bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Deep Learning Techniques in UAV Image Recognition and Tracking
Authors: Li Hong , Hussin Norriza 
Published: June 2024

Abstract
The advancement of computer vision technology, coupled with developments in deep learning theory, has facilitated the widespread application of deep learning across various domains. This paper leverages the YOLOv7 deep learning algorithm to incorporate the MobileVITv3 lightweight Transformer architecture and a positional attention mechanism, culminating in the creation of the MobileVIT-YOLO-Tiny algorithm for uncrewed aerial vehicle (UAV) image recognition and detection. Furthermore, utilizing the principles of the Kalman filter algorithm, this study designs an optimized Kalman filter tracking algorithm specifically for UAVs. Subsequent experimental evaluations are conducted to validate the efficacy of the proposed algorithms in UAV image detection and tracking. The results indicate that the overall average precision (AP) of the UAV image detection algorithm is 0.8931, with leading performance in simple and complex background scenarios among participating algorithms. Moreover, the UAV tracking optimization algorithm achieves success rates of 0.827 and 0.629, respectively, fulfilling the real-time operational demands of UAV image tracking. The findings underscore the significant potential for practical applications of the UAV image detection and tracking algorithm developed in this study, as evidenced by the experimental outcomes.
A meta-analysis-based study of the effects of rural tourism activities on tourists’ physical and mental health
Authors: Li Yan , Xu Baoli 
Published: June 2024

Abstract
As a convenient and easily accessible green space place in the environment, the countryside provides people with the opportunity to get in touch with nature intimately, so in the process of this tourism experience, people’s health benefits have been more and more widely concerned by academia. Based on this, this study utilizes the questionnaire survey method and scientific experiments to conduct an in-depth exploration of the mechanism of the impact of rural tourism activities on the physical and mental health of tourists by taking tourists who have had rural tourism experiences as the research object. This paper derives the physical and mental health indicators of tourists before tourism based on the evaluation method of physiological indicators and the test method in psychology, proposes a perceptual measurement tool to study the obtained data of tourists’ state of mind, and concludes that after tourism, the overall mean values of tourists’ average heart rate, minimum heart rate, and maximum heart rate were reduced by 0.65 bpm, 3.28 bpm, and 7.5 bpm, respectively. The mean value of ventilation per minute was reduced by 1.11 L- min-1, indicating that there is a significant effect of different ages on the physical and mental health benefits of tourists. Tourists with tour time under half an hour had a poor state of mind, and tour time of 4 hours or more had the highest POMS index of 298, with a more favorable emotional state, indicating that there is also a significant effect of tour time length on tourists’ state of mind.
A study on the application of optimization algorithms in vocal music teaching strategies
Authors: Sun Ke 
Published: June 2024

Abstract
Subjective interference is a common difficulty in vocal music teaching, and human ear audition cannot fully objectively analyze the students’ problems in vocal practice due to the influence of environment and other factors. This paper takes the convolutional neural network as the vocal music recognition algorithm and the Mel spectrum as the vocal music feature extraction algorithm and constructs the vocal music analysis model based on the optimization and improvement of the two algorithms. Then select the support vector machine, the nearest neighbor node, Wavenet, LSTM, GAN, SAGAN, CLDNN_BILSTM, and other models, as well as this paper’s model, for comparison experiments. Finally, the model was utilized in the vocal education classroom to evaluate the singing practice of four students. It is found that the MSE value of Arousal’s algorithm in this paper is the lowest, and the R2 values of 0.51197 and 0.71058 are the highest in the test of the MFCC vocal music feature dataset. Valence’s model in this paper has the MSE value of 0.51996, which is still the lowest, and the R² value of 0.76946, which is still the highest. This paper’s model has the best performance and results. The average rate of professional singers is 61 beats, and the model calculates the average singing rate of the four students as 77, 66, 63, and 61 beats. The first three still have a large gap compared to the standard level, and the student D level is higher. The problem of student practice analysis and vocal feature extraction and recognition in vocal teaching can be solved using new ideas and methods provided in this study.
Applications and Challenges of Statistics in Large-Scale Data Mining
Authors: Yang Siwen , Xie Wanqiu 
Published: June 2024

