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In view of diminishing the transmission of the coronavirus pandemic (COVID-19) in the community, an essential intervention strategy has been the consideration of public health measures. However, at the present scenario, these measures can be considered as the only available tools for mitigation of this virus impact. An attempt was made in this study with the use of grey technique for order of preference by similarity to ideal solution (Grey-TOPSIS) method for prioritizing the precautionary measures for the public health in order to enable taking appropriate steps by the general public of India to protect them from virus transmission. © 2022 IGI Global. All rights reserved.
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Social media platforms often become environments of information ambiguity during crisis events. We studied the discussion around four”cures” for COVID-19 in India, where the highest number of cases were recorded between 2020 and May 2021, focusing on the role played by high network accounts on social media such as those of journalists, politicians, and celebrities. We find that information scarcity and anxiety among citizens enabled non-experts, particularly the aforementioned social media influencers. We find that this undermined institutional sources of information and led to massive spikes in online interest around unproven cures during the peak of the crisis. © 2022 IEEE Computer Society. All rights reserved.
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Activity-based learning is one of the most trending methodology of learning. It is a teaching methodology which enables a learner to learn as per his or her natural pace using a series of activities which is more interactive, engaging and beneficial for young learners. It also has the facility of monitoring and evaluating the activities. However, the online activity-based learning became a new mode of teaching during the COVID-19 when the entire world went under lockdown. The situation was indeed very difficult for everyone to make their ends meet during that time. People were not allowed to come out of their homes except to purchase the daily needs. All the educational institutions including schools, colleges and universities were shut down for an indefinite period. The faculties were left with no other alternative but to take classes through online mode. This was a challenging task not only for students but also for the teachers. Therefore, the basic objective of the paper would be to discuss about the effectiveness of the implementation of activity-based learning through online mode and how far it has succeeded in creating an impact among the trainers as well as the learners. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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One day in the month of November, 2019, the world received a major setback when it understood that a new pandemic called COVID-19, or the Novel Coronavirus had taken over to create havoc among the people. It was first started in a wet market of a small province in China. After that it has spread all over the world like a bonfire. Many countries got under its grip, namely USA, South Korea, and Italy where the situation was totally out of control. During the last two years, India suffered huge loss due to COVID-19 in terms of life, property, and other assets. India is the second largest most populous country with 130 crores was highly affected due to COVID-19. Out of all the aspects, education was the worst affected sector. There was no option left but to implement e-learning as a methodology of teaching and learning. It has emerged as one of the major sources of business, like e-commerce, learning methodology, and e-learning. E-learning is a methodology of teaching and learning where the teacher teaches using multimedia, and the learner learns using the digital mode of education. This mode of teaching and learning has indeed brought a revolution in the education process because neither the teacher nor the student needs to be together in one place. There are numerous subjects which can be taught online, ranging from technical to non-technical subjects. Literature is an imitation of fiction or non-fiction. Online could be the best mode of instruction for literature students. Therefore, this paper is an attempt to make an analysis of the implications of e-learning in education, and its implementation to teach literature by the teachers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In medical science, imaging is the most effective diagnostic and therapeutic tool. Almost all modalities have transitioned to direct digital capture devices, which have emerged as a major future healthcare option. Three diseases such as Alzheimer's (AD), Haemorrhage (HD), and COVID-19 have been used in this manuscript for binary classification purposes. Three datasets (AD, HD, and COVID-19) were used in this research out of which the first two, that is, AD and HD belong to brain Magnetic Resonance Imaging (MRI) and the last one, that is, COVID-19 belongs to Chest X-Ray (CXR) All of the diseases listed above cannot be eliminated, but they can be slowed down with early detection and effective medical treatment. This paper proposes an intelligent method for classifying brain (MRI) and CXR images into normal and abnormal classes for the early detection of AD, HD, and COVID-19 based on an ensemble deep neural network (DNN). In the proposed method, the convolutional neural network (CNN) is used for automatic feature extraction from images and long-short term memory (LSTM) is used for final classification. Moreover, the Hill-Climbing Algorithm (HCA) is implemented for finding the best possible value for hyper parameters of CNN and LSTM, such as the filter size of CNN and the number of units of LSTM while fixing the other parameters. The data-set is pre-processed (resized, cropped, and noise removed) before feeding the train images to the proposed models for accurate and fast learning. Forty-five MR images of AD, Sixty MR images of HD, and 600 CXR images of COVID-19 were used for testing the proposed model ‘CNN-LSTM-HCA'. The performance of the proposed model is evaluated using six types of statistical assessment metrics such as;Accuracy, Sensitivity, Specificity, F-measure, ROC, and AUC. The proposed model compared with the other three types of hybrid models such as CNN-LSTM-PSO, CNN-LSTM-Jaya, and CNN-LSTM-GWO and also with state-of-art techniques. The overall accuracy of the proposed model received was 98.87%, 85.75%, and 99.1% for COVID-19, Haemorrhage, and Alzheimer's data sets, respectively. © 2023 John Wiley & Sons Ltd.
