Browsing by Author "Topalli, Ayca Kumluca"

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A Focused Survey on Patient Simulation with Large Language Models
(Institute of Electrical and Electronics Engineers Inc., 2025) Katarci, Selin; Topalli, Ayca Kumluca
Green Synthesized Silver Nanoparticles in Two Stages: Box Behnken Design To Machine Learning
(Taylor & Francis Inc, 2024) Çalışkan, Gülizar; Kumluca Topallı, Ayca; Topalli, Ayca Kumluca
In order to solve the modeling issues due to data scarcity problems in the disciplines utilizing statistical approximations, a novel two-stage idea is proposed. As a use case, nanoparticle biosynthesis was selected, for which an environmentally friendly process is of vital importance. First, Box Behnken Design was used for experimental setup, quadratic model formulation and data generation. The second stage consists of Machine Learning, in which the data generated in the previous stage were fed into a Neural Network to determine the relationship between the parameters. Obtained results showed that the proposed combined strategy provided better nanoparticle size estimations than the statistical approach alone. In the absence of publicly available databases, data generation using experimental design and machine learning, as proposed here, could be a faster, lower-cost, and greener solution. Our proposed method can be applied to a wide range of biotechnology and bioengineering applications with significant advanced knowledge.
Citation - WoS: 1
Citation - Scopus: 1
A Unified Diagnosis Kit Design for Telemedicine
(IEEE, 2022) Ozek, Oke; Akgun, Cem; Kilic, Kemal; Akan, Aydin; Kumluca Topallı, Ayça; Topalli, Ayca Kumluca
Vital signs of vulnerable adults, such as heart beat, oxygen saturation in blood and body movement, are tracked continuously in real-time while they are at home and alone. These data are used to present person's past and current status to the related physician and carer, as well as to detect emergencies. The data are sent by the wellbeing sensors via Bluetooth BLE technology, collected and processed by a developed mobile application, stored in the cloud, and shown numerically and graphically on a Web page. Together with the data presentation, abnormalities are detected, alarms are raised and early warnings are given to the medical staff. Such system would be beneficial for both patients and health workers, having affordable sensors and a remote diagnose support tool.