bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom
Authors: Li Shuhui
Published: June 2024

With the advancement of intelligent technology, CNN-based recognition technology has been integrated into public English teaching in universities. This implementation contributes to enhancing teaching quality, nurturing students’ English proficiency, and holds significant educational and practical value. To address the issue of low traditional attendance efficiency in intelligent classrooms for public English teaching, a face recognition model based on CNN recognition technology has been developed. R-CNN is utilized for object detection, along with pyramid pooling and non-maximum suppression to acquire the optimal candidate region for face detection. Furthermore, K-Means clustering is combined to enhance Fast R-CNN, thereby improving detection accuracy. Experimental results demonstrated that among the three networks - Fast R-CNN, Faster R-CNN, and CNN -Faster R-CNN maintained a high recognition rate and exhibited faster convergence speed, showcasing superior overall performance. Specifically, at 500 iterations, the three networks require 23.7 seconds, 26.8 seconds, and 34.2 seconds, respectively. For facial expression recognition, Faster R-CNN achieved the highest recognition rate, indicating its exceptional detection efficiency and potential for aiding teaching management. This study offers novel technical support for public English teaching in intelligent university classrooms, effectively enhancing teaching efficacy and learning experiences. Its practical significance extends to promoting educational reform and improvement.
Social Media and Cloud Computing Impact on Human Resource Management in Multinational Enterprises
Authors: Du Tianliang
Published: June 2024

To address the issues of high storage costs for human resource data, resource management of human resource systems, and employee social interaction in human resource management, this study proposes an adaptive intelligent HR data placement strategy based on genetic algorithms. Additionally, a fitness model and an employee social interaction model are presented. The fitness model utilizes genetic algorithms to optimize data storage strategy and server load balancing, resulting in reduced overall costs and efficient resource utilization. This is achieved by comprehensively considering data transmission latency and storage costs. The employee social interaction model analyzes social media blog post topics and employee ranks, and employs a co-training method to predict employee interactions. The experimental results indicated that the placement strategy can improve the HR data transfer by nearly 50% and effectively reduce the standard deviation of server load by about 10-15%. The accuracy, recall, F1 value, and precision of the employee social interaction model were 77.89%, 63.97%, 70.32%, and 71.78%, respectively. The proposed strategy and model demonstrated higher accuracy and robustness in predicting and analyzing employee social interactions, thus providing more reliable decision support for human resource management.
Environmental Protection Control System Based on IoT and Deep Learning Intelligent Monitoring Sensors
Authors: Cao Shichao , Liu Xiaoying , Li Nan
Published: June 2024

In order to improve the multi-sensor intelligent control performance of environmental pollution detection, this paper proposes a design model of environmental pollution detection intelligent system based on Internet of Things technology. Firstly, this paper uses ZigBee’s Internet of Things networking technology to design the network of the pollution detection system. Secondly, in order to verify the effectiveness of the environmental pollution detection method, this paper carries out system debugging analysis and simulation test, and uses visual dsp++simulation system to debug the system. The network of ZigBee Internet of Things networking technology is adopted to realize the network design of pollution detection system, and VIX bus technology is adopted to carry out environmental pollution detection data communication and information processing, so as to realize the optimal design of intelligent system of environmental pollution detection system. Finally, the experimental test and analysis were performed. The collection period of environmental pollution data is 10 s and the monitoring time is 22 h. Finally, according to the environmental model and parameters, we determine that the system can accurately detect environmental pollution information, and the results are accurate and reliable.
The Application Characteristics and Creation of Digital Sculpture Based on Parameter Model
Authors: Xu Zhao , Li Qiuyang
Published: June 2024

Recently, digital sculpture based on parametric models has emerged both domestically and internationally. To improve the accuracy and efficiency of constructing digital sculpture parameter models, this study proposes a method for constructing digital sculpture parameter models based on improved point cloud registration algorithms by iterating the nearest point algorithm and multi-scale matrix descriptors to increase the iteration efficiency and accuracy of point cloud registration algorithms. The results show that under the Bunny model, the number of three scale feature points is 2146, and the coarse registration error is 0.925 * 10-6 m; As the noise increases, the efficiency of the algorithm proposed in the study increases by an average of 10.1%. The average relative rotation error is reduced by 1-2 orders of magnitude, and the registration time is shortened by 62.7% on average. For a 30dB noise point cloud, it is reduced by one order of magnitude, and as the noise increases, the efficiency of the algorithm proposed in the study increases by an average of 10.1%. In the Dragon model, the number of three scale feature points is 2235, the coarse registration time is only 26 seconds, and the registration error is 0.285 * 10-5m; The average relative rotation error is reduced by 1-2 orders of magnitude, and the registration time is shortened by 62.7% on average. The digital sculpture creation method on the ground of parameter models proposed in this study has improved the registration accuracy and efficiency of parameter models.
Virtual Reality Scenario Analysis of Art Design Taking into Account Interactive Digital Media Pattern Generation Technology
Authors: Liu Yun
Published: June 2024