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With the digitalization of the services across various sectors in an Indian setting, health care is also influenced by the same. It was evidenced during the COVID-19 pandemic that Indian patients were orienting themselves toward more teleconsultations and digital and smartphone-based health care. This not only saves time and money but also reduces the chances of hospital-acquired cross infections. This is more important for patients with rheumatic diseases who try to avoid frequent hospital visits despite the need for regular health-care consultations due to the aforementioned reasons. Apart from the telemedicine and smartphone apps, health care is expanding to robotics and artificial intelligence-based machine learning. Healthcare digitalization will lead to the expansion of precision based medicine. When more robust genomics, proteomics, metabolomics, and transcriptomics data become available for Indian patients with rheumatic diseases, management then would be more personalized than blanket therapy. However, such futuristic advancements face challenges of their own which are neither time nor knowledge bound. We are currently just at the tip of this massive iceberg. We describe various aspects of the future of digital health and precision medicine in rheumatology in an Indian setting. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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In the current circumstances, when lakhs of people are dying as a result of the COVID-19 Pandemic, imposing lockdown and shutdown disrupts the socioeconomic conditions of the entire globe to the point that the country's backbone is compromised. A highly modified, effective sterilizing and disinfection system is essential to improve the country or human civilization. To combat the many adverse effects of radiation, not only on human health but also on the environment, ultra violet technology has been introduced. UV technology is also a source of radiation that has health risks;however, changes in wavelength can make the risk low. UVM-30A sensor module is used in the sanitization to detect UV irradiation in this study effort. An IoT-based system based on Arduino uno microcontroller, GSM A6 WiFi module, and UVM-30A is presented to create an automated implanted UV radiation detection device. © 2022 Wiley Periodicals LLC.
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One day in the month of November, 2019, the world received a major setback when it understood that a new pandemic called COVID-19, or the Novel Coronavirus had taken over to create havoc among the people. It was first started in a wet market of a small province in China. After that it has spread all over the world like a bonfire. Many countries got under its grip, namely USA, South Korea, and Italy where the situation was totally out of control. During the last two years, India suffered huge loss due to COVID-19 in terms of life, property, and other assets. India is the second largest most populous country with 130 crores was highly affected due to COVID-19. Out of all the aspects, education was the worst affected sector. There was no option left but to implement e-learning as a methodology of teaching and learning. It has emerged as one of the major sources of business, like e-commerce, learning methodology, and e-learning. E-learning is a methodology of teaching and learning where the teacher teaches using multimedia, and the learner learns using the digital mode of education. This mode of teaching and learning has indeed brought a revolution in the education process because neither the teacher nor the student needs to be together in one place. There are numerous subjects which can be taught online, ranging from technical to non-technical subjects. Literature is an imitation of fiction or non-fiction. Online could be the best mode of instruction for literature students. Therefore, this paper is an attempt to make an analysis of the implications of e-learning in education, and its implementation to teach literature by the teachers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The