Prediction of Child Birth Weight Using Kernel Extreme Reservoir Machine and QPSO for Optimization

Autor:	Liu Zongying, Tooba Samad, Ghalib Ahmed Tahir, Hammad A. Qureshi, Murtaza Ashraf, Sundus Abrar
Rok vydání:	2021
Předmět:	Labor management business.industry Computer science Birth weight Kernel (statistics) Particle swarm optimization Child birth Model parameters Artificial intelligence business Machine learning computer.software_genre computer
Zdroj:	SN Computer Science. 2
ISSN:	2661-8907 2662-995X
Popis:	Birth weight is considered a major factor when monitoring any signs of abnormalities in the growth of the fetus and taking timely decisions related to labor management. Existing methods involve specialized equipment and training, which makes them less feasible for underdeveloped areas. Therefore, this study proposed a system for prediction of childbirth weight through kernel extreme reservoir machines and optimized the model parameters by the use of particle swarm optimization. Experimental results showed a significant improvement in the recommended method over existing models. The proposed approach is more economical than the traditional ultrasound making it extremely suited to underprivileged communities.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bc9c2f0d93d42a3fc9c5e36a4c988f48 https://doi.org/10.1007/s42979-021-00601-z Zobrazit plný text záznamu Full text from SpringerLink