An Effective Technique using MobileNet-V2 for Face Mask Detection

Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc. © 2023 IEEE.

Research on the Construction Scheme of Wuhan Emergency Logistics System Under the Background of Public Health Emergencies

In order to improve the emergency logistics support capacity of Wuhan city and build a transportation power pilot, based on the background of public health emergencies and on the basis of comprehensively summarizing the experience, practices and prominent problems of emergency logistics support work of COVID-19 in Wuhan City, this paper studies from the aspects of development foundation, overall thinking and main tasks, Put forward the systematic framework and specific implementation path of emergency logistics system construction of "building three guarantee systems of reserve facilities, transportation capacity and command and dispatching, and building an information platform”. At the same time, in the construction of emergency logistics command and coordination information platform, K-means clustering method is adopted to achieve scientific matching and efficient connection between emergency materials transit stations and demand points. For other cities It is of practical significance to improve the regional emergency logistics system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Convolutional Neural Network-based Covid-19 Analysis with Internet of Things

The very hazardous respiratory illness known as COVID-2 (SARS-CoV-2), which is the root cause of the even more serious illness known as COVID-19, was caused by the COVID-2 virus. The COVID-19 virus was identified in Wuhan City, China, in the month of December in 2019. It began in China and then spread to other parts of the world before it was officially classified as a pandemic. It has had a significant impact on day-To-day life, the welfare of people in general, and the economy of the whole globe. It is of the utmost importance, particularly in the beginning stages of treatment, to pinpoint the constructive experiences that are useful at the proper time. The identification of this virus involves a substantial number of tests, each of which takes a certain amount of time;nevertheless, there are currently no other automated tool kits that can be used in their place. X-ray photos of the chest that are obtained via the use of radiology imaging methods may provide significant insight into the COVID-19 infection if they are analysed carefully. An accurate diagnosis of the infection may be obtained via the application of deep learning techniques, which are applied to radiological images and make use of cutting-edge technology such as artificial intelligence. Patients who reside in distant places, where it may not be feasible for them to have rapid access to medical facilities, may benefit from this kind of analysis throughout the course of their therapy. One of the deep learning strategies that are used in the creation of the model that has been proposed is the use of convolutional neural networks. The images of chest X-rays are analysed by these networks to detect whether a person has a positive or negative result for the Covid gene. © 2022 IEEE.

Hazmat Cooling Vest Temperature Using Heat Pipe and Thermoelectric

Corona Virus (COVID-19) is a virus that is endemic almost all over the world, including Indonesia. COVID-19 was first confirmed by the World Health Organization (WHO) on December 31, 2019, in Wuhan City, Hubei Province, China, and then rapidly expanded outside of China. To suppress the Covid-19 case, medical volunteers are needed as the main actors in efforts to handle Covid-19 patients. This makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. This also makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. The use of hazmat clothes is one of the efforts to protect health workers when in contact with Covid-19 patients. Hazmat clothes are technically referred to as "encapsulated waterproof protective clothing” which is PPE that must be used for officers from the risk of contracting the Covid-19 virus through airborne droplets and contact with patients and patient body fluids. Although hazmat clothing is an important PPE for health workers to stay protected, the use of hazmat clothing for a long time often makes medical personnel feel uncomfortable when providing services. Based on the problems above, the researchers conducted a study on the heat pipe - thermoelectric hazmat suit cooling vest. This technology can absorb more heat than other methods by simply applying the principle of capillarity to the wicks on the pipe walls. schematic of testing a cooling vest on a hazmat suit. The loading on the thermoelectric is given through the DC - Power supply. The temperature data read by the sensor will be detected by the computer system using the NI 9123 and C-DAQ 9174 modules. The test results can be viewed using the NI LabView 2017 software. The temperature used in this experiment is the result of tests carried out for 30 min. Based on the tests that have been carried out, the heat pipe-based thermoelectric hazmat suit cooling vest has been able to reach the lowest thermoelectric temperature of 24,42 ∘C, which is distributed through heat pipes to body parts. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis and Prediction of COVID-19 Spread in Ernakulam District, Kerala

The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Computational Approach for Predicting the Termination of COVID-19

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field. © 2022 IEEE.

