Comprehensive analysis and machine learning-based solutions for drift behavior in ambient Atomic Force Microscope conditions

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dc.contributor.authorDeveci, Derya Gemici
dc.contributor.authorBarandır, T. Karakoyun
dc.contributor.authorÜnverdi, Ö.
dc.contributor.authorÇelebi C.
dc.contributor.authorTemur, L.Ö.
dc.contributor.authorAtilla, D.Ç.
dc.date.accessioned2025-08-14T15:11:40Z
dc.date.available2025-08-14T15:11:40Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programı
dc.descriptionArticle number : 111678 CODEN : EAAIE
dc.description.abstractThis study outlines the effectiveness of combining numerical methods, Computer Vision (CV) and Machine Learning (ML) approaches to analyze and predict drift behavior in high-resolution Atomic Force Microscope (AFM) scanning procedures. Using Long Short-Term Memory (LSTM) models for time series analysis and the Light Gradient Boosting Machine (LightGBM) algorithm for predictive modeling, significant progress was achieved in understanding the dynamic and variable nature of drift and mitigating its impact on scanning. The models demonstrated a robust predictive capability, achieving approximately 94% accuracy in drift predictions. The study emphasizes the nonstationary characteristics of drift and demonstrates how the selection of features directly related to the target variable enhances the efficiency of the model and enables adaptive real-time correction. These findings confirm the predictive strength of the models and highlight the potential for integrating ML predictions with real-time feedback mechanisms to improve the resolution and stability of AFM imaging in both scientific and industrial applications.
dc.description.sponsorshipFunding agency : Yaşar University Grant number : BAP143
dc.identifier.citationDeveci, D. G., Barandır, T. K., Ünverdi, Ö., Celebi, C., Temur, L. Ö., & Atilla, D. Ç. (2025). Comprehensive analysis and machine learning-based solutions for drift behavior in ambient Atomic Force Microscope conditions. Engineering Applications of Artificial Intelligence, 159, 111678. 10.1016/j.engappai.2025.111678
dc.identifier.doi10.1016/j.engappai.2025.111678
dc.identifier.issn0952-1976
dc.identifier.scopus2-s2.0-105010562030
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5859
dc.identifier.volume159
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorDeveci, Derya Gemici
dc.institutionauthorTemur, Lütviye Özge
dc.institutionauthorAtilla, Doğu Çağdaş
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectAtomic Force Microscope
dc.subjectComputer Vision
dc.subjectDeep learning
dc.subjectDrift
dc.subjectMachine Learning
dc.titleComprehensive analysis and machine learning-based solutions for drift behavior in ambient Atomic Force Microscope conditions
dc.typeArticle

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