Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

Basit Öğe Kaydı
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorJovanovic, Luka
dc.contributor.authorZivkovic, Miodrag
dc.contributor.authorBacanin, Nebojsa
dc.contributor.authorDobrojevic, Milos
dc.contributor.authorSimic, Vladimir
dc.contributor.authorSadasivuni, Kishor Kumar
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2025-04-18T09:48:33Z
dc.date.available2025-04-18T09:48:33Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThis study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, Institute of Physics Belgrade, Science Fund of the Republic of Serbia, Science Fund of the Republic of Serbia
dc.identifier.citationJovanovic, L., Zivkovic, M., Bacanin, N., Dobrojevic, M., Simic, V., Sadasivuni, K. K., & Tirkolaee, E. B. (2024). Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction. Neural Computing and Applications, 1-30
dc.identifier.doi10.1007/s00521-024-09850-4
dc.identifier.endpage14756
dc.identifier.issn09410643
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85192782986
dc.identifier.scopusqualityQ1
dc.identifier.startpage14727
dc.identifier.urihttp://dx.doi.org/10.1007/s00521-024-09850-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6868
dc.identifier.volume36
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofNeural Computing and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCrop Yield Prediction
dc.subjectMetaheuristics
dc.subjectReptile Search Algorithm
dc.subjectWeight Agnostic Neural Networks
dc.titleEvaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Endüstri Mühendisliği Bölümü Makale Koleksiyonu