dc.contributor.author | Zhang, Dongning | |
dc.contributor.author | Cao, Ying | |
dc.contributor.author | Tan, Jiao | |
dc.contributor.author | Men, Ke | |
dc.contributor.author | Shi, Mingjuan | |
dc.contributor.author | Ma, Yonghong | |
dc.date.accessioned | 2023-02-21T09:25:02Z | |
dc.date.available | 2023-02-21T09:25:02Z | |
dc.identifier.citation | Ma 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.other | vv_1032021 | |
dc.identifier.other | av_2db5286a-0eeb-47b8-a5fb-93bc8b161372 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12627/187459 | |
dc.identifier.uri | https://doi.org/10.1007/978-3-030-99616-1_25 | |
dc.description.abstract | At 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.iso | eng | |
dc.subject | Bilgisayar Bilimleri | |
dc.subject | Biyoenformatik | |
dc.subject | Mühendislik ve Teknoloji | |
dc.subject | Teorik Bilgisayar Bilimi | |
dc.subject | Genel Mühendislik | |
dc.subject | Mühendislik (çeşitli) | |
dc.subject | Bilgisayar Bilimi Uygulamaları | |
dc.subject | Bilgisayar Ağları ve İletişim | |
dc.subject | Bilgisayar Bilimi (çeşitli) | |
dc.subject | Genel Bilgisayar Bilimi | |
dc.subject | Mühendislik | |
dc.subject | Fizik Bilimleri | |
dc.subject | Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği | |
dc.subject | TELEKOMÜNİKASYON | |
dc.subject | Mühendislik, Bilişim ve Teknoloji (ENG) | |
dc.subject | Bilgisayar Bilimi | |
dc.subject | BİLGİSAYAR BİLİMİ, TEORİ VE YÖNTEM | |
dc.title | Lung Cancer Based on Big Data Technology Disease Data Management Status Quo | |
dc.type | Bildiri | |
dc.contributor.department | Xi''an Medical University , , | |
dc.identifier.volume | 129 | |
dc.contributor.firstauthorID | 3454863 | |