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dc.contributor.authorZhang, Dongning
dc.contributor.authorCao, Ying
dc.contributor.authorTan, Jiao
dc.contributor.authorMen, Ke
dc.contributor.authorShi, Mingjuan
dc.contributor.authorMa, Yonghong
dc.date.accessioned2023-02-21T09:25:02Z
dc.date.available2023-02-21T09:25:02Z
dc.identifier.citationMa Y., Tan J., Zhang D., Men K., Shi M., Cao Y., "Lung Cancer Based on Big Data Technology Disease Data Management Status Quo", 2nd International Conference on Forthcoming Networks and Sustainability in the IoT Era (FoNeS-IoT), ELECTR NETWORK, 8 - 09 Ocak 2022, cilt.129, ss.189-196
dc.identifier.othervv_1032021
dc.identifier.otherav_2db5286a-0eeb-47b8-a5fb-93bc8b161372
dc.identifier.urihttp://hdl.handle.net/20.500.12627/187459
dc.identifier.urihttps://doi.org/10.1007/978-3-030-99616-1_25
dc.description.abstractAt present, lung cancer is very common, with the highest mortality rate. As for the pathology of lung cancer, we are still at the research stage. The main research of this paper is the analysis of lung cancer based on big data technology disease data management status. This article collected a total of 50 medical examination data. The field types include three types: numeric type, sub-type, and text type. The data mining model of this article is mainly realized by the data mining function of Clementinel 2.0. The feature selection component can calculate the importance of each field and the predicted field, and can select fields with greater importance for analysis. This paper extracts the features of the last convolutional layer of the model, and then performs batch quadratic superposition and fusion of the feature maps, and finally takes the average value as the input data for the secondary detection. Experiments have found that the parallel algorithm designed in this paper can increase the computational efficiency by about 9 times. The results show that big data technology improves the efficiency of lung cancer diagnosis.
dc.language.isoeng
dc.subjectBilgisayar Bilimleri
dc.subjectBiyoenformatik
dc.subjectMühendislik ve Teknoloji
dc.subjectTeorik Bilgisayar Bilimi
dc.subjectGenel Mühendislik
dc.subjectMühendislik (çeşitli)
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectBilgisayar Ağları ve İletişim
dc.subjectBilgisayar Bilimi (çeşitli)
dc.subjectGenel Bilgisayar Bilimi
dc.subjectMühendislik
dc.subjectFizik Bilimleri
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectTELEKOMÜNİKASYON
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectBilgisayar Bilimi
dc.subjectBİLGİSAYAR BİLİMİ, TEORİ VE YÖNTEM
dc.titleLung Cancer Based on Big Data Technology Disease Data Management Status Quo
dc.typeBildiri
dc.contributor.departmentXi''an Medical University , ,
dc.identifier.volume129
dc.contributor.firstauthorID3454863


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