Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach

The spread of the corona virus or better known as covid-19 in Indonesia is a serious matter that needs to be dealt with immediately considering the number of victims caused by the virus that was first discovered in Wuhan. As in mid-March, after the first death in Indonesia due to the corona virus and the designation of the catastrophic virus as a global pandemic by WHO or the World Health Organization, this indicates that the government must be tougher in controlling corona virus transmission in Indonesia. Even since March 15, 2020 Indonesia has determined the status of the co-19 distribution as a national disaster. The implications of the outbreak of the pandemic are the impact on problems both in the health and economic fields. In connection with health issues, the Indonesian government itself has established a public health emergency as stated in the Decree of the Head of the National Disaster Management Agency. This is as stated in Article 10 of Law Number 6 Year 2018 on Health Outrageousness as seems to be a lockdown consideration, there are large-scale social restrictions which are clearly evident in article 59 paragraph (2) which includes the consolation of schools and workplaces, restrictions on religious activities and / or restrictions on activities at public places or facilities.

The spread of the corona virus or better known as covid-19 in Indonesia is a serious matter that needs to be dealt with immediately considering the number of victims caused by the virus that was first discovered in Wuhan. As in mid-March, after the first death in Indonesia due to the corona virus and the designation of the catastrophic virus as a global pandemic by WHO or the World Health Organization, this indicates that the government must be tougher in controlling corona virus transmission in Indonesia. Even since March 15, 2020 Indonesia has determined the status of the co-19 distribution as a national disaster. The implications of the outbreak of the pandemic are the impact on problems both in the health and economic fields. In connection with health issues, the Indonesian government itself has established a public health emergency as stated in the Decree of the Head of the National Disaster Management Agency. This is as stated in Article 10 of Law Number 6 Year 2018 on Health Outrageousness as seems to be a lockdown consideration, there are large-scale social restrictions which are clearly evident in article 59 paragraph (2) which includes the consolation of schools and workplaces, restrictions on religious activities and / or restrictions on activities at public places or facilities.

Introductionfacemask use is well recognized as an effective public health strategy for preventing COVID-19. However, facemask can only provide enough protection if people recognize its importance and learn how to use it properly. The objective of the study was to assess the knowledge, attitudes, and practices (KAP) of patients or caregivers regarding the use of facemasks as a COVID-19 preventive measure and identifies the factors associated with its practice.Methodsa cross-sectional study where 480 patients or caregivers attending the Family Medicine Clinic were systematically selected over four weeks. A self-administered questionnaire was used to collect data on KAP regarding facemasks use. Student t-test and analysis of variance (ANOVA) were used to examine the relationship between the socio-demographic characteristics and KAP. Pearson's correlation coefficient was used to determine the relationship between knowledge, attitudes and practices. A p-value ≤ 0.05 was considered statistically significant.Resultsabout 82% of the respondents knew the correct steps of wearing a facemask, but with low positive attitudes. Further analyses showed that respondents were more likely to wear a facemask at clinics and public places than at home. There was a moderately strong correlation between knowledge and practices but weak correlations between attitude and knowledge, and attitude and practices of facemask use.Conclusionthe study revealed good knowledge and practices but low attitudes towards facemask use. Therefore, public health programmes or interventions on facemask usage as a COVID-19 preventive measure, should address the attitudinal problems and also involve the family and community leaders to enhance compliance.

Many lives have been put in danger since the start of the COVID-19 outbreak. According to WHO (World Health Organization)’s statements, breathing without a mask is highly dangerous in public and crowded places. Wearing a mask is essential as long as the vaccination is not widely used and does not fully protect everyone. Wearing masks does minimize the chances of becoming infected and the transmission of Coronavirus. Due to this, many public service providers may mandate customers to wear masks in order to get service.However, manually monitoring whether people are wearing a mask or not in public is inefficient and difficult. This paper proposes replacing manual inspection with a deep learning-based method using YOLOX, the most powerful objection detection algorithm. The results of the experiments show that the algorithm described in this work can efficiently detect face masks and enable more effective personnel monitoring than other YOLO series models. In addition, as compared to other models, our technique has significant benefits in terms of speed and accuracy in small and crowded areas.

Responding to COVID-19 pandemic: Why a strong health system is required.

The world is in the midst of a paramount pandemic owing to the rapid dissemination of coronavirus disease (COVID-19) brought about by the spread of the virus ‘SARS-CoV-2’. It is mainly transmitted among persons through airborne diffusion of droplets containing the virus produced by an infected person sneezing or coughing without covering their face. The World Health Organization (WHO) has issued numerous guidelines which state that the spread of this disease can be limited by people shielding their faces with protective face masks when in public or in crowded areas. As a precautionary measure, many nations have implemented obligations for face mask usage in public spaces. But manual monitoring of huge crowds in public spaces for face masks is laborious. Hence, this requires the development of an automated face mask detection system using deep learning models and related technologies. The detection system should be viable and deployable in real-time, predicting the result accurately so as to be used by monitoring bodies to ensure that the face mask guidelines are followed by the public thereby preventing the disease transmission. In this paper we aim to perform a comparative analysis of various sophisticated image classifiers based on deep learning, in terms of vital metrics of performance to identify the effective deep learning based model for face mask detection.

