Hybrid Greylag Goose deep learning with layered sparse network for women nutrition recommendation during menstrual cycle

A complex biological process involves physical changes and hormonal fluctuation in the menstrual cycle. The traditional nutrition recommendation models often offer general guidelines but fail to address the specific requirements of women during various menstrual cycle stages. This paper proposes a n...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 5959 - 22
Main Authors:	Logapriya, E., Rajendran, Surendran, Zakariah, Mohammad
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 18.02.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/705/117 692/700/2814 692/700/784 Algorithms Anser anser Aquatic birds Bidirectional gated recurrent unit Deep Learning Female Greylag Goose optimization Humanities and Social Sciences Humans Layered sparse autoencoder Menstrual cycle Menstrual Cycle - physiology Menstruation multidisciplinary Nutrition Pre-processing Science Science (multidisciplinary) Waterfowl Deep learning Menstrual cycle Pre-processing Greylag Goose optimization Nutrition recommendation Classification Layered sparse autoencoder Bidirectional gated recurrent unit
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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