In art design, 3D printing technology is crucial, and more and more creators conceive scenes using 3D modeling software to get a three-dimensional and beautiful work. Due to the large amount of noise and redundant points in the raw data collected during the modeling process, the generation speed and rendering effect of 3D models are reduced. Given the above problems, the study designed an interactive 3D lightweight modeling system based on the combination of hand-drawn sketching and laser 3D scanning based on the streamlined algorithm. The experimental results showed that when the hand-drawn speed was 300, the number of triangular slices, model size, and time required to generate the model of the hand-drawn sketching model based on the streamlined algorithm were reduced by 67.39%, 65.48%, and 63.79%, respectively. In the real-time point cloud data streamlining process of the laser 3D scanning model, the point cloud data reduction ratio and the streamlining goodness index of the point cloud streamlining algorithm are 71.99% and 3.06%, respectively. The system performance is robust, and the data processing speed and rendering effect are good.
SCAN Algorithm to Optimize Dynamic Instructional Information Network for Predicting Student Behavior
Authors: Teng Junting
Published: June 2024

With the integration of work, study and life on campus taking shape gradually in major universities across China, the concept of education from classroom to life is more widely accepted and the smart education model has become a trend in education informatization. However, most of the existing studies are not applicable to student behaviour data in the campus environment, and the temporal as well as cyclical characteristics in the data are not accessible in the student behaviour prediction problem. In this study, a hypergraph-based dynamic campus behaviour information network is designed to address the needs of the student behaviour prediction problem, and a student campus behaviour prediction algorithm is proposed in the dynamic campus behaviour information network-based student behaviour prediction problem. The effectiveness and rationality of the algorithm is verified through experiments with real campus data sets. The experimental results demonstrated that the periodic nature of the data acquired by the cycle gated cyclic unit module needs to be built on top of the snapshot gated cyclic unit module to help the algorithm achieve better results. The area under the curve of the algorithm proposed in the study achieves more advantageous results on both the 21-day and 35-day datasets. The cycle gated cyclic unit module of the student campus behaviour prediction algorithm proposed in this study can more effectively extract the cyclic features present in the dynamic campus behaviour information network and better accomplish the prediction of student campus behaviour.
Complex Event Information Mining and Processing for Massive Aerospace Big Data
Authors: Li Lin , Jia Libin
Published: June 2024

This paper intends to analyze the existing problems of remote sensing data from the perspectives of space remote sensing information data capacity and data types. Then, a framework for rapidly analyzing and processing space remote sensing information is constructed. Then, LSTM is used to realize the fault diagnosis of remote sensing data continuity, discrete sample mixing and strong correlation of sample variation. LSTM conducts a multimodal analysis of remote-control commands, which is applied to modeling. The multi-stage LSTM prediction model is established and integrated efficiently to improve its adaptive ability in complex space environments. In this way, the anomaly recognition of remote sensing information is realized. Experiments show that the algorithm can improve the anomaly detection rate of remote sensing data. Experiments show that the algorithm is feasible. It can provide reliable data interpretation function for space remote sensing information control system.
Application of High-Dimensional Data Visualization and Visual Communication Technology in Virtual Reality Environment
Authors: Liu Dan
Published: June 2024

