scholarly journals RESCNN: A Deep Learning Approach for Unmasking Face Mask

2021 ◽  
Vol 7 (1) ◽  
pp. 49-55
Author(s):  
Bhusra Fatima ◽  
Arun Kumar Jhapate

Due to outbreak of COVID-19 pandemic, the trend of wearing mask is rising all over the world. Before such pandemic people wear mask only to protect themselves from pollution. While other people are self-conscious about their looks, they hide their emotions from the public by hiding their faces. But in current scenario, after pandemic, it is compulsory to wear mask everywhere as researchers and doctors have proved that wearing face masks works on impeding COVID-19 transmission. Nowadays, all attendance system or surveillance systems, etc. are integrated with AI technology in which face recognition is considered as input variable. So, there is need to determine all facial landmarks to recognize an individual. In this research work, Residual Convolution Neural Network (ResCNN), network is designed and simulated which unmasks the face mask present on face and restore mask area and recognize an individual. The result analysis is performed in three different cases or scenario, one normal frontal facial region with mask, in another case the masked face is tilted and in third case the noisy masked face is taken as input. The noise in image occurs due to many physical conditions. The dataset for training of ResCNN is prepared by masking facial images taken from CelebA dataset and MFR datasets to prove the efficiency of the proposed model.

Author(s):  
Yasutaka Umayahara ◽  
Zu Soh ◽  
Kiyokazu Sekikawa ◽  
Toshihiro Kawae ◽  
Akira Otsuka ◽  
...  

Cough peak flow (CPF) is a measurement to evaluate the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on both patients and their caregivers. Therefore, this study develops a novel cough strength evaluation method using cough sounds. This paper presents an exponential model to estimate CPF from the cough peak sound pressure level (CPSL). We investigated the relationship between cough sounds and cough flows and the effects of a measurement condition of cough sound, microphone type, and participant’s height and gender on CPF estimation accuracy. The results confirmed that the proposed model estimated CPF with a high accuracy. The absolute error between CPFs and estimated CPFs were significantly lower when the microphone distance from the participant’s mouth was within 30 cm than when the distance exceeded 30 cm. Analysis of the model parameters showed that the estimation accuracy was not affected by participant’s height or gender. These results indicate that the proposed model has the potential to improve the feasibility of measuring and assessing CPF.


2021 ◽  
Vol 96 ◽  
pp. 01001
Author(s):  
Lei Feng ◽  
Juxiu Huang ◽  
Jingxing Liao

Survey on public satisfaction of quality work is one of the effective methods for connecting the quality management department with the public. In this paper, the application of techniques and methods of survey on public satisfaction of quality work is studied and explored from the aspects of the design of survey content index system, data collection, and statistical result analysis based on the survey on public satisfaction of quality work in Yunnan Province, so as to provide enlightenment and reference for the relevant research work in the field of quality.


2009 ◽  
pp. 1577-1591
Author(s):  
Gabor Laszlo

This chapter introduces L-PEST model as the proposed tool for better understanding the fields are influenced by motivations and adaptation policy on FLOSS of public authorities and governments. Software usage in the public sector is a highly complex topic. In the confines of this chapter the selected case studies will show consideration to the vastly different needs and capacities and the different approaches and motivations towards the utilization of FLOSS by governments and/or local authorities. The primary objective of this chapter is to identify and describe the actors associated to the usage of FLOSS within and by the public sector. This chapter has made an attempt to fill this research gap and place the different actors into one complex model. It is hoped the proposed model assists better clarifying the intricate relationship between relevant factors. Nevertheless, much more research work is needed in the years to come. According to Michel Sapin, French Minister in charge of Public Administration and e-Government (2001), “The next generation e-government has two requirements: interoperability and transparency. These are the two strengths of open source software. Therefore, I am taking little risk when I predict that open source software will take a crucial part in the development of e- Government in the years to come.”


Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1628
Author(s):  
Hoang Pham

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States.


2021 ◽  
Vol 2020 (1) ◽  
pp. 370-376
Author(s):  
Samuel Ady Sanjaya ◽  
Suryo Adi Rakhmawan

Corona Virus Desease (COVID-19) pandemic is causing health crisis in every region in the world, especially in Indonesia. One of the effective methods against the virus is wearing face mask in public place as the regulation made by the authorities. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. On the other hand, this solution can be used as communication tool to evaluate people’s habit on wearing face mask. The face mask recognition in this study is developed with machine learning algorithm through the image classification method: MobileNetv2. The proposed model can be integrated with surveillance camera to impede the Covid-19 transmission by allowing the detection of people who are not wearing face mask. After the training, validation, and testing phase, the model can provide the percentage of people using face mask in some cities with high accuracy. The data produced also have a strong correlation to the vigilance index of COVID-19.


Author(s):  
Gabor Laszlo

This chapter introduces L-PEST model as the proposed tool for better understanding the fi elds are influenced by motivations and adaptation policy on FLOSS of public authorities and governments. Software usage in the public sector is a highly complex topic. In the confines of this chapter the selected case studies will show consideration to the vastly different needs and capacities and the different approaches and motivations towards the utilization of FLOSS by governments and/or local authorities. The primary objective of this chapter is to identify and describe the actors associated to the usage of FLOSS within and by the public sector. This chapter has made an attempt to fill this research gap and place the different actors into one complex model. It is hoped the proposed model assists better clarifying the intricate relationship between relevant factors. Nevertheless, much more research work is needed in the years to come. According to Michel Sapin, French Minister in charge of Public Administration and e-Government (2001), “The next generation e-government has two requirements: interoperability and transparency. These are the two strengths of open source software. Therefore, I am taking little risk when I predict that open source software will take a crucial part in the development of e-Government in the years to come.”


