Academic Journal of Computing & Information Science最新文献

Algorithmic Implementation for Insurance Fraud Detection 保险欺诈检测的算法实现

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060914

引用次数: 0

Research on the Static WTA of Terminal Cooperative Air Defense Impacted by Coupling Factors 耦合因素影响的末端协同防空静态WTA研究

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060804

引用次数: 0

Emergency Network Public Opinion and Coping Strategies Based on Emotion Feature Extraction Algorithm 基于情感特征提取算法的突发事件网络舆情及应对策略

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061009

{"title":"Emergency Network Public Opinion and Coping Strategies Based on Emotion Feature Extraction Algorithm","authors":"","doi":"10.25236/ajcis.2023.061009","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061009","url":null,"abstract":"With the development of the Internet in today's society, the prevalence of public opinion also heralds the trend of networking. More and more people are starting to express their opinions and opinions on the Internet. Therefore, the analysis of emergency Internet public opinion and the research on coping strategies are becoming more and more important. Although there are many studies on emergency Internet public opinion analysis and coping strategies, the existing research still needs to be supplemented. This article was a certain discussion on the empathy strategy of characteristic diplomatic language. First, the relevant background of the title was introduced at the beginning of the introduction section. Emergency Internet public opinion analysis and coping strategies were analyzed and studied, and thinking was made. Second, various algorithms were proposed. The algorithm was established based on the emotion feature extraction algorithm, which has provided a theoretical basis. Third, for the response methods of emergency Internet public opinion, the emergency characteristics of college Internet public opinion in the era of big data were introduced. The application of big data in Internet public opinion has carried out emergency Internet public opinion analysis research. Finally, this paper conducted experimental research on the subject of emergency Internet public opinion analysis and coping strategies research based on emotion feature extraction algorithm. The research results showed that the research model constructed in this paper has improved the effectiveness of emergency Internet public opinion by 15.12%.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"140 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":"135156302","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

Lightweight Real-time Detection Method for Dress Code of Anti-static Equipment 防静电设备着装规范轻量化实时检测方法

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061002

{"title":"Lightweight Real-time Detection Method for Dress Code of Anti-static Equipment","authors":"","doi":"10.25236/ajcis.2023.061002","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061002","url":null,"abstract":"Detection of dress code for anti-static equipment is an important management link in clean workshops. To address the issue of difficulty in deploying multi-scale dress code detection methods for anti-static equipment in embedded systems, a lightweight real-time detection method for dress code of anti-static equipment is proposed. This article uses the MobileNetV3-small backbone network to extract features of anti-static equipment, making the model lightweight and easy to deploy. Adopting BiFPN structure to enhance the feature fusion ability of anti-static equipment at multiple scales, and using CIoU Loss and DIoU-NMS to accurately locate anti-static equipment targets, and improving the problem of missed detection of anti-static equipment when people are crowded, and improving the accuracy of dress code detection for anti-static equipment. The experimental results show that the algorithm improves accuracy by 2.1%, reduces parameter count by 43.8%, and reduces model size by 40.6% compared to YOLOv5s. The recognition speed on the Jeston Xavier NX system is 27FPS, and the recognition accuracy of wearing anti-static hats, anti-static clothing, and anti-static shoes is 98.1%, 96.2%, 95.8%, 94.2%, and 94.1%, respectively. It meets the requirements of real-time detection of anti-static equipment dress code.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"140 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":"135157610","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

A Study of Arrhythmia Risk Level Discrimination Based on K-Means Algorithm and Analytic Hierarchy Method 基于k均值算法和层次分析法的心律失常危险等级判别研究

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061018

{"title":"A Study of Arrhythmia Risk Level Discrimination Based on K-Means Algorithm and Analytic Hierarchy Method","authors":"","doi":"10.25236/ajcis.2023.061018","DOIUrl":"https://doi.org/10.25236/ajcis.2023.061018","url":null,"abstract":"Arrhythmia is one of the major causes of cardiac risk events, so the study and analysis of this cause can reduce the lethality of cardiac risk events. In this paper, based on the K-Means algorithm and hierarchical analysis method, a specific research and analysis of cardiac risk events is carried out. In this paper, the K-Means algorithm is used to establish the data classification model of abnormal heart beats, the Euclidean distance is chosen as the method of data similarity calculation, and the arrhythmia is classified through the analysis of the number of clusters, and through the deviation of the coordinates of the center point of the clusters, the corresponding objects are re-divided according to the minimum distance until the coordinates of the center point of the clusters are no longer shifted. The final field variability analysis was derived and solved for the frequency and percentage of classification for each category. Then, based on the comprehensive analysis of the classification results and the characteristics of each type of arrhythmia in sinus arrhythmia, five categories were derived: sinus arrhythmia, sinus bradycardia, sinus tachycardia, sinus conduction block, and sinus arrest. Further, this study used hierarchical analysis to establish an evaluation model to evaluate the risk level of each arrhythmia category, and the higher the score, the higher the risk level. A pairwise comparison matrix was constructed by comparing each category, and the weight vector and eigenvalues of each category were calculated, resulting in a ranking of the risk level of each arrhythmia category from highest to lowest: sinus arrest, sinus block, sinus tachycardia, sinus bradycardia, and sinus arrhythmia. This methodology enables healthcare organizations to more accurately assess arrhythmia categories and their corresponding risk levels, which provides an important reference for medical decision-making and contributes to more timely and effective interventions and treatments, thus improving patients' survival rates and quality of life.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"29 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":"135156094","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

Analysis and Comparison of Loan Default Prediction Models Based on XGBoost and LightGBM Algorithm 基于XGBoost和LightGBM算法的贷款违约预测模型分析与比较

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060905

引用次数: 0

Digital image stabilization method based on variational mode decomposition and sampling fluctuation analysis 基于变分模态分解和采样波动分析的数字稳像方法

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060802

引用次数: 0

Research on Distinguishing Biological Species by Data Model and Linear Discriminant Analysis 基于数据模型和线性判别分析的生物物种识别研究

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.060903

{"title":"Research on Distinguishing Biological Species by Data Model and Linear Discriminant Analysis","authors":"","doi":"10.25236/ajcis.2023.060903","DOIUrl":"https://doi.org/10.25236/ajcis.2023.060903","url":null,"abstract":"This article explores the classification of lizards based on their distinct pholidosis and morphological characteristics using various data attributes. The authors aim to construct a classification model that takes advantage of data attributes for both simplicity and accuracy. Additionally, the article aims to propose an adaptive model that provides recommendations according to the precision requirements of biologists and the computational environment, enhancing the model's applicability. The authors employ Fisher's and Bayesian methods from linear discriminant analysis for classification, leveraging the linear structure to ensure the model's simplicity. A novel aspect of this work is the development of a discriminative power index for variables. This index prioritizes variables with strong discriminative abilities, thus simplifying computations and improving efficiency. The results align with those obtained through exhaustive searches for optimal solutions. Furthermore, the constructed model offers classification criteria and prediction accuracy under different variable combinations, enabling biologists to adjust variables based on accuracy needs and computational constraints. This functionality enhances the model's suitability for various real-world research scenarios.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"82 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":"135750604","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

Research on hybrid energy storage and demand response strategy of high proportion solar energy microgrid 高比例太阳能微电网混合储能与需求响应策略研究

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061010

引用次数: 0

Recognition and Evaluation Algorithm for English Pronunciation Syllables Based on Neural Prediction Model 基于神经预测模型的英语语音音节识别与评价算法

Academic Journal of Computing & Information Science Pub Date : 2023-01-01 DOI: 10.25236/ajcis.2023.061008

引用次数: 0