How to efficiently find what you want from massive data has become a hot topic. It is precisely because network information is developing so fast that so many data-oriented software systems and pages are used to manage so much network information. Aiming at problems such as insufficient information mining of visual communication websites in ASPNET and poor visual communication effects, a design scheme for integrating high-dimensional data into visual communication websites is proposed. Its working architecture includes high-dimensional data integration, multi-type visual integration, and visual support services. The main functions of the website include comprehensive management, charts, data sources, projects, systems, etc. We have conducted specific design and Implementation of high-dimensional information integration on the website, completed the design and Implementation of integration of high-dimensional data, integration of multiple visualization types, and integrated access to visualization-supported functions. Use a deep web crawler to search the visual site. It uses a search-based approach to filter URLs and crawl valuable web pages. Practice has proved that the intelligent distribution network management page implemented by the solution proposed in this article has good performance. The system shows good performance in visualization implementation and the ability to discover information. The system achieved sound and visual communication effects.
Cross-border E-commerce Logistics Optimization Algorithm for Collaboration between the Internet of Things and Logistics
Authors: Pan Shiling , Cheng Juan
Published: June 2024

This paper proposes the shortest path optimization algorithm for domestic and overseas e-commerce logistics based on a bilateral search method. This paper uses the logistics distribution route optimization algorithm based on the shortest path to set the collaborative parameters. Then, it builds an adaptive optimization model for the grid planning of domestic and overseas e-commerce logistics path. The route is optimized. Then, the PSO and genetic algorithm are integrated to establish the logistics path planning model of domestic and overseas e-commerce. The superiority of the proposed route optimization algorithm in domestic and overseas e-commerce logistics distribution is verified through simulation experiments. This algorithm has high spatial positioning efficiency and high transportation efficiency.
Indoor Space Layout Design based on Differential Evolution Algorithm
Authors: Meng Sha
Published: June 2024

To enhance the interactivity of spatial design, this study proposes an indoor spatial layout design method based on differential evolution algorithm, which combines backtracking strategy and reverse learning strategy to improve the interactive differential evolution algorithm. The experimental data demonstrated that the proposed method for indoor space layout design achieved an average user satisfaction of 81.7%, which increased by 16.8% compared with traditional interactive genetic algorithms. In addition, the improved human-computer interaction interface scored higher than 0.8 in terms of usability, reliability, customizability, and interactive feedback. This means that the improved interface can better meet user needs and provide a better user experience. This study shows that the indoor space layout design method ground on differential evolution algorithm and the improved human-computer interaction interface can significantly improve user satisfaction and user experience. This has brought more efficient and convenient solutions to the field of spatial design.
The MD-BK-means Construction Method for Library Reader Portraits
Authors: Zhu Zhiyu
Published: June 2024

Due to the rapid development of internet technology, knowledge acquisition has become more convenient and efficient in network operations. University libraries serve as important resources for readers to acquire knowledge, and online resources and services in libraries have become the main direction for readers to acquire knowledge at present. Research the use of binary K-means clustering algorithm and library reader portrait technology to optimize the design of the reader portrait module and construct a multidimensional and multi perspective reader feature system. Reuse Spark programming language and support vector machine to perform computational processing on reader profile data to ensure accurate segmentation of the dataset. Finally, three datasets were used to test the accuracy and efficiency of the algorithm. The experimental comparison shows that the mining and precision segmentation of parallel SVM on the dataset are 93.20%, 85.16%, and 79.35% on the sample set, respectively, in order to optimize the mining performance of the data. The MD multi view binary K-means algorithm has a total Mahalanobis distance of 3.543, 5.268, and 22.385 on the sample dataset, respectively, to demonstrate its superiority in clustering performance. Therefore, the multi view binary K-means algorithm based on Mahalanobis distance has high advantages in reader portrait technology design, and provides technical support and theoretical reference for library reader portrait technology.
The Recognition of Aerobics Movements Using Bone Data Combined with ST-GCN
Authors: Yang Huahua , Zhao Yansheng , Xia Li
Published: June 2024

To solve the aerobics action recognition and promote the gradual intelligence and standardization of aerobics teaching and evaluation, a network model based on spatial temporal graph convolution and combined with attention mechanism was proposed. This model improved the extraction efficiency of spatiotemporal features and channel features by introducing spatiotemporal graph and channel attention mechanisms, respectively, thereby improving the accuracy of action recognition. And a time extension module was introduced into each basic module, and additional features between adjacent vertices were extracted by extending the time graph between frames. These experiments confirmed that this model exhibited high accuracy in identifying aerobics movements. The recognition accuracy of basic actions was above 93.6%, and the recognition accuracy of two actions had reached 98.4% and 98.6% respectively. For advanced actions, the recognition accuracy of the model had slightly decreased, but the average value was still above 95%. The accuracy of difficult motion recognition had also achieved good results, reaching a maximum of 94.5%. These data indicate that this model can achieve high accuracy in handling action recognition tasks of different difficulty levels, and can identify aerobics movements of different difficulty levels.
Stock Quantitative Intelligent Investment Model Based on Machine Learning Algorithms
Authors: Wang Kangyi
Published: June 2024

