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

ABSTRACT: 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 novel Optimization Hybrid Deep Learning (OdriHDL) model to provide a personalized health nutrition recommendation for women during their menstrual cycle. It involves pre-processing the data through Missing Value Imputation, Z-score Normalization, and One-hot encoding. Next, feature extraction is accomplished using the Layered Sparse Autoencoder Network. Then, the extracted features are utilized by the Hybrid Attention-based Bidirectional Convolutional Greylag Goose Gated Recurrent Network (HABi-C

SUBMITTER: Logapriya E 

PROVIDER: S-EPMC11836056 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature