bluuebunny/crossref_metadata_2025_split huggingface dataset:
Text Study of Reader Magazine in the Context of Big Data
Authors: Zhang Limin , Xia Yiming 
Published: September 2023

Abstract
Exploring the textual emotional value of the Reader is to help readers understand the Reader’s embedded emotions in an all-around way. In this paper, two text analysis methods, latent semantic analysis and probabilistic latent semantic analysis are described, starting from the analysis model of text research, and the automatic text classification technique is illustrated. The principles of support vector machines are explained, the optimal decision function selection solution is performed using kernel functions, and a PLSA-SVM text analysis model is also constructed using the PLSA method jointly with SVM. The structural features of Reader magazine were analyzed and explained, and the PLSA-SVM text analysis model was used to analyze the data on the narrative features of Reader magazine. In terms of narrative themes, the main narrative themes of Reader magazine were distributed as affection and love, which increased by 16.84% and 17.42% from 2012 to 2021, respectively. In terms of narrative perspectives, the proportion of first-person and third-person narrative perspectives is comparable, increasing by 9.03% and 12.3% from 2012 to 2021, respectively. Thus, in the context of big data, the PLSA-SVM text analysis model can be used to analyze the text narrative features of Reader magazine effectively, and Reader magazine can use the analysis to optimize further the text content and issue texts that are attuned to readers’ emotions.
Analysis of artistic features of ancient Chinese literary works based on multi-scale feature fusion network
Authors: Zhang Yanan 
Published: September 2023

Abstract
Against the background of the rapid development of artificial intelligence technology, the use of artificial intelligence technology to improve the efficiency of artistic feature analysis of ancient Chinese literary works has become a hot topic of current research in literature. In this paper, we propose a multi-scale feature fusion network for the artistic feature analysis of ancient Chinese literary works to address the problems of a single structure and inflexible adaptation of features that appear in RPN networks and path aggregation networks. Then, features are extracted from ancient Chinese literary works, and several adaptive multi-scale feature extraction modules are used to squeeze the incentive and adaptive gating mechanisms for artistic feature extraction and fusion. Finally, the evaluation index system of artistic features of literary works is constructed, and the multi-scale feature fusion network weights the artistic features of ancient Chinese literature. The results show that the average weights of humanistic spiritual features, national patriotic features, literary emotional features and transformative artistic features in ancient Chinese literature and art features are 26.11%, 24.97%, 23.89% and 25.03%, respectively, and the average weight of humanistic spiritual features performs better compared with the other three. This study analyzes the artistic characteristics and cultural values of ancient literary works, which is an effective initiative for modern people to study ancient culture and inherit the national spirit and has important historical significance for developing Chinese literature.
Innovation of teaching methods of international Chinese language education in universities based on artificial intelligence analysis technology
Authors: Zhang Xiangli 
Published: September 2023

Abstract
Exploring the innovative teaching methods of international Chinese language education in colleges and universities under the new media environment is to transmit better and develop Chinese culture. This paper constructs an international Chinese language education platform using graph convolutional neural network, RNN algorithm, and long and short-term memory neural network combined with big data technology under artificial intelligence analysis technology and gives that the platform consists of an infrastructure layer, data resource layer, application service layer, and user business layer. The plurality and interactivity indexes are analyzed for the constructed international Chinese education platform. Regarding plurality, A, B, C, and D ratings account for 40.82%, 35.4%, 14.71%, and 9.07%, respectively. Regarding interactivity, the percentage of overall user evaluation data above grade C is 93.4%. This shows that building an international Chinese language education platform can effectively enhance the diversified development of Chinese language education and meet the learning needs of diverse people. The international Chinese education platform uses strong interactivity to achieve communication between users and realize the development of Chinese culture transmission.
A Study on Innovative Strategies for Teaching English to College Students Based on Knowledge Mapping
Authors: Wang Lun 
Published: September 2023

