Proceedings of the ... IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) : ... IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium on Computational Intelligence in Healthcare and e-h...最新文献

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Computational Intelligence in Healthcare 医疗保健中的计算智能

Proceedings of the ... IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) : ... IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium on Computational Intelligence in Healthcare and e-h... Pub Date : 2021-11-15 DOI: 10.1007/978-3-030-68723-6

Meenu Gupta, Shakeel Ahmed, Rakesh Kumar, Chadi Altrjman

引用次数: 2

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage. FDT 2.0:提高模糊决策树归纳工具的可扩展性——集成数据库存储。

Erin-Elizabeth A Durham, Xiaxia Yu, Robert W Harrison

{"title":"FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.","authors":"Erin-Elizabeth A Durham, Xiaxia Yu, Robert W Harrison","doi":"10.1109/CICARE.2014.7007853","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007853","url":null,"abstract":"<p><p>Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.</p>","PeriodicalId":92121,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) : ... IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium on Computational Intelligence in Healthcare and e-h...","volume":"2014 ","pages":"187-190"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CICARE.2014.7007853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35242309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Semantic interoperability in sensor applications making sense of sensor data 传感器应用中的语义互操作性使传感器数据有意义

Paul Brandt, T. Basten, Sander Stuiik, Vinh T. Bui, P. D. Clercq, L. F. Pires, M. V. Sinderen

{"title":"Semantic interoperability in sensor applications making sense of sensor data","authors":"Paul Brandt, T. Basten, Sander Stuiik, Vinh T. Bui, P. D. Clercq, L. F. Pires, M. V. Sinderen","doi":"10.1109/CICARE.2013.6583065","DOIUrl":"https://doi.org/10.1109/CICARE.2013.6583065","url":null,"abstract":"Much effort has been spent on the optimization of sensor networks, mainly concerning their performance and power efficiency. Furthermore, open communication protocols for the exchange of sensor data have been developed and widely adopted, making sensor data widely available for software applications. However, less attention has been given to the interoperability of sensor networks and sensor network applications at a semantic level. This hinders the reuse of sensor networks in different applications and the evolution of existing sensor networks and their applications. The main contribution of this paper is an ontology-based approach and architecture to address this problem. We developed an ontology that covers concepts regarding examinations as well as measurements, including the circumstances in which the examination and measurement have been performed. The underlying architecture secures a loose coupling at the semantic level to facilitate reuse and evolution. The ontology has the potential of supporting not only correct interpretation of sensor data, but also ensuring its appropriate use in accordance with the purpose of a given sensor network application. The ontology has been specialized and applied in a remote patient monitoring example, demonstrating the aforementioned potential in the e-health domain.","PeriodicalId":92121,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) : ... IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium on Computational Intelligence in Healthcare and e-h...","volume":"68 6 1","pages":"34-41"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83521098","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: 20