Abstract
As mathematical statistics evolve, their incorporation across diverse fields has markedly increased. This study examines specific challenges within statistical applications to data mining. By synthesizing theoretical frameworks and practical applications, this research delves into the utilization of statistical methods in data mining, enriched with practical examples. Notably, enhancements to the K-Means clustering algorithm are introduced through the optimization of initial clustering centers and the integration of a Gini index-based weighting system. This refined algorithm is subsequently applied to segment student behavioral groups, utilizing behavioral data from university students as the sample. Additionally, multiple linear regression models are employed to scrutinize variables related to student performance and to formulate a predictive model for their academic achievements. The analysis results in the identification of eight consumer behavior groups and nine academic effort groups, facilitating the classification of students. The variables exhibit varying levels of correlation with student performance, which are statistically significant (p < 0.05). Specifically, the total time spent on the Internet shows a negative correlation (-0.074), whereas grades from the previous semester display a positive correlation (0.593), both of which are particularly pronounced. The predictive model demonstrates a high accuracy, exceeding 80%, in forecasting student grades. Although the convergence of data mining and mathematical statistics presents challenges, it simultaneously offers substantial opportunities for the advancement of the field.
Product development strategy of non-heritage cultural and creative products under the fusion of traditional crafts and modern technology
Authors: Deng Haibei 
Published: June 2024

Abstract
In the contemporary digital era, the transformation of intangible cultural heritage (ICH) products represents a critical trend in their evolutionary trajectory. This study advocates for the synergistic integration of traditional craftsmanship with advanced technologies within the ICH sector. A standardized electronic image repository was established by scanning these items under uniform lighting conditions, forming the basis for generating non-heritage experimental datasets. Subsequently, a clustering algorithm was applied to extract color features from the images. Owing to its resilience against rotation, scale, and noise variations, the Scale-Invariant Feature Transform (SIFT) algorithm was utilized to delineate pattern features of non-heritage items. The analysis primarily focused on the predominant colors in contemporary ICH products, paving the way for innovative developments that incorporate augmented reality (AR) technology with non-heritage characteristics. Quantitative analyses indicated that the average hue of these products is 118.15°, predominantly within the warmer green spectrum of the color wheel. Moreover, the evaluation of pattern style similarity of non-heritage items, as determined by the SIFT algorithm, yielded an average of 88.36%. Further, regression analysis demonstrated a significant influence of hue, brightness, pattern, and texture on the effectiveness of product development, with regression coefficients of 0.276, 0.236, 0.237, and 0.159, respectively, at a significance level of P<0.001. This research highlights the potential of contemporary science and technology to invigorate traditional arts, thereby contributing significantly to the preservation and revitalization of valued cultural legacies.
Pattern Recognition and Automatic Rendering of Silkscreen Printmaking Technology in Animation and Game Production
Authors: Du Chuanyong 
Published: June 2024

Abstract
With the continuous progress of image processing technology and deep neural networks, image style conversion has a wide range of applications in game production. This paper focuses on the application of silk screen printmaking technology in animation game production, using the DiracNet algorithm to express the features of image data, to realize the feature extraction of silk screen printmaking, and add attention mechanism and auxiliary classifier on the AnimeGANv2 model for improvement, to complete the style migration process of silk screen printmaking in animation game production, and to complete the silk screen printmaking technology’s pattern recognition and automatic rendering. Through the key point matching test of the model and the subjective and objective quantitative evaluation of the style migration of screen prints, the model is mined for its effect in pattern recognition and automatic rendering of screen prints technology. The model in this paper has better performance in feature extraction in multi-scenes, and the matching scores and mAP values are improved by 7.14%~25.82% and 11.51%~35.14% over the comparison algorithms, respectively. Most of the SSIM values of the sample screen print style migration images are greater than 0.3, and the overall aesthetic and migration effect evaluations are greater than 3.5. The model in this paper is capable of recognizing screen print patterns and auto-rendering effects, and it has a wide range of potential applications in the production of animation games.
Evaluation Model Construction of School-Enterprise Cooperation Innovation Ability Based on Big Data Analysis
Authors: Zhou Mingyi 
Published: June 2024