Preventive measurement and Prediction of Covid-19 in India through Business Intelligence Tools

From December 2019, a major outbreak called novel corona virus is infecting people all over the world now. It is believed to be a beta corona virus of SARS-CoV and MERS-CoV. Infected people are unable to detect this disease as they feel normal till 10-12 days. After that, the virus infects the whole body and starts to find another body to infect, multiplying it day by day. As per the media news and other sources, epidemic is spreading globally, especially in countries like China, Italy where its effect is at peak, killing thousands of people. Based on the data of infected Covid-19 people in India, we systematically discuss the outbreak of epidemic corona virus in India. Defining the structure of active cases day by day, we predict the future of Covid-19 in India. We also suggest important measures to help prevent the spread of Covid-19 in India. © 2022 IEEE.

Hazmat Cooling Vest Temperature Using Heat Pipe and Thermoelectric

Corona Virus (COVID-19) is a virus that is endemic almost all over the world, including Indonesia. COVID-19 was first confirmed by the World Health Organization (WHO) on December 31, 2019, in Wuhan City, Hubei Province, China, and then rapidly expanded outside of China. To suppress the Covid-19 case, medical volunteers are needed as the main actors in efforts to handle Covid-19 patients. This makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. This also makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. The use of hazmat clothes is one of the efforts to protect health workers when in contact with Covid-19 patients. Hazmat clothes are technically referred to as “encapsulated waterproof protective clothing” which is PPE that must be used for officers from the risk of contracting the Covid-19 virus through airborne droplets and contact with patients and patient body fluids. Although hazmat clothing is an important PPE for health workers to stay protected, the use of hazmat clothing for a long time often makes medical personnel feel uncomfortable when providing services. Based on the problems above, the researchers conducted a study on the heat pipe - thermoelectric hazmat suit cooling vest. This technology can absorb more heat than other methods by simply applying the principle of capillarity to the wicks on the pipe walls. schematic of testing a cooling vest on a hazmat suit. The loading on the thermoelectric is given through the DC - Power supply. The temperature data read by the sensor will be detected by the computer system using the NI 9123 and C-DAQ 9174 modules. The test results can be viewed using the NI LabView 2017 software. The temperature used in this experiment is the result of tests carried out for 30 min. Based on the tests that have been carried out, the heat pipe-based thermoelectric hazmat suit cooling vest has been able to reach the lowest thermoelectric temperature of 24,42 ∘C, which is distributed through heat pipes to body parts. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automatic COVID-19 Detection from Chest X-Rays using Deep Learning Techniques

COVID-19 emerged in November 2019 in the Wuhan city of China. Since then, it has expanded exponentially and reached every corner of the world. To date, it has infected more than three hundred eighty-five million people and caused more than five million seven hundred deaths. Traditional COVID-19 diagnostic tests lack sensitivity and result in false-negative reports several times. Using X-Rays and CT scans to detect covid-19 can aid the diagnosis process when powered by deep learning techniques. Using deep learning will provide accurate results in a fast and automatic manner. The proposed research work has performed a total of twenty-eight experiments. This research work has experimented with seven different Deep Learning models including, DenseNet201, MobileNetV2, DenseNet121, VGG16, VGG19, InceptionV3, and ResNet50. The performance of each model is tested based on the distinct image enhancement techniques. The four different experiments include raw data, data preprocessed with gamma correction for two different gamma values (0.7 and 1.2), and Contrast Limited Adaptive Histogram Equalization (CLAHE). Gamma Correction with gamma value 1.2 performed the best. Lastly, this research work has created an ensemble of three best-performing algorithms including, DenseNet201, MobileNetV2, DenseNet121, and achieved an accuracy, precision, recall, f1 score, and AUC of 98.34%, 98.61%, 98.78%, 98.2%, and 99.8%, respectively. © 2022 IEEE.

Implementation of Deep Learning Using Matlab-Based Convolutional Neural Network for Covid-19 Forecasting and Classification

The outbreak of the Corona Virus Disease or better known as the Korona virus or Covid-19 was first detected to appear in China precisely in China's Wuhan city at the end of 2019, suddenly becoming a terrible terror for the world community, especially after taking the lives of hundreds of people in a relatively short time. Almost approximately 200 countries in the world infected with Corona viruses including Indonesia, the number of virus infection status known as Garry-19 is increasing there are cases that are easy to do forecasting and some are difficult to predict, forecasting process and classification depends on the following that is related to the related factors, mathematical model to be used and the existence of the data owned. In this study can be produced percentage accuracy of the training data for classification with CNN method of 89.79% and for predictions of 90.47% for the type of positive cases Garry with the output data of emergency status with 3 status i.e.Transition, standby and responsiveness. © 2021 IEEE.

