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Academic Journal of Computing & Information Science最新文献

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Optimization Analysis of Service Quality of Cainiao Station 菜鸟站服务质量优化分析
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061020
{"title":"Optimization Analysis of Service Quality of Cainiao Station","authors":"","doi":"10.25236/ajcis.2023.061020","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061020","url":null,"abstract":"The rapid development of e-commerce platforms has provided consumers with diverse shopping choices. This study focuses on the service quality of Taobao, the largest e-commerce brand in China, and Cainiao Station, its service logistics system. Through actual investigation, it was found that customer expectations can affect the business performance of e-commerce. There is a correlation between service performance and satisfaction, as well as customer complaints. Customer complaints can even affect customer loyalty and repurchase rate.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135156093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Lossy Compression Approaches Based on Vector Quantization 基于矢量量化的有损压缩方法
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061001
{"title":"Lossy Compression Approaches Based on Vector Quantization","authors":"","doi":"10.25236/ajcis.2023.061001","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061001","url":null,"abstract":"Vector Quantization (VQ) is an effective lossy compression technology developed in the late 1970s. Its theoretical basis is Shannon's rate distortion theory. The basic principle of vector quantization is to use the index of the codeword in the codebook that best matches the input vector for transmission and storage, while decoding only requires a simple table lookup operation. Its outstanding advantages are high compression ratio, simple decoding, and the ability to preserve signal details well. In this article, several VQ approaches are introduced for lossy compression.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135157267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mobile Guidance Model of Opportunity Network Node Based on Knowledge Graph 基于知识图的机会网络节点移动引导模型
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061006
{"title":"Mobile Guidance Model of Opportunity Network Node Based on Knowledge Graph","authors":"","doi":"10.25236/ajcis.2023.061006","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061006","url":null,"abstract":"When guiding the movement of network nodes, due to the lack of comprehensive analysis of node attributes, the success rate of network information delivery is low. Therefore, an opportunity network node movement guidance model based on Knowledge graph is proposed. The Knowledge graph of opportunity network nodes including degree centrality, proximity centrality, PageRank algorithm and Structural holes is constructed; When building the mobile guidance model of opportunity network nodes, the optimal solution of the Knowledge graph is taken as the result of node mobile guidance. In the test results, when the proportion of abnormal nodes in the test network is within 10.0%, the success rate of information delivery remains stable at over 0.90.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135157915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction and Analysis of Global Temperature Based on BP and ELMAN Neural Networks 基于BP和ELMAN神经网络的全球温度预测与分析
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061019
{"title":"Prediction and Analysis of Global Temperature Based on BP and ELMAN Neural Networks","authors":"","doi":"10.25236/ajcis.2023.061019","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061019","url":null,"abstract":"In order to explore the global climate evolution and change patterns, this article uses global temperature data from 1881 to 2020 for nearly 140 years, and based on the global temperature zone division model, constructs BP neural network and ELMAN neural network prediction models to analyze the spatiotemporal evolution trend of global temperature historical data. It is found that the average temperature in the northern and southern hemispheres began to significantly increase around 1950; based on the above model, it is predicted that the global annual average temperature will reach its peak around 2050 and continue to maintain around 16.6433℃ for the next fifty years.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135158141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Applied Research on AQI Prediction Based on BP Neural Network Modeling 基于BP神经网络建模的空气质量预测应用研究
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061014
{"title":"Applied Research on AQI Prediction Based on BP Neural Network Modeling","authors":"","doi":"10.25236/ajcis.2023.061014","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061014","url":null,"abstract":"In recent years, air environment quality has become a hot issue of concern for people all over the world, and the prediction of air quality is of great significance for air pollution prevention and control. There is mainly a nonlinear relationship between air quality data and influencing factors, and BP neural network has a strong nonlinear mapping ability, which can fit the more complex nonlinear mapping relationship. Based on this, this paper utilizes BP neural networks to establish an air quality index AQI prediction model to predict the AQI in Nanjing, with an average relative error of about 1% and a prediction accuracy of 99%. The establishment of this model can provide reliable reference and decision-making basis for government departments and citizens.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135158142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
LOGO recognition system based on deep learning 基于深度学习的LOGO识别系统
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060902