Abstract
This study analyzes SCIE papers published by 39 universities as a substantial big-data sample, employing latent class analysis to explore the characteristics of university-enterprise cooperation in Chinese institutions. It identifies five categorical attributes to construct a latent class model. This paper integrates hierarchical analysis with fuzzy mathematics to develop a comprehensive fuzzy evaluation model. By organizing and layering the factors, a rational set of indicators is established, and a hierarchical analysis structure is constructed. Utilizing a judgment matrix, the study calculates the importance rankings of all indicators, thereby highlighting the significant influence of the factors. Empirical results indicate that the model achieves optimal fit with five latent categories. Within the five primary indicators for evaluating the innovation capability of university-enterprise cooperation, the weight of innovation outcomes is the highest at 0.24, followed by industrial innovation at 0.22. The analysis reveals a regional imbalance in university-enterprise collaboration across the eastern coastal area, with Guangdong achieving the highest rating of 84.03, closely followed by Jiangsu. In assessing the innovation capability of university-enterprise cooperation in Guangdong's applied universities, the overall scores for universities categorized as B, C, and D are 3.641, 3.522, and 3.409, respectively. Notably, the scores for R&D investment intensity and Chinese patent applications are relatively low, suggesting these areas require enhancement to bolster support and development, thereby improving university-enterprise cooperation and innovation capabilities.
A Mechanism Study of Corporate Culture and Organizational Innovation Enhancement for Structural Equation Modeling
Authors: Guo Yali , Zhang Miao 
Published: June 2024

Abstract
The role of innovation capability is becoming more and more important for the survival and development of an enterprise, and how to improve organizational innovation capability is an urgent problem for contemporary enterprises. This study adds the mediating variable of corporate learning to the model of corporate culture's influence on organizational innovation capability, puts forward hypotheses, and uses structural equation modeling to study the mechanism of corporate culture-corporate learning-organizational innovation capability. First, the sample data is tested for reliability and validity. The initial structural equation model is then established and tested. Finally, the initial model has been corrected. The correction results show that the effects of innovation values on all three dimensions of corporate learning are significant, with standardized estimates of 0.396, 0.842, and 0.489, respectively. The effects of the innovation incentive system on all three dimensions of corporate learning are confirmed. The hypothesis that innovative behavioral patterns affect vision sharing is rejected. The assumption that all three dimensions of corporate learning have an impact on organizational innovation capability is adopted. This paper concludes with managerial insights on innovation management in firms, makes recommendations, and suggests ways to enhance organizational innovation capability.
Construction and validation of a new media marketing influence assessment model
Authors: Hu Xia 
Published: June 2024

Abstract
In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.
Optimization Exploration of Digital Identity Authentication Algorithm Based on Blockchain
Authors: Lan Fuji , Jiang Yihuo 
Published: June 2024

Abstract
In order to solve the problems of the cumbersome user authentication process, insecure identity information, and vulnerable authentication system in traditional identity authentication, a distributed identity authentication method based on blockchain technology is proposed, which utilizes decentralization and is not easy to tamper with characteristics of blockchain and asymmetric encryption to improve the security and stability of user identity authentication system. On this basis, a digital identity authentication optimization scheme is designed, including the use of a hash chain to improve the data structure and the consistency of node data by optimizing the consensus mechanism to enhance authentication security and efficiency. Finally, the automation of authentication is also achieved by introducing smart contracts. The experimental results show that the computational spend of blockchain technology is 55.47% and 97.36% less than the two traditional algorithms, and the overall security score is 22, while the conventional scheme is only 12.5. The blockchain-based authentication mechanism improves the reliability and efficiency of the authentication system.
Evaluating the Effectiveness of the ACT Model in Enhancing Students’ Coping Skills in Higher Education Counseling
Authors: Li Yongbo 
Published: June 2024

Abstract
The outbreak of a new coronavirus pneumonia epidemic in early 2020 has had a great impact on the mental health status of college students, especially elevating the prevalence of PTSD among students. Based on the cognitive theory of ACT, this study develops a mobile networked intervention program to enhance students’ psychological state and students’ coping ability under the intervention of the ACT model and realizes the function of ACT intervention in the form of networked self-help. Through Latent Gold analysis software, the potential profiles of multiple characteristics of the subjects were categorized in order to scientifically examine the effect of the ACT model on enhancing students’ coping abilities. The results indicated that the ACT intervention group was able to significantly alleviate students’ depression levels (d = 0.71), and enhance students’ positive mental health (d = 0.42), compared to the waiting group. The results verified the effectiveness of the ACT network intervention application, which can provide a reference for the networked reform of psychological counseling in colleges and universities.
Research on the Innovation of Teaching Mode of Accounting Computerization Based on Internet
Authors: Lin Lin 
Published: June 2024

