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)Article Reconstructing Lost Heritage: Digital Presentation of 19th Century Rural Landscape of Gülbahçe (İzmir, Türkiye)(Elsevier Ltd, 2026) Tabur, Beylem Doğa; Kul, F.N.This study aims to provide an original methodological framework for the digital reconstruction of Gülbahçe, a historically layered settlement in western Anatolia, Türkiye, which has experienced significant transformations and heritage loss over time. Confronting the challenge of limited documentation regarding its original condition, the study employs hypothetical spatial assumption by integrating comparative typologies, oral history, architectural drawings, and environmental data to digitally reconstruct the village's 19th century spatial and cultural character. This character evolved dramatically following the 1922 population exchange and was further transformed in the 1970s through tourism-driven urban development, the establishment of a university campus, and counter-urbanisation triggered by pandemics and earthquakes. The novelty of this research lies in addressing a critical methodological gap within digital heritage studies by introducing a geometry-based reconstruction technique specifically created for data-scarce heritage contexts often excluded from approaches reliant on rich archival or photogrammetric datasets. The proposed method integrates limited data within a transparent, evidence-based process that presents both the reliability level and the interpretive assumptions behind each modelling decision. By producing a historically grounded and immersive digital environment, the approach responds to the technical and ethical challenges of representing lost heritage, reinforcing discussions on interpretive accountability, community memory, and intercultural dialogue. Ultimately, this interdisciplinary and ethically informed methodology positions digital reconstruction as both an analytical and communicative tool—an adaptable model for documenting, responsibly interpreting, and conveying heritage that has been physically lost but remembered for its cultural significance and is under threat from urbanisation or environmental change. © 2025 Elsevier Ltd.Article An AI-Based Solution for Warehouse Safety: Video Surveillance System Based Anomaly Detection in Equipment-Human Interactions with Vanilla Autoencoder(Elsevier Ltd, 2025) Elçi, T.; Unlu, M.The significant growth of the logistics sector in recent years has resulted in the expansion of warehouse operations and an increased use of equipment, leading to a rise in workplace accidents. These incidents are predominantly attributed to factors such as carelessness, fatigue, high work intensity, individual behaviors, lack of experience, insufficient training, and employee negligence. To enhance warehouse safety, it is essential to implement a system capable of real-time prediction of human-equipment interactions. This study proposes a comprehensive video surveillance framework designed to improve occupational safety in warehouse environments. The system integrates key components, including object detection, object tracking, action recognition, and alarm classification, to effectively reduce risks and prevent accidents.The system employs YOLOv7, a deep learning model with the ability to quickly and accurately detect objects in a single network pass, as the object detection methodology, and DeepSORT, an algorithm for object tracking that assigns unique identifiers to each object and utilizes deep learning techniques to improve tracking performance. The action detection component of the system introduces a novel approach by analyzing and identifying actions and movements while detecting anomalies and potential risks. By leveraging features such as the speed, tags, movement direction, and coordinate data of individuals and equipment, the system estimates alarm levels and generates corresponding alarms, providing an innovative and dynamic solution for real-time risk assessment. The system, tested to demonstrate technological capabilities such as real-time responsiveness and high operational success rates, is designed to predict accidents in warehouse environments, generate alarms, and significantly reduce the risk of occupational accidents. © 2025 The Franklin Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Editorial Preface of Special Issue: Recent Advances in Cancer Biosensors & Diagnostics(Elsevier Ltd, 2025) Yildiz, A.A.; Parlak, O.; Gürsan, A.E.Article Citation - Scopus: 2Digital Sensing Technologies in Cancer Care: a New Era in Early Detection and Personalized Diagnosis(Elsevier Ltd, 2025) Yucel, M.; Önder, A.; Kurt, T.; Keles, B.; Beyaz, M.; Karadağ, Y.; Yildiz, U.H.Digital sensor platforms are systems that integrate sensors with digital technology, which revolutionize data collection, processing, and transmission for enabling real-time, high-precision and automated diagnostics. These platforms often serve as the backbone of modern monitoring systems, enabling real-time data acquisition and analysis for a wide range of applications. Recent advancements in digital sensor platforms have paved the way for transformative innovations in cancer diagnosis. These cutting-edge technologies offer unprecedented opportunities to facilitate early detection, improve diagnostic accuracy, and personalize treatment methods. This review explores the landscape of digital sensor platforms in the context of cancer diagnosis, providing an overview of their principles, functionalities, and clinical applications. The review further illustrates that biosensors, lab-on-a-chip (LOC) devices and wearable sensors have leveraged on nanotechnology, biorecognition materials and artificial intelligence (AI) for revolutionizing cancer diagnosis. It consolidates the recent advances in digital sensor platforms for cancer diagnosis and the associated critical challenges, such as regulatory concerns, standardization, and ethical considerations. Further, the review summarizes the feasibility for the integration of digital sensor platforms with routine clinical practices for the development of efficient cancer diagnosis and treatment methods. © 2025 The AuthorsArticle Citation - Scopus: 13Modification of Pea Protein Isolates by High-Intensity Ultrasonication: Functional, Structural and Nutritional Properties(Elsevier Ltd, 2024) Ozkan,G.; Tataroglu,P.; Gulec,S.; Capanoglu,E.The current study aims to modify the functional, physical, structural and nutritional characteristics of pea protein isolate. High-intensity