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1.
Data Analytics in Bioinformatics: A Machine Learning Perspective ; : 283-291, 2021.
Article in English | Scopus | ID: covidwho-1888501

ABSTRACT

Trends of teaching and learning has changed its shape from offline teaching to online teaching as full mode and physical mode of teaching may become substitution of academic keeping in view the pandemic covid-19. Data science has become part of parcel of our daily life and most of the technical apps we are using contains machine Learning algorithms and helps us in many ways. With rising conditions, artificial intelligence will be the most prominent transformative technology and enabler for society in the present era. here is no uncertainty that AI and analogous frameworks are built to change global efficiency, working habits, and lifestyles and support healthcare, Pharma Industry and Transformation in diagnosis process, disease treatment and early identification of symptoms has been fuelled machine learning techniques and tools such as Generative Adversarial Networks (GAN), Deep Convolutional Networks, Deep Reinforcement Learning (DRL), Gradient-boosted-tree models (GBM), etc. MRI and other sophisticated imaging systems immensely used for neural disorders, cancer diagnostics. In this chapter we are discussing various resources of medical datasets which can be used for diagnosis of dementia with the usage of machine learning approaches. We are presenting how various machine learning approaches can be useful in early diagnosis of many diseases and explained where machine learning and deep learning can be used on electronically stored medical data. Recent developments are achieved in what way machine learning can be applicable in multi-disciplinary research areas. The main emphasis of this chapter is to elaborate on the applicability of machine learning in the domain of healthcare. In the past, there had been substantial signs of progress in the way where machine learning can apply in innumerable research and industries. This chapter deliberates the prospect of using machine learning technologies in the healthcare sector and sketches several industry ingenuities implementing machine learning initiatives. © 2021 Scrivener Publishing LLC.

2.
Pharmaceutical and Biomedical Research ; 6(SpecialIssue1):27-36, 2020.
Article in English | EMBASE | ID: covidwho-1884824

ABSTRACT

Background: The whole planet is facing one of the scariest pandemic situations in this era. On 11th February, 2020 the World Health Organization announced the name of an unknown disease as COVID-19, which is caused by the ssRNA virus SARS-CoV-2 (formally recognized as a sister of SARS-CoV and MERS-CoV). The epicenter of this disease is Wuhan, Hubei Province, China. COVID-19 can affect all age groups, but particularly affects immune compromised and aged persons with co-morbid conditions. It is highly contagious disease that involves mild to severe respiratory symptoms along with breathing difficulties. Objectives: As SARS-CoV-2 is a new strain of β-coronavirus that spreads from animals to humans via an unknown intermediate host, no vaccines have been developed yet and only supportive treatment is given to the infected patients. The review paper highlights the pharmacological therapy as a supportive treatment given to the COVID-19 patients and nonpharmacological therapeutic approaches for the prevention. Methods: Methods: Authors were surveyed and reviewed numerous articles, magazines, news papers, conference proceedings from different search engines and made the review successful. Results: Some drugs of different categories are approved and prescribed to the patients and some others are still under investigation and have gone through clinical trials. Conclusion: As no specific treatment or drugs for this disease have been developed till the date;therefore, social distancing, home quarantine, and proper healthy lifestyle management are the best current short-term options to avoid further spread of this pervasive virus.

3.
Journal of Punjab Academy of Forensic Medicine and Toxicology ; 21(2):53-57, 2021.
Article in English | Scopus | ID: covidwho-1876081

