Deveci, Derya GemiciBarandır, T. KarakoyunÜnverdi, Ö.Çelebi C.Temur, L.Ö.Atilla, D.Ç.2025-08-142025-08-142025Deveci, 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.2025.1116780952-1976https://hdl.handle.net/20.500.12939/5859Article number : 111678 CODEN : EAAIEThis 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.eninfo:eu-repo/semantics/closedAccessArtificial IntelligenceAtomic Force MicroscopeComputer VisionDeep learningDriftMachine LearningArticle10.1016/j.engappai.2025.1116781592-s2.0-105010562030Q1Q1