Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Collabpersona: A Framework for Collaborative Decision Analysis in Persona Driven LLM-Based Multi-Agent Systems(IEEE Computer Society, 2025) Tamer, O.A.; Gumus, A.Large Language Model (LLM) agents have recently demonstrated impressive capabilities in single agent and adversarial settings, but their ability to collaborate effectively with minimal communication remains uncertain. We introduce CollabPersona, a simulation framework that combines persona-grounded memory with one-shot feedback to study team-based reasoning among LLM agents. In a multi-round variant of the Guess 0.8 of the Average game, agents reason entirely through structured prompts without fine-tuning. Our results show that minimal feedback significantly improves intra-team coordination and stabilizes strategic behavior, while cognitive style remains a primary driver of competitive outcomes. These findings suggest that lightweight scaffolding can elicit emergent collaboration in LLM agents and provide a flexible platform for studying cooperative intelligence. © 2025 IEEE.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.Article Citation - WoS: 1Citation - Scopus: 1Real-Time Superficial Vein Imaging System for Observing Abnormalities on Vascular Structures(Springer, 2024) Altay, A.; Gumus, A.Circulatory system abnormalities might be an indicator of diseases or tissue damage. Early detection of vascular abnormalities might have an important role during treatment and also raise the patient’s awareness. Current detection methods for vascular imaging are high-cost, invasive, and mostly radiation-based. In this study, a low-cost and portable microcomputer-based tool has been developed as a Near-Infrared (NIR) superficial vascular imaging device. The device uses NIR Light-Emitting Diode (LED) light at 850 nm along with other electronic and optical components. It operates as a non-contact and safe infrared (IR) imaging method in real-time. Image and video analysis are carried out using OpenCV (Open-Source Computer Vision), a library of programming functions mainly used in computer vision. Various tests were carried out to optimize the imaging system and set up a suitable external environment. To test the performance of the device, the images taken from three diabetic volunteers, who are expected to have abnormalities in the vascular structure due to the possibility of deformation caused by high glucose levels in the blood, were compared with the images taken from two non-diabetic volunteers. As a result, tortuosity was observed successfully in the superficial vascular structures, where the results need to be interpreted by the medical experts in the field to understand the underlying reasons. Although this study is an engineering study and does not have an intention to diagnose any diseases, the developed system here might assist healthcare personnel in early diagnosis and treatment follow-up for vascular structures and may enable further opportunities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.