In order to cope with sudden changes in market style, the author designed and implemented an investment system. In response to the inability to cope with sudden changes in market style, the author proposed an improved scheme when using a classifier for training, using a hidden Markov model to select time points in the same market state as the training samples for the classifier. The results of backtesting and real market testing show that the improved investment system can capture changes in market style, and during the testing period, the test results obtained higher returns compared to the pre improved classifier. At present, the investment system implemented by the author has become the core backend module of the officially launched intelligent investment advisory product Zhiyu Liangtou APP, providing strong investment decision-making suggestions for small and medium-sized investors. In the future, we can provide backend support for more user end products.
The Digital Media Art and Design System under the Integration of Computer Big Data ChatGPT Technology
Authors: Zhang ErHua , Lu ShengWen
Published: June 2024

Traditional digital media art and design systems take a long time to run and cannot handle a large number of objects simultaneously. Therefore, the author proposes a digital media art and design method based on ChatGPT technology. ChatGPT technology is an important part of networking.Prepare to provide conference support for various applications such as customer service, personal assistant, robotic interview, and so on. ChatGPT can not only answer the question that is set, but also solve the question openly.The comprehensiveness and intelligence of ChatGPT have attracted widespread attention since its launch. The hardware system adopts dual core TMS320C6657 and DDR3Thel1333 series processors as the core circuit, together with high speed external storage, to expand the image transmission line of multi-data; System software uses module architecture to design image modules, video modules, and voice modules, all of which are integrated into the core of the system as modules. In this experiment, 10 images were transferred and the running time was tested. Experimental results showed that, the traditional digital media art and design system had three errors, which took more time to run.
Intelligent Matching Method for College Dormitory Roommates: Chameleon Algorithm Based on Optimized Partitioning
Authors: Wang Cuiping
Published: June 2024

A chameleon algorithm based on optimized partitioning was studied to solve the intelligent matching problem of college dormitory roommates. Using quantitative research methods, data on personal preferences and lifestyle habits of college students were collected, and the K-center object chameleon algorithm was used for data analysis and roommate matching. Test the algorithm performance on the BBC dataset, compare clustering quality indicators such as entropy, purity, and RI value, and verify the effectiveness of the algorithm. This algorithm can accurately assign students to their respective dormitories, avoiding overlapping situations and achieving excellent matching results. In terms of matching accuracy and running time, the K-center object chameleon algorithm shows superior performance compared to other algorithms. In terms of clustering quality evaluation, comparisons were made from three dimensions: entropy value, purity, and RI value. The experimental results show that the closer the entropy value is to 0, the closer the purity and RI value are to 1, and the better the matching effect. This result further validates the effectiveness of the algorithm in the intelligent matching problem of college dormitory roommates. The matching accuracy of this algorithm on the BBC dataset reached 98.82%, showing better clustering quality than other algorithms in terms of entropy, purity, and RI values. The entropy value approached 0, while the purity and RI values approached 1, verifying the efficiency of matching quality. The chameleon algorithm based on optimized partitioning proposed in the study has shown excellent performance in intelligent matching of college dormitory roommates, with the characteristics of high-precision matching and fast running time. It has important practical significance for improving the quality of life and learning efficiency of college dormitory students, and provides new research methods and ideas for related fields.
Intelligent Education Managemnt System Design for Universities based on MTCNN Face Recognition Algorithm
Authors: Li Lin , Zhang Qi
Published: June 2024

