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  • Open Access

    ARTICLE

    Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

    Kyamelia Roy1, Sheli Sinha Chaudhuri1, Sayan Pramanik2, Soumen Banerjee2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 647-662, 2023, DOI:10.32604/csse.2023.024997 - 01 June 2022

    Abstract In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning… More >

  • Open Access

    ARTICLE

    Increasing rate of pathologic upgrading in low risk prostate cancer patients in the active surveillance era

    Facundo Davaro1, David Weinstein1, Ryan Wong1, Sameer Siddiqui1, Leslie Hinyard2, Zachary Hamilton1

    Canadian Journal of Urology, Vol.29, No.2, pp. 11059-11066, 2022

    Abstract Introduction: Management of prostate cancer has seen an increasing predilection for active surveillance in low risk (LR) patients. We aimed to evaluate the rate of pathologic upgrading in patients with very low (VLR) or LR prostate cancer after prostatectomy.
    Materials and methods: The National Cancer Database (NCDB) and the Surveillance, Epidemiology, and End Results (SEER) Database were queried for patients diagnosed with Gleason 6 prostate cancer and prostate specific antigen (PSA) < 10 ng/mL from 2010 to 2016. All patients underwent 12-core biopsy and a subsequent prostatectomy for final pathologic staging. Our primary outcome was rate of… More >

  • Open Access

    REVIEW

    Treatment options in stage I seminoma

    UROS BUMBASIREVIC1,2,*, MARKO ZIVKOVIC1, MILOS PETROVIC1, VESNA CORIC2,3, NIKOLA LISICIC1, NEBOJSA BOJANIC1,2

    Oncology Research, Vol.30, No.3, pp. 117-128, 2022, DOI:10.32604/or.2022.027511 - 12 January 2023

    Abstract Seminomas are most commonly diagnosed in clinical stage I (CSI). After orchiectomy, approximately 15% of patients in this stage have subclinical metastases. Adjuvant radiotherapy (ART) delivered to the retroperitoneum and ipsilateral pelvic lymph nodes has been the mainstay of treatment for many years. Although highly efficient, with long-term cancer-specific survival (CSS) rates approaching almost 100%, ART is associated with considerable long-term consequences, particularly cardiovascular toxicity and increased risk of secondary malignancies (SMN). Therefore, active surveillance (AS) and adjuvant chemotherapy (ACT) were developed as alternative treatment options. While AS prevents patient overtreatment, it is associated with… More >

  • Open Access

    ARTICLE

    Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos

    R. Haripriya*, G. Kousalya

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 947-965, 2022, DOI:10.32604/csse.2022.024149 - 09 May 2022

    Abstract COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosed in December 2019 in China. As of 25th Aug 2021, there are 165 million confirmed COVID-19 positive cases and 4.4 million deaths globally. As of today, though there are approved COVID-19 vaccine candidates only 4 billion doses have been administered. Until 100% of the population is safe, no one is safe. Even though these vaccines can provide protection against getting seriously ill and dying from the disease, it does not provide 100% protection from getting… More >

  • Open Access

    ARTICLE

    Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

    Erssa Arif1,*, Syed Khuram Shahzad2, Muhammad Waseem Iqbal3, Muhammad Arfan Jaffar4, Abdullah S. Alshahrani5, Ahmed Alghamdi6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4615-4630, 2022, DOI:10.32604/cmc.2022.027571 - 21 April 2022

    Abstract The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution More >

  • Open Access

    ARTICLE

    Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

    Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Malek Z. Alksasbeh3, Bassam A. Y. Alqaralleh2,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5593-5605, 2022, DOI:10.32604/cmc.2022.026666 - 21 April 2022

    Abstract Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved… More >

  • Open Access

    ARTICLE

    Key Frame Extraction Algorithm of Surveillance Video Based on Quaternion Fourier Significance Detection

    Zhang Yunzuo1,*, Zhang Jiayu1, Cai Zhaoquan2

    Journal of New Media, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jnm.2022.027054 - 21 April 2022

    Abstract With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data. Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval. It can extract key frame sets that can express video content from massive videos. However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately. To this end, this paper proposes a key… More >

  • Open Access

    ARTICLE

    Background Subtraction in Surveillance Systems Using Local Spectral Histograms and Linear Regression

    S. Hariharan1,*, R. Venkatesan2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 407-422, 2022, DOI:10.32604/iasc.2022.025309 - 15 April 2022

    Abstract Background subtraction is a fundamental and crucial task for computer vision-based automatic video analysis due to various challenging situations that occur in real-world scenarios. This paper presents a novel background subtraction method by estimating the background model using linear regression and local spectral histogram which captures combined spectral and texture features. Different linear filters are applied on the image window centered at each pixel location and the features are captured via these filter responses. Each feature has been approximated by a linear combination of two representative features, each of which corresponds to either a background More >

  • Open Access

    ARTICLE

    Bayesian Feed Forward Neural Network-Based Efficient Anomaly Detection from Surveillance Videos

    M. Murugesan*, S. Thilagamani

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 389-405, 2022, DOI:10.32604/iasc.2022.024641 - 15 April 2022

    Abstract Automatic anomaly activity detection is difficult in video surveillance applications due to variations in size, type, shape, and objects’ location. The traditional anomaly detection and classification methods may affect the overall segmentation accuracy. It requires the working groups to judge their constant attention if the captured activities are anomalous or suspicious. Therefore, this defect creates the need to automate this process with high accuracy. In addition to being extraordinary or questionable, the display does not contain the necessary recording frame and activity standard to help the quick judgment of the parts’ specialized action. Therefore, to… More >

  • Open Access

    ARTICLE

    IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

    Wahidur Rahman1, Naif Al Mudawi2,*, Abdulwahab Alazeb2, Muhammad Minoar Hossain1, Saima Siddique Tashfia1, Md. Tarequl Islam1, Shisir Mia1, Mohammad Motiur Rahman1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2033-2053, 2022, DOI:10.32604/cmc.2022.025025 - 24 February 2022

    Abstract On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain.… More >

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