bluuebunny/crossref_metadata_2025 huggingface dataset:
Studying the Influence of School Culture on Health-related Knowledge, Attitudes and Practices of Traditional Chinese Medicine
Authors: Li Bin
Published: June 2024

With the development of the mobile internet and the intensification of the differentiated behaviour of  communication audiences, there are many challenges and difficulties in content dissemination to younger generations about promoting Traditional Chinese Medicine (TCM) and improving health-related behaviour. Current literature lacks information on the association between knowledge, attitude, practice, and campus culture construction. This article investigates the relationship between traditiona Chinese medicine (TCM) campus culture construction and health-related knowledge, attitudes, and practices (KAP) among primary campus students in China. It employed structural equation modelling (SEM) to analyze the data. For this study, the data was collected through an online questionnaire from a group of students. Exploratory factor analysis (EFA) and Confirmatory FactorAnalysis (CFA) was conducted on data for validation and reliability. The study contributes to the existing literature by providing empirical evidence on the influence of TCM campus culture construction on health-related KAP among primary campus students.
A Railway Roadbed Deformation Monitoring System Using Deep Learning and AI Intelligent Technology
Authors: Pan Pengfei , Li Peng , Zhang Shihao
Published: June 2024

The author has designed an automated monitoring system for settlement and deformation of high-speed railway subgrade. Firstly, an automated monitoring system is designed based on sensors, data collection and transmission, client tracking and querying, monitoring result processing, automated warning, manual monitoring data analysis, monitoring data analysis and evaluation. Secondly, the system software and hardware are designed, the author calculated and analyzed engineering examples from the perspective of practical applications using artificial neural network methods, obtained corresponding deformation analysis models, and predicted deformation. Finally, the practical application of the system was analyzed for its effectiveness. The experimental results indicate that, the BP artificial neural network method is used to model and predict the Deformation monitoring data. On the premise of 20 learning samples and 4 prediction samples, the Root-mean-square deviation of the prediction is 0.32mm, which shows that the deformation prediction using BP model is feasible and effective in a certain precision range. It has been proven that the application of artificial neural network methods in monitoring and prediction of practical engineering has certain practical significance.
Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design
Authors: Huang Jinyi , Zhang Xiaofang
Published: June 2024

With the rapid development of the fashion industry and the increasing diversification of consumer needs, accurately predicting the fashion trends of clothing elements has become an urgent problem in the field of art design. In order to solve these problems, this paper uses deep learning technology for fashion trend prediction and optimization of prediction models. Firstly, the EfficientNet-b7 model is constructed as an attribute predictor to accurately extract the attributes of clothing image elements. Then, based on user information, popular elements are grouped and counted to solve the problem of different populations having different opinions on popular trends. The prediction model is constructed based on the bidirectional long short-term memory network encoder decoder framework, which trains trend information as a whole and utilizes element coexistence relationships to assist in trend prediction. Meanwhile, the study uses random sampling method for clothing original adoption, and the experimental results show that the model considering coexistence relationship performs the best in terms of mean absolute error and mean absolute percentage error, which are 0.0132 and 14.68%, respectively. In addition, the model that introduces attention mechanism based on trend similarity has improved by 5.09% and 4.54% on two indicators compared to the latest model. The experimental results indicate that coexistence relationships can help improve the performance of prediction models. The attention mechanism based on trend similarity can further improve model performance by similarity comparison between historical information and changing trend, and selecting similar historical information as an important influencing factor for future trends.
Evaluation and Selection of Manufacturing Suppliers under the Perspective of Smart Manufacturing Combined with Bilevel Programming
Authors: Du Feng
Published: June 2024

To address the issue of supplier evaluation and selection in the current intelligent manufacturing industry, a supplier intelligent evaluation and selection model combining bilevel programming is proposed. Firstly, based on existing research results and the current situation of the manufacturing market, a manufacturing supplier evaluation index system is constructed, and the principal component analysis method is used to simplify it; Then, the simplified indicator system is input into the back propagation neural network to build a supplier intelligent evaluation model and achieve supplier intelligent evaluation; A mathematical model for supplier selection problem is constructed based on bilevel programming, and an optimized sparrow search algorithm is put forward to solve it. A supplier intelligent selection model is constructed to achieve intelligent supplier selection. The lab findings denote that the F1 value of the supplier intelligent evaluation model is 0.953, the accuracy is 0.985, the area under the curve is 0.962, and the error of the output comprehensive evaluation value is 0.003; The output accuracy of the supplier intelligent selection model is 0.8. The above results indicate that the model raised in the article has good  application effects in supplier evaluation and selection, and has a certain promoting effect on the development of the intelligent manufacturing industry.
Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design
Authors: Yan Wen , Cao Xiaojing , Ye Peng
Published: June 2024