Abstract
This paper proposes a personalized teaching method based on knowledge mapping and the commonly used personalized learning methods. A web crawler is used to crawl English exercises data on the Internet and classify them according to the knowledge categories, establish the connection between topics according to their cosine similarity, and build an English knowledge graph using NEO4J. After creating the knowledge map, a select few nodes are categorized with labels that reflect the characteristics of various pupils, and the node categories are divided into two categories: mastered and unmastered, after which the whole knowledge map is labeled using a graph convolutional neural network to generate a personalized knowledge map. After testing, the model works best when the threshold value is increased to 0.8 when generating the English knowledge map. And the overall English score of the experimental class taught with this paper’s method improved by 5.27 points, among which the average scores of completion, writing, and reading increased by 2.58, 1.32, and 1.17 points, while the overall English level of the control class did not change significantly. As a result, the approach used in this research to create tailored learning routes based on personalized knowledge mapping has excellent applicability.
Application of machine learning based models in computer network data
Authors: Liu Huixia 
Published: September 2023

Abstract
Computer networks, as one of the indispensable infrastructures in today’s world, play an extremely important role in industrial production and daily life. This paper revolves around the intelligent diagnosis of computer network data faults using machine learning methods. Firstly, the support vector machine network fault minimization principle is proposed based on the machine learning model, which leads to under-learning or over-learning when the samples are limited, or there is noise. Then, a loss function is introduced for the under-learning or over-learning problem to ensure that the support vector machine can accurately achieve fault diagnosis. Finally, the evaluation index of computational network fault diagnosis is constructed for the experimental purpose, and four algorithms are selected as the experimental control group to analyze the data. It is obtained that SVM and ANN models have high DR and low FAR. Their DR is 87.9% and 84.5%, respectively, while their FAR is only 5.4%. This further validates the superiority of SVM in computer network data fault detection. This study possesses low training time complexity and can overcome the problem of uneven distribution of the number of faulty and normal samples in network fault diagnosis to some extent.
Design of teaching quality evaluation system for higher vocational education based on data mining technology
Authors: Li Yuanai 
Published: September 2023

Abstract
It is advantageous for schools to realize exact education management, the cornerstone for developing abilities, by constructing higher vocational education teaching quality evaluation systems. The division of frequent item sets based on the modified Apriori algorithm under the association rule algorithm is used for the evaluation algorithm of this system to examine the evaluation data in the evaluation system, which is created in this work using data mining technology. Data validation was carried out from system performance and application analysis of association rules for the teaching quality evaluation system constructed in this paper. Regarding system performance, the highest response time of the system in this paper is 8.34s, which is about 56.94% and 40.68% shorter than that of the evaluation system based on AHP and gray correlation theory, respectively. This shows that using association rule algorithms can realize the operation of the evaluation system faster, improve evaluation efficiency, and use visual data to achieve precise education management in schools.
Electromyographic characterization of limb flexion, extension and their special exercise movements in high level distance runners
Authors: Zhang Shichao 
Published: September 2023

Abstract
The study of EMG characteristics of high-level distance runners in limb flexion, extension, and their special exercise movements is beneficial to developing competitive distance running sports. In this study, high-level distance runners were used as the research object, and the sEMG test in EMG analysis was used to compare the general distance runners, and the changes of MPF, FC, and IEMG were obtained during the limb flexion, extension, and special exercise movements of high-level distance runners. When the FC was 100Hz, the MPF values of high-level distance runners were 64MV2/Hz, while the MPF values of average-distance runners were 45 MV2/Hz. When the muscle strength reached 50%, the IEMG values of rectus abdominis, biceps femoris, and gastrocnemius were 52%, 45 and 56%, respectively, in high-level distance runners, while the IEMG values of average distance runners were 47%, 42%, and 50%, respectively and 50%, respectively. Through the data analysis, the high-level long-distance runners could maintain a higher exercise state and perform at a stable level despite the gradual muscle fatigue, their muscle strength and contraction ability were stronger, and their explosive muscle power and potential were greater than the average long-distance runners.
Research on the analysis of traditional music tunes in the context of network information technology
Authors: Ma Xintao , Wang Dongmei 
Published: September 2023

