Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Comprehensive Analysis and Machine Learning-Based Solutions for Drift Behavior in Ambient Atomic Force Microscope Conditions(Pergamon-Elsevier Science Ltd, 2025) Deveci, D. Gemici; Barandir, T. Karakoyun; Unverdi, O.; Celebi, C.; Temur, L. O.; Atilla, D. C.This 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.Editorial Comments on “Relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems”(Ieee-inst Electrical Electronics Engineers inc, 2025) Sahan, Gokhan; Trenn, StephanThis study addresses the deficiencies in the assumptions of the results in (Chen and Yang, 2017) due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then, we prove why they are wrong in that form and show the errors in the proof of the main result of (Chen and Yang, 2017). Next, we compare the assumptions and related results of (Chen and Yang, 2017) with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them.Article Citation - Scopus: 20Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arıkan, İ.; Ayav, T.; Seçkin, A.Ç.; Soygazi, F.Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors.