{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:03:27Z","timestamp":1771286607466,"version":"3.50.1"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaf034","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T07:44:09Z","timestamp":1746690249000},"source":"Crossref","is-referenced-by-count":0,"title":["Forecasting livestock activity through interpretable neuroevolutionary transfer learning"],"prefix":"10.1093","volume":"34","author":[{"given":"Aymeric","family":"Vellinger","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, University of Namur , Namur, B-5000, Wallonia, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesc","family":"Rodr\u00edguez D\u00edaz","sequence":"additional","affiliation":[{"name":"Data Science and Big Data Lab, Universidad Pablo de Olavide , ctra de Utrera km. 1, ES-41013, Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federico","family":"Divina","sequence":"additional","affiliation":[{"name":"Data Science and Big Data Lab, Universidad Pablo de Olavide , ctra de Utrera km. 1, ES-41013, Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Francisco Torres","sequence":"additional","affiliation":[{"name":"Data Science and Big Data Lab, Universidad Pablo de Olavide , ctra de Utrera km. 1, ES-41013, Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"2026021618131031000_ref1","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/IC2IE53219.2021.9649271","article-title":"Forecasting pet food item stock using arima and lstm","volume-title":"2021 4th International Conference of Computer and Informatics Engineering 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