Face recognition technique has made significant advancements in security and attendance, but its application in teaching management is minimal. To address the issues of insufficient teacher resources and declining educational quality, the paper designs an intelligent education management system for colleges and universities based on improved Multi-task Cascaded Convolutional Neural Networks (MTCNN) face recognition. The purpose is to achieve accurate recognition of faces through improved facial recognition technology, thereby analyzing the attendance status of students, and improving the efficiency and quality of educational resource utilization. Firstly, an improved MTCNN facial recognition technology is adopted to achieve realtime monitoring of student status and attendance in the classroom through a B/S network structure. Secondly, through cluster deployment and load balancing, system stability and response speed can be improved. The results indicated that the improved MTCNN had better facial recognition accuracy and GPU utilization than traditional systems under different occlusion conditions. When there was no occlusion, recognition accuracy was 99.4%. However, when occlusion was presented at 10%, 20%, and 30%, the accuracy dropped to 92.3%, 84.25%, and 73.4%, respectively. Additionally, when the number of concurrent users was 1000, the maximum GPU utilization rate was 75%, which was 11% lower than traditional MTCNN systems. The use of an improved MTCNN facial recognition-based intelligent education management system in universities can effectively enhance the quality of classroom teaching and monitor the status of students. Further optimization of algorithm performance is needed in subsequent research to support larger-scale concurrent user usage while reducing hardware resource consumption.
Approximate Computing Based Low-Power FPGA Design for Big Data Analytics in Cloud Environments
Authors: Dova Murali , M Sandi Anuradha
Published: June 2024

As cloud computing continues to evolve, the demand for scalable and energy-efficient infrastructure to handle extensive applications becomes paramount. Traditional transistor scaling and microprocessor design methods no longer suffice to meet the growing scale of cloud usage. This research explores the potential of approximate computing (AC) as an innovative solution to these challenges, particularly in high-demand computational settings. AC, known for its ability to make controlled accuracy trade-offs, is identified as a key strategy for improving both the performance and energy efficiency of cloud infrastructure, with a focus on low-power Field-Programmable Gate Array (FPGA) designs. This paper introduces novel methodologies that harness the strengths of AC, emphasizing its application in neural-based and machine-learning techniques for energy-efficient solutions. By targeting the performance of AC, especially in varied application domains and complex data mining scenarios, we propose two groundbreaking approaches that significantly enhance computational speed and reduce energy consumption. Our empirical analysis demonstrates notable improvements over existing techniques, highlighting the effectiveness of AC in optimizing cloud infrastructure. The proposed model on FPGA through cloud computing attains substantial elevation rates by 89 % and energy reduction by 122%, which had been good outcomes. This study not only confirms the benefits of integrating AC with low-power FPGA designs for cloud environments but also sets a new benchmark for future research in achieving more sustainable and efficient cloud computing via VLSI FGPA design analysis solutions.
Classification of Diabetes Using Ensemble Machine Learning Techniques
Authors: GR Ashisha , Mary X Anitha , Raja J Mahimai
Published: June 2024

Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In this context, Machine Learning (ML) might be used to detect health problems early, diagnose them, and track their progress. Ensemble techniques are a promising approach that combines many classifiers to improve forecast accuracy and resilience. This study investigates the categorization of diabetes using an ensemble machine learning technique known as a voting classifier. Using a variety of classifiers, including Light Gradient Boosting Machine (LightGBM), Gradient Boost classifier (GBC), and Random Forest (RF). The predictions are aggregated using voting methods to get a final classification result. The research is carried out using two benchmarking datasets: the Pima Indian Diabetes Dataset (PIDD) and the German Dataset. The Boruta technique is used to choose the best attributes from the datasets, while the Random Over Sampling approach balances the range of classes and eliminates abnormal data using the interquartile range approach. The findings showed that the combination of the Boruta feature selection algorithm and ensemble Voting Classifier performed better for both PIDD and German datasets with an accuracy of 93% and 90% respectively. These algorithms are evaluated and the maximum accuracy is produced using the combination of the Boruta feature selection algorithm and ensemble Voting Classifier. This research helps medical professionals in the early prediction of diabetes, reducing physician’s time.
Prediction of Diabetes Mellitus using Artificial Intelligence Techniques
Authors: G L SumaLata , C Joshitha , Kollati Meenaksh
Published: June 2024