ultrasound treatment was used at 20 kHz frequency and 25 % amplitude for 10 (US10), 20 (US20), or 30 (US30) min. Results indicated that ultrasound application enhanced the protein solubility and zeta potential. When compared to control samples, the foaming capacity (FC) and stability (FS) as well as emulsion activity (EA) and stability (ES) were also increased from 157.5, 42.03, 46.25 and 53.75 % up to 182.5, 81.57, 72.50 and 67.50 %, respectively. Besides, particle size was found to be lower for ultrasound treated samples (92.9–131.1 nm) in comparison to that of untreated commercial pea protein isolate (161.9 nm). Moreover, while the bioaccessibility of pea protein in untreated sample was calculated as 28.90 %, ultrasonication increased the retention of pea protein up to 49.36 %. It can be concluded from the results that the ultrasonication process can be used as an advantageous, green and non-thermal tool for obtaining protein isolates with improved techno-functional properties and nutritional quality. Therefore, this treatment might improve the characteristics, and thus increase the utilization of plant-based proteins, especially pea protein, in various food systems. © 2024Conference Object Citation - Scopus: 2Influence of Non-Uniform Noise Levels on Modal Identification Procedures(Elsevier Ltd, 2021) Hızal,Ç.; Ceylan,H.Considerable deficiencies and errors in measurement systems are frequently encountered in vibration based structural health monitoring (SHM) applications. Some SHM algorithms are capable of considering such kinds of problems as prediction errors and/or sensor channel noise. As a general intention, however, either a uniform channel noise distribution is assumed or the corresponding measurement channel which produces significant noise in the measured data is generally omitted by researchers. From this perspective, this paper presents a comparative study to investigate the performance of two different SHM algorithms in case of non-uniform sensor channel noise spectral densities. In this context, first the considered problem is illustrated based on the disruptions in the spectral coherence between the noisy and noise free data. Then, a numerical example is presented in which the modal identification of a three degree-of-freedom (DoF) system is performed by using Bayesian Fast Fourier Transform Approach (BFFTA) and Covariance-based Stochastic Subspace Identification (SSI–COV). Results show that both techniques can be adversely affected by the non-uniform levels of channel noise. However, SSI–COV performs better in this case. © 2021Article Citation - Scopus: 5Risk Analysis for Groundwater Intakes Based on the Example of Neonicotinoids(Elsevier Ltd, 2024) Pietrzak,D.; Kania,J.; Kmiecik,E.; Baba,A.Neonicotinoids are a class of broad-spectrum insecticides that are dominant in the world market. They are widely distributed in the environment. Understanding the sources, distribution, and fate of these contaminants is critical to mitigating their effects and maintaining the health of aquatic ecosystems. Contamination of surface and groundwater by neonicotinoids has become a widespread problem worldwide, requiring comprehensive action to accurately determine the mechanisms behind the migration of these pesticides, their properties, and their adverse effects on the environment. A new approach to risk analysis for groundwater intake contamination with emerging contaminants was proposed. It was conducted on the example of four neonicotinoids (acetamiprid, clothianidin, thiamethoxam, and imidacloprid) in relation to groundwater accessed by a hypothetical groundwater intake, based on data obtained in laboratory tests using a dynamic method (column experiments). The results of the risk analysis conducted have shown that in this case study the use of acetamiprid and thiamethoxam for agricultural purposes poses an acceptable risk, and does not pose a risk to the quality of groundwater extracted from the intake for food purposes. Consequently, it does not pose a risk to the health and life of humans and other organisms depending on that water. The opposite situation is observed for clothianidin and imidacloprid, which pose a higher risk of groundwater contamination. For higher maximum concentration of neonicotinoids used in the risk analysis, the concentration of clothianidin and imidacloprid in the groundwater intake significantly (from several to several hundred thousand times) exceeds the maximum permissible levels for drinking water (<0.1 μg/L). This risk exists even if the insecticides containing these pesticides are used according to the information sheet provided by the manufacturer (lower maximum concentration), which results in exceeding the maximum permissible levels for drinking water from several to several hundred times. © 2024 Elsevier LtdArticle Citation - Scopus: 3Development of Chrono-Spectral Gold Nanoparticle Growth Based Plasmonic Biosensor Platform(Elsevier Ltd, 2024) Sözmen,A.B.; Elveren,B.; Erdogan,D.; Mezgil,B.; Bastanlar,Y.; Yildiz,U.H.; Arslan Yildiz,A.Plasmonic sensor platforms are designed for rapid, label-free, and real-time detection and they excel as the next generation biosensors. However, current methods such as Surface Plasmon Resonance require expertise and well-equipped laboratory facilities. Simpler methods such as Localized Surface Plasmon Resonance (LSPR) overcome those limitations, though they lack sensitivity. Hence, sensitivity enhancement plays a crucial role in the future of plasmonic sensor platforms. Herein, a refractive index (RI) sensitivity enhancement methodology is reported utilizing growth of gold nanoparticles (GNPs) on solid support and it is backed up with artificial neural network (ANN) analysis. Sensor platform fabrication was initiated with GNP immobilization onto solid support; immobilized GNPs were then used as seeds for chrono-spectral growth, which was carried out using NH2OH at varied incubation times. The response to RI change of the platform was investigated with varied concentrations of sucrose and ethanol. The detection of bacteria E.coli BL21 was carried out for validation as a model microorganism and results showed that detection was possible at 102 CFU/ml. The data acquired by spectrophotometric measurements were analyzed by ANN and bacteria classification with percentage error rates near 0% was achieved. The proposed LSPR-based, label-free sensor application proved that the developed methodology promises utile sensitivity enhancement potential for similar sensor platforms. © 2024 The Author(s)