Abstract
The development of information technology such as big data, intelligence, mobile Internet, and cloud computing and their application in the field of accounting have expanded the function of accounting and promoted the development of accounting informatization. This paper innovates and applies the teaching mode of accounting computerization under the Internet mode by constructing a complete teaching system of accounting computerization, introducing the Bayesian knowledge tracking model, and improving the K-means algorithm of the teaching mode of the Internet. The application test validation shows that the average score after the implementation of the teaching mode is higher than the average score before the implementation of the teaching mode, indicating that there is a significant difference between the scores before and after the experiment.... All the learning behaviors in this teaching mode have a strong correlation with the learning effect, and the correlation degree is more than 0.6. Through the knowledge tracking model, this teaching mode successfully achieves timely mastery of students’ learning levels and strengthens their cognition.
Collaborative Filtering Algorithms in Financial Performance Evaluation Indicators for Intelligent Innovation Dynamic Research
Authors: Liu Hong 
Published: June 2024

Abstract
This study develops a financial performance evaluation system for S Group, employing a collaborative filtering algorithm to address the limitations inherent in traditional financial performance evaluation methodologies. Improvements have been made to the similarity measure method, data imputation technique, and rating prediction approach within the model. Subsequently, the financial status of S Group is scrutinized, with a thorough exploration of the input and output indicators, leading to the calculation of the efficiency values within the financial indicator system. Through sensitivity analysis, this research investigates the impact of input and output indicators on the outcomes derived from collaborative filtering. Based on the study, recommendations are formulated in alignment with the evaluation outcomes. The findings reveal that the average comprehensive efficiency of S Group over the past decade is 0.772, with a median comprehensive efficiency of 0.788, indicative of a robust financial condition. This research provides a valuable reference for the formulation of financial performance evaluation systems and the analysis of financial performance across various industries.
Exploration of Quantum Cryptography Security Applications for Industrial Control Systems
Authors: Li Hengyu , Dong Yun , Zhang Yusheng , Wang Haiping 
Published: June 2024

Abstract
The exploration of security applications of quantum cryptography for industrial control systems is a key research effort aimed at enhancing the security of industrial control systems through quantum cryptography. In this paper, we study the security threats faced by industrial control systems, including network attacks, data leakage, and system tampering, and propose to utilize quantum key distribution and quantum invisible state transfer algorithms to ensure the secure transmission of industrial control system data. The simulation test environment of the upper and lower computers of the industrial control system is built. The quantum encryption and decryption algorithms are deployed in the embedded environment and PCs to test the effectiveness of quantum cryptography to enhance the advanced encryption standard key scheme. The experimental results show that the quantum cryptography technology successfully realizes the encryption and decryption of data, and the total time consumed in the whole process is less than 61.8 seconds, which meets the requirements of a real-time industrial control system. Therefore, quantum cryptography is suitable for protecting field-level data in industrial control systems.
Constructing and Experimental Research on the Feedback Mechanism of Mathematics Teaching Based on Error Analysis
Authors: Liu Ji , Zuo Lijuan 
Published: June 2024

Abstract
In the current teaching context, mathematics has become the most popular subject among all disciplines, and the establishment of mathematics teaching evaluation and feedback mechanisms is particularly important. The study constructs a feedback mechanism for mathematics teaching based on error analysis using the improved hierarchical analysis method and fuzzy comprehensive evaluation method. The mathematics teaching feedback mechanism and the traditional manual feedback mechanism are used to compare the performance of error analysis. Completely evaluating math teachers in Z-school using the math teaching evaluation index system. Compare the students’ math scores before and after the implementation of the math teaching feedback mechanism to explore the application effect of the feedback mechanism. The results show that the overall error of the math teaching feedback mechanism (1.12) is better than that of the traditional manual feedback mechanism (3.19), and the error fluctuation of the former is also smaller than that of the latter. The feedback results from the math teaching feedback mechanism are more accurate than the actual feedback results. The fuzzy comprehensive evaluation results of Teacher A are in the following order from highest to lowest: teaching method (93.069) > teaching content (92.531) > teaching objectives (91.645) > teaching effect (90.537) > teaching attitude (88.543). After the experiment of the math teaching feedback mechanism, the mean score of math achievement of the experimental group increased by 12 points. The control group decreased by 1.32 points, and the teaching feedback mechanism achieved significant results.