Diabetes Mellitus (DM) is a global health challenge, demanding proficient predictive models for early identification and intervention. This study adopts a comprehensive strategy for diabetes prediction with Machine learning algorithms, utilizing PIMA Indian diabetes dataset which encompasses clinical, demographic and lifestyle data. Employing techniques like Recursive Feature Elimination (RFE) and correlation analysis, the feature selection process identifies influential predictors, including glucose levels, Body Mass Index (BMI), Blood Pressure and diabetic history of family. A distinctive facet of this study involves integrating IBM Auto AI, automating the machine learning pipeline for tasks like feature engineering, hyperparameter tuning and model selection. Through comparative analysis, the research evaluates the efficiency and performance enhancements achieved through automation in contrast to manually-tailored models. Evaluation metrics encompass accuracy, precision, recall, and F1 score. Crossvalidation, particularly k-fold cross-validation, ensures model generalization to diverse subsets of the dataset. The research outcomes offer valuable insights into the optimal amalgamation of AI techniques for diabetes prediction, underscoring the significance of interpretability, performance, and automation in healthcare analytics. The proposed Methodology is evaluated with different classifiers with Auto AI and without Auto AI techniques. Using IBM Auto AI,Gradient boosting algorithm performed well with 84.4 % accuracy and Logistic Regression showed good accuracy of 84. 4% among conventional machine learning techniques without Auto AI using Pima Indian Diabetes Dataset.
Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model
Authors: Gu Yunzi
Published: June 2024

An accurate short-term passenger flow forecast of rural tourism can avoid accidents as much as possible. However, the short-term passenger flow of rural leisure tourism shows nonlinear, seasonal, random, and other complex characteristics. Meanwhile, the traditional forecasting methods are often difficult to achieve accurate forecasting. Therefore, this study now used the nonlinear mapping function in the support vector regression to convert the passenger traffic training sample into a high-dimensional feature space and established a linear decision function. Then the influence of periodicity on the prediction effect through seasonal index adjustment was reduced. Finally, event triggering combined with core embedding technology for tamper-proof detection was adopted to improve the security of the platform. The results showed that the minimum absolute error of prediction with improved SVM model was 0.27% compared with traditional model and autoregressive integrated moving average model. After the introduction of the Internet search factor, the traffic prediction result was more accurate, which was 0.0425 smaller than that without the introduction of the Internet search factor. When the concurrency was less than 100 times/s, the average response time difference before and after adding the core embedded program was small, indicating that the security of page tampering technology was high. This research method can effectively predict the passenger flow of rural leisure tourism industry and ensure the safety of the platform.
Research on the Application of VR Technology based on Human Posture Recognition in Online Physical Education
Authors: He Fuyang
Published: June 2024

Offline and current online physical education classes are characterized by low learning efficiency and poor teaching quality. To solve this problem, the study combines a dynamic time regularization algorithm and local image feature action recognition with virtual technology, which is applied to online teaching to improve teaching quality. Experimental results indicate that the overall model accuracy reaches more than 94%, and in the public dataset MSR, the model accuracy reaches 91.4%. In the UTK dataset, the accuracy reaches 85.8%. When the accuracy of pose recognition is compared, the research model is superior to the two traditional models. The validation results against KNN and SVM are 62% and 71% as well as 79% and 84%, respectively, and the experimental results of the research model have improved by 9% and 5%. The research technique achieves an overall fit of more than 90% for different parts in the analysis of motion capture, which is superior to the traditional online instruction. The overall teaching satisfaction reaches more than 93.5% in the comparison of online physical education courses with offline courses. In summary, the optimized and improved posture recognition system is better for human motion capture, and the applied virtual technology can effectively improve the learning effect and teaching quality of online physical education courses.
Interior Scene Coloring Design Model Combining Improved K-means and SAA
Authors: Xu Jiahui
Published: June 2024

Due to technological progress and changes in people’s aesthetic standards, traditional design models need to be constantly broken through, seeking more efficient and accurate design methods. Seeking effective design models to improve design efficiency and prediction accuracy is an important task. Therefore, this study proposes an indoor scene coloring design model that combines improved K-means clustering and simulated annealing algorithm for this important task. Based on the analysis of indoor scene coloring, particle swarm optimization algorithm is used to optimize K-means clustering to achieve color classification. Combined with simulated annealing algorithm, adaptive adjustment of lighting conditions is achieved to enhance the naturalness and realism of coloring. These results confirmed that the proposed method had the highest average F-value, with an average F-value of 92.524 and 143.601 on both datasets, respectively. The average ARI values were 0.361 and 0.897, respectively. The designed algorithm performed the best and converged faster than other three. Therefore, the proposed method can effectively ensure the consistency between the distribution of data objects after clustering and the actual situation. For indoor scene coloring design, it has important practical significance and provides new possible paths for  improving design efficiency and prediction accuracy.
A Deep Learning Model-Based Feature Extraction Method for Architectural Space and Color Connection in Interior Design
Authors: Liang Tao , Xiao Zhizhong , Guo Lingzi
Published: June 2024

