ABSTRACT
The Coronavirus made a new normalization of life where communal distancing and use of masks for covering their face perform an essential part in monitoring the effects of spreading of the corona virus, still the majority of population are found not using face shields or masks in public areas that accelerates the spreading of the corona virus. This might lead to the serious issue of rise in scattering of the disease. Therefore, to neglect any kind of circumstances we are in need to explore and alert the public for wearing masks. Persons can't be deployed for this procedure, as the risk of getting affected by corona virus increases. Henceforth, the presented model for mask detection is surrounded along the theories of artificial intelligence (AI), deep learning, object detection technologies and convolutional neural networks (CNN) which are the key subject of this project. The project performs by recognizing the people are wearing their face shields or masks or not in public areas via utilizing image processing and deep learning practices and transmitting data to the governing authorities. These algorithms for abject detection have been optimized for recognition of people with face masks or not. This paper is attempting for development of a model for real-time monitoring which will turn out to be pretty effective and simple. This model magnificently recognizes whether an individual is wearing a mask or not up to 98% of accuracy as achieved till date and observed that it has yielded outstanding outcomes for the detection. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
The Coronavirus made a new normalization of life where communal distancing and use ofmasks for covering their face perform an essential part in monitoring the effects of spreading of thecorona virus, still the majority of population are found not using face shields or masks in public areasthat accelerates the spreading of the corona virus. This might lead to the serious issue of rise inscattering of the disease. Therefore, to neglect any kind of circumstances we are in need to exploreand alert the public for wearing masks. Persons can't be deployed for this procedure, as the risk ofgetting affected by corona virus increases. Henceforth, the presented model for mask detection is surroundedalongthetheoriesofartificialintelligence(AI), deep learning, object detection technologies andconvolutionalneuralnetworks(CNN)whicharethekeysubjectofthisproject.Theprojectperformsbyrecognizing the people are wearing their face shields or masks or not in public areas via utilizingimage processing and deep learning practices and transmitting data to the governing authorities.These algorithms for abject detection have been optimized for recognition of people with face masksor not. This paper is attempting for development of a model for real-time monitoring which will turnout to be pretty effective and simple. This model magnificently recognizes whether an individual iswearing amask or not up to 98% of accuracy as achieved till date and observed that it has yieldedoutstandingoutcomesforthedetection. Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method comprises of four stages, preprocess the covid-19 data, attribute selection, tune parameters, and classify cov-id-19 data. Initially, images are fed to preprocess and features are selected using Convolutional Neural Network (CNN). Next, Multi-objective Black Widow Optimization (MBWO) method is imparted to finely tune the hyper parameters of CNN. Lastly, Extreme Learning Machine Auto Encoder (ELM-AE) is used to check the existence of corona virus and further classification is done to classify the covid-19 data into respective classes. The suggested MBWO-CNN model was evaluated for effectiveness by undergoing experiments and the outcomes attained were matched with the outcome stationed by prevailing methods. The outcomes confirmed the astonishing results of the ELM-AE model to classify cov-id-19 data by achieving maximum accuracy of 97.53%. The efficacy of the proposed method is validated and observed that it has yielded outstanding outcomes and is best suitable to diagnose and classify covid-19 data. © 2022, Tech Science Press. All rights reserved.