The poor application effect of traditional sports training methods and the difficulty of recording data due to the time and space constraints of sports make it difficult for trainers to improve their learning outcomes. Based on this, the study proposes to apply human posture recognition in sports teaching design, and use VGGNet-19 as a feature extractor and OpenCV open source software to capture posture movements, and introduce the concepts of joint angle and movement similarity to design a sports assessment system for physical education based on the geometric spatial feature variability analysis of posture based on limb angle information. The testing outcomes demonstrate that the study’s improved gesture recognition algorithm has a recognition rate of more than 90% on gesture movements, and the maximum recognition error value (0.010) is smaller than that of the dynamic time-regularised gesture algorithm (0.014) and the convolutional neural network algorithm (0.017). The assessment system is also better able to improve students’ professional performance and satisfaction, with its average professional score and satisfaction reaching 86 and 92%, which is significantly better than other comparative algorithms. The method is effective in providing trainers with data-based training scenarios and helping them to improve their learning in sport.
Teaching Optimization Algorithm and Simulation Analysis based on Self-learning Mechanism and Multi-class Interaction
Authors: Ma Qianli
Published: June 2024

Teaching optimization algorithm is an intelligent optimization algorithm applied in the education. At the same time, it can solve complex optimization problems in other fields such as traffic flow optimization and logistics optimization. In response to the weak development ability, a teaching optimization algorithm based on self-learning mechanism is proposed by referring to general reverse learning methods. Meanwhile, a multi class interactive teaching optimization algorithm is proposed by combining clustering and partitioning methods based on Euclidean distance. By combining the two algorithms, a personalized and collaborative learning teaching environment is provided. When the function dimension is 30, the average function evaluations for the teaching optimization algorithm based on self-learning mechanism on unimodal function f1 is only 3859. On the multimodal function f2, the average function evaluations for this algorithm are only 4735, which is 2057 and 1367 less than the other two algorithms, respectively. Meanwhile, the success rates of this algorithm are all 100%. In addition, on the unconstrained function f6, the multi class interactive teaching optimization algorithm tends to converge when the function evaluations are 0.1×104. Traditional teaching optimization algorithms tend to converge only at 1.0×104. The two improved algorithms proposed in the study have better solution accuracy and stability, providing a reliable method reference for solving modern complex engineering problems.
Development and Mode Innovation of Cultural Tourism Resource based on Industrial Integration
Authors: He Shan
Published: June 2024

The cultural tourism industry contributes to the growth of both cultural and tourism industries. Cultural Tourism Resource (CTR) is the prerequisite for the integrated development of the cultural tourism industry. Developing potential CTR can promote the new transformation and upgrading of the urban cultural tourism industry. Luoyang has the advantages of location and resources in the development of CTR, but its tourism industry develops slowly. Therefore, Luoyang City was taken as the research object and the relevant evaluation index system was established. The entropy method and grey correlation model were used to analyze the development trend and influencing factors of the city’s CTR. The entropy values of the eight indicators of the evaluation system of CTR established in the study were all above 0.96, indicating that the evaluation system was relatively stable. The grey correlation degree of CTR of historical culture and folk customs was high, which was 0.668 and 0.642, respectively. The grey correlation degree of religious culture and calligraphy and painting CTR was low, which was 0.625 and 0.617, respectively. The CTR of historical culture and folk customs have developed well, but the resources of religious culture, calligraphy, and painting need to be improved. The knowledge of cultural tourism location theory and regional economic growth theory are applied. According to the evaluation results and problems of the development of CTR in Luoyang, this paper puts forward targeted strategies for CTR.
Film and Television Special Effects AI System Integrating Computer Artificial Intelligence and Big Data Technology
Authors: Ju Yao , Wei Guobin
Published: June 2024

Particle systems can achieve many scenarios that are difficult to achieve in the field or expensive in reality. In this paper, the requirements of 3D film special effects and the design process of particle systems are studied. Unity3D engine was used to simulate 3D movie special effects. Then, the motion trajectory planning of 3D video group animation characters based on particle swarm optimization is proposed. Then, the system models the animated characters’ moving track to achieve the realism’s dynamic effect. This project intends to use the gravity optimization method for particle swarm optimization. The aim is to overcome the optimization difficulty caused by particle swarm optimization, which is easy to fall into local extreme values. Finally, the generated trajectory information is input into the 3D simulation system for conflict detection and clustering tests. Experiments show that the proposed algorithm can effectively render memorable scenes such as movies and TV. The picture has a high real-time frame rate and is realistic.
Task-dependent Slicing with Convolutional Network Model for Histopathological Image Analysis
Authors: N Hari Babu  , Enireddy Vamsidhar
Published: June 2024