In architectural interior design, color is one of the important design elements. Through the reasonable combination of various color elements, it can effectively improve the interior environment and create an atmosphere that meets the preferences and needs of users. And with the continuous development of social economy, the application of color in interior design is becoming more and more widespread. Using different colors in interior design to harmonize not only can relieve people’s visual fatigue, but also can bring people a pleasant mood. Different colors have different meanings, therefore, the use of color in interior design should be more flexible and color matching should be more innovative. The warm and cold, near and far, expansion and contraction of color make the color space the most dynamic key element in design. The grasp of color and scale of architectural space and the flexible use of color will directly affect the quality of architectural space design. Color can strengthen the form of interior space or destroy its form. In order to accurately grasp the connection between architectural space and color in interior design, this paper proposes a deep learning model-based feature extraction method for the connection between architectural space and color in interior design. First, we construct a product color sentiment imagery dataset; then, we build a model for generating architectural interior space layout and color design schemes based on the product color sentiment imagery dataset and conditional deep convolutional generation adversarial network, and innovatively generate product color design schemes. This algorithm can better balance the chromaticity, saturation, and clarity of images. When determining the similarity of indoor space colors, depth features are superior to point-to-point pixel distance and aesthetic features of indoor space colors. Finally, the effectiveness and applicability of the proposed method are verified in relevant experiments.
Dynamic Security Rule Optimization Based on Deep Learning and Adaptive Algorithms
Authors: Hang Feilu , Xie Linjiang , Zhang Zhenhong , Liu Yuting , Hu Jian
Published: June 2024

Compared with traditional computer network security identification techniques, deep learning algorithms are based on their own distributed network structure for storage, processing, classification, comparison, and automatic identification functions. The distribution and dynamism of cloud databases increase the difficulty of route prediction and recognition in the cloud, affecting the efficiency of cloud computing. In response to the above issues, the author proposes a dynamic path optimization process for cloud databases based on adaptive immune grouping polymorphic ant colony algorithm. By setting up two states of ant colony, reconnaissance ant and search ant, and introducing an adaptive polymorphic ant colony competition strategy, the defect of general ant colony algorithms being prone to falling into local optima is improved; On this basis, an artificial immune algorithm with fast global search capability is further integrated to improve the search ant path optimization process, improving  search speed and accuracy. Simulation experiments show that the IPANT algorithm outperforms the other three algorithms, maintaining a throughput of 1000 kbps and relatively stable; The data of OSPF, SPF, and FR are not significantly different, significantly lower than IPANT. The immune polymorphic ant colony algorithm (IPANT) has the lowest time delay for packet routing and performs better than the other three algorithms, with FR and SPF having higher latency. It has been proven that this algorithm can better solve convergence speed and global optimization problems, and can quickly and reasonably find the database to be accessed in the cloud.
Construction of Power System Network Security Defense Behavior Decision-making Model based on Artificial Intelligence Technology
Authors: Hang Feilu , Xie Linjiang , Zhang Zhenhong , Hu Jian
Published: June 2024

According to the needs of power grid monitoring architecture and information security cooperation protection, this project builds a multi-level, deeply distributed active security cooperation defense mode. A complete  implementation method is proposed from the perspective of model architecture and function mechanism. The optimal defense strategy based on grey correlation is studied according to the characteristics of cooperation between regions. In this way, the coordination between the equipment is realized to achieve multi-level protection from the host layer to the security equipment layer and then to the network layer. Multiple detection mechanisms are used to realize the comprehensive detection and integrated judgment of abnormal documents in the cloud environment. This provides maximum protection for cloud users. Experiments show that this method can effectively suppress the malicious attacks of malicious users and reduce the damage caused by viruses. In this way, both the cloud and the customer are protected.
Artificial Intelligence Retrieval System Based on Computer Big Data Technology
Authors: Yu Honghua
Published: June 2024

In order to achieve automatic retrieval of library literature, the author proposes a robot retrieval method based on intention recognition for library literature automatic retrieval. Firstly, propose a robot retrieval approach; Then, the keywords of the user’s question are automatically extracted, and the intention of the user’s question is identified. Finally, combined with intention recognition, the semantic similarity between the intention recognition literature and the search literature is calculated. The latest results indicate that: The accuracy of keyword automatic extraction based on KL and TF-IDF for each sentence keyword is 98.3%, indicating that this extraction method performs better. The retrieval accuracy of the automatic retrieval robot reaches 98%, indicating that this method can accurately achieve accurate retrieval of user intentions and candidate literature.
Evaluation Method of Comprehensive Ability of Students Majoring in Preschool Education Based on Computer Technology
Authors: Wen YunFang
Published: June 2024

