scholarly journals Comparative Study of Deep Learning Methods in Detection Face Mask Utilization

2020 ◽  
Author(s):  
Ivan Muhammad Siegfried
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Face Mask ◽  
Face Image ◽  
Learning Methods ◽  
Accuracy And Precision ◽  
The World ◽  
Image Dataset ◽  
Emerging Virus

In 2020, the world is facing new and emerging virus called COVID-19 where the transmission could be halted using a face mask. A method and model needed to anticipate the spread of such virus. We study some transfer and deep learning methods: MobileNetV2, ResNet50V2, and Xception. The result is that the usage of ResNet50V2 and Xception for face image dataset using mask has better accuracy and precision than that of MobileNetV2 method.

scholarly journals Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study

2020 ◽  
Vol 140 ◽  
pp. 110121 ◽  
Author(s):  
Abdelhafid Zeroual ◽  
Fouzi Harrou ◽  
Abdelkader Dairi ◽  
Ying Sun
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Comparative Study ◽  
Time Series Data ◽  
Series Data ◽  
Learning Methods

scholarly journals Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2692 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Monjur Mourshed ◽  
Yuanjun Guo ◽  
Yimin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Learning Methods ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

scholarly journals A comparative study of rice variety classification based on deep learning and hand-crafted features

2020 ◽  
Vol 14 (1) ◽  
pp. 1-10
Author(s):  
Vinh TRUONG HOANG ◽  
Duc Phan Van Hoai ◽  
Thongchai Surinwarangkoon ◽  
Huu-Thanh Duong ◽  
Kittikhun Meethongjan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Comparative Study ◽  
Efficient Method ◽  
Rice Variety ◽  
Automation System ◽  
Rice Seed ◽  
Quality Inspection ◽  
The World ◽  
Variety Classification

Rice is vital to people all around the world. The demand for an efficient method in rice seed variety classification is one of the most essential tasks for quality inspection. Currently, this task is done by technicians based on experience by investigating the similarity of colour, shape and texture of rice. Therefore, we propose to find an appropriate process to develop an automation system for rice recognition. In this paper, several hand-crafted descriptors and Convolutional Neural Networks (CNN) methods are evaluated and compared. The experiment is simulated on the VNRICE dataset on which our method shows a significant result. The highest accuracy obtained is 99.04% by using DenNet21 framework.

scholarly journals A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction

2020 ◽  
Vol 24 (4) ◽  
pp. 179
Author(s):  
Zhuonan He ◽  
Cong Quan ◽  
Siyuan Wang ◽  
Yuanzheng Zhu ◽  
Minghui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Methods ◽  
Unsupervised Deep Learning ◽  
Mri Reconstruction

scholarly journals Deep Learning Model Based on Mobile-Net with Haar-like Algorithm for Masked Face Recognition at Nuclear Facilities

2021 ◽  
Vol 10 (7) ◽  
pp. 18-23
Author(s):  
Nadia.M. Nawwar* ◽  
Kasban . ◽  
Salama May
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Nuclear Facility ◽  
Nuclear Facilities ◽  
Novel Technique ◽  
The World ◽  
The Individual ◽  
Deep Learning Model

During the spread of the COVID-I9 pandemic in early 2020, the WHO organization advised all people in the world to wear face-mask to limit the spread of COVID-19. Many facilities required that their employees wear face-mask. For the safety of the facility, it was mandatory to recognize the identity of the individual wearing the mask. Hence, face recognition of the masked individuals was required. In this research, a novel technique is proposed based on a mobile-net and Haar-like algorithm for detecting and recognizing the masked face. Firstly, recognize the authorized person that enters the nuclear facility in case of wearing the masked-face using mobile-net. Secondly, applying Haar-like features to detect the retina of the person to extract the boundary box around the retina compares this with the dataset of the person without the mask for recognition. The results of the proposed modal, which was tested on a dataset from Kaggle, yielded 0.99 accuracies, a loss of 0.08, F1.score 0.98.

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

2021 ◽  
Vol 19 (6) ◽  
pp. 994-1001
Author(s):  
Diego Gonzalez Dondo ◽  
Javier Andres Redolfi ◽  
R. Gaston Araguas ◽  
Daiana Garcia
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
Learning Methods

scholarly journals Autonomic Face Mask Detection with Deep Learning: an IoT Application

2021 ◽  
Author(s):  
◽  
V. H. Benitez-Baltazar
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Image Tracking ◽  
Critical Areas ◽  
Machine Learning Model ◽  
The World ◽  
The Face ◽  
Health Organization

A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 3 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.

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