Due to its high rates of both mortality and morbidity, cancer stands as the primary cause of death globally. The examination of histopathological images plays a crucial role in the early prediction of cancer, relying on manual practices for each individual affected. However, this phase is prone to errors, time-consuming, and doesn’t facilitate early-stage decision-making for pathologists. Despite significant advancements in computer-aided image processing, the analysis of histopathological morphology remains challenging due to the intricate nature of these images. Moreover, limited annotations restrict sample analysis. This work focuses on developing efficient deep-learning methods to analyze histopathological images by considering both global and local features. It involves the analysis of image patches and features to address issues related to image annotations. A unique task-specific slicing model, integrated with the convolutional network model (ts-CNN) is proposed. This framework aims to conduct patch-level feature examination and combine multiple samples to achieve the necessary outcomes for classification. The envisioned model aims to rectify inefficiencies present in various existing approaches. Using MATLAB 2020a, the proposed model was employed to classify cancer-based histopathological images,  demonstrating superior predictive performance. It assisted pathologists in early cancer prediction with an accuracy of 98.2 % and aided in decision-making processes, offering promising results.
An Effective Investigation for Quality of Service Enhancement of Content Delivery Network for HTTP Live Streaming Using H.265
Authors: Sangeetha Kiran Babu , Reddy V S K
Published: June 2024

In the contemporary era, the prevailing trend among individuals is the widespread utilization of streaming technology to indulge in audio or video content emanating from a server. This innovative approach negates the necessity for downloading the content beforehand, allowing users to seamlessly revel in the material through the streaming mechanism. However, this cutting-edge technology mandates a substantial bandwidth to uphold its Quality of Service (QoS) at an optimal level. Challenges may arise, such as packet loss, attributed to the restricted bandwidth provided by the network service provider, thereby compromising the delivered content’s quality. To address this issue, one viable solution is the integration of CDN (Content Delivery Network), a technology proven to enhance the quality of content delivered through streaming services. This research endeavors to elevate the quality of live video streaming by leveraging CDN. The HTTP Live Streaming (HLS) format for content streaming, the study empirically demonstrates that CDN significantly improves the Quality of Service. As a result, the proposed method Content Delivery Network (CDN) using H.265 plays vital role in live video streaming. The result shows that live video streaming with CDN outperforms without CDN. Specifically, the live video streaming experience with CDN showcases a remarkable throughput average of 5899.7 kbps, coupled with a minimal packet loss ratio average of 0.05%. This stands in stark contrast to the version without CDN, which exhibits a comparatively diminished throughput average of 5287.3 kbps and a higher packet loss ratio average of 0.22%. These empirical findings strongly emphasize the effectiveness of CDN in elevating the Quality of Service for live video streaming. The tangible enhancements in throughput and the reduction of packet loss achieved through CDN implementationcontribute significantly to an enriched user experience. The evidence supports the notion that integrating CDN technology not only optimizes the technical performance of live video streaming but also plays a pivotal role in enhancing the overall satisfaction and engagement of users with the content delivery system.
Influential Effects between Positive HR Practices and Employee performance – Mediating Moderating Effects based on Work Engagement
Authors: Zhang Bin
Published: June 2024

The development of the knowledge economy society has made enterprises pay more attention to the subjectivity and initiative of ”people” in their human resource management practices, and the performance of employees is related to their level of work engagement. Therefore, this study is based on the impact of positive human resource practices on employee performance, and analyzes the mediating effect of work engagement through experimental data processing, scale design, and hypothesis testing. The results showed that: a) There is a significant positive correlation between positive human resource practices and employee performance, and the regression coefficients corresponding to work performance and relationship performance are both above 0.35 and 0.40. b) There is a significant correlation between positive human resource practices and work performance, and a significant correlation between positive human resource practices and work engagement. c) Work engagement plays a mediating role between positive human resource practices and work performance. Mastering the mediating effect of work engagement can effectively provide management insights for enterprises to promote human resource practices.
Application of Heterogeneous Data Analysis based on SEA Grid in User Investment Analysis
Authors: Liu Aqian , Huang Shuke
Published: June 2024