present Covid-19 pandemic caused by corona virus (SARS-CoV-2) is an unpredictable public health burden in India and abroad. Worldwide emergency steps are taken to counter the current situation. Odisha state (Eastern India) is now passing through a crucial period with a huge number of corona positive cases with high degree of mortality and morbidity. The aim of the present study is to correlate the demographic, clinical and radiological profiles of Covid-19 patients. The present study was carried out in the Covid-19 Hospital of S C B medical college Cuttack. This was a record based cross sectional study of 196 patients from 01/05/2021 to 01/06/2021. Plain X Rays were taken in all the patients to evaluate the incidence of disease. Total number of 196 Covid-19 cases was included in this study with male female ratio being 2.16:1. Maximum male patients were seen amounting (68%) and females (32%) respectively. Severity was mild to moderate in 80 % of cases. The diagnostic features of novel SARS-CoV-2 infection were observed in 32 % cases in x ray images of thorax. The co-morbid conditions for mortality were diabetes mellitus and hypertension. The chest X rays of corona virus 2 (SARS-CoV-2) infected patients are showing typical ground glass opacity (GGO), mixed GGO with consolidation bilaterally in the peripheral part of middle and lower lobe of lungs. The clinical co-morbid condition observed to be associated with high mortality in SARS-CoV-2 cases were diabetes and hypertension. Copyright © 2022, Global Research Online. All rights reserved.
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The COVID-19 pandemic requires social distancing to prevent transmission of the virus. Monitoring social distancing is difficult and expensive, especially in "travel corridors" such as elevators and commercial spaces. This paper describes a low-cost and non-intrusive method to monitor social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behaviour of CSI with a cubic SVM classifier, we count the number of people in an elevator with an accuracy of 92%, and count the occupancy of an office to 97%. As opposed to using a multi-class counting approach, this paper aggregates CSI for the occupancies below and above a COVID-Safe limit. We show that this binary classification approach to the COVID safe decision problem has similar or better accuracy outcomes with much lower computational complexity, allowing for real-world implementation on IoT embedded devices. Robustness and scalability is demonstrated through experimental validation in practical scenarios with varying occupants, different environment settings and interference from other WiFi devices.
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Aim and background: Delirium is the disturbance of consciousness characterised by acute onset, rapid fluctuations in mental status, and impaired cognitive functioning. The patient's ability to receive, process, store, and recall information is impaired in delirium. Objective: To study the incidence of delirium in patients in COVID and non-COVID ICU. To also study various risk factors associated with delirium. Materials and methods: After ethical committee approval and written informed consent, this study was carried out over a period of 1 year (August 2020 to July 2021). Each patient meeting the inclusion criteria was evaluated on the RAAS score within 24 hours of admission, then screened for delirium according to CAM-ICU worksheet every 6th hourly after admission in MICU. 50 patients were studied each in COVID and non-COVID ICU. Patients found to have delirium after the first assessment were classified as new cases. Various risk factors were evaluated prospectively. Results: Incidence of delirium in non-COVID ICU was 29%, while in COVID ICU was 37%. Delirium is present in a patient who has risk factors including smoking, higher severity of illness, oversedation, and mechanical ventilation. Antipsychotics can be used for patients who develop delirium. Conclusion: Delirium is a preventable issue in ICU patients that can be managed by preventing the risk factors that will decrease overall length of stay in ICU.
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Background: Hospitalised COVID-19 patients are known to exhibit varying degrees of immune dysfunction, few modifiable risk factors have been identified to improve this state of which one is the immune modulator effects of vitamin D. Vitamin D is being prescribed as a treatment of COVID-19 in a few guidelines as there is generalised assumption that vitamin D enhances immunity during this illness. So this is an attempt to find out whether a deficiency of vitamin D is associated with the severity of COVID-19. Aim: To study the relationship of serum 25 hydroxy vitamin D [25(OH)D] deficiency with disease severity in hospitalised COVID-19 patients. Materials and methods: The present case-control study compared serum 25(OH)D levels among Mild to moderate and severe COVID- 19 patients. Around 39 diagnosed and Hospitalised Severe COVID- 19 disease are compared with 39 Hospitalised Mild and Moderate COVID-19 disease in Care Hospital, Bhubaneswar, Odisha, India between April 1, 2021, ad August 31, 2021. Patients were divided into 2 groups. The Group 1-Mild to Moderate infection with CT Severity index < 10/25 and Group 2-Severe Infection with HRCT Chest of CTSI >10/25. As per hospital policy, severe infection patients were kept in Critical Care Area and Mild infection patients were kept in Ward/Cabin areas. Any patients becoming sick and being transferred to critical areas are shifted from Group 1 to Group 2 after HRCT chest. Vitamin D levels (25 D Cholecalciferol) are done on the day of admission by chemiluminescence immunoassay test after taking due consent from the patients/attenders. The level of cut-off used in our study is 20 ng/mL. The association was analysed using regression analysis and other statistical methods. Results: The status of 25(OH)D deficiency (present/absent with cut-off being 20 ng/mL) showed no significant difference among cases and control at p < 0.05. Chi-square statistics with Yates correction is 1.8909. The p value is 0.169099. So there were no significant differences in vitamin D3 levels between Mild to moderate and Severe COVID- 19 patients. Conclusion: 25(OH)D levels appear to have no strong association with disease severity amongst hospitalised COVID-19 patients. Hence, its prescription for COVID-19 treatment as well as prevention needs to be reconsidered.
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Aim and background: Due to the resurgence of COVID cases many doctors, medical students, and nurses from varied backgrounds, many a time novice to COVID management are deployed in turn from time to time at different COVID care centers and hospitals across India, before they are properly trained and skilled for effective management of COVID and post COVID syndromes, as the disease is relatively new, leading to non-uniform management and documentation. COVID being a contagious disease with newer symptomatology and ongoing research outputs suggesting new guidelines from time to time, which sometimes are conflicting in nature for novice healthcare workers. For uniform and appropriate management to reduce morbidity and mortality, it mandates a unique and effective solution towards guided and error-free disease management, authentic high volume data capture for future research and to trace patient to post COVID condition in the community outside the hospital, virtual patient counselling cum relative visit, generation of the daily patient bulletin, simultaneous teleround of multiple units, and sharing patient's data across multiple specialities and investigation areas. Objective: To have all these above-mentioned facilities over one platform, we aim to test run a cloud-based dynamic mobile application based dedicated device, the C O V I D Device (Covid Operation Vital Information Delivery device) across many hospitals in India simultaneously for COVID and post COVID syndrome management and data retrieval for research. Materials and methods: Two institutes, namely IMS and SUM Hospital and ITER have collaborated to design a cloud-based device having recent COVID guidelines on the management of adult COVID patients. The software has been incorporated into a dedicated handheld device (tablet or android mobile phone), the COVID Device in a dynamic way (when new symptomatology surfaces and new research outcomes on management are published). The important modules pertaining to this COVID Device are Web-based application for Registration Desk and Device-based application for Doctor's Module/Care-givers Module and Patient's/Patient's relative's module. Results: In a pilot, we have successfully test run the COVID device on virtual patients and 2 actual patients in a secondary level COVID ICU and HDU to examine the different functionality of the cloud-based application, namely error-free and guided patient management without missing any point, daily patient relative's counselling and virtual patient visiting by relatives, generating daily patient bulletin, simultaneous tele round of multiple units, and sharing patient's data across multiple specialities and investigation areas and tracing patient to the community after discharge to enquire about post COVID condition and retrieval of data across all module and incorporation of new guideline in a dynamic way and checking the facilities for incorporating other modules namely pediatric module. Conclusion: COVID Device (Adult module) is a very effective tool for COVID and post COVID condition management and research. It has the potential to incorporate other modules namely obstetric, pediatric, and neonatal modules. If used across all hospital of India, it will be a real boost to digital health mission and centralized COVID data management and research in India.
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Background & Aim: Background: Traditionally, ‘fresh’ Hematopoietic progenitors cell (HPC) infusions have been preferred over cryopreserved HPC in Allo-HCT because cryopreservation and thawing leads to cell loss, besides DMSO-related adverse reactions in patients. Emergence of COVID-19 pandemic has severely affected fresh HPC infusions and most professional bodies recommend cryopreservation of HPC products before initiating conditioning chemotherapy. Although some western studies suggest no significant impact of graft manipulation on patient outcome, there is no available data from the developing world.Aim: We compare neutrophil and platelet engraftment in patients undergoing Allo-HCT with fresh and cryopreserved HPC products. Methods, Results & Conclusion: Material and Method: Allo-HCT data from October 2018 to October 2021 were analyzed. Cryopreservation was performed by controlled-rate freezing using 10% DMSO, plasmalyte- A and human albumin ( 1:2:1) as cryoprotectant. Cryopreserved products were stored in vapour-phase of Liquid nitrogen tank. CD34+ enumeration and viablity( by 7-AAD) was done on Flow-cytometry on fresh and post-thaw HPC samples. Neutrophil engraftment was defined as absolute neutrophil count >0.5 ×109/L for 3 days. Platelet engraftment was defined as independence from platelet transfusion for at least 7 days with a platelet count >20 × 109/L. Statistical analysis using Wilcoxon Rank Sum test. Results: Ninety-six patients underwent allo-HCT (46 received fresh and 50 received cryopreserved HPC products) (Table 1). There was no significant difference in neutrophil engraftment with fresh and cryopreserved grafts (p>0.05) in different types of transplants( Matched related/unrelated and haploidentical). 22% (11/50) of cryopreserved graft infusions were associated with Grade-1 DMSO-related adverse reactions, which were managed with symptomatic treatment. Cryopreservation increased the cost of related allogeneic transplants by USD1100. No cryopreserved HPC product was culture positive on microbiological assessment. Conclusion: In our experience, the engraftment kinetics were similar with fresh and cryopreserved HPC products as CD34+cell dose administered was almost the same. Cryopreserved grafts had a median 7% CD34+cell loss, associated with mild DMSO-related adverse reactions and cost increment. Even though, graft cryopreservation is a feasible alternative during the pandemic, it is crucial to ensure graft quality and promptly manage DMSO-related adverse reactions.(Table Presented) Table 1 Comparison of Fresh and cryopreserved HPC products in Allo-HCT
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Background and Aims: ACLF is a condition in which 2 insults to liver operate simultaneously, 1 being chronic, and other acute. Complementary & alternative medicine (CAM) are important causes for ACLF. India is the birth place of Ayurveda & CAM is considered safe by the common population with around 80% of the population relying on it. CAM consumption has increased in recent years. Due to the pandemic and the focus on improved immunity, the consumption of CAM has gone up. India has reported 4.8 lakh COVID 19 related deaths till December 2021. However, WHO has estimated 4.7 million deaths directly or indirectly related to COVID-19. We documented a case series of CAM related DILI-ACLF, with CAM being consumed for COVID prevention Methods: ACLF established with APASL defining criteria. USG was done to assess for features of CLD. Liver biopsy was done where feasible. Results: Case 1-39-year-old diabetic taking Giloy Kwath for 2 months for COVID prevention. Presenting with jaundice & ascites having MELD score 18 and CTP class B, he had NASH related cirrhosis on biopsy and is still on follow up. Case 2- f/u/c of CTP A alcoholic cirrhosis who consumed a crushed herb for protection against COVID given by a quack for 3 months, with no alcohol intake in 2 years. He presented with jaundice and encephelopathy, had MELD score 38 & CTP C & succumbed to illness. Case 3 49-year-old lady consuming Giloy Kwath for 4 months for COVID prevention. She was diagnosed with AIH type 1 with MELD score 39. She succumbed to illness with post-mortem liver biopsy showed features of AIH cirrhosis Conclusion: CAM is the most common cause of drug induced ACLF. CAM consumption increased during the pandemic and may have lead to increase in indirect COVID related deaths
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The death of Indian film star Sushant Singh Rajput at the peak of the COVID lockdown triggered chaos on the news cycle in India with a range of conspiracy theories that led to a witch hunt of sorts, and the hounding of several entertainers and public figures in the months that followed. Using data from Twitter, YouTube, and an archive of debunked misinformation stories, we examine the drivers and consequences of social media outrage in this case. We analyse these patterns from the framework of conspiracy and astroturfing and contextualize our findings to the socio-political background currently prevalent in India. Primarily, retweet rates on Twitter suggest that commentators benefited from talking about the case, which got higher engagement than other topics. Moreover, we report evidence of political hands in the way the discourse has shaped online, but more importantly that the story bears warnings for the shape and impact of witch-hunts in the backdrop of a fractured media environment. In conclusion, we consider the effects of Rajput's outsider status as a small-town implant in the film industry within the broader narrative of systemic injustice, as well as the gendered aspects of mob justice that have taken aim at his former partner in the months since. © 2022 ACM.
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Background: With reference to the National vaccination drive against COVID-19 disease (rolled out on January 16, 2021 by Government of India), this study was undertaken to analyze the patterns of antibody response among fully vaccinated adult individuals, to find the spectrum of adverse events following immunizations and knowledge component of the participants regarding the COVID-19 vaccines as well as its side effects. Materials and Methods: A total of 500 vaccinated individuals (with two doses of Government approved Covishield vaccine) were studied over a period of 9 weeks following the second dose of their vaccine. They were tested for the development of antibodies against SARS-CoV-2 spike protein, using an immunoglobulin G ELISA kit on three occasions, and the seroconversion pattern was analyzed. Results: A postvaccination seroconversion rate of 63.8% (at 2-3 weeks), 83.2% (at 4-5 weeks), and 93.2% (overall seroconversion rate at 8-9 weeks) was found. While 77.4% participants (at 4 weeks) and 65.9% participants (at 8 weeks) showed rise in optical density (OD) values, 7.4% showed a declining in OD values (at 8 weeks) and 6.8% remained seronegative throughout the study period. Sixty-two percent had experienced at least one form of adverse effect postvaccination, which were mostly mild in nature not requiring hospitalization. Conclusion: This study found that the timeline for seroconversion postvaccination by COVISHIELD varies between individuals, with few showing decline in the OD values as well and that majority of the adverse reactions observed in this population were only mild and manageable not requiring hospitalization.
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Outbreaks of the COVID-19 that emanated in Wuhan city of China have been causing worldwide health concerns since December 2019 resulting in a global pandemic declared by World Health Organization (WHO) on March 11, 2020. It has highly affected social, financial matters and health too. In the study, COVID-19 affected people’s statistics are taken into account for predicting the upcoming day’s movement in a total count of infected cases in India. Regression models especially multiple linear regression, support vector regression are implemented on the dataset for observing the curve of the infected cases and forecast the total active, total deaths and total recovered cases for next coming days. The usefulness of regression techniques is studied. These techniques analyze and predict the rise and spread of COVID-19. We investigate how well mathematical modeling can forecast the rise using datasets from https://covid19india.org. Here, a comparison of multiple regression and support vector regression is done. It can be concluded that these models acquired remarkable accuracy in forecasting COVID-19. We also want to compare the distribution of COVID-19 in different nations and try to predict potential instances as soon as possible. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The COVID-19 epidemic continues to have a devastating influence on the global population's well-being and economy. One of the most important advances in the fight against COVID-19 is thorough screening of infected individuals, with radiological imaging using chest radiography being one of the most important screening methods. Early studies revealed that patients with abnormalities in chest radiography images were infected with COVID-19. Persuaded by this, a variety of computerized reasoning and simulated intelligence frameworks based on profound learning have been suggested, with promising results in terms of precision in differentiating COVID-infected individuals. COVID-Net, a neural system configuration custom-fit for the recognition of COVID-19 instances from chest radiography photographs that is open source and accessible to the general public, is presented in this study. Many techniques have been used for the detection of COVID-19, but here we are going to focus on the chest radiography technique with the application of machine learning and image processing concepts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus, it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, there are not enough image datasets containing a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application was developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals' 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.