to buy by recommending new products and activating old ones, and their sales increased to $32,527 in the fourth quarter. The impact of growth-type customers using consumption coupons and discount codes to stimulate consumption and purchases is flat, with no significant growth. This indicates that the operation strategy in this paper is more obvious and can be used as a precursor for further optimization.
Optimization study of stochastic process and probabilistic model for line loss management efficiency in low-voltage station areas
Authors: Li Tao , Dong Xin , Wang Xuesi 
Published: August 2024

Abstract
Line loss management efficiency is one of the main indicators used to judge the effectiveness of electric power enterprises’ work, as well as a comprehensive indicator used to reflect their operation level and production technology. In this paper, we analyze the types of line losses in low-voltage station areas, present a method for calculating these losses, examine the influence indices of these losses, and outline the process for collecting line loss data in these areas. The maximum load loss amount is used for outlier construction, and the improved K-Means clustering algorithm is utilized for clustering processing of line loss data, which is combined with the isolated forest algorithm to solve the anomaly scores of the line loss data and then to obtain the positioning of abnormal data of line loss in low-voltage station areas. Then, based on the Markov chain, the probabilistic modeling of the time series random variable data of line loss in the LV station area is carried out, and the OLS-optimised RBF neural network is used to process its time series variation data. Applying the combined model to the detection of line loss anomalies in LV station areas, the global maximum value of its time-voltage parametric difference is only 34.28 V, and the value of the directional electron transmission distance is only 3.41 μm. When the expansion constant is 5.42, the OLS-RBF model’s optimal prediction accuracy and minimum mean square error reach 98.43% and 0.0012, respectively. The average residual mean value for the model prediction results and the actual line loss data is 4.4 kW, and the average relative error is only 0.12%. Therefore, this paper’s method effectively locates anomalies, detects stochastic line loss data in low-voltage stations, and accurately processes time-series random variable data.
Analysis of the correlation between the AI model and adolescents’ physical and mental development from the perspective of humanistic sociology of sport
Authors: Ma Haolin 
Published: August 2024

Abstract
Adolescent physical and mental development is a perspective that the humanistic sociology of sports focuses on in the field of youth sports. This paper investigates how the use of AI technology in sports will affect the physical and mental development of youth. In order to overcome the shortcomings of small samples, high dimensionality, and high noise in youth physical and mental development data, this paper adopts dimensionality reduction methods such as principal component analysis and linear discriminant analysis to reduce the dimensionality of high-dimensional data. Using the processed data, the physical and mental development data of youth were successfully classified into four categories: social interaction, training intensity, psychological state, and cultural influence. The study on the correlation between the AI model and youth physical and mental development takes soccer as an example and adopts the method of pre and post-tests of actual experiments to select students from a high school to conduct the test of physical and psychological indexes before and after the experiments. Physical and psychological index tests before and after the experiment were significantly better for students in the experimental group using AI technology compared to those in the control group not using AI technology.