Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article A Machine Learning Framework for Advanced Analytical Detection of CD36 Using Immunosensors Below Limit of Detection(Elsevier Ltd, 2026) Yeke, M.C.; Gelen, S.S.; Fil, H.; Yalcin, M.M.; Gumus, A.; Yazgan, I.; Odaci, D.We introduce a machine learning (ML)-based regression framework for quantitative electrochemical analysis, representing a paradigm shift from traditional univariate methods to a multivariate approach. Conventional analysis is constrained by reducing the entire signal to a single peak current feature to define a linear range and calculate a limit of detection (LOD). In contrast, our methodology treats the Differential Pulse Voltammetry (DPV) curve as time-series data, creating a high-dimensional fingerprint by systematically evaluating multiple data windows with varying widths around the main signal peak to identify the most informative segment. To validate this approach, a biosensor was developed by immobilizing Anti-CD36 antibodies on polydopamine-modified screen-printed carbon electrodes for the detection of CD36, a key protein in metabolism and immunity. Measurements were collected across 12 concentrations, including blank samples, spanning a range of 0 to 25 ng/mL. Following data augmentation, nine different regression models were evaluated, with the top-performing models achieving near-perfect prediction accuracy (R2>0.99) across this entire range. This high accuracy across the full concentration spectrum quantitatively demonstrates the method's ability to operate without relying on traditional concepts like linear range or LOD, enabling reliable detection at ultra-low levels. Furthermore, the immunosensor exhibited high selectivity against common interferents and excellent recovery in human serum. This methodology represents a significant advancement in analytical electrochemistry, providing a transferable approach for enhancing sensitivity in biomarker detection with potential applications in clinical diagnostics and biomedical research. The codes and dataset are made publicly available on GitHub to support further research: https://github.com/miralab-ai/biosensors-AI. © 2026 The Author(s)Conference Object Current Sensing in Phase-OTDR Systems Using Deep Learning(SPIE, 2025) Yeke, M.C.; Sirin, S.; Yüksel, K.; Gumus, A.Fiber optic current sensors are marked by a number of advantages such as light-weight, small-size and inherently insulated nature when compared to conventional current transformers which get bulkier and costlier as the desired values of current to be measured increase. Phase-OTDR is a widely known technology especially in acoustic and thermal sensing, but it suffers from noise that limits its usage for current sensing especially for low currents. In order to interpret the noisy data retrieved from Phase-OTDR current sensor simulator, deep learning techniques can have promising performance. In this paper, 3 different types of deep learning models were proposed and applied on the data generated by Phase-OTDR current sensor simulator tool to improve the ability to distinguish low and similar current levels. The current measurements were analyzed as a classification problem where different current ranges with different current increments are selected as different classes. The proposed method provided 100% accuracy at a difference of 20 A between the current levels. In addition, other scenarios where the current levels were increased by 15 A and 10 A were also studied. In this case, the accuracies 97% and 89% were obtained, respectively. © 2025 Elsevier B.V., All rights reserved.