WSEAS TRANSACTIONS ON COMPUTER RESEARCH最新文献

Corporate Accounting Management Risks Integrating Improved Association Rules and Data Mining 整合改进关联规则和数据挖掘的企业会计管理风险

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-07-22 DOI: 10.37394/232018.2024.12.34

Haiyan Li

{"title":"Corporate Accounting Management Risks Integrating Improved Association Rules and Data Mining","authors":"Haiyan Li","doi":"10.37394/232018.2024.12.34","DOIUrl":"https://doi.org/10.37394/232018.2024.12.34","url":null,"abstract":"With the development of the times, enterprises need to face more data in operational decision-making. Traditional data analysis strategies cannot handle the growing amount of data well, and the accuracy of analysis will also decrease when faced with uneven data types. The research uses a corporate accounting management risk analysis technology that combines big data algorithms and improved clustering algorithms. This method combines big data processing ideas with a clustering algorithm that incorporates improved weighting parameters. The results show that on the data sets DS1, DS2, and DS3, the NMI values of the GMM algorithm are all 0; while the NMI values of the MCM algorithm correspond to 0.9291, 0.9088 and 0.8881 respectively. At the same time, the Macro-F1 values of the Verify2 algorithm correspond to 0.9979, 0.9501, and 0.9375 respectively, and the recognition accuracy of the data remains above 85%. In the running time comparison, when the number of samples in the data set reaches 5,000, the calculation time of the Verify2 algorithm remains within 5 seconds. In terms of practical application results, the study selected the profitability risk indicators of 40 companies for analysis. After conducting risk ratings, it can be seen that companies No. 5, 6, 7, and 39 have the highest risk levels, and companies No. 33 and 34 have the highest risk levels. The lowest level. After conducting risk assessments on the 40 selected listed companies, the risk level of net asset income of each company remained at level 5, and the risk level of earnings per share remained at level 3. The above results show that this technology has good performance in terms of calculation accuracy and calculation time, can assess enterprise risks, and can provide data support for enterprise operation decisions.","PeriodicalId":267474,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815987","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

Optimizing the Energy Efficiency of a Lighting Network using Graph Theory 利用图论优化照明网络的能效

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-04-23 DOI: 10.37394/232018.2024.12.28

Ekaterina Gospodinova

引用次数: 0

Intrusion Detection System using CNNs and GANs 使用 CNN 和 GAN 的入侵检测系统

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-04-19 DOI: 10.37394/232018.2024.12.27

Nabeel Al-Milli, Y. Al-Khassawneh

{"title":"Intrusion Detection System using CNNs and GANs","authors":"Nabeel Al-Milli, Y. Al-Khassawneh","doi":"10.37394/232018.2024.12.27","DOIUrl":"https://doi.org/10.37394/232018.2024.12.27","url":null,"abstract":"This study investigates the effectiveness of deep learning models, namely Generative Adversarial Networks (GANs), Convolutional Neural Networks with three layers (CNN-3L), and Convolutional Neural Networks with four layers (CNN-4L), in the domain of multi-class categorization for intrusion detection. The CICFlowMeter-V3 dataset is utilized to thoroughly evaluate the performance of these models and gain insights into their capabilities. The primary approach involves training the models on the dataset and assessing their accuracy. The GAN achieves an overall accuracy of 93%, while CNN-3L demonstrates a commendable score of 99.71%. Remarkably, CNN-4L excels with a flawless accuracy of 100%. These results underscore the superior performance of CNN-3L and CNN-4L compared to GAN in the context of intrusion detection. Consequently, this study provides valuable insights into the potential of these models and suggests avenues for refining their architectures. The conclusions drawn from this research indicate that CNN-3L and CNN-4L hold promise for enhancing multi-class categorization in intrusion detection systems. It is recommended to further explore these models with diverse datasets to strengthen overall comprehension and practical applicability in this crucial field.","PeriodicalId":267474,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684243","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

Digital Management Mode of Real Estate Marketing based on Big Data and Artificial Intelligence 基于大数据和人工智能的房地产营销数字化管理模式

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-03-29 DOI: 10.37394/232018.2024.12.26

Shuangxin Chen

{"title":"Digital Management Mode of Real Estate Marketing based on Big Data and Artificial Intelligence","authors":"Shuangxin Chen","doi":"10.37394/232018.2024.12.26","DOIUrl":"https://doi.org/10.37394/232018.2024.12.26","url":null,"abstract":"To cope with the pressure on sales information processing as the real estate industry grows, the study builds a real estate digital marketing management system design based on the analysis of real estate marketing needs to meet the needs of real estate marketers for digital information processing, and builds a hybrid recommendation model using a combination of Gradient Boosting Decision Tree (GBDT) technology and Logistic Regression (LR) to accurately recommend real estate potential purchase users. The GBDT-LR model performance test results show an accuracy of 94.63% and a regression rate of 94.82%, which is particularly good in terms of classification accuracy, and the system CPU occupancy rate basically stays below 30% during the whole script running period, and the system still maintains good system stability when the TPS user concurrency is 150, and it’s using experience is better. The comparison of the ROC curve of the GBDT-LR model shows that the GBDT-LR model's accuracy is as high as 92%, which is better than the performance of most of the classification models, and it can meet the practical application requirements of the real estate industry and provide a good solution for the real estate industry. It can meet the actual application requirements of the real estate industry and provide a scientific and systematic digital management solution for the real estate industry.","PeriodicalId":267474,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","volume":"24 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368523","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