To solve the problem of yield calculation in complex investment scenarios, a time-cost double-weighted rate of return calculation method based on SEA grid is proposed, and its effectiveness is verified by comparing with  traditional methods and researching the quantitative evaluation and analysis method of user investment based on structured data. To solve the above problems, a data-heterogeneous federated learning method based on user investment analysis FedPSG is proposed, which changes the data form transmitted from the client to the server from model parameters to model scores, and only a small number of clients need to upload model parameters to the server in each round of training, thereby reducing communication costs. At the same time, a model retraining strategy is proposed, which uses server data to train the global model for second iteration, and further improves the model performance by alleviating the impact of data heterogeneity on federated learning. The method of event dimension analysis of user investment is designed, and a credibility index is proposed to evaluate the analysis results. Experiments show that by combining event data, it can effectively provide users with event factors in the fluctuation of investment profit and loss, and help users better analyze their own investments.
Application of BIM Technology in Structural Design of Prefabricated Building Based on Big Data Simulation Modeling Analysis
Authors: Xu Lili , Wang Lin , Zhu Minmin
Published: June 2024

Aiming at the complex steel bar layout problem in prefabricated building, this research proposes a structural design method of prefabricated building based on big data simulation modeling building information modeling technology. It includes the reinforcement arrangement model based on agent path planning, the intelligent reinforcement arrangement of frame based on artificial potential field method and path optimization, and the modeling of Building information modeling. When analyzing the reinforcement arrangement effect of beam column joints in Prefabricated building, the calculation time of top corner joints is the shortest, 76.8 seconds, while that of middle layer joints is the longest, 141.7 seconds. In direction Y and direction X, differential evolution has the longest calculation time, 27s and 26.57s respectively, while particle swarm optimization algorithm has the shortest calculation time, 2.84s and 3.02s respectively. The algorithm designed through research is significantly superior to other algorithms in terms of computational time. In general, through building information modeling technology and big data simulation modeling, this study realized the collision free layout of rebar in beam column joints of prefabricated building. This improves the speed and efficiency of deepening design, and provides a new solution for structural design of prefabricated building.
Application of Health Education Program based on Intelligent Recommendation Algorithm in the Development of School-age Children's Hand Hygiene Behavior
Authors: Zhao Xin , Li Chong , Wang Linghong , Dong Chao , Meng Zhaoxuan
Published: June 2024

The cultivation of hand hygiene behavior among school-age children is an important way to prevent the spread of diseases and ensure children’s health. However, traditional health education methods lack personalized programs tailored to each child, which cannot effectively improve their hand hygiene awareness and behavior. In response to this issue, the study combines multi-objective particle swarm optimization algorithm to provide personalized hand hygiene behavior development recommendations for school-age children, improving their hand hygiene awareness and behavioral level. The study adopted multi-objective particle swarm optimization algorithm and convolutional neural network, combined with the personalized needs of school-age children and the goal of cultivating hand hygiene behavior, and proposed a health education plan based on intelligent recommendation algorithm. Through deep learning, match the hand hygiene needs of school-age children with corresponding health education plans to achieve personalized recommendations. The average accuracy of the health education recommendation plan for hand hygiene behavior cultivation of school-age children based on multi-objective particle swarm optimization algorithm reaches 99.07%. Meanwhile, after introducing convolutional neural networks, the feature matching error between the recommended scheme and school-age children ranges from 10-1 to 10-2. After testing, the designed algorithm performs more stably and has less fluctuations under different sparsity conditions. Health education solutions based on intelligent recommendation algorithms can provide personalized solutions for school-age children, effectively cultivate their hand hygiene behavior, and meet various health needs.
An Explainable AI Model in Heart Disease Classification using Grey Wolf Optimization
Authors: G Varun , J Jagadeeshwaran , K Nithish , DS Ashick Sanjey , V Venkatesh , P Ashokkumar
Published: June 2024

Heart disease is one of the world’s leading causes of death. It is estimated that around one-third of all deaths are caused by heart disease in the entire world. Recently many research works have focused on using machine learning models to detect and warn patients about the occurrence of heart disease at the early stage. However, machine learning models provide promising results, and the performance of the classification is affected by various reasons which include imbalanced training, and missing values. There are three main contributions of this research work. Firstly, missing values are addressed by employing a grouping of instances. Secondly, a dual filter based feature selection is introduced to pick the most effective features and lastly, we make of Grey Wolf Optimization for optimizing the hyperparameters of the machine learning models. Together, these contributions aim to improve the robustness and efficiency of machine learning applications by addressing missing data, optimizing feature selection, and fine-tuning model parameters. The accuracy of 98.41% indicates the superiority of the proposed classification which is more than 17.15% than the existing machine learning models. On the other hand, we use Explainable AI (XAI) methods to make our proposed model interpretable.
Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm
Authors: Zhang Qiong
Published: June 2024

The study presents a hybrid model for recommending travel routes that takes into account multiple constraints. This model is based on a collaborative filtering recommendation algorithm and addresses the issue of disorganized travel route recommendations. To improve upon the k-mean clustering algorithm, the proposed model introduces the Dynamic Gaussian Kernel Density K-means algorithm. After the data was processed, the initial clustering center was determined and k-means clustering was performed. Subsequently, the travel route recommendation model was created by integrating various constraints. The study’s proposed algorithm was compared with alternative algorithms, and the experimental results demonstrated superior performance across a range of datasets, with the minimum sum of squared errors and a running time of approximately 1.4 seconds - a noteworthy improvement. Comparative experiments were conducted on various forgetting coefficients in the model, and the forgetting coefficient with the lowest sum-of-squares of errors was selected to replace the existing one. Upon comparing the proposed research model with other models, it was found that the former had greater accuracy and recall, amounting to 98.1% and 96.8% respectively. This suggests that the proposed research model serves as a more efficient solution for travel route recommendation.
Intelligent Advanced Attack Detection Technology based on Multi-modal Data Fusion
Authors: Hang Feilu , Xie Linjiang , Zhang Zhenhong , Hu Jian
Published: June 2024

This paper proposes an adaptive Wedman intrusion detection algorithm (AID-DFS) for data fusion. Firstly, feature extraction of abnormal text detection is carried out using a BI-gated loop (Bi-GRU). Multi-branch convolutional recurrent neural network (CNN-RNN) extracts hierarchical features from abnormal images. The multi-mode dynamic fusion uses the intermodal and intramodal attention mechanisms. In this way, a joint representation of multiple modes is obtained. The visual perception mechanism is used to realize multichannel integration and strengthen the function of original information in multichannel. The experimental results show that the proposed method has 99.6% accuracy and 94.9% accuracy. Compared with other algorithms, the proposed method can improve the performance of the intrusion system by about 10.2%.
Analysis of Lift-apriori-DP Joint Algorithm-based Data Extraction in Business English Achievement in Colleges and Universities
Authors: Xiao Hongying
Published: June 2024

This paper investigates the application of data mining based on a correlation-rule algorithm in business English performance in colleges and universities. The extracted correlation degree rules are screened by adopting three indexes of support degree confidence degree and lifting degree to measure the correlativity. Experimental validation is carried out on different sets of data sets, and the experimental results show the effectiveness of the Lift-Apriori-DP algorithm. Based on the improved Lift-Apriori-DP algorithm, it is applied to the analysis of students’ performance. Taking the chapter test scores of students in business English courses in colleges and universities as an example, the student’s achievements are extracted and analyzed, and the final appropriate parameter values are selected according to the evaluation of the extraction results under different privacy pre-estimation. The experimental results verify the effectiveness of the Lift-Apriori-DP algorithm in the analysis of student grades and evaluate the accuracy of the algorithm application. The results of this paper show that data mining based on the correlation degree rule algorithm has a wide range of applications in business English grades in colleges and universities, which can provide useful references for teaching and at the same time protect students’ private information from being leaked. In addition, this paper also explores the evaluation class method of the mining results under different privacy pre-estimation, which provides a useful reference for the application of privacy-protecting relevance degree rule mining type algorithms.
Onward and Autonomously: Expanding the Horizon of Image Segmentation for Self-Driving Cars through Machine Learning
Authors: Tirumalapudi Raviteja , Kumar M Nanda , J Sirisha
Published: June 2024

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to perform image segmentation. CNNs are a type of deep learning model that is well-suited for image processing tasks. The CNN in this paper was trained on a local city dataset, and it was able to achieve a mean intersection over union (IoU) of 73%. IoU is a measure of how well the segmentation results match the ground truth labels. A score of 100% indicates that the segmentation is perfect, while a score of 0% indicates that the segmentation is completely wrong. This means that the method can segment images at a very fast rate, which is important for autonomous vehicles that need to make real-time decisions. Overall, the proposed method is a promising approach for image segmentation in autonomous vehicles. It can achieve high accuracy and speed, and it is easy to implement using Python. The proposed method attains an accuracy of 98.34 %, a Sensitivity of 97.26 % and a sensitivity of 96.37 % had been attained. The method could be used to improve the safety and efficiency of autonomous vehicles by enabling them to better understand their surroundings.
Teaching Resource Recommendation of Online Sports Collaborative Learning Platform Based on Optimized K-means Algorithm
Authors: Li Weiguo , Feng Ke , Shi Tianjiao , Hua Jing
Published: June 2024

