In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.
{"title":"Application Research of an Intelligent Detection Algorithm for Vehicle Trajectory Route Deviation","authors":"Jianfei Luo, Yadong Xing, Cheng Chen, Weiqing Zhang, Zhongcheng Wu","doi":"10.4236/jcc.2023.1110001","DOIUrl":"https://doi.org/10.4236/jcc.2023.1110001","url":null,"abstract":"In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"98 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":"136301467","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}
Pub Date : 2023-01-01DOI: 10.4236/jcc.2023.1110004
Sesugh Gabriel Abenga, Kehinde Seyi Olalekan, Francis Akogwu Alu, Stephen Yavenga Uyoo
Diabetic Kidney Disease (DKD) is a common chronic complication of diabetes. Despite advancements in accurately identifying biomarkers for detecting and diagnosing this harmful disease, there remains an urgent need for new biomarkers to enable early detection of DKD. In this study, we modeled publicly available transcriptome datasets as a graph problem and used GraphSAGE Neural Networks (GNNs) to identify potential biomarkers. The GraphSAGE model effectively learned representations that captured the intricate interactions, dependencies among genes, and disease-specific gene expression patterns necessary to classify samples as DKD and Control. We finally extracted the features of importance; the identified set of genes exhibited an impressive ability to distinguish between healthy and unhealthy samples, even though these genes differ from previous research findings. The unexpected biomarker variations in this study suggest more exploration and validation studies for discovering biomarkers in DKD. In conclusion, our study showcases the effectiveness of modeling transcriptome data as a graph problem, demonstrates the use of GraphSAGE models for biomarker discovery in DKD, and advocates for integrating advanced machine-learning techniques in DKD biomarker research, emphasizing the need for a holistic approach to unravel the intricacies of biological systems.
{"title":"Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network","authors":"Sesugh Gabriel Abenga, Kehinde Seyi Olalekan, Francis Akogwu Alu, Stephen Yavenga Uyoo","doi":"10.4236/jcc.2023.1110004","DOIUrl":"https://doi.org/10.4236/jcc.2023.1110004","url":null,"abstract":"Diabetic Kidney Disease (DKD) is a common chronic complication of diabetes. Despite advancements in accurately identifying biomarkers for detecting and diagnosing this harmful disease, there remains an urgent need for new biomarkers to enable early detection of DKD. In this study, we modeled publicly available transcriptome datasets as a graph problem and used GraphSAGE Neural Networks (GNNs) to identify potential biomarkers. The GraphSAGE model effectively learned representations that captured the intricate interactions, dependencies among genes, and disease-specific gene expression patterns necessary to classify samples as DKD and Control. We finally extracted the features of importance; the identified set of genes exhibited an impressive ability to distinguish between healthy and unhealthy samples, even though these genes differ from previous research findings. The unexpected biomarker variations in this study suggest more exploration and validation studies for discovering biomarkers in DKD. In conclusion, our study showcases the effectiveness of modeling transcriptome data as a graph problem, demonstrates the use of GraphSAGE models for biomarker discovery in DKD, and advocates for integrating advanced machine-learning techniques in DKD biomarker research, emphasizing the need for a holistic approach to unravel the intricacies of biological systems.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"115 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":"135158153","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}
Pub Date : 2023-01-01DOI: 10.4236/jcc.2023.1111002
Mussa Ali Makame, Mudiarasan Kuppusamy, Apparow Sannasai
The aim of the paper is to explore e-government services technological readiness as a mediating factor impacting the development of Zanzibar SMEs. The ultimate objective is to provide actionable insights and recommendations that could inform future strategies and policies to enhance Zanzibar SMEs on the successful implementation and acceptance of e-government services in Zanzibar. The study involves SMEs which are Small and Medium Industries Development Agency (SMIDA), Zanzibar Association of Tourism Investors (ZATI) and Zanzibar National Chambers of Commerce (ZNCC). Quantitative research design holds significant merit for this study, whereby primary data was in this study ensures that all variables are accurately and comprehensively captured. The study involved 384 respondents. Survey questionnaire used to collect the required information. Generally, the results displayed that, while Technological Readiness does not exhibit a mediating effect between either Human Resource Capital or Financial Source Capital and E-government Services Adoption, it does play a significant mediating role in the relationships of both Government Policies and Information Communication Technology with E-government Services Adoption. This highlights the importance of considering intermediary factors like Technological Readiness when understanding the influences on E-government service adoption to Zanzibar SMEs.
{"title":"Technological Readiness as Mediator to Adoption of E-Governemnt Services in Zanzibar SMEs","authors":"Mussa Ali Makame, Mudiarasan Kuppusamy, Apparow Sannasai","doi":"10.4236/jcc.2023.1111002","DOIUrl":"https://doi.org/10.4236/jcc.2023.1111002","url":null,"abstract":"The aim of the paper is to explore e-government services technological readiness as a mediating factor impacting the development of Zanzibar SMEs. The ultimate objective is to provide actionable insights and recommendations that could inform future strategies and policies to enhance Zanzibar SMEs on the successful implementation and acceptance of e-government services in Zanzibar. The study involves SMEs which are Small and Medium Industries Development Agency (SMIDA), Zanzibar Association of Tourism Investors (ZATI) and Zanzibar National Chambers of Commerce (ZNCC). Quantitative research design holds significant merit for this study, whereby primary data was in this study ensures that all variables are accurately and comprehensively captured. The study involved 384 respondents. Survey questionnaire used to collect the required information. Generally, the results displayed that, while Technological Readiness does not exhibit a mediating effect between either Human Resource Capital or Financial Source Capital and E-government Services Adoption, it does play a significant mediating role in the relationships of both Government Policies and Information Communication Technology with E-government Services Adoption. This highlights the importance of considering intermediary factors like Technological Readiness when understanding the influences on E-government service adoption to Zanzibar SMEs.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","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":"135659287","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}
{"title":"Research on User Pairing Techniques Based on NOMA in Cognitive Radio Networks","authors":"Yongming Huang, Xiaoli He, Yuxin Du","doi":"10.4236/jcc.2023.113010","DOIUrl":"https://doi.org/10.4236/jcc.2023.113010","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936253","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}
{"title":"Application Research of Artificial Intelligence in the Innovation of Zhuang Brocade Digital Art: Taking “The Speech of Zhuang Brocade” as an Example","authors":"Yuelin Hu","doi":"10.4236/jcc.2023.115004","DOIUrl":"https://doi.org/10.4236/jcc.2023.115004","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936655","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}
Eric Michel Deussom Djomadji, Ivan Basile Kabiena, Valery Nkemeni, Ayrton Garcia Belinga À Njere, M. E. Sone
{"title":"Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques","authors":"Eric Michel Deussom Djomadji, Ivan Basile Kabiena, Valery Nkemeni, Ayrton Garcia Belinga À Njere, M. E. Sone","doi":"10.4236/jcc.2023.116005","DOIUrl":"https://doi.org/10.4236/jcc.2023.116005","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937396","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}
{"title":"Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems","authors":"J. Ally","doi":"10.4236/jcc.2023.116003","DOIUrl":"https://doi.org/10.4236/jcc.2023.116003","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937645","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}
{"title":"Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques","authors":"Nasimalsadat Saesi, M. Taleghani","doi":"10.4236/jcc.2023.117004","DOIUrl":"https://doi.org/10.4236/jcc.2023.117004","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937952","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：