Comparison of Machine Learning Approaches for Anticipating of COVID-19 Active, Recovered and Death Cases in India

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.

Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

A Robust Approach of COVID-19 Indian Data Analysis Using Support Vector Machine

COVID-19, as the name suggests it is coronavirus disease 2019, comes from severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS) virus family, which is a life-threatening disease. The place of origin of disease has been from country China. It was first spread in Wuhan's city in China and was declared as a pandemic in March 2020 by WHO. To date, no vaccination exists to kill the virus;however, it is being cured and prevented using medicines like hydroxychloroquine, Metformin, dexamethasone, and plasma therapy. In this paper, we have analyzed the state-wise as well as district-wise dataset of country India and classified the states as profoundly affected states and less affected states by performing preprocessing and applying support vector machine model for classification on the state-wise dataset. We also have classified districts into the red, orange, and green zone after analyzing and preprocessing district-wise COVID-19 dataset using Weka Framework. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Role of Artificial Intelligence and Machine Learning in Covid-19 LED Pandemic

The leading life threatening and fatal virus which is spreaded all around the globe causing pandemic Covid-19 tends to originate in the wuhan city of China in Nov 2019 affecting the life of million every single day, Multiple clinical approaches were performed taking in consideration latest technology: AI and Ml have contributed a lot so as to control its wide spread. This paper presents some of the application of AI and ML which will help us to tackle this situation. There various branches of helping hands are the following: helped us by detection and testing of covid-19,building up of smart hospital using ML, mask detection using ML model and maintaining the social distancing and sanitization plays a crucial role for controlling the virus and lastly predicting the anxiety disorder is also important to understand the effects the lockdown has caused.We have also emphasised on the the challenges faced while predicting its accuracy of the model since the dataset wasn't up the mark due to absence of historical data it wasn't proficient, also considering the opportunity this pandemic has brought in our life's by introducing digital platforms facilities in everyday life by improving the quality services. Considering the future scope of this skill oriented technology, the world is going to experience a drastic transformation and we will hope scientists and researchers make utmost use of AI and ML to bring us the best potential resources from it. © 2021 IEEE.

IRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages

Emerged in Wuhan city of China in December 2019, COVID-19 continues to spread rapidly across the world despite authorities having made available a number of vaccines. While the coronavirus has been around for a significant period of time, people and authorities still feel the need for awareness due to the mutating nature of the virus and therefore varying symptoms and prevention strategies. People and authorities resort to social media platforms the most to share awareness information and voice out their opinions due to their massive outreach in spreading the word in practically no time. People use a number of languages to communicate over social media platforms based on their familiarity, language outreach, and availability on social media platforms. The entire world has been hit by the coronavirus and India is the second worst-hit country in terms of the number of active coronavirus cases. India, being a multilingual country, offers a great opportunity to study the outreach of various languages that have been actively used across social media platforms. In this study, we aim to study the dataset related to COVID-19 collected in the period between February 2020 to July 2020 specifically for regional languages in India. This could be helpful for the Government of India, various state governments, NGOs, researchers, and policymakers in studying different issues related to the pandemic. We found that English has been the mode of communication in over 64% of tweets while as many as twelve regional languages in India account for approximately 4.77% of tweets. © 2021, Springer Nature Switzerland AG.

COVID'19 in India: Emotion of the Nation A novel Spatio-Temporal Unsupervised Sentiment Analysis

The coronavirus was originated in Wuhan City in China in 2019, and it led to something for which the world was not prepared. In the world where the extent of COVID'19 was ubiquitous, India was one of the countries that witnessed multiple phases of its spread. Moreover, since India has one of the largest populations, this made analyzing the sentiment of people during this time a task that held significance. COVID'19 brought a mix of emotions across the different periods in its first 18 months. During this time, social media was flooded with tweets and hashtags expressing both favorable and negative opinions about COVID'19, pandemic, lockdown, and vaccines. © 2021 IEEE.

Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Model

At the end of December 2019, the COVID-19 virus was the initial report case in China Wuhan City. On March 11, 2020. The Department of Health (WHO) announced COVID-19, a global pandemic. The COVID-19 spread rapidly out all over the world within a few weeks. We will propose to develop a forecasting model of COV-19 positive case predict outbreak in Pakistan using Deep Learning (DL) models. We assessed the main features to forecast patterns and indicated The new COVID-19 disease pattern in Pakistan and other countries of the world. This research will use the deep learning model to measure several COVID-19 positive case reports in Pakistan. LSTM cell to process time-series data forecasts is very efficient. Recurrent neural network processes to handle time-dependent and involve hidden layers are confirmed and predict positive cases and weekly cases reported in the future. Bidirectional LSTM (Bi-LSTM) processes data and information in one direction to predict and analyze the weekly 6-9 days readily forecast the number of positive cases of COVID-19 © 2021 IEEE.

Modeling the COVID-19 Epidemic in PR China

This study is to investigate the impacts of the strategies against COVID-19 epidemic in China, so as to provide a solid reference to control its spread in the world. A two-stage dynamics transmission model is proposed using 'lockdown of Wuhan city' as the time line. The first stage is a SEIR derived model that considers the contagious of the exposed ones. It simulates the COVID-19 epidemic in Hubei Province before 'lockdown of Wuhan city'. The second stage is the new transmission dynamics model proposed in this paper and referred to as SEIRQH. It takes into account the influence over the COVID-19 epidemic from the series of strategies taken by Chinese government, such as travel restriction, contact tracing, centralized treatment, the asymptomatic infected patients, hospitalized patients and so on. It simulates the COVID-19 epidemic in China after 'lockdown of Wuhan city'. The least square method is used to estimate the parameters of the SEIR derived model and the SEIRQH model based on the collected data of COVID-19 from Hubei Province and the mainland of China before April 30, 2020. The experimental results found that the SEIR derived model simulates the actual data in Hubei Province before 'lockdown of Wuhan city', and the basic reproduction number of COVID-19 epidemic in Hubei Province is 3.2035. The SEIRQH model simulates the number of the hospitalized persons of COVID-19 in Hubei Province and the mainland of China after the 'lockdown of Wuhan city' perfectly. The control reproductive number is 0.11428 and 0.09796 in Hubei Province and the mainland of China, respectively. Our two-stage dynamics transmission model simulates the COVID-19 epidemic in China, especially our SEIRQH model simulates the actual data very well. The strategies taken by Chinese government are effective, and plays significant role in preventing the spread of COVID-19 epidemic in China. This study gives the reference to World Health Organization and other countries against the COVID-19 epidemic. © 2021 IEEE.

Performance Analysis of Deep Convolutional Features using Support Vector Machines for COVID-19 Diagnosis on X-ray Images

Since the first case of COVID-19 appeared in Wuhan city, China, in December 2019, the disease has affected more than millions of people worldwide. Therefore, early detection of COVID-19 is important to prevent transmission to more people. One method widely used to detect COVID-19 through X-ray images is Convolutional Neural Networks (CNN). However, CNN needs large amounts of image data to build models with high accuracy, while the medical image has limited amounts of data. To overcome this problem, transfer learning technique where CNN is used as a feature extraction method is usually be chosen as an alternative. However, most studies use the extraction results of the final layers such as fully connected layer or the last convolutional layer. In this study, all layers will be used by turns to analyze how the extraction results affect the performance of classification method. The CNN models used are pre-trained models VGG16 and VGG19, while the classification method used is Support Vector Machines (SVM). Based on the results of the study, the extraction results by the initial layer gave a better performance on SVM compared to the layers that are deeper in the selected CNN architecture. Several layers in CNN model did not analyze due to limited source capability in doing computation. Therefore, as the future work, the rest layers of CNN in this study can be analyzed as well as the other CNN models and the classification method. © 2021 IEEE.

Communicable disease transmission model for the prevention and control of COVID-19 in Wuhan City, China

Epidemic prevention and control strongly affect people's lives in cities, but existing communicable disease models cannot accurately simulate the effects of prevention and control procedures. A city simulation model for the 2019 coronavirus epidemic was developed based on an Agent model for Wuhan, China to model the epidemic transmission process. The model includes the government control measures and the hospital diagnosis and treatment levels during the epidemic with analyses of the infection rates and spatial distributions for various epidemic control measures. The model was also used to model the active anti-epidemic impact of nucleic acid testing after people returned to work. The results show that this modeling method accurately reproduces the spatio-temporal transmission characteristics of the Wuhan epidemic. Thus, this method can be used to evaluate government control measures and to implement diagnosis and treatment plans for decision-making for infectious disease prevention and control. © 2021, Tsinghua University Press. All right reserved.