The online collaborative learning platform for physical education is an interactive and open physical education teaching mode. To improve students’ learning interest and efficiency, the online sports collaborative learning platform is designed. From the perspective of person-post matching, the role in the group is designed and the improved clustering algorithm is used to realize the grouping. The combination of the k-mean algorithm and the firefly algorithm is used to enhance the real-time and accuracy of learning resource recommendation. The outcomes demonstrated that the Firefly algorithm had obvious advantages in convergence speed and other aspects. Relative to the classical K-means algorithm and the Firefly algorithm, the average clustering accuracy of the presented algorithm was improved by 7.23 % as well as 2.18 %, and the average processing time was improved by 4.35 % and 2.26 %, respectively. In the dataset Iris, the average clustering accuracy and processing time were 91.29 and 8.65, respectively. The optimal, worst, and average values of the online collaborative learning platform on the ground of the firefly-optimized K-means algorithm were 0.3006, 3.2176, and 1.5234, respectively. The fusion algorithm proposed in this study can optimize the recommendation of teaching resources on sports online collaborative learning platforms, improve learners’ learning passion, learning efficiency, and satisfaction, and relieve teachers’ teaching pressure.
An AI-Based Classification and Recommendation System for Digital Libraries
Authors: Alomran Abdulaziz I. , Basha Imtiaz
Published: June 2024

The immense volume of online content linked to digital libraries has given emergence to the advancement of screening and recommendation systems. A recommendation system is vitally important in both academic institutions and elibraries to assist professors, instructors, students, and researchers in finding appropriate sources of information. Distributed or collaborative screening is the most common method used in current recommendation systems. However, collaborative approaches cannot promote library repositories, including unrated or unpurchased electronic information. Thus, this paper deals with the automated classification and recommendation of a multiclass corpus found in virtual repositories (cloud databases). In various stages, Neuro-Fuzzy (NF) and Support Vector Machine (SVM) techniques are used as the base classifiers for the categorization of the essential subjects (contents). Later, a high-level ensemble learning strategy is utilized to recommend appropriate subjects from the available multiclass corpus. The methods use a CoC (Coherence of Content)-based inference mechanism to extract and filter the critical components before beginning the recommendation process. Experiments demonstrated that a recommended approach based on detailed conceptual descriptions instead of a handful of phrases/words might help academic and research communities to find relevant sources. Observing the results over a period of months shows that the suggested method increases user comfort, proving the system’s acceptability to users in this way. In addition, compared to previous models, the accuracy in categorizing therequisite subjects is more than 97.16 per cent.
Research on the Application of Artificial Intelligence Technology in the Banking Internet Finance Industry
Authors: Zhang Tianhao
Published: June 2024

This paper presents a collaborative filtering algorithm based on reinforcement learning theory. Then, the personalized bank financial recommendation system for users is constructed in the massive data environment. Tags mimic different types of user interest points to build a representative personalized data set. The collaborative screening of bank financial products is realized using the simulation results and users’ historical access records. The ranking calculation of related financial products is added to the general bank financial product recommendation system. This method can more accurately express the query results for a specific user. It is found that the collaborative filtering algorithm based on enhanced learning theory can improve the efficiency of collaborative screening of bank financial products. The best results can be obtained by combining the two organically. This paper proposes that the recommendation algorithm of reinforcement learning bank financial products based on user preference and collaborative filtering is feasible.
The Application of MATLAB in the Mathematics Teaching of Computer Majors
Authors: Wang Jiangang
Published: June 2024

Under the background of new engineering courses, professional mathematics teaching needs to keep pace with the times and improve the progressiveness and scientific nature of teaching methods.In order to improve the mathematics teaching effect of computer majors, This article constructs a new teaching environment through simulation system construction methods, analyzes the shortcomings of traditional teaching models through comparative analysis methods, and improves the teaching platform based on the actual needs of mathematics professionals in current society. The curve interpolation technology is improved, and a forward-looking real-time Matlab simulation algorithm is designed. Through the evaluation and analysis, it can be seen that the mathematics teaching system for computer majors based on MATLAB can effectively improve the intuitive effect of advanced mathematics teaching, and help to improve the teaching quality of advanced mathematics.
A Multi-Level Deep Neural Network-Based Tourism Supply Chain Risk Management Study
Authors: Xu Liping
Published: June 2024

