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  • Open Access

    ARTICLE

    An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

    Maha Farouk S. Sabir1, Irfan Mehmood2,*, Wafaa Adnan Alsaggaf3, Enas Fawai Khairullah3, Samar Alhuraiji4, Ahmed S. Alghamdi5, Ahmed A. Abd El-Latif6

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4151-4166, 2022, DOI:10.32604/cmc.2022.017865 - 07 December 2021

    Abstract Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places, by presenting an automated system that automatically… More >

  • Open Access

    ARTICLE

    Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

    S. Ushasukhanya*, M. Karthikeyan

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1261-1275, 2022, DOI:10.32604/iasc.2022.022654 - 17 November 2021

    Abstract Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective… More >

  • Open Access

    ARTICLE

    Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

    Saleh Albahli1, Tahira Nazir2,*, Aun Irtaza2, Ali Javed3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691 - 05 February 2021

    Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR… More >

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