Health Information Prediction System of Infant Sports Based on Deep Learning Network.
The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sop...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research Journal Article |
| Publicado: |
Wiley-Blackwell
7/31/2022
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| 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=158264826&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158264826 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/31/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158264826 158264826 158264826 10.1155/2022/4438251 158264826 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Health Information Prediction System of Infant Sports Based on Deep Learning Network. aug: au: Qi, Jie Zhang, Jun affil: College of Physical Education, Shanghai Normal University, Shanghai 200234, China sug: subj: Deep Learning Health Information Systems Human ab: The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST-DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity. pubtype: Academic Journal doctype: equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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