Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading

The dataset, methods, and machine learning prediction framework on the Covid-19 theme have been published widely and complex. Special publications on the spread of virus infection 19 in the form of a time series need to be mapped more comprehensively. This literature review aims to identify and analyze research trends, datasets, and methods used in predicting Covid-19 with Machine Learning Engineering research between 2019 and 2021. Identifying the need, specifying the research question evaluating review protocol, searching for papers, scanning papers, and reporting results are the eight major steps of this systematic literature review. The most critical aspect of systematic analysis is defining the research questions. The PICOC techniques are used to identify research questions. Journal candidates were filtered out using inclusion and exclusion criteria techniques to shrink the SLR scope area. based on a literature study it was found that research in 2019-2021 on the Covid-19 distribution prediction system used variables: susceptibility, infection, mortality, geography, weather, and patient clinical data to be processed into ANFIS machine learning prediction models and neural networks are several models. A classification model that is widely used for hybrid processing in calculating covid-19 infection prediction. The datasets that are often used do not fully meet the epidemiological aspects that trigger the spread of COVID-19 infections. ANFIS and NN are several classification methods that are widely used for hybrid processing in calculating predictions of the spread of COVID-19 infection. © 2022 IEEE.

Masked Face Recognition Dataset and Application

During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.. IEEE

Environmentally persistent free radicals in PM2.5 from a typical Chinese industrial city during COVID-19 lockdown: The unexpected contamination level variation

The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM2.5) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM2.5-bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM2.5, still should not be ignored. © 2022

COVID-19 Growth Curve Forecasting for India Using Deep Learning Techniques

Due to sudden evolution and spread of COVID-19, the entire community in the globe is at risk. The covid has affected the health and economy and caused loss of life. In India, due to social economic factors, several thousands of people are infected, and India is seen as one of the top countries seriously impacted by the pandemic. Despite of having a modern medical instruments, drugs, and technical technology, it is very difficult to contain the spread of virus and save people from risk. Healthcare system and government personnel need to get an insight of covid outbreaks in the near future to decide on stepping up the healthcare facilities, to take necessary actions and to implement prevention policies to minimize the spread. In order to help the government, this study aims to build model a forecast COVID-19 model to foretell growth curve by predicting number of confirmed cases. Three variant models based on long short-term memory (LSTM) were built on the Indian COVID-19 dataset and are compared using the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The findings have revealed that the proposed stacked LSTM model outperforms the other proposed LSTM variants and is suitable for forecasting COVID-19 progress in India. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Using Deep Learning Algorithm for Prediction and Detection of Covid-19

As the Covid-19 puts the great impact on the world health and economic situations, which directly leads toward the crisis. Prediction helps us to take precaution accordingly. Currently, more than 293 million of positive cases have been detected and more than 5.4 million deaths have been recorded. To prevail the spread of virus many countries open sourced datasets of Covid-19 positive cases for scientists to predict the curve. Therefore, countries can take the measures accordingly. It helps to obtain a rough idea about the pandemic end date, which is very difficult to predict because of its uncertainty. This article takes the dataset of many countries and predicts the curve of positive cases of the top 10 countries. We used this data to integrate it with logistic regression model to have a future view of pandemic. The article consists of two parts. First part includes the prediction by using logistic regression. This function used Python programming, Panda's machine learning library, whereCovid-19 dataset has been taken from the open-source dataset available on the internet. Second part includes the detection of Covid-19 using Deep Learning Convolution neural network method. CNN method is used by training the model with the dataset of X-ray Images. CNN can detect the virus at early stages because of its powerful deep learning multiple layers ‘algorithm. There are several stages of detection such as processing image datasets and applying image-processing techniques to have a clear understanding of features in X-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Robotic Systems Deployed to Combat COVID-19 Pandemic: A Review

The COVID-19 epidemic has been deemed a pandemic by the World Health Organization. It is triggered due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated and spread from Wuhan, China, in December 2019. At present, the entire world is struggling from this virus due to large confirmed positive and death cases of COVID-19. People of every nation have been isolated, and lockdowns are instituted. Despite the introduction of several precautionary measures, the spread of the virus is still increasing at an alarming pace. Although promising development has been made for the development of vaccines for SARS-CoV-2, no vaccines have been reported to cure the infection. Different antiviral therapies have also been attempted but do not seem to be successful for every patient. To deter the dissemination and control the spread of virus, the frontline healthcare staff and police officers deployed numerous autonomous systems for an increased line of protection. Robots are deployed to conduct different operations including decontamination, package delivery, etc. It also acts as a mediator for two-way communication between the doctors and patients. Recent advancement in robotics for its application in healthcare facilities has been found very effective for the healthcare officials to communicate with the virus affected patients, and this literature has addressed it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Intelligent mask recognition and voice dialogue system based on Raspberry Pi control

