EAI endorsed transactions on context-aware systems and applications最新文献

Integration and Recommendation System of Profiles based on Professional Social Networks 基于专业社交网络的档案整合与推荐系统

EAI endorsed transactions on context-aware systems and applications Pub Date : 2024-01-15 DOI: 10.4108/eetcasa.4500

Paul Dayang, Ulriche Mbouche Bomda

{"title":"Integration and Recommendation System of Profiles based on Professional Social Networks","authors":"Paul Dayang, Ulriche Mbouche Bomda","doi":"10.4108/eetcasa.4500","DOIUrl":"https://doi.org/10.4108/eetcasa.4500","url":null,"abstract":"The aim of our investigation is to personalize bilateral recommendation of job-related proposals based on existing professional social networks. In a context where the points of view of job seekers and employers can be contradictory, our approach consists in trying to bring the both in a best possible matching. To this end, we propose an integration system that gives a minimum of credit to the users’ data in order to facilitate the discovery of relevant proposals based on the users’ behaviors, on the characteristics of the proposals and on possible relationships. The main contribution is the proposal of an architecture for the recommendation of profiles and job offers including social and administrative factors. The particularity of our approach lies in the freedom from the recommendation problem by using metrics proven in the literature for the estimation of similarity rates. We have used these metrics as default values to appropriate data dimensions. It emerges that, the user’s behavior is exclusively responsible for the recommendations. However, the cross-analysis of randomly generated behaviors on real profiles collected on Cameroonian sites dedicated to job offers, shows the influence of the most active users. But, for requests via the search bar (interface with the script respecting the path of our architecture) the central subject remains the user. Our current work is limited by a data set that is not very representative of changing socio-economic conditions.","PeriodicalId":500308,"journal":{"name":"EAI endorsed transactions on context-aware systems and applications","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140507626","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

Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning 利用机器学习增强流感和COVID-19的诊断

EAI endorsed transactions on context-aware systems and applications Pub Date : 2023-10-10 DOI: 10.4108/eetcasa.v9i1.4030

Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc

{"title":"Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning","authors":"Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc","doi":"10.4108/eetcasa.v9i1.4030","DOIUrl":"https://doi.org/10.4108/eetcasa.v9i1.4030","url":null,"abstract":"The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.","PeriodicalId":500308,"journal":{"name":"EAI endorsed transactions on context-aware systems and applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296275","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: 1

Manipulation of the Multi-Vehicle System for the Industrial Applications 工业应用中多车系统的操作

EAI endorsed transactions on context-aware systems and applications Pub Date : 2023-10-02 DOI: 10.4108/eetcasa.v9i1.3978

Lourve Vincent

引用次数: 0

Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam 克里格插值模型:预测越南新冠肺炎死亡人数随时间变化的问题

EAI endorsed transactions on context-aware systems and applications Pub Date : 2023-09-25 DOI: 10.4108/eetcasa.v9i1.3954

Nguyen Cong Nhut

{"title":"Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam","authors":"Nguyen Cong Nhut","doi":"10.4108/eetcasa.v9i1.3954","DOIUrl":"https://doi.org/10.4108/eetcasa.v9i1.3954","url":null,"abstract":"The COVID-19 pandemic can be considered a human disaster, it has claimed the lives of many people. We only know the number of deaths due to COVID-19 through government statistics, but on days when there are no statistics, how do we know whether people died that day or not? This study aims to predict the number of new deaths per day due to COVID 19 in Vietnam on days when observational data is not available and predict the number of deaths in the future. The study used COVID-19 data from the World Health Organization (WHO). A total of 260 days were collected and the author processed and standardized the data. Based on available data, the author uses Kriging interpolation statistical method to build a forecast model. As a result, the author has selected a prediction model suitable for a highly reliable data set, the regression coefficient and correlation coefficient are close to 1, the error between the model’s prediction results compared to data. There are days when the prediction error is almost zero. The study has built a future forecast map of the number of new deaths per day due to COVID-19. The article concludes that applying the Kriging statistical methodis appropriate for COVID-19 data. This research opens up new research directions for related fields such as earthquakes, mining, groundwater, environment, etc.","PeriodicalId":500308,"journal":{"name":"EAI endorsed transactions on context-aware systems and applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859471","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

Study of Robot Manipulator Control via Remote Method 机器人机械手远程控制方法研究

EAI endorsed transactions on context-aware systems and applications Pub Date : 2023-09-25 DOI: 10.4108/eetcasa.v9i1.3884

None Tuan Nguyen

引用次数: 0

Facial Sentiment Recognition using artificial intelligence techniques. 使用人工智能技术进行面部情绪识别。

EAI endorsed transactions on context-aware systems and applications Pub Date : 2023-09-22 DOI: 10.4108/eetcasa.v9i1.3930

Vuong Xuan Chi, Phan Cong Vinh

引用次数: 0