Yazar "Kahraman, Idris" seçeneğine göre listele

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    AR Based App for Tourist Attraction in ESKI CARSI (Safranbolu)
    (Copernicus Gesellschaft Mbh, 2016) Polat, Merve; Karas, Ismail Rakip; Kahraman, Idris; Alizadehashrafi, Behnam
    This research is dealing with 3D modeling of historical and heritage landmarks of Safranbolu that are registered by UNESCO. This is an Augmented Reality (AR) based project in order to trigger virtual three-dimensional (3D) models, cultural music, historical photos, artistic features and animated text information. The aim is to propose a GIS-based approach with these features and add to the system as attribute data in a relational database. The database will be available in an AR-based application to provide information for the tourists.
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    Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm
    (Mdpi, 2024) Kahraman, Idris; Karas, Ismail Rakip; Turan, Muhammed Kamil
    Protozoa detection and classification from freshwaters and microscopic imaging are critical components in environmental monitoring, parasitology, science, biological processes, and scientific research. Bacterial and parasitic contamination of water plays an important role in society health. Conventional methods often rely on manual identification, resulting in time-consuming analyses and limited scalability. In this study, we propose a real-time protozoa detection framework using the YOLOv4 algorithm, a state-of-the-art deep learning model known for its exceptional speed and accuracy. Our dataset consists of objects of the protozoa species, such as Bdelloid Rotifera, Stylonychia Pustulata, Paramecium, Hypotrich Ciliate, Colpoda, Lepocinclis Acus, and Clathrulina Elegans, which are in freshwaters and have different shapes, sizes, and movements. One of the major properties of our work is to create a dataset by forming different cultures from various water sources like rainwater and puddles. Our network architecture is carefully tailored to optimize the detection of protozoa, ensuring precise localization and classification of individual organisms. To validate our approach, extensive experiments are conducted using real-world microscopic image datasets. The results demonstrate that the YOLOv4-based model achieves outstanding detection accuracy and significantly outperforms traditional methods in terms of speed and precision. The real-time capabilities of our framework enable rapid analysis of large-scale datasets, making it highly suitable for dynamic environments and time-sensitive applications. Furthermore, we introduce a user-friendly interface that allows researchers and environmental professionals to effortlessly deploy our YOLOv4-based protozoa detection tool. We conducted f1-score 0.95, precision 0.92, sensitivity 0.98, and mAP 0.9752 as evaluating metrics. The proposed model achieved 97% accuracy. After reaching high efficiency, a desktop application was developed to provide testing of the model. The proposed framework's speed and accuracy have significant implications for various fields, ranging from a support tool for paramesiology/parasitology studies to water quality assessments, offering a powerful tool to enhance our understanding and preservation of ecosystems.