News and Notes

Introduction:COVID-19 is the disease caused by an infection of the SARS-CoV-2 virus, previously known as 2019 Novel Coronavirus (2019-nCoV) respiratory disease. World Health Organization (WHO) declared the official name as COVID-19 in February 2020 and in 11th March 2020 declared COVID-19 as Global Pandemic. In June 6th 2020, over 7 million cases registered in the world, recovered 3.4 million and death over 402.000.Aim:The aim of this study is to retreive published papers about COVID-19 infection deposited in PubMed data base and analyzed current results of investigations regarding morbidity and mortality rates as consequences of COVID-19 infection and opinions of experts about treatment of afected patients with COVID-19 who have Cardiovascular diseases (CVDs).Methods:It’s used method of descriptive analysis of the published papers with described studies about Corona virus connected with CVDs.Results:After searching current scientific literature (on PubMed till today is deposited more than 1.000 papers about COVID-19 with consequences in almost every medical disciplines), we have acknowledged that till today not any Evidence Based Medicine (EBM) study in the world. Also, there are no unique proposed ways of treatments and drugs to protect patients, especially people over 65 years old, who are very risk group to be affected with COVID-19, including patients with CVDs. Vaccine against COVID-19 is already produced and being in phases of testing in praxis in treatment of COVID-19 at affected patients, but the opinions of experts and common people whole over the world about vaccination are full of controversis.Conclusion:Frequent hand washing, avoiding crowds and contact with sick people, and cleaning and disinfecting frequently touched surfaces can help prevent coronavirus infections are the main proposal of WHO experts in current Guidelines, artefacts stored on a web site. Those preventive measures at least can help to everybody, including also the patients who have evidenced CVDs in their histories of illness. Authors analyzed most important dilemmas about all aspects of CVDs, including etipathogenesis, treatment with current drugs and use of potential discovered vaccines against COVID-19 infection, described in scientific papers deposited in PubMed data base.

IntroductionThailand ratified the World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC) on November 8, 2004. The WHO FCTC requires all parties to inform all persons of the health consequences of tobacco consumption and exposure to tobacco smoke. Each party has agreed to develop, implement and evaluate effective tobacco control programs to measure progress in reaching the goals of the WHO FCTC.MethodsThe Global Youth Tobacco Survey (GYTS) was developed to provide data on youth tobacco use to countries for their development of youth-based tobacco control programs. Data in this report can be used as baseline measures for future evaluation of the tobacco control programs implemented by the Ministry of Public Health.ResultsOverall, about 1 in 10 Thai students are current smokers, this number including 4 times more boys than girls (17% versus 3.9%). Almost 2 in 10 Thai students start smoking before the age of 10, and almost 7 in 10 students are reported to have been exposed to smoke from others in public places. About 4 in 10 students are reported to have an object with a cigarette brand logo on it.ConclusionThe key for Thailand is to implement and enforce the provisions on indirect tobacco advertising, smoking in public places, selling tobacco to youths under 18 years of age, and to use the data from the GYTS to monitor progress toward achieving the goals of the WHO FCTC. When these goals are met, tobacco consumption and exposure in Thailand will have declined substantially.

Face masks in the general healthy population. Scientific and ethical issues

The COVID-19 virus is causing a global pandemic. The total number of new coronavirus cases worldwide by the end of November 2020 had already surpassed 60 million. The World Health Organization (WHO) has determined that wearing masks is a crucial precaution during the COVID-19 epidemic to limit the growth of viruses, and facemasks are frequently seen in public places worldwide. Also, many public service providers wear face masks (covering their mouths and noses). These events brought attention to the need for automatic computer-vision-based object detection (masked face detection) methods to track public behavior. Therefore, it is necessary to develop tools for monitor people who have not used masks in public service areas in real-time. Reducing the spread of infectious diseases can occur when masked face detection techniques are used for authentication instead of mask removal for face matching. A superior framework of masked face detection could improve security systems and lower the rate of crime. Masked face detection is a computer vision method standard in people's daily lives to recognize, discover, and recognize masked faces in pictures and videos. This study provides a thorough and systematic analysis of masked face detection algorithms. With the help of examples, we have thoroughly examined and reviewed the studies done concerning face mask identification and techniques for masked face detection.Additionally, we compared and explained different masked face detection dataset types, libraries, and techniques. We also discussed the challenges with masked face detection and whether the researchers could overcome them. We have discussed and conducted a thorough evaluation of the accuracy, pros, and cons of various approaches by comparing their performance on multiple datasets. As a result, this study aims to give the researcher a broader viewpoint to aid him in finding patterns and trends in masked face detection in various COVID-19 contexts, overcoming challenges that are still present, and creating future algorithms for masked face detection that are more reliable and accurate.

Legal Aspects of the Single Monetary Policy in the Euro Area: From the Establishment of the Eurosystem to the Current Pandemic Crisis

COVID-19 in Kidney Transplantation: Outcomes, Immunosuppression Management, and Operational Challenges.

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

PurposeThe purpose of this paper is to provide commentary on the challenges and impacts of the pandemic crisis to higher education institutions (HEIs) in Malaysia. It also outlines mitigation plans, innovative strategies adopted and implications and recommendations to HEIs.Design/methodology/approachThe research study uses a discourse analysis to examine the higher education challenges and impacts of the pandemic in Malaysia.FindingsThe findings reveal that the coronavirus disease 2019 (COVID-19) pandemic has affected the higher education sector in Malaysia significantly. Both lecturers and students in HEIs are faced with multiple challenges in teaching and learning. The effect was more severe in East Malaysia due to poor Internet connectivity for online learning. Nevertheless, COVID-19 also provides an opportunity to HEIs to reconstruct the education system and establish updated programmes and assessments. The success of online learning to mitigate the impacts is still unknown.Research limitations/implicationsThe review of the literature and news are not comprehensive as the current pandemic crisis is not over.Originality/valueThis paper is presumably representing a frontier review with more empirical research studies to be conducted to investigate the extent of the current crisis affecting teaching and learning in HEIs in Malaysia. Though there were some commentaries on how the pandemic affects education, to the best of the authors’ knowledge, this is the first commentary paper in higher education realm in Malaysia.