Abstract
People’s traditional music passed down from generation to generation should be heard by the world, and music tunes, as one of the indispensable basic elements of traditional music, need to be studied more deeply and systematically. In this paper, based on modern network information technology, we use big data visualization technology to analyze and study traditional music tunes. Using the RGB color space principle to understand the visualization technology, the number of research literature related to traditional music tunes in the past 20 years is obtained through literature collation, and the chart way of visualization using Sankey diagrams is analyzed to conclude traditional music tunes research. The data show that most research literature on traditional music tunes features an average of 6 articles per year. The number of ephemeral articles has gradually increased over the decade, with the total effective number of articles being about 7 in 2002 and increasing to about 39 in 2022. The research institutions are mainly concentrated in many universities in Henan, Yunnan, Hunan, Guangdong, and Guangxi, but the main traditional music tunes studied in each place also Have differences. The research on traditional music tunes by universities has a guiding and leading role, and the increase and depth of related research literature are also conducive to the protection and inheritance of traditional music tunes and integration and innovation in each region.
Cluster control technology of transmission line intelligent inspection drones based on 5G communication
Authors: Zhang Ying , Zhu Ling , Liao Ruchao , Zhang Feng , Chen Yun 
Published: September 2023

Abstract
This paper firstly analyzes the network topology model of the UAV cluster network and wireless 5G communication channel model by modeling and briefly analyzes the idea of topology movement control for flying self-organized networks. Then, a cluster-based structure and reinforcement learning clustered routing protocol is proposed for the problem of easy breakage of routing forwarding paths caused by smart inspection of transmission lines based on UAV clusters for 5G communication. Finally, a cluster structure-based precedence routing protocol is designed, an adaptive routing protocol based on location and link quality Q-learning is used between clusters, and fast and reliable routing is achieved by combining the routing table maintained by itself. The simulation results show that ARP-L-Q (average end-to-end delay 4.22, average packet loss rate 88.09%, average packet rate 2.37, average control overhead 2.52) protocol performs better than GPSR and GACB protocols, and the experiment verifies that ARP-L-Q protocol can better achieve the high dynamic reconfiguration, high stability and reliability, and low communication delay of UAV cluster-based 5G communication network. Characteristics and requirements. This study has application prospects in both civil emergency and military mobile communication and has certain military significance, theoretical value and application value for thus promoting UAV innovation.
Optimization of the innovation path of practical teaching of college Civics based on VR technology under information fusion technology
Authors: Si Yang 
Published: September 2023

Abstract
With the rapid growth of computer technology and multimedia technology, VR technology is regularly developed and applied to entire walks of life. This paper first introduces the VR double master teaching model into the practical teaching of Civics in colleges and universities (CU) and analyzes the shortcomings and limitations of the traditional teaching design scheme. Secondly, under the guidance of constructivist learning theory and contextual cognitive theory, as well as the basic theory of humanistic theory, combined with the characteristics of venue learning, and also with the implementation of gesture interaction technology and face expression detection technology with the help of depth sensors, a teaching design scheme with immersion and interactivity based on red VR venues is designed. The results show that the response time of logging into the business through the performance test of the VR Civics teaching system is 5.239s - 3s, and the thinking time is 2.271 s, which is in line with the result of logging in less than 3 s. Moreover, the Civics performance of students taught through VR has improved by 11.2 percentage points, and the teaching effect and system performance are in line with the practice expectation. It shows that the virtual reality technology proposed in this paper and Civics teaching have achieved an effective combination and realized the fundamental purpose of implementing moral education.
Optimization study of intelligent decision-making system for coal processing plant based on big data analysis
Authors: Zhang Jianhua , Li Rui , Wang Lin 
Published: September 2023

Abstract
Optimizing the intelligent decision-making system of coal processing plants is better to improve the economic efficiency of coal processing plants and realize the high-quality development of coal processing plants. In this paper, an integrated intelligent decision-making platform for a coal processing plant is constructed based on big data technology, and the intelligent data analysis techniques of the platform are optimized by using an improved whale optimization algorithm and BP neural network. Examples analyze the optimized crude coal slurry and flotation systems’ processes, and the economic benefits are analyzed. From the optimization of the crude coal slurry sorting system, the ash content in the 0.25mm particle size region was reduced from 55.37% to 13.12%, and the ash content in the −0.125mm particle size region was reduced from 42.68% to 15.96%. From the flotation system optimization, when the flotation time increases from 120s to 180s, the ash content increases from 16.27% to 17.19%, and then to 240s, the ash content increases to 19.44%. Using the integrated intelligent decision-making platform can achieve a net increase in revenue of 4,276,800 yuan for the crude coal slurry sorting system and a net increase of 11,274,200 yuan for the flotation system. This shows that the integrated intelligent decision-making platform can improve the coal processing plant’s quality and efficiency and promote intelligent production.
Analysis of Intelligent Control Strategy for Heavy Media Coal Separation Process Based on Deep Learning Model
Authors: Wang Yu , He Jiexin , Bai Dongyan 
Published: September 2023

