{
  "abstract": "Traditional sun-drying methods for seaweed face many challenges, such as moisture content, weather conditions, and inconsistent drying rates. This study addresses the needs of Malaysian seaweed farmers for a dependable and effective drying process by a v-Groove Hybrid Solar Dryer (v-GHSD). Data on seaweed was obtained using smart farming technology sensors attached to the v-GHSD. There are 29 main drying parameters, and by adding the second-order interaction, the parameters increased to 435 parameters to capture interaction effects. For non-linear and high-dimensional data, traditional models cannot handle the problem. Then, we applied the Elman Neural Network (ENN) based on the Whale Optimization Algorithm, support vector regression, ENN, and partial least squares regression methods to develop the predictive models. From the results, the hybrid WOA-ENN model is better, with the root mean square error of prediction of 0.0710, a correlation coefficient of 0.9974, and the relative percent deviation of 3.1165.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Olayemi Joshua Ibidoja"
    },
    {
      "name": "Wan Rosli Wan Ishak"
    },
    {
      "name": "Majid Khan Majahar Ali"
    },
    {
      "name": "Nurraihana Hamzah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ff51x962ke9bv.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-65129-9",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-65129-9",
  "id": "ff51x962ke9bv",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-65129-9",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1106|Food Drying and Modeling"
    ]
  ],
  "openalex": "W7223109917",
  "pdf_url": "https://www.nature.com/articles/s41598-026-65129-9.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/ff51x962ke9bv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-65129-9",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-65129-9"
    },
    {
      "first_seen": "2026-10-30T13:45:58Z",
      "native_id": "9bfd6781f60f4d949ed442b36bcc304a",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-65129-9"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Whale-optimized elman neural network for predicting moisture content during seaweed drying in a v-groove hybrid solar dryer",
  "title_text": "Whale-optimized elman neural network for predicting moisture content during seaweed drying in a v-groove hybrid solar dryer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:58Z",
  "url": "https://research.draconitic.com/paper/ff51x962ke9bv",
  "venue": "Scientific Reports",
  "versions": []
}