Mammography is one of the key method used for detecting the breast cancer, several researcher has proposed the detection and segmentation method, however absolute solution has not developed till now and they have certain limitation and still it is one of the major challenge for finding the region in masses. Hence in this research work we have developed and design a novel method named as DL-CNN (Dual Layered) architecture CNN. The main intention of the model is segmentation and probable region identification. DL-CNN is based on the Convolution Neural Network. It has two layer first layer is applied for identifying the probable region whereas the second layer is used for segmentation and minimizing the false positive Reduction. In order to evaluate the DL-CNN algorithm by using the In Breast Dataset. Moreover the proposed model is compared against the various model in terms of ROI(Region of Interest), Dice_Index and False positive per Image. Result analysis shows that our model outperforms the existing


Author(s):  
Marcelo Pereira da Silva ◽  
◽  
Jessica de Cassia Rossi ◽  
Ana Carolina Trinidade ◽  
◽  
...  

Society, countries, chaps and organizations are going through an atypical moment in history because of the Covid-19 pandemic, which highlights the need to strategically propose local, regional and planetary solutions for phenomena that affect the health and the economy. In this context, the communication of organizations is driven by the frenetic circulation of content and meanings disseminated by digital media, a phenomenon that has changed - and continues to change - the modes of subjectivation, conversation and sociability, as it elevates the actors, the shares to infinity and the connections. This challenging environment constitutes a favorable territory for building relationships, but also foments of conflicts and reputational crises that may involve people, governments, institutions, brands, organizations, etc. Hence, the current scenario shows that the production of information and the building of relationships through digital media reflect on identity, image and reputation. We highlight the existence of a fine line that can become unfavorable through controversial and contradictory communicative actions, given that the contents are disseminated and subject to all kinds of evaluation by the “inhabitants” of the online ecosystem. The organizational performance must align ethical, transparent and coherent principles with corporate and public objectives, as well as paying attention to the rapid changes on the contemporary society. The monitoring of communication becomes a fundamental tool so that, given the fast connections made possible by the development of digital technologies and media, organizations can prevent and manage possible crises and conflicts. These theoretical-pragmatic scenario reinforce the nature of the Public Relations activity as a true sine qua non, as poorly planned speeches and strategies can trigger negative reactions when produced in a time of heightened social sensitivity. In conclusion, the scenario also points to challenges related to the surveillance of citizens, which cover issues related to security, integrity and privacy. Thus, the objective of this article is to analyze, discursively, based on the notion of ethos, two audiovisual utterances from Rede Globo de Televisão on its official Youtube channel, in July 2020, entitled: “Globo – Qualidade também é respeito” and “Globo – Qualidade também é brasilidade”. The methodological procedures correspond to bibliographic research, discussing themes such as digital media, public relations, identity, image, reputation and organizations, and principles of discourse analysis, discussing the production of meaning effects in images of the self (ethos) that can produce cognitive dissonance in the audience. We investigated surveys carried out and made available by the Public Relations consultancy entitled Edelman to understand the trust of citizens in the Brazilian media, which show that more than half of the interviewees distrust the mass media. We infer that ethical, honesty and truth issues become central to reputation due to the acceleration of mediatization processes, marked by interactivity, participation, and clashes, launching organizations into an ocean of vulnerability characterized by praise, boycotts and cancellations who place the strategic management of public relations as imperative in the face of reverberations and negative conversations about the image and reputation of organizations on the digital media.


Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2381 ◽  
Author(s):  
Yasutaka Umayahara ◽  
Zu Soh ◽  
Kiyokazu Sekikawa ◽  
Toshihiro Kawae ◽  
Akira Otsuka ◽  
...  

Cough peak flow (CPF) is a measurement for evaluating the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on both patients and their caregivers. Therefore, this study develops a novel cough strength evaluation method using cough sounds. This paper presents an exponential model to estimate CPF from the cough peak sound pressure level (CPSL). We investigated the relationship between cough sounds and cough flows and the effects of a measurement condition of cough sound, microphone type and participant’s height and gender on CPF estimation accuracy. The results confirmed that the proposed model estimated CPF with a high accuracy. The absolute error between CPFs and estimated CPFs were significantly lower when the microphone distance from the participant’s mouth was within 30 cm than when the distance exceeded 30 cm. Analysis of the model parameters showed that the estimation accuracy was not affected by participant’s height or gender. These results indicate that the proposed model has the potential to improve the feasibility of measuring and assessing CPF.


Author(s):  
Ashamol P R

COVID -19 pandemic is the defining global health crisis of our time which eventually led to the use of face mask and maintaining safe social distancing, which became mandatory for reducing the rate of transmission of virus. This has parallelly raised a challenge in identifying people since most of the face regions are hidden inside the mask. So we came up with a system which identifies maked face along with which it ensures whether people follows safe social distancing or not. For this purpose we are using deep convolutional neural network (CNN) along with this MLP is also used for classification process. We also incooperates an efficient system that makes real time automated monitoring of people to detect safe social distancing and use of thermal cameras for detecting the body temperature. Thus the entire system favours the society by saving time and the automated inspection reduces the manpower to inspect the public.


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