Role of semiconductor photo catalysts on mask pollution management

Covid 19 pandemic taught us many lessons. Wearing mask becomes an essential to save our lives. At the same time, management of mask wastages is becoming threat to the environment as they release harmful dyes and fibres. Here in this work we attempt to degrade the mask dyes from the surface of the mask using nano metal oxide based silicate photo catalysts. Two nanocomposites PbO-SiO2 and TiO2-SiO2 were studied on the degradation of coloured mask. The percentage degradation was found to be more than 90%. The results are encouraging so that the proposed catalysts can be used for coating on the masks or used as photo catalyst to degrade the used masks. © 2023 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.

Face Mask Detection Based Entery

The Covid-19 pandemic has introduced several challenges to the society and safety measures are of utmost importance. Hence, to contain and reduce the spread, mask detection-based entry has emerged as a very fascinating topic in the domains of image processing, computer vision, and the Internet of Things (IoT). Convolutional architectures are being used to develop a number of new algorithms that will improve the accuracy of the algorithm. Such convolutional architectures have also made possible the extraction of pixel details. The project aims to build a binary face classifier which can detect the presence of a mask and accordingly people will be granted entry. The classifier is created by using Convolutional Neural Network (CNN), Region based CNN (R-CNN), ThingSpeak. © 2022 IEEE.

A Real-Time Face Mask Detection Using SSD and MobileNetV2

After a rapid spread of Coronavirus (COVID-19) in Wuhan-China in December 2019, the World Health Organization (WHO) confirmed that this was a dangerous virus that could spread from person to person through droplets and airborne contaminants. To prevent the spread of the Covid19, people should wear a mask during the epidemic. During this pandemic, it is becoming increasingly difficult to keep track of human beings the one who wears a mask as a usual practice or not. It will not solely depend on human efforts to keep track the whole world so there is a need to build software that automatically detects whether people in public places wearing a mask or not. Many new models are developed utilizing convolutional Neural Network to build a model as accurately as possible. The method proposed in this paper uses the ResNet model to obtain multiple faces with a single (SSD-Single Shot Multibox Detector) image using a network (model) and MobileNetV2 Architecture used as face mask detectors. This proposed model has 99% more accuracy than most other face recognition models. This mask detector model uses a dataset of hidden morphed masked images to obtain more accurate model. This system should be used in Real-Time applications which require face mask discovery for protection purpose due to the sudden happening of Covid-19. © 2021 IEEE.

COVID-19 classification using feature based deep learning approach

According to consensus, the use of Computerized Tomography (CT) methodology for early finding of several disease, yields both quick and reliable results. Expert radiologists reported that COVID19 has exhibit severalmanners in CT images. In this research, a novel technique of fusing and rankingfeatures based Deep Learning Approach was proposed to detect COVID-19 in its early stages. To create sub-datasets, 32x32 as Subset-1 and 64x64 as Subset-2, within the framework of the proposed procedure, 300 patch images as COVID-19 and Non-COVID-19 were used in the training and testing phases. A VB-Net Deep learning-based segmentation system was created to segment the infection regions in CT scans image of COVID-19 patients. To improve the proposed methodperformance, feature fusion and a ranking method were used.The Convolutional Neural Network (CNN) technique is used in transfer learning. The processed data was then categorized into two types as by using a Support Vector Machine (SVM). This study compares the proposed two subsets with different CNN architecture as Resnet-50, VGG-16 and GoogleNet performance result.By this comparison, the proposed model Subset-2 achieved a better accuracy of 97.58% than other comparison models.