As New Coronavirus continues to mutate, virulence and infectivity are constantly strengthened. 2019-nCoV has become the most deadly virus in recent years. Its main transmission route is droplet transmission. Wearing masks can effectively block the spread of viruses. Mask is an important defense line to prevent respiratory infectious diseases, which can reduce the risk of New Coronavirus infection. The mask can not only prevent the patient from spraying droplets and reduce the amount and speed of droplets.but also block the droplet core containing virus and prevent the wearer from inhaling. In public places.especially in places with large passenger flow, it is a drop in the bucket to rely solely on manual supervision of customers wearing masks. In order to solve this problem.an intelligent mask detection system is designed, It supports the corresponding models of all mainstream frameworks for face mask detection (pytorch, tensorflow, keras,mxnet and cafe) (the model trained by keras and other framework models converted),and provides the reasoning code of all five frameworks. At the same time.it also has the voice dialogue function,which can wake up the voice robot in real time and achieve man-machine communication. Let inspectors feel more at ease and consumers feel more at ease. © 2022 IEEE.

Big Data-Enabled Solutions for Covid-19

In the time of the pandemic like CORONA, Covid-19, everyone is ftghting against this deadly virus. Besides, governments are looking for a barrier that stops spread of virus until the vaccine is made. In modern era, technology plays an important role. This paper brings the way by using a powerful technology called Big data. Big data know for handling a large amount of data and provide powerful insights into the data. Big data integrated with Artificial Intelligence is a powerful tool to ftght against this pandemic. Many countries like Taiwan, China with the use of Big Data stop this pandemic up to some extent. But the collection of data itself comes up with the big challenge of PRIVACY AND SECURITY. In the recent times, the world has seen the effect of data leaking whether by Facebook or by Google. Many European countries due to this big challenge will not be able to use this technology. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

GeoSpread: An Epidemic Spread Modeling Tool for COVID-19 Using Mobility Data

We present an individual-centric agent-based model and a flexible tool, GeoSpread, for studying and predicting the spread of viruses and diseases in urban settings. Using COVID-19 data collected by the Korean Center for Disease Control & Prevention (KCDC), we analyze patient and route data of infected people from January 20, 2020, to May 31, 2020, and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of population mobility and is used to parameterize GeoSpread to capture the spread of the disease. We validate simulation predictions from GeoSpread with ground truth and we evaluate different what-if counter-measure scenarios to illustrate the usefulness and flexibility of the tool for epidemic modeling. © 2022 Owner/Author.

Thermal Performance of the Heat Pipe and Thermoelectric Sterilization Face Mask

The spread of the coronavirus has been the focus the world's attention. The government has issued a new regulation on the coronavirus that obligates the entire community to always wear a face mask and implement social distancing to prevent the spread of viruses. These issues have been impacted by an increase in the use of face masks throughout the world and in Indonesia. Moreover, the World Health Organization (WHO) suggested using medical face masks. Furthermore, in the health care sector, the possible loading of pathogens in sub-micrometer sizes and properties such as splash resistance and the ability to prevent infection by reducing the concentration of inhaled particulates has created limitations on the types of face masks that can be used. Therefore it is necessary to design a face mask sterilizer device that can extend the life span of masks, thereby decreasing the masks consumption. An investigation on mask sterilizer devices based on heat pipes and thermoelectrics was conducted in this study. The objective of this study was to investigate the thermal performance of the mask sterilizer device. The method used was an experimental method using variations in the input voltage on the thermoelectric, namely 9 V, 10 V, and 11 V, and the microcontroller was also adjusted to control the temperature by 70 ℃, 80 ℃, and 90 ℃. The results showed that the thermoelectrics can generate heat with a temperature difference between the hot side and the cold side that can reach 80.11 ℃. In terms of voltage, the greater voltage that was given led to a greater resulting temperature of the sterilization device and reduced the amount of time to achieve the minimum sterilization temperature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Epidemic Modeling of the Spatiotemporal Spread of COVID-19 over an Intercity Population Mobility Network

Intercity traveling has been recognized as a leading cause for the continuation of the COVID-19 global pandemic. However, there lacks credible prediction of the spatiotemporal spread of COVID-19 with humans traveling between metropolitan areas. This study attempts to establish a novel framework to simulate human traveling and the spread of virus across an intercity population mobility network. A Markov process was introduced to capture the stochastic nature of travelers’ migration. A backward derivation algorithm was adopted and the Nelder-Mead simplex optimization method applied to overcome the limitation of existing deterministic epidemic models, including the difficulties in estimating the initial susceptible population and the optimal hyper-parameters required for simulation. We conducted two case studies with data from 24 cities in China and Italy. Our framework yielded state-of-the-art accuracy while being modular and scalable, indicating the addition of population mobility and stochasticity significantly improves prediction performance compared to using epidemic data alone. Moreover, our results revealed that transmission patterns of COVID-19 differ significantly with different population mobility, offering valuable information to the understanding of the correlation between traveling activities and COVID-19 transmission. © 2022, Springer Nature Switzerland AG.

