Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 1Pedestrian Equipment Anomaly Detection With Computer Vision in Warehouses(Avestia Publishing, 2024) Elçi,T.; Ünlü,M.Z.; Kantar,D.; Türker,A.Y.; Güney,H.; Ustaoğlu,A.The rapid growth of the logistics sector in recent years caused the expansion of warehouse areas and the increase in the number of equipment used. With the increase in these activities, the possibility of work accidents in warehouses also increases. In defiance of this situation, it has been determined that a real-time prediction system of pedestrian and equipment interaction is needed to ensure in-warehouse reliability. This system should address the urgent need to reduce the risk of work accidents and focus on the overall goal of reducing the possibility of work accidents in warehouse environments. To overcome this challenge, we propose a comprehensive Warehouse Anomaly Detection and Control System consisting of object detection, object tracking, action detection, and alarm classification components which will play an important role in increasing work safety in warehouse environments. YOLOv7 (You Only Look Once version 7) is a deep learning model that detects objects quickly and accurately in a single network pass. The deep learning-based Deep SORT algorithm used for object tracking provides a dynamic understanding of the warehouse environment by continuously storing these identified problems in real-time. The action detection part of this system is designed to identify and analyze actions and movements, recognizing anomalies and potential risks. In this part, the speed of pedestrians and equipment are detected utilization of 3D bounding boxes of objects and perspective transformation. The possible accident risks are measured using the intersection percentage of these areas, the magnitude of speed, the direction of the motion vector of pedestrian and equipment, and the distances between objects. Alert levels can be considered as encounter, near-miss, and emergency. Using this system in warehouses will reduce the risk of possible work accidents that may even result in death. © 2024, Avestia Publishing. All rights reserved.Conference Object Citation - Scopus: 19Thquad: Turkish Historic Question Answering Dataset for Reading Comprehension(Institute of Electrical and Electronics Engineers Inc., 2021) Soygazi,F.; Çiftçi,O.; Kök,U.; Cengiz,S.Question answering(QA) is a field in natural language processing and information retrieval, it aims to give answers to the questions using natural language. In this paper, we present the Turkish question answering dataset, which is THQuAD and baseline results with contextualized word embeddings. THQuAD consists of two different datasets one of them is TQuad on Turkish Islamic Science history within the scope of Teknofest 2018 "Artificial Intelligence competition", the second dataset on Ottoman history within the scope of Teknofest 2020 "Dogal Dil íçleme Yarismasi" prepared by us. THQuAD is a reading comprehension dataset, consisting of questions, answers, and passages. Our objective is to give an answer to a specific question by understanding the passage and extracting the answer from this passage. We generate contextualized word embeddings from pre-trained Turkish Bert, Electra, Albert language models after fine-tuning on different hyperparameters with neural networks. © 2021 IEEEArticle Citation - WoS: 2Citation - Scopus: 2Improving Outdoor Plane Estimation Without Manual Supervision(Springer, 2022) Uzyıldırım, Furkan Eren; Özuysal, MustafaRecently, great progress has been made in the automatic detection and segmentation of planar regions from monocular images of indoor scenes. This has been achieved thanks to the development of convolutional neural network architectures for the task and the availability of large amounts of training data usually obtained with the help of active depth sensors. Unfortunately, it is much harder to obtain large image sets outdoors partly due to limited range of active sensors. Therefore, there is a need to develop techniques that transfer features learned from the indoor dataset to segmentation of outdoor images. We propose such an approach that does not require manual annotations on the outdoor datasets. Instead, we exploit a network trained on indoor images and an automatically reconstructed point cloud to estimate the training ground truth on the outdoor images in an energy minimization framework. We show that the resulting ground truth estimate is good enough to improve the network weights. Moreover, the process can be repeated multiple times to further improve plane detection and segmentation accuracy on monocular images of outdoor scenes.Conference Object Citation - Scopus: 2Label-Free Detection of Rare Cancer Cells Using Deep Learning and Magnetic Levitation Principle(SPIE, 2021) Delikoyun, Kerem; Demir, Ali Aslan; Tekin, Hüseyin CumhurMagnetic levitation is an effective tool for separating target cells within a heterogeneous solution by utilizing density differences among cell lines. However, magnetic levitation cannot be used to identify target cells which have similar density profile as the other cells in the solution. Therefore, accuracy of cell identification can dramatically reduce. In this study, we introduce, for the first time, the use of deep learning-based object detection approach for label-free identification of rare cancer cells within levitated cells. As a result, our novel and hybrid detection strategy could be used to identify circulating tumor cells for diagnosis and prognosis of cancer. © 2021 SPIE.Article Citation - WoS: 55Citation - Scopus: 56Evaluation of an Artificial Intelligence System for Diagnosing Scaphoid Fracture on Direct Radiography(Springer Verlag, 2020) Özkaya, Emre; Topal, Fatih Esad; Bulut, Tuğrul; Gürsoy, Merve; Özuysal, Mustafa; Karakaya, ZeynepPurpose The aim of this study is to determine the diagnostic performance of artificial intelligence with the use of convolutional neural networks (CNN) for detecting scaphoid fractures on anteroposterior wrist radiographs. The performance of the deep learning algorithm was also compared with that of the emergency department (ED) physician and two orthopaedic specialists (less experienced and experienced in the hand surgery). Methods A total 390 patients with AP wrist radiographs were included in the study. The presence/absence of the fracture on radiographs was confirmed via CT. The diagnostic performance of the CNN, ED physician and two orthopaedic specialists (less experienced and experienced) as measured by AUC, sensitivity, specificity, F-Score and Youden index, to detect scaphoid fractures was evaluated and compared between the groups. Results The CNN had 76% sensitivity and 92% specificity, 0.840 AUC, 0.680 Youden index and 0.826Fscore values in identifying scaphoid fractures. The experienced orthopaedic specialist had the best diagnostic performance according to AUC. While CNN's performance was similar to a less experienced orthopaedic specialist, it was better than the ED physician. Conclusion The deep learning algorithm has the potential to be used for diagnosing scaphoid fractures on radiographs. Artificial intelligence can be useful for scaphoid fracture diagnosis particularly in the absence of an experienced orthopedist or hand surgeon.