ABSTRACT

Introduction: The Coronavirus led lockdown has put all of our strength and zeal to a hard test by interrupting our regular lives in recent history. It is indeed testing times for all of us. In an effort to overcome the effects of the pandemic, teaching institutions all around the globe are switching to online teaching-learning mode. However, the transition to online learning could be a challenge for everyone. Aim: This Study's aim is to evaluate whether online learning when compared to offline learning can improve learning outcomes of undergraduate medical students. Methods: We developed Google classroom in which teachers of all subjects uploaded lectures by power point.A total of 97 students of first year MBBS of GMC KATUA participated in online classes. The study was conducted through a prestructured and prevalidated on line questionnaire sent to the 97 students. The consent from the Ethical committee of GMC KATUA was duly sought. The study was carried out after 6 months of online classes. A set of self-designed questionnaire was developed based on 5-point Likert scale and given to the students through the e-classes. Convenience sampling technique was used to select the participants. Out of a total of 97 undergraduate students of Phase 1 GMCkathua, 83 responses to the questionnaire were received. All students voluntarily participated in the study. Results: 91.6 % students were taking on line classes for the first time. 82.2% were using mobile as the choice gadget, majority of the students 42.2% preferred video as mode of delivery of on-lineclasses compared to PPT 31.3%. In our study, ZOOM platform was utilized by 54.2% and Google platforms by 39.8% with overlap in use of applications. On the whole, the participants preferred video, PPT, PDF content for clearing of doubts on content covered during the lectures 48.2% participants are not happy with Corona Pandemic in India and 47 % are anxious for this. Only 31.3% of participants were satisfied with the class material provided during online classes, 48.2% were neutral and 14.5% were dissatisfied with the material. The method of recorded classes for better understanding 42.2 % participants found it somewhere less effective, 13.3% found it somewhat more effective. Only 12% participants think that online teaching/learning is better than conventional teaching/ learning method. Among the participants 65.1% would not like to continue with online classes as a part of regular classes in future. Conclusion: According to our study concept of online classes is still evolving and students prefer conventional teaching over online teaching. To overcome the identified barrier during this study, mix method study i.e. online & offline should be started as the pandemic situation normalizes. © 2021, Punjab Academy of Forensic Medicine and Toxicology. All rights reserved.

4.
Frontiers in Education ; 7:9, 2022.
Article in English | Web of Science | ID: covidwho-1869364

ABSTRACT

With the sudden onset of the COVID-19 pandemic, teachers across the globe felt the need to respond overnight to unprecedented adversity. Rapid adaptation to unfamiliar modes of teaching was required, leading teachers to face overwhelm on two fronts-adapting to a global pandemic, and teaching in the absence of resources and infrastructure. The lack of a physical classroom and face-to-face interaction also impacted a teachers' sense of purpose and identity. Shifts in teacher identity affect their motivation, commitment, job satisfaction, and self-image, which collectively influences their resilience. By focusing on the impact of COVID-19 on their teaching experience, this study attempted to understand the resilience of the teaching community while exploring the social and emotional factors that helped them adapt. Using the Life Story Interview method, 20 school teachers in the Delhi National Capital Region were asked to reflect on their teaching experiences during the lockdown to explore their resilience in adapting to a new reality. Reflexive thematic analysis revealed adjustment to change and loss, disengagement, and recognising small victories as themes.

5.
Internal Medicine Journal ; 52:39-39, 2022.
Article in English | Web of Science | ID: covidwho-1865997
6.
Lung India ; 39(SUPPL 1):S20, 2022.
Article in English | EMBASE | ID: covidwho-1857224

ABSTRACT

Background: The second wave of COVID19-Pandemic was associated with massive case surge causing overwhelming of health-care-system. Early monitoring and identification of at-risk patients through digital mode, can reduce mortality and judicious use of hospitalresources. Objectives: To study the efficacy of CareShare®- application in predicting hospital-admission for homeisolated- mild-COVID19-patients (HIMCP). Methods: CareShare® an android-application, designed in collaboration, to follow-up HIMCP. A baseline-profile is entered on first contact and includes demography, comorbidities, and current symptoms. Application generated a baseline-symptom-score for follow-up. Subjects were automatically reminded to refill their symptom-severities on daily basis. Change in symptoms-score led to one of the three-flags: Red (critical), Orange (review) and Green (Safe). Results: Over 2 months, 550 patients were screened and 485 were enrolled on the application-platform with 2511 entries. Most-common reason for missing out was lack of an android-phone. The mean age was 35.1±12 years with 40.8% being females and 82.1% from urban background. Average number of entries/ patients were 5.18±4.9 (1 to 22). 1072 daily entries were given orange-flag, 298 were reviewed on call and remaining were reclassified as falsewarnings. Out-of-298, 16 were called in hospital and 6 were admitted. 690 entries were given a red-flag, leading to urgent-review, and calling of 108. 86 cases were called for in-person review, leading to admission of 27 patients. Out of 33, 27 required ICU admission. There was no mortality in the study population. Conclusion: CareShare® is a reliable method of symptommonitoring of HIMCP taking pressure off overwhelmed health care system.

7.
Lung India ; 39(SUPPL 1):S24, 2022.
Article in English | EMBASE | ID: covidwho-1857047

ABSTRACT

Background: High-flow nasal oxygen therapy (HFOT) has fueled a growing interest in non-invasive management of acute hypoxemic respiratory failure (AHRF). ROX index, a bedside index can be used to prevent excess mortality associated with delayed intubation. Methods and Objectives: This was a single-center prospective observational cohort study including 55 patients with AHRF treated with HFOT. Identification was through ROC analysis and Kaplan Meier survival estimation of ROX association with HFOT outcome. The most specific cutoff of the ROX index to predict HFOT failure and success was assessed. Results: Among the 55 patients treated with HFOT in the validation cohort, 19 (34.54%) required intubation. Baseline 39 patients were COVID19 positive. The prediction accuracy of the ROX index increased over time (AUC of ROC curve at 2 h, 0.922;6 h, 0.971;12 h, 0.980). Sensitivity and specificity of 4.88/4.4 cut off were: 85.7/89.5,73.2/94 at 2 hours, 78.6/84.2, 80.5/97.2 at 6 hours, 78.6/84.2, 78.05/97.2 at 12 hours) ROX greater-than-orequal- to 4.4 was a better cut off than 4.88 on the basis of diagnostic accuracy and was consistently associated with a lower risk for intubation. A ROX less than 4.31, less than 4.61 and less than 4.33 at 2, 6 and 12 hours of HFOT initiation, respectively, were predictors of HFOT failure. ROX ≥ 4.4 at 2 hours had HR of 0.022 with 95% confidence interval of 0.004-0.127 with p value of <0.001. Conclusion: ROX index can help identify low and high risk for intubation in patients of AHRF treated with HFOT.

8.
Studies in Computational Intelligence ; 1024:401-415, 2022.
Article in English | Scopus | ID: covidwho-1826335

ABSTRACT

In the past decade, the world has seen rapid advancements in the field of healthcare services due to the state of the arts in technologies. Several real-time health monitoring applications and products are designed to assist the human to take the timely precautionary measures to avoid the unseen abnormalities. However, current healthcare monitoring infrastructures are not ready to provide efficient health services during the sudden and unknown pandemic situations such as COVID-19. The COVID-19 started in the later part of 2019, rapidly spread across the countries and labeled as a pandemic in the very early part of the 2020. Several people died due to the lack of the healthcare infrastructure and lack of access to health facilities. This book chapter explores the various technologies such as augmented reality, connected e-health along with the time series analysis of COVID-19 waves in India to know the implication of COVID-19 on society for a social good. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

9.
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 ; 286:295-303, 2022.
Article in English | Scopus | ID: covidwho-1826296

ABSTRACT

The whole world is passing through a very difficult time since the outbreak of Covid-19. Wave after wave of this pandemic hitting people very hard across the globe. We have lost around 3.8 million lives so far to this pandemic. Moreover, the impact of this pandemic and the pandemic-induced lockdown on the lives and livelihoods of the people in the developing world is very significant. Till now there is no one-shot remedy available to stop this pandemic. However, spread can be controlled by social distancing, frequent hand sanitization, and using a face mask in public places. So, in this paper, we proposed a model to detect face mask of people in public places. The proposed model uses OpenCv module to pre-process the input images, it then uses a deep learning classifier MobileNetV3 for face mask detection. The accuracy of the proposed model is almost 97%. The proposed model is very light and can be installed on any mobile or embedded system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
Medical Science ; 26(121):7, 2022.
Article in English | Web of Science | ID: covidwho-1812226

ABSTRACT

Objective: To analyse the effects of the COVID-19 outbreak on behavior management strategies in paediatric dentistry. Study Design: For this cross sectional research, before the COVID-19 pandemic and after the lift of lockdown during COVID-19 pandemic, a standardized dose-ended set of 26 questions on behaviour management and paediatric dental practise was developed and forwarded to pedodontist in India. The data of their responses was collected and put into a worksheet in Excel, then analysed statistically and inferences were drawn. Results : The preference for non-pharmacological and pharmacological behavior management techniques has been changed;before COVID-19, non-pharmacological behaviour management techniques were widely prevalent but after the lift of lockdown the preference for pharmacological behavior management techniques have noticeably increased. Conclusion: Because of the threat of cross-infection in the COVID-19 pandemic, use of strategies for behavior management has be changed. So the paediatric dentist should cope-up with the situations such as the COVID-19 outbreak, adapt to changes in behavior management strategies and become competent enough to effectively perform treatment in paediatric patients.

11.
Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture ; : 1-24, 2021.
Article in English | Scopus | ID: covidwho-1810411

ABSTRACT

The chapter starts with a focus on the current scenario of the digitalization in agriculture space. It pinpoints the reason behind the need and explains the emergence of new Agtech-based startups that work on new innovative digital technologies. The chapter also tries to discuss the post-COVID implications along with the merits of digitalization in the agricultural domain. Apart from this, it also discusses different aspects of the digitalization on the agriculture space in general that includes the concept of telematics, precision farming, blockchain, artificial intelligence, etc. At last, some of the main challenges like the issue of connectivity, interoperability, portability, and need of public and private sector cooperation were discussed. © 2021, IGI Global.

12.
Journal of Laboratory Physicians ; : 5, 2022.
Article in English | Web of Science | ID: covidwho-1805730

ABSTRACT

Background Expansion of the testing capacities for severe acute respiratory syndrome-coronavirus-2 is an important issue in the face of ever-increasing case load. So, there is need of point-of-care diagnostic tests in the existing laboratory capacities for early treatment, isolation, and clinical decision making, especially in resource limited settings. Materials and Methods This prospective cohort study was conducted at Jai Prakash Narayan Apex Trauma Center, All India Institute of Medical Sciences, New Delhi. Nasopharyngeal samples and blood samples were collected for antigen and antibody testing. Rapid antigen test was performed as per the kit's instructions. The performance of the kit was compared with the gold standard reverse transcription polymerase chain reaction (RT-PCR) testing. Results Eighty-eight out of 110 patients tested positive by RT-PCR for coronavirus disease 2019 in last 48 to 72 hours were included in the study. Overall, the sensitivity of combined antibody test was 52%, antigen test 26%, and combined sensitivity of both antigen and antibody was 72.7%, respectively. Conclusion The combo kit needs to be used with caution in low prevalence settings, where cases may be missed.

13.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333524

ABSTRACT

Purpose To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease evaluation and clinical risk stratification. Materials and Methods A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on anterior-posterior CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ~160,000 images from CheXpert and transfer learning on 314 CXRs from patients with COVID-19. The algorithm was evaluated on internal and external test sets from different hospitals, containing 154 and 113 CXRs respectively. The PXS score was correlated with a radiographic severity score independently assigned by two thoracic radiologists and one in-training radiologist. For 92 internal test set patients with follow-up CXRs, the change in PXS score was compared to radiologist assessments of change. The association between PXS score and subsequent intubation or death was assessed. Results The PXS score correlated with the radiographic pulmonary disease severity score assigned to CXRs in the COVID-19 internal and external test sets (rho=0.84 and rho=0.78 respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (rho=0.74). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operator characteristic curve=0.80 (95%CI 0.75-0.85)). Conclusion A Siamese neural network-based severity score automatically measures COVID-19 pulmonary disease severity in chest radiographs, which can be scaled and rapidly deployed for clinical triage and workflow optimization.

14.
2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1794849

ABSTRACT

Covid-19 first originated in Wuhan, China, became a global pandemic and spread to many countries due to its high transmissibility. Digital technology could prove to be of great use in pandemic management with many strategies like screening for infection, tracking, contact tracing, and many more. Covid-19 tracker is one such application of digital technology. This helps in visualizing the pattern of public health through graphs, search results, and tables that can be easily understood by the user. It is built using HTML and JavaScript. Covid-19 Tracker uses data from certain authentic sources and helps to visualize the spread of Covid-19 throughout India. The main features incorporated in our dashboard are: total cases reported, graphs for daily trends, search functionality, state-wise comparative evaluations, and hotspot distribution map of the entire country. This dashboard provides a variety of user involvement possibilities and extracts useful data in a simple and easy-to-understand way. © IEEE 2022.

15.
Aerosol and Air Quality Research ; 22(4), 2022.
Article in English | Scopus | ID: covidwho-1792159

ABSTRACT

South Asia is a hotspot of air pollution with limited resilience and hence, understanding the mitigation potential of different sources is critically important. In this context the country lockdown initiated to combat the COVID-19 pandemic (during March and April 2020 that is the pre-monsoon season) provides an unique opportunity for studying the relative impacts of different emission sources in the region. Here, we analyze changes in levels of air quality species across the region during selected lockdown periods using satellite and in-situ datasets. This analysis compares air quality levels during the lockdown against pre-lockdown conditions as well as against regional long-term mean. Satellite derived AOD, NO2, and CO data indicates an increase of 9.5%, 2%, and 2.6%, respectively, during the 2020 lockdown period compared to pre-lockdown over the South Asia domain. However, individual country statistics, urban site data, and industrial grid analysis within the region indicate a more varied picture. Cities with high traffic loads reported a reduction of 12–39% in columnar NO2 during lockdown, in-situ PM2.5 measurements indicate a 23–56% percent reduction over the country capitals and columnar SO2 has an approximate reduction of 50% over industrial areas. In contrast, pollutant emissions from natural sources e.g., from biomass burning were observed to be adversely affecting the air quality in this period potentially masking expected lockdown related air quality improvements. This study demonstrates the need for a more nuanced and situation specific understanding of sources of air pollutants (anthropogenic and natural) and for these sources to be better understood from the local to the regional scale. Without this deeper understanding, mitigation strategies cannot be effectively targeted, wasting limited resources as well as risking unintended consequences both for the atmosphere and how mitigation action is perceived by the wider public. © The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

16.
Journal of the Indian Chemical Society ; 99(5), 2022.
Article in English | Scopus | ID: covidwho-1788122

ABSTRACT

In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH2OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV. © 2022 Indian Chemical Society

17.
Journal of Physics: Conference Series ; 2223(1):011001, 2022.
Article in English | ProQuest Central | ID: covidwho-1778859

ABSTRACT

We are very pleased to present the conference proceedings of ICIAST-2021. The conference has been organized in virtual mode due to worldwide COVID Protocol and safety measures by Department of Applied Sciences, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India during December, 21-23, 2021, in Greater Noida, Uttar Pradesh, India.In this conference approx. 300 young researchers, engineers, scientists, academicians and industrial delegates participated from more than 20 countries of the globe on common virtual platform of zoom to share and exchange their research findings including theoretical results, novel scientific models and work in progress in the different areas of Science & Technology. During virtual conversations participants from remote areas faced sometime network issues but overall communication was good throughout the even. More than 30 international resource persons from different fields share their expertise with the young academicians and researcher with case studies and concrete examples within allowed time span. The scientific & technical program of the three days international conference have review talks, invited lectures, contributed oral presentations.This proceeding includes papers from Physics, Mathematics and their applications. A sound understanding of the same shall help emergence of new ideas that can be helpful in building trained professionals who can serve in the knowledge-based industries. We are pleased to appreciate our dynamic reviewers who took time and effort for providing their valuable comments in time and help towards the improvement of quality of papers through rigours review process.List of Organizing Committee, image, ICIAST-2021 Proceeding Editorial Team are available in this pdf.