The Effect of Face Mask and Approach Pattern on Interpersonal Distance in COVID-19 Pandemic Using VR Technology

In the COVID-19 pandemic, control measures including wearing masks, ensuring hand hygiene, and maintaining a physical distance of at least 1 m were recommended to prevent the spread of virus. The purpose of this study was to investigate the influence of face mask, approach pattern and participants’ gender on interpersonal distance in the pandemic environment. Virtual reality (VR) technology was applied to build the experimental environment. This study recruited 31 participants including 17 males and 14 females, who were asked to interact with virtual confederates with and without a face mask. The interpersonal distance was recorded when participants actively walk towards the virtual confederate or approached passively by the confederate. Three-way ANOVA results showed that face mask and approach pattern had significant effects on interpersonal distance. The distance when facing the confederate with a face mask was significantly closer than without a face mask. Moreover, participants preferred a significantly larger distance in the passive pattern than in the active pattern. The participants’ gender showed no significant effect on interpersonal distance and no interaction effects were found. The findings in this study helped to further investigate the nature of interpersonal distance and contributed to a better understanding of the human behaviors in the pandemic environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Vaccine Awareness and Enhancing Networked Healthcare System Across the Globe

Immunisation is the procedure of providing a vaccine to a person in order to protect him or her against disease. This process has been widely recognized and adopted as one of the world's most successful and cost-effective health interventions. Vaccines have been saving millions of lives worldwide from deadly infectious diseases and viruses, such as hepatitis, measles and polio. However, the COVID outbreak in the late 2019 has witnessed huge devastation on the global health front. For now, vaccine is the only cost-effective health intervention to control the spread of virus and completely eradicating it. Technological breakthroughs are making a significant contribution to the improvement of healthcare. Blockchain technology is one example of such a disruptive technology. Blockchains have the potential to improve the healthcare system in a variety of ways. In this paper, we have thoroughly discussed how we can create vaccine awareness across the globe by focusing on parents, healthcare workers, frontline workers, policymakers, media, and ultimately how everyone must work together to ensure that every individual in every country gets the vaccine. We also discussed how blockchain technology may be applied to many sectors of healthcare and the benefits it can provide in terms of enhancing global network healthcare systems. © 2022 IEEE.

Social Distance Monitoring Method with Deep Learning to prevent Contamination Spread of Coronavirus Disease

The ongoing COVID-19 virus pandemic has resulted in a global tragedy due to its lethal spread. The population's vulnerability grows as a result of a lack of effective helping agents and vaccines against the virus. The spread of viruses can be mitigated by minimizing close connections between people. Social distancing is a critical containment tool for COVID-19 prevention. In this paper, the social distancing violations that are being made by the people when they are in public places are detected. As per CDC (Centers for Disease Control and Prevention) minimum distance that should be maintained by people is 2-3 meters to prevent the spread of COVID- 19, the proposed tool will be used to detect the people who are maintaining less than 2-3 meters of distance between themselves and record them as a violation. As a result, the goal of this work is to develop a deep learning-based system for object detection and tracking models in social distancing detection. For object detection models, You Only Look Once, Version 3 (YOLO v3) is used in conjunction with deep sort algorithms to balance speed and accuracy. To recognize persons in video segments, the approach applies the YOLOv3 object recognition paradigm. An efficient computer vision-based approach centered on legitimate continuous tracking of individuals is presented to determine supportive social distancing in public locations by creating a model to generate a supportive climate that contributes to public safety and detect violations through camera. © 2022 IEEE.

NEW NORMAL INTERIOR ARCHITECTURE: SPACE IN HOTEL DESIGN

In the year 2021, the world has been facing the Covid-19 pandemic. The tourism industry in Northern Thailand was affected by restricted travel, and the country lockdown and Social distancing practical guidance effect all physical environment, especially in public areas such as hotels must adjust the interior layout for the new use of space. The research aimed to propose guideline for design new normal interior architecture of hotel for the future. This study emphasized research through surveys and assessments of elements within a building to design a space for the new normal, using minimized spread of virus as key criteria. Results showed that the public areas of the hotel must contain . openings for ventilation within the building, limiting users number with rotate to reduce time of use, space for personal distancing, and interior decoration that uses surface materials that are easy to sanitize. Within an interior privacy atmosphere that lead to green space perspective, it enable guests who must have a longer stay experience happiness and leisure in the new normal environment.

CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching

Worldwide 219 million people have been infected and 4.5 million have lost their lives in ongoing Covid-19 pandemic. Until vaccines became widely available, precautions and safety measures like wearing masks, physical distancing, avoiding face touching were some of the primary means to curb the spread of virus. Face touching is a compulsive human behavior that can not be prevented without constantly making a conscious effort, even then it is inevitable. To address this problem, we have designed a smartwatch-based solution, CovidAlert, that leverages Random Forest algorithm trained on accelerometