Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

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dc.contributor.authorNaseri, Raghda Awad Shaban
dc.contributor.authorKurnaz, Ayça
dc.contributor.authorFarhan, Hameed Mutlag
dc.date.accessioned2023-01-24T09:32:34Z
dc.date.available2023-01-24T09:32:34Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe growth of the “Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a “Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2.en_US
dc.identifier.citationNaseri, R. A. S., Kurnaz, A., Farhan, H. M. (2023). Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach. Applied Soft Computing, 109933.en_US
dc.identifier.issn1568-4946
dc.identifier.scopus2-s2.0-85145980584
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3189
dc.identifier.volume134en_US
dc.identifier.wosWOS:000967884600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorNaseri, Raghda Awad Shaban
dc.institutionauthorKurnaz, Ayça
dc.institutionauthorFarhan, Hameed Mutlag
dc.language.isoen
dc.publisherElsevier Ltden_US
dc.relation.ispartofApplied Soft Computing
dc.relation.isversionof10.1016/j.asoc.2022.109933en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive Sailfish Moth Flame Optimizationen_US
dc.subjectCOVID-19 Pandemicen_US
dc.subjectFace Mask Detectionen_US
dc.subjectHybrid ResMobileNeten_US
dc.subjectInternet of Medical Thingsen_US
dc.subjectSingle Shot Multibox Detectoren_US
dc.titleOptimized face detector-based intelligent face mask detection model in IoT using deep learning approach
dc.typeArticle

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