Optimizing structural parameters for accurate prediction of height and diameter relationships in Himalayan pine using artificial intelligence based neural networks

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dc.contributor.authorM. Iqbal Jeelani
dc.contributor.authorSouad Baowidan
dc.contributor.authorFehim Jeelani Wani
dc.contributor.authorTauseef A. Bhat
dc.contributor.authorFarheen Naqash
dc.contributor.authorElsiddig Idriss Mohamed
dc.contributor.authorFatma Mansour
dc.contributor.authorNahla Zidan
dc.contributor.authorMohamed Sakran
dc.contributor.authorAlaa Baazeem
dc.contributor.authorMansha Gul
dc.date.accessioned2025-04-14T06:33:26Z
dc.date.available2025-04-14T06:33:26Z
dc.date.issued2024-11-14
dc.departmentFakülteler, İktisadi İdari Bilimler Fakültesi, İşletme Bölümü
dc.description.abstractAccurate prediction of height and diameter relationships in the context of Himalayan Pine (Pinus wallichiana) holds immense ecological significance. Leveraging the capabilities of Artificial Intelligence (AI) through neural network models provides a promising avenue for achieving such predictions. This study focuses on investigating the impact of structural parameters on the accuracy of AI-based neural network models designed specifically for this purpose. By identifying the optimal combination of parameters such as the number of layers, neurons per layer, and the choice of activation functions, the research aims to enhance the precision of predictions regarding the growth patterns of Himalayan Pine. The results of this study have practical implications for ecological research and conservation efforts in the Himalayan ecosystem. By optimizing the structural parameters of AI-based neural network models, researchers can achieve more accurate predictions of height and diameter relationships for Himalayan Pine. Such predictions are instrumental for informed decision-making regarding forest management, conservation strategies, and environmental sustainability.
dc.identifier.doi10.30848/pjb2025-2(38)
dc.identifier.issn0556-3321
dc.identifier.issn2070-3368
dc.identifier.issue2
dc.identifier.scopus2-s2.0-86000116446
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.30848/pjb2025-2(38)
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8580
dc.identifier.volume57
dc.identifier.wosWOS:001443943900001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorMansour, Fatma
dc.publisherPakistan Journal of Botany
dc.relation.ispartofPakistan Journal of Botany
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectNeural network model
dc.subjectNeurons
dc.subjectHeight
dc.subjectDiameter
dc.subjectOptimization
dc.subjectPine
dc.titleOptimizing structural parameters for accurate prediction of height and diameter relationships in Himalayan pine using artificial intelligence based neural networks
dc.typejournal-article
oaire.citation.issue2
oaire.citation.volume57

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