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  1. Article: A new YOLO-based method for social distancing from real-time videos.

    Gündüz, Mehmet Şirin / Işık, Gültekin

    Neural computing & applications

    2023  Volume 35, Issue 21, Page(s) 15261–15271

    Abstract: The coronavirus disease (COVID-19) is primarily disseminated through physical contact. As a precaution, it is recommended that indoor spaces have a limited number of people and at least one meter apart. This study proposes a real-time method for ... ...

    Abstract The coronavirus disease (COVID-19) is primarily disseminated through physical contact. As a precaution, it is recommended that indoor spaces have a limited number of people and at least one meter apart. This study proposes a real-time method for monitoring physical distancing compliance in indoor spaces using computer vision and deep learning techniques. The proposed method utilizes YOLO (You Only Look Once), a popular convolutional neural network-based object detection model, pre-trained on the Microsoft COCO (Common Objects in Context) dataset to detect persons and estimate their physical distance in real time. The effectiveness of the proposed method was assessed using metrics including accuracy rate, frame per second (FPS), and mean average precision (mAP). The results show that the YOLO v3 model had the most remarkable accuracy (87.07%) and mAP (89.91%). On the other hand, the highest fps rate of up to 18.71 was achieved by the YOLO v5s model. The results demonstrate the potential of the proposed method for effectively monitoring physical distancing compliance in indoor spaces, providing valuable insights for future use in other public health scenarios.
    Language English
    Publishing date 2023-04-07
    Publishing country England
    Document type Journal Article
    ZDB-ID 1480526-1
    ISSN 1433-3058 ; 0941-0643
    ISSN (online) 1433-3058
    ISSN 0941-0643
    DOI 10.1007/s00521-023-08556-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models.

    Gündüz, Mehmet Şirin / Işık, Gültekin

    Journal of real-time image processing

    2023  Volume 20, Issue 1, Page(s) 5

    Abstract: As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. ...

    Abstract As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. Depending on the size of the indoors, the number of persons that can fit in that area varies. Then, the size of the indoor area should be measured and the maximum number of people should be calculated accordingly. Computers can be used to ensure the correct application of the capacity rule in indoors monitored by cameras. In this study, a method is proposed to measure the size of a prespecified region in the video and count the people there in real time. According to this method: (1) predetermining the borders of a region on the video, (2) identification and counting of people in this specified region, (3) it is aimed to estimate the size of the specified area and to find the maximum number of people it can take. For this purpose, the You Only Look Once (YOLO) object detection model was used. In addition, Microsoft COCO dataset pre-trained weights were used to identify and label persons. YOLO models were tested separately in the proposed method and their performances were analyzed. Mean average precision (mAP), frame per second (fps), and accuracy rate metrics were found for the detection of persons in the specified region. While the YOLO v3 model achieved the highest value in accuracy rate and mAP (both 0.50 and 0.75) metrics, the YOLO v5s model achieved the highest fps rate among non-Tiny models.
    Language English
    Publishing date 2023-01-30
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2280192-3
    ISSN 1861-8219 ; 1861-8200
    ISSN (online) 1861-8219
    ISSN 1861-8200
    DOI 10.1007/s11554-023-01276-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications.

    Gharehchopogh, Farhad Soleimanian / Ucan, Alaettin / Ibrikci, Turgay / Arasteh, Bahman / Isik, Gultekin

    Archives of computational methods in engineering : state of the art reviews

    2023  Volume 30, Issue 4, Page(s) 2683–2723

    Abstract: Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper ... ...

    Abstract Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.
    Language English
    Publishing date 2023-01-12
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2276736-8
    ISSN 1886-1784 ; 1134-3060
    ISSN (online) 1886-1784
    ISSN 1134-3060
    DOI 10.1007/s11831-023-09883-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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