Abstract
Intelligent control of heavy dielectric coal beneficiation in coal plants is achieved with the help of deep learning models to optimize the control effect. In this paper, through the study of heavy dielectric coal separation methods and processes, a coal separation control optimization strategy based on a radial basis neural network optimized by the ant colony algorithm is proposed, and the RBF network is optimized by clustering using ant colony algorithm, which is used to determine the center and radius of the basic function of the RBF network. The suspension density, ash content of the fine coal and the level of the Hopper bucket, which affect the control effect, are selected as the inputs of the optimized model, and the control strategy is formulated according to the effect after adjusting the parameters. The experimental simulation results show that the ACO-RBF model has less oscillation when the ash value is changed, the final change is smoother, and the root mean square error of the ash value is 0.075%, which is 36.6% less than that of the PID algorithm. With the control strategy optimized by deep learning, the fluctuation range of the level of the qualified media barrel is controlled between 15 and 25 cm, and the volatility pattern of the level is more regular. The control system based on deep learning can better meet the requirements of the coal processing process and effectively improve the efficiency of a coal processing plant.
Optimization of smart tourism destination management based on linear regression model
Authors: Ma Yue , Wang Yan 
Published: September 2023

Abstract
With the rapid development of Internet communication technology, the tourism industry will certainly transform and upgrade to the direction of wisdom, and the organization and dissemination of tourist destination information will become a breakthrough in tourism development. This paper firstly constructs a tourism management system (DMS) in line with the idea of smart tourism development by constructing a process line from market opportunity analysis, interest motivation analysis, strategic planning stage, strategy implementation stage and evaluation improvement stage, respectively. Secondly, the evaluation model of tourism destination development based on logistic regression is systematically constructed, and the parameter estimation method is used to estimate the value for 8 influencing factors that affect the development of tourism destinations. The results show that through the empirical analysis of the Guizhou smart tourism destination management system, it is found that the AUC value shows an increasing trend, and the 5km grid has the smallest AUC value. When the grid continues to increase, the AUC value shows a decreasing trend, slowly decreasing from 0.836 (20km) to 0.844 (25km). It shows that the intelligent tourism destination management system constructed in this paper is reasonable and lays the theoretical foundation for realizing regional barrier-free tourism and achieving the development goal of integrated symbiosis.
The use of graphic design elements in commercial architectural design in the context of intelligence
Authors: Song Yimin 
Published: September 2023

Abstract
In this paper, firstly, in the context of intelligence, using techniques such as hierarchical features, local perceptual field, weight sharing, and spatial subsampling make the convolutional neural network with representational learning ability, certain translation, scaling, and deformation invariance, which can accurately extract the features of target objects in graphic design elements. Next, the network structure, loss function, and optimization methods of MaskR-CNN are analyzed, and the MaskR-CNN convolutional neural network is implemented based on the TensorFlow framework, and the network is applied to the extraction of buildings from graphic design elements. Then the borrowed landscape technique in graphic design is used to enrich the combination form of elements of architectural design through the mirror effect of water landscape. The selected IALD dataset is composed of 180 data images and 180 corresponding labeled images, and the effect of high-resolution building extraction of graphic design elements based on the MaskR-CNN model is verified by simulation experiments. The simulation results show that Mask R-CNN improves the accuracy of building extraction from graphic design elements by 5.11%, mAP also improves by about 10.09%, and the inference time is less than 1 second; Mask R-CNN has good robustness and accuracy for building extraction from graphic design elements. This study has important research significance for the depth and development of the commercial architectural design.
A study on the measurement and standardized assessment model of student learning outcomes in vocational institutions
Authors: Zhao Minxiao 
Published: September 2023

Abstract
As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.