{"title":"LOGO recognition system based on deep learning","authors":"","doi":"10.25236/ajcis.2023.060902","DOIUrl":"https://doi.org/10.25236/ajcis.2023.060902","url":null,"abstract":"We used the deep learning architecture designed by ourselves to identify the logo, with good effect and accuracy. Our architecture uses four convolutional neural network architectures, two pooling structures and two fully connected neural network architecture.The characteristic of our architecture is that it is relatively simple. We can use the limited things we learn to create a program that meets our requirements.The results of the test were relatively successful. The logo recognition accuracy for our own data set can reach 95.83%.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135750224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Real-time Monitoring and Analysis of Computer Image Processing in Intelligent Transportation System 智能交通系统中计算机图像处理的实时监控与分析
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060908
{"title":"Real-time Monitoring and Analysis of Computer Image Processing in Intelligent Transportation System","authors":"","doi":"10.25236/ajcis.2023.060908","DOIUrl":"https://doi.org/10.25236/ajcis.2023.060908","url":null,"abstract":"As one of the foundations of building a \"smart city\", and as an effective means to improve the current transportation situation, which is particularly reflected in cities, intelligent transportation can provide great help for people's daily travel, but the development of intelligent transportation system (ITS) is accompanied by some problems and shortcomings. This article believed that computer image processing technology can be used to assist the real-time monitoring and analysis system in the system, helping it carry out daily traffic monitoring and management work. Computer image processing technology can improve image quality and even restore some damaged and incomplete images, making them easy to observe. It can also uses methods such as frame difference to analyze vehicle and road conditions in real-time, thereby locating and processing illegal vehicles, and improving road conditions.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135750233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Application of Data Encryption Technology in Computer Network Security 数据加密技术在计算机网络安全中的应用
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061007.
{"title":"Application of Data Encryption Technology in Computer Network Security","authors":"","doi":"10.25236/ajcis.2023.061007.","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061007.","url":null,"abstract":"With the rapid development of computer technology, computer network security is an important issue that cannot be ignored in modern society, and data encryption technology plays a key role in ensuring the security of computer network. As one of the important means of computer network security, data encryption technology is widely used to protect sensitive information. Data encryption technology plays an important role in computer network security and needs to be continuously studied and explored to meet the increasingly severe challenges of network security. This paper will discuss the classification, advantages and application of data encryption technology in computer network security, and discuss the application of data encryption technology in computer network security.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135508646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial Intelligence Algorithm and Device for Big Data Processing of the IoT System 物联网系统大数据处理的人工智能算法与装置
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060810
{"title":"Artificial Intelligence Algorithm and Device for Big Data Processing of the IoT System","authors":"","doi":"10.25236/ajcis.2023.060810","DOIUrl":"https://doi.org/10.25236/ajcis.2023.060810","url":null,"abstract":"With the rapid development of the Internet of Things (IoT), there are more and more big data generated in the IoT system, which requires effective processing and analysis. Traditional data processing methods cannot meet the processing needs of big data in the IoT system, so it is necessary to study new big data processing technologies in the IoT system. This paper has proposed a big data algorithm, which uses data mining technology in big data to process sensor and device data. In the data pre-processing stage, the algorithm and device use data cleansing and other technologies to ensure data quality and reliability. In the feature extraction and selection stage, the algorithm and device adopt adaptive feature extraction and selection techniques to extract key features of the data and reduce the dimensionality and complexity of the data. In the experiment, this article tested and evaluated the algorithm to verify its performance. The experimental results showed that the F1 value of the model established in this study was 0.87, and the training time was the shortest, only 9 seconds. This algorithm and device can effectively improve the efficiency of data processing and analysis, as well as improve the accuracy and reliability of data processing. Compared with traditional data processing methods, this algorithm and device have better performance and application prospects. The algorithm and device also have good robustness and scalability, and can adapt to different data processing and analysis needs. The algorithm based on big data mining technology is an effective big data processing technology of the IoT system, which can improve the efficiency of data processing and analysis, and improve the accuracy and reliability of data processing.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135749926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Orientation Estimation Method Based on Directional Gradient 基于方向梯度的方向估计方法
Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060918
{"title":"Orientation Estimation Method Based on Directional Gradient","authors":"","doi":"10.25236/ajcis.2023.060918","DOIUrl":"https://doi.org/10.25236/ajcis.2023.060918","url":null,"abstract":"In fingerprint recognition system, fingerprint features must be extracted. Fingerprint preprocessing is the premise of fingerprint feature extraction, which can improve the effect of feature extraction. This paper introduces the orientation estimation method based on the direction gradient, and applies this method to improve the effect of fingerprint image preprocessing. Experimental results show the effectiveness of this method.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135750228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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