With the rapid advancement of the tourism, the capital demand of tourism enterprises has gradually risen, but the confusion of market management has increased the difficulty of risk assessment of tourism enterprises by financial institutions, which has led to difficulties in financing tourism enterprises and seriously hindered their development. On this basis, this research first analyses the risk structure of the tourism supply chain (TSC) from the financial aspect and establishes a relevant risk assessment system. After confirming the assessment indexes, the data is dativized and normalized, and finally a multi-level deep neural network (DNN) is used to construct a TSC risk prediction model to calculate the transformed indexes and assess the risk degree of the enterprise according to the results. The experimental results indicate that the model has the best performance when the H-Net hidden layer is 3 layers and the L-Net hidden layer is 4 layers, and its accuracy reaches 93.35%, sensitivity reaches 84.13%, convergence starts at 25 iterations, the final loss value is only 0.8, and the predicted and the real value error is within 2.5%. Therefore, the multi-level DNN model constructed in this experiment has certain application value in TSC risk management.
Application of Unet-SE-Bisru Algorithm for Music Signal Processing in Music Source Separation
Authors: Zhang Tao
Published: June 2024

At present, the use of time-domain deep learning for end-to-end neural network models has the problem of long training time and poor performance in music source separation. To address this issue, a U-network squeezing excitation bidirectional simple recursive unit model was proposed based on the deep extractor model. Replace Unet SE Bisru with Unet SE Bisru in the following text. This model improves the bidirectional long short-term memory network into a bidirectional simple recurrent unit, and then introduces attention mechanisms in the generalized encoding and decoding layers. The squeezing excitation block is used to selectively extract features based on the type of audio to be separated. Finally, group normalization is added after onedimensional convolution, And its effectiveness was verified. The experimental results show that the signal noise distortion ratio in the improved model is 5.68 decibels compared to the bidirectional simple recursive unit value, which is higher than the 5.55 decibels of bidirectional long short-term memory. After adding the squeezing excitation module, the overall increase is about 0.1-0.5 decibels. In addition, in the model comparison, the three indicators of the improved model with the same number of channels were 5.68 decibels, 5.91 decibels, and 11.28 decibels, respectively, higher than the benchmark model. Compared with other music source classification models, the improved model has better comprehensive separation performance. Although some indicators are lower than the comparison model, the signal noise distortion ratio of drum and bass is 6.11 decibels and 6.36 decibels, which is better than the comparison model. Overall, the improved model has high performance in music source separation for music signal processing and can be effectively applied in practical music source separation.
Verification and Optimization of Network Security Defense System from the Perspective of Blue Army in Actual Offensive and Defensive Exercises
Authors: Liao Zhouyuan , Zhang Zhenhong , Yan Ying
Published: June 2024

From the point of view of signal camouflage, this paper proposes an active network defense system. Then, the optimal camouflage target selection scheme is proposed. Its goal is to solve the problems existing in the information constructionof Chinese military equipment support. This method establishes a multistage game model based on weapon support informationnetwork attack and defense. The attack and defense benefits are quantitatively calculated based on the cost of signal concealment. The solution to the refined Bayesian balance problem is given. Then, a multistage optimization algorithm for camouflage signal selection is proposed. Finally, experimental research proves the proposed algorithm to be reasonable and practical.
Fault Tolerance Analysis and Optimization of Centralized Control Platform Based on Artificial Intelligence and Optimization Algorithm
Authors: Yang Longbao , Ma Yuejiao , Zhou Liheng
Published: June 2024

To enhance the reliability and self-healing of the system, the research on fault tolerance of the reconfigurable modular centralized control center is its development trend. Most of the previous research has focused on hardware redundancy. Improving fault tolerance performance is an essential topic in the research of centralized control platforms. Firstly, the problem of centralized fault tolerance in the working configuration of a reconfigurable manipulator is studied. The effect of each hinge on fault tolerance in the existing configuration is studied with the criterion of manoeuvrability and tolerable space. The fault module was first modelled to represent the system architecture information. A modular motion rule based on autonomous recombination technology is proposed. A self-organizing deformation algorithm with fault tolerance is studied. The fault tolerance of the motion pairs is compensated by adding a small number of motion pairs to ensure the configuration characteristics. With the addition of a failure compensation device, the joint’s range of motion was reduced, and the fault tolerance rate was enhanced. After the failure of the robot arm, the fault tolerant control method can still ensure that the robot arm can perform work in its tolerable working space. The test results show that the fault tolerance analysis method is practical and feasible. It lays a theoretical foundation for the application in aerospace, industry and other fields.
Research on Computer Intelligent Collaborative Filtering Algorithm for Personalized Network Data Recommendation System
Authors: Yu Yong
Published: June 2024