18.
Journal of Clinical & Translational Research ; 8(2):125-137, 2022.
Article in English | MEDLINE | ID: covidwho-1777224

ABSTRACT

Background and Aim: The present study intends to investigate COVID-19 by targeting their main proteins with 17 selected drugs used for treating Oral Lichen Planus (OLP) which is a chronic muco-cutaneous disorder. Here, an attempt is made to gain better insight into the structure of various drugs targeting specific proteins which will be helpful in developing drugs useful for therapeutic and preventive measures. Method: In silico studies, molecular docking and molecular dynamic simulations were performed to repurpose the therapeutic drugs (n = 17) which were used to treat OLP against COVID-19. In addition, the maximum binding affinities of the key protein spike glycoprotein, main-protease (Mpro) of coronavirus, and Angiotensin-Converting Enzyme-2 (ACE-2) in the human body were evaluated with the selected drugs. Results: Epigallocatechin-3-gallate (EGCG) showed the highest docking values among the drugs selected for repurposing. Among the target proteins, EGCG has shown maximum binding affinity with ACE-2 receptor. Further, according to the molecular dynamic simulation studies, EGCG has shown the least conformational fluctuations with Mpro. Conclusion: EGCG can be a potential inhibitor drug which can bind with ACE-2 receptor thus inhibiting the interaction of mainly Mpro protein and spike glycoprotein of SARS-CoV-2. Relevance for Patients: EGCG, a natural compound shows antiviral potential having considerably high affinity and stability with SARS-CoV-2. It might be further employed as a lead drug against selective inhibitors of SARS-CoV-2 for the therapeutic management of COVID-19 patients after necessary clinical trials.

19.
3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021 ; : 2082-2084, 2021.
Article in English | Scopus | ID: covidwho-1774602

ABSTRACT

A new Coronavirus has caused panic wave among the public all over the world. It is being discussed extensively in various news channels and papers each day. The most affected countries are China, Italy, Spain, and USA. In India, more than 5000 cases have been reported and the number is increasing day by day. This paper has undertaken good use of Google Trends to analyse the public interest in COVID-19 outbreak. Google Trends has been used to collect data pertaining to Indian public interest in Corona Virus.Methods: Current data pertaining to public interest in Corona virus is extracted from Google Trends website by entering the search topic: COVID-19 with location set as India. The reported period is 10th March 2020 to 8th April 2020. The second data regarding mental health query of Indians is also extracted from the same tool.Results: As per the Google Trends observed for Indian public interest in COVID-19, the interest started rising from 10th March, 2020 and was gradually moving up till 21stMarch 2020 while number of reported corona cases in India had started emerging and lockdown was enforced on the public movement. The interest in COVID-19 doubled in just a time of one week from 21st march, 2020.Similar trend has been found with Indian mental hearth search queries showing first peak on 13th March, second on 19th and third on 24th March 2020. The last peak which is highest one involves almost triple population than the first peak. Hence Google trends can be used to predict the mental health and sensitivity of the people towards disease. © 2021 IEEE.

20.
1st International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2021 ; 1534 CCIS:563-585, 2022.
Article in English | Scopus | ID: covidwho-1750541

ABSTRACT

Nowadays, people are required to wear masks due to the COVID-19 pandemic. The COVID-19 is an ongoing crisis that has resulted in a large number of fatalities and safety concerns. People also carry masks to cover themselves to effectively prevent the transmission of this virus. In this situation recognizing a face is very challenging. In certain cases, like facial attendance, face access control, facial security, this makes traditional facial recognition technology ineffective, for that urgent requirement to improve this recognition performance and use the technology on the masked face. During the current pandemic, the main objective of researchers is to deal with these problems through quick and accurate approaches. Throughout this report, suggest a clear way centred on removing masked areas and deep learning related techniques to resolve the issues of mask detection. Another way of finding the masked face is to go through TensorFlow, YOLOv5, SSDMNV2, SVM, OpenCV, Keras. Deep Learning of artificial intelligence (AI) is an exciting future technology with explosive growth. Masked face recognition is a mesmerizing topic that contains several AI technologies including classifications, SSD object detection, MTCNN, FaceNet, data preparation, data cleaning, data augmentation, training skills, etc. Takes two datasets, CASIA-Web face datasets used for training purpose and LFW is used for testing purpose. Here, face alignment has been done by SSD and MTCNN. After face alignment, deleting the misleading image. Then wear a face mask in the image by using Dlib. From Dlib get the facial landmark. So take the mouth part for the face mask. Then detect the face mask. In green box shows that the person is wearing the mask. In red it shows that person is not wearing the mask. After detecting the face, also have to recognize the person in the mask. In training, it gives 99% of accuracy and in testing, it gives 96% of accuracy. © 2022, Springer Nature Switzerland AG.

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