In order to improve the comprehensive ability evaluation effect of students majoring in preschool education, this paper combines computer technology to evaluate the comprehensive ability of students majoring in preschool education. Through linearization, this paper deduces the state space model of data load of students’ comprehensive ability and the transfer function model of student’s comprehensive ability data load. Moreover, this paper establishes an analytical expression between the model coefficients of the transfer function of student’s comprehensive ability data load and the transfer parameters of student’s comprehensive ability data load. In addition, this paper analyzes and selects the identification method of the parameters of students’ comprehensive ability data load model. Since the parameters of the student comprehensive ability data load model have physical meanings, the parameter identification of the student comprehensive ability data load model is a gray box identification, and constraints are required when identifying the parameters of the student comprehensive ability data load model. The experimental study has verified that the comprehensive ability evaluation model of students majoring in preschool education based on computer technology can play an important role in the comprehensive ability evaluation of students majoring in preschool education. This article adopts the comprehensive load model in the Comprehensive Stability Calculation Program (PSASP) to eliminate the shortcomings of factor analysis in traditional teaching comprehensive evaluation, and has a certain reference value for the promotion of intelligent teaching in the future.
SVM-based Support Vector Type Recognition Machine for Smart Things in Soccer Training Motion Recognition
Authors: Wang Shouwei
Published: June 2024

With the development of IoT technology, machine learning, and other artificial intelligence technologies, there have been many related technologies applied to the sports industry. Soccer, as the world’s number one sport, has a wide range of popularity, a high degree of attention, and a high degree of commercialization. In the traditional soccer training action recognition methods, there is insufficient collection and in-depth profiling of real data, and what is not available is soccer movement action capture and recognition based on kinematic knowledge. To address the above shortcomings, this study designs a Support Vector Machine SVM-based intelligent IoT-type soccer training movement recognition and evaluation framework, and constructs a machine learning algorithmic model to recognize, evaluate and analyze soccer training movements. Common feature extraction methods are suitable for recognizing most monotonous movements, but soccer movements are highly variable and athletes’ ankle movements are flexible and changeable. The actual acquired soccer data streams are noisy and the data patterns are not obvious, and the performance of the model to recognize the data will be degraded. To extract the effective feature values in the complex data stream and improve the correct degree of pattern recognition, the classification pattern of attitude angle type solving + SVM classification algorithm is constructed. The experimental results show that the designed algorithmic pattern based on the posture angular pattern solving + SVM classification algorithm pattern for soccer training movement recognition can reach 90% accuracy in recognizing different movements, which is extremely suitable for the recognition of soccer training movements.
Heterogeneous High Performance Data Mining System for Intelligent Data
Authors: Wang Xinke , Li Kai , Li Xiaoling
Published: June 2024

In order to improve the utilization rate of internet data under heterogeneous distribution, increase the diversified usage functions and data transmission rate of the internet, and reduce the running time of the internet, it is necessary to mine internet data under heterogeneous distribution. The author proposes an ontology based optimization method for internet data mining under heterogeneous distribution; This method first preprocesses and selects data features from internet data under heterogeneous distribution, and uses a feature selection decision system to select features from the mining data. Based on this, information entropy is used to filter internet data under heterogeneous distribution. During the filtering process, the theoretical values filtered by information entropy are reduced to obtain the optimal data filtering value, finally, based on the various data information obtained in the preprocessing, the iterative calculation results of the information gain value in the decision tree generation algorithm are used to high-precision mine internet data under heterogeneous distribution; The simulation experimental results demonstrate that theproposed method improves the flexibility of internet data operations under heterogeneous distribution, increases the recyclability of internet data, and makes internet operations under heterogeneous distribution more concise and efficient, providing a strong basis for research and development in this field.
Application of Multi-objective Evolutionary Algorithms for Multidimensional Sensory Data Prediction and Resource Scheduling in Smart City Design
Authors: Liu Liya
Published: June 2024

Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm’s performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms’ superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm’s optimization of the YOLO-v3 model enhances target detection performance and precision.