Formulation, Proposition and Application of Interval Semigraph 区间半图的提出、命题和应用

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-03-29 DOI: 10.37394/232018.2024.12.25

Abdur Rohman, Surajit Kr. Nath, Sheetal Sonawane

引用次数: 0

Garbage Detection using YOLO Algorithm for Urban Management in Bangkok 利用 YOLO 算法检测垃圾，促进曼谷城市管理

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-02-27 DOI: 10.37394/232018.2024.12.23

Mathuros Panmuang, Chonnikarn Rodmorn

引用次数: 0

Analyzing Banking Firms: Impacts of Credit, Currency Conversion Rates, Mortgage Rates on Equity Yields with Profit Margins 分析银行企业：信贷、货币兑换率、抵押贷款利率对股票收益率和利润率的影响

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-02-21 DOI: 10.37394/232018.2024.12.22

Fahrudin Zain Olilingo, Rita Alfin, Listiyana Listiyana, Sonny Leksono

{"title":"Analyzing Banking Firms: Impacts of Credit, Currency Conversion Rates, Mortgage Rates on Equity Yields with Profit Margins","authors":"Fahrudin Zain Olilingo, Rita Alfin, Listiyana Listiyana, Sonny Leksono","doi":"10.37394/232018.2024.12.22","DOIUrl":"https://doi.org/10.37394/232018.2024.12.22","url":null,"abstract":"Using profit margins as an intermediate variable, this research examines the effects of credit risk (X1), exchange rate risk (X2), and mortgage rates (X3) on equity yields in the context of banking businesses listed between 2020 and 2022 on the Bursa Efek Indonesia (BEI). A purposive sampling approach was used to determine the sample size, which was 43 firms. Path analysis and the Sobel Z test were applied to the data. The findings of the investigation may be summed up as follows: (1) The statistical significance of credit risk's impact on profit margins is shown by its p-value of 0.001, which is below the 0.05 cutoff. With a p-value more than 0.05, mortgage rates, however, do not show a statistically significant impact on profit margins. Moreover, a p-value greater than 0.05 indicates that profit margins do not substantially impact equity yields. (4) With a p-value >ccr0.05, credit risk has no discernible effect on equity yields. (5) In contrast, with a p-value smaller than 0.05, mortgage rates have a substantial impact on equity yields. (6) The Z-Sobel result drops below 1.96 at -0.87363822, indicating that credit risk does not directly influence equity yields via profit margins after doing an indirect impact analysis using Sobel's Z test route analysis. (7) In a similar vein, the Z-Sobel result of 0.35789034 stays below 1.96, indicating that mortgage rates do not directly affect equity yields via profit margins in the indirect impact study conducted using Sobel's Z-test route analysis.","PeriodicalId":267474,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442725","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

Financial Fraud Identification Model of Listed Companies based on Time-Series Information 基于时间序列信息的上市公司财务欺诈识别模型

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-02-21 DOI: 10.37394/232018.2024.12.21

Lili Wang

{"title":"Financial Fraud Identification Model of Listed Companies based on Time-Series Information","authors":"Lili Wang","doi":"10.37394/232018.2024.12.21","DOIUrl":"https://doi.org/10.37394/232018.2024.12.21","url":null,"abstract":"The aim of this research is to establish a high-precision financial fraud identification model for listed companies, which is mainly based on the financial indicators of time series. Support vector machine and K-means clustering algorithm are especially used in the research process. Firstly, local linear embedding is used to reduce the dimensionality of the selected financial indicators to extract the low-dimensional characteristics. Then the samples are classified into financial fraud and non-fraud by support vector machine, and the recognition model is constructed. At the same time, the research also uses K-means clustering algorithm to analyze the pattern of financial fraud. The experiment of dimensionality reduction proves that the model has a high effect on the processing of financial data, and the error between the data after dimensionality reduction and the original data is small. In addition, the clustering effect of the model also shows a clear pattern of fraud. In practical application, the accuracy rate of this model is as high as 94.89%, showing high accuracy and recall rate, and its F1 value is 87.08%, showing its feasibility and effectiveness in practice. The results highly prove that the performance of the financial fraud identification model proposed in this study is excellent, and it has a wide application prospect in the future.","PeriodicalId":267474,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","volume":"228 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443734","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

WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model WeightedSLIM：一种新颖的项目权重丰富基线推荐模型

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-02-14 DOI: 10.37394/232018.2024.12.20

Haiyang Zhang, Xinyi Zeng, Ivan Ganchev

引用次数: 0

WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model WeightedSLIM：一种新颖的项目权重丰富基线推荐模型

WSEAS TRANSACTIONS ON COMPUTER RESEARCH Pub Date : 2024-02-14 DOI: 10.37394/232018.2024.12.20

Haiyang Zhang, Xinyi Zeng, Ivan Ganchev

引用次数: 0