Application of Deep-Learning Methods to Real Time Face Mask Detection

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

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MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques

Digital Technologies and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-73882-2_9 ◽

2021 ◽

pp. 87-97

Author(s):

Ismail Nasri ◽

Mohammed Karrouchi ◽

Hajar Snoussi ◽

Abdelhafid Messaoudi ◽

Kamal Kassmi

Keyword(s):

Deep Learning ◽

Real Time ◽

Face Mask ◽

Learning Techniques

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Real-Time Deep Learning Face Mask Detection Model During COVID-19

Communication and Intelligent Systems - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-1089-9_11 ◽

2021 ◽

pp. 121-131

Author(s):

Amit Juyal ◽

Aditya Joshi

Keyword(s):

Deep Learning ◽

Real Time ◽

Face Mask ◽

Detection Model

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Application of deep learning methods to predict ionosphere parameters in real time

E3S Web of Conferences ◽

10.1051/e3sconf/202019602007 ◽

2020 ◽

Vol 196 ◽

pp. 02007

Author(s):

Vladimir Mochalov ◽

Anastasia Mochalova

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Architecture ◽

Short Term Memory ◽

Neural Network Architecture ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

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Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

Traitement du signal ◽

10.18280/ts.380632 ◽

2021 ◽

Vol 38 (6) ◽

pp. 1875-1885

Author(s):

Ruchi Jayaswal ◽

Manish Dixit

Keyword(s):

Deep Learning ◽

Real Time ◽

Preventive Measure ◽

Face Mask ◽

Histogram Equalization ◽

Test Accuracy ◽

The Face ◽

Novel Coronavirus ◽

Face Detector

A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

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Ransomware Traffic Classification Using Deep Learning Models

International Journal of Web Portals ◽

10.4018/ijwp.2020010101 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1-11

Author(s):

Arivudainambi D. ◽

Varun Kumar K.A. ◽

Vinoth Kumar R. ◽

Visu P.

Keyword(s):

Deep Learning ◽

Real Time ◽

Network Traffic ◽

Classification Model ◽

Traffic Classification ◽

Learning Models ◽

Learning Methods ◽

Novel Method

Ransomware is a malware which affects the systems data with modern encryption techniques, and the data is recovered once a ransom amount is paid. In this research, the authors show how ransomware propagates and infects devices. Live traffic classifications of ransomware have been meticulously analyzed. Further, a novel method for the classification of ransomware traffic by using deep learning methods is presented. Based on classification, the detection of ransomware is approached with the characteristics of the network traffic and its communications. In more detail, the behavior of popular ransomware, Crypto Wall, is analyzed and based on this knowledge, a real-time ransomware live traffic classification model is proposed.

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