In order to meet the protection needs of user privacy data in social networks, this paper proposes a computer intelligent collaborative filtering algorithm for personalized network data recommendation systems. This algorithm predicts user preferences for specific items by utilizing user evaluation information on groups of similar feature items, thereby achieving personalized recommendations. The experimental results show that as the number of project feature selections N increases, the MAE, RMSE, and NDCG5 of the algorithm gradually improve. This is mainly attributed to increasing the number of features under a fixed similarity threshold, which makes the data granularity finer and helps to describe project features more accurately. In the case of a fixed number of project feature selections N, the impact of the number of nearest neighbors s in similar groups on algorithm performance was further studied. The results showed that with the increase of s, MAE, RMSE, and NDCG5 showed a decreasing trend. Although the algorithm suffers from certain losses in recommendation accuracy, it is still within an acceptable range. It is worth noting that due to the system only using generalized data as input, user privacy data is effectively protected. Based on the comprehensive experimental results, this algorithm has significant value in practical applications.
Performance Evaluation Model of Corporate Financial Sustainability based on Swarm Algorithm
Authors: Chang Lingjie
Published: June 2024

In traditional financial performance evaluation models, parameter settings are often too large or too small, resulting in significant model errors. To address this issue, an improved artificial bee colony algorithm was proposed and applied to optimize the parameters of performance evaluation models. This method first constructs a corporate financial performance evaluation system, and then improves the artificial bee colony algorithm with differential evolution algorithm to optimize the parameters of the long short-term memory network, in order to improve the accuracy of the long short-term memory network in corporate financial performance evaluation. The results showed that the improvement of the ABC algorithm was effective. The improved ABC algorithm converged on the Ackley function in the 800th iteration, and the ABC algorithm converged in the 1400th iteration. The evaluation error of the proposed method is the lowest, with the algorithm having the lowest four errors of -0.0121, 0.0453, 0.0683, and 0.0047, respectively. Among the other algorithms, the comprehensive error of the financial performance evaluation model based on Long Short Term Memory (LSTM) network is relatively low, but still lower than the algorithm proposed in the study. The research proposes a long short-term memory network optimized based on improved artificial bee colony algorithm, which can accurately evaluate the financial performance of enterprises, help them review their own development level, and clarify their future development direction.
Research on Laboratory Construction and Management Based on Internet of Things and Deep Learning
Authors: Cao Min
Published: June 2024

A computer laboratory intelligent management and control system based on Internet of Things technology and deep learning is proposed to address the shortcomings of traditional university laboratory management.The system adopts the main embedded technology, mobile communication technology, wireless sensor technology and database storage technology in the Internet of Things technology, laboratory areas and equipment, identify and collect relevant data, monitor abnormal conditions in real time, and alarm. At the same time, the user is provided with a preview of the experimental content and a demonstration of the results through the handheld client APP. The experimental results show that: when the system is powered by 0.35A current, the total system power is about 3W. The NTC thermistor used has a B value of 3435. Under normal temperature, T2 = 298.15K,R = 10KΩ,R0 = 10KΩ. The current temperature value can be calculated according to the value obtained after the analog-to-digital conversion of the microcontroller. The output frequency of the humidity detection circuit is measured by the single-chip microcomputer, and the current ambient humidity can be obtained by substituting the result into the above formula, that is, when the output frequency dimension of the circuit is 6853Hz, the ambient humidity is 40%. Through the application of this system, the inefficient and extensive manual management method of the computer experimental teaching center is completely solved, so that it can bring more intelligent and efficient services to the teachers and students of the whole school, so as to comprehensively promote the experimental teaching of the school.
Application of Improved Genetic Algorithm and Deep Learning in Cold Chain Logistics Distribution Demand Prediction
Authors: Li Hailong , Lu Guangyao
Published: June 2024

In order to solve the problem of inaccurate prediction results caused by the excessive impact of downstream on upstream suppliers in the process of cold chain logistics transportation demand prediction, the author proposes a demand prediction system Multi agent based on improved genetic algorithm and deep learning. The system will improve genetic algorithm, combine deep learning with practical problems in cold chain logistics supply chain, and evaluate the improved model through instance simulation. The results are as follows: After optimization, the order quantity of each stratum reduces the influence of retailers on upstream suppliers by more than 70%. As for the overall transportation cost, it shows a continuous upward trend within 20 cycles, while the total cost of each cycle fluctuates in a lower range after optimization, reducing the overall total cost by about 50%. It shows the reliability of the improved demand forecasting system in this study to greatly reduce storage and transportation costs.