Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM).
Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important parameters for predicting the concentration of indoor culturable fungi (ICF). For model training and statistical analysis, parameters that contained indoor o...
| Publicado en: | International Journal of Environmental Health Research Vol. 30; no. 3; pp. 344 - 357 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143356066&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143356066 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09603123 57L jtl: International Journal of Environmental Health Research issn: 09603123 maglogo: Y pubinfo: dt: Jun2020 vid: 30 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 143356066 143356066 143356066 10.1080/09603123.2019.1609659 143356066 ppf: 344 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM). aug: au: Liu, Zhijian Ma, Shengyuan Wu, Lifeng Yin, Hang Cao, Guoqing affil: Department of Power Engineering, North China Electric Power University, Baoding, Hebei, PR China sug: subj: Information Science Methods Fungi Environmental Exposure Extreme Learning Machines Human Algorithms Descriptive Statistics China ab: Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important parameters for predicting the concentration of indoor culturable fungi (ICF). For model training and statistical analysis, parameters that contained indoor or outdoor PM10 and PM2.5, RH, Temperature, CO2 and ICF were measured in 85 residential buildings of Baoding, China, from November 2016 to March 2017. The variable selection process contains four different cases to identify the optimal input combination. The results indicate that root mean square error of the optimal input combinations can be improved 5.6% from 1 to 2 input variables, while that could be only improved 1.9% from 2 to 3 input variables. However, considering both precision and simplicity, the combination of indoor PM10 and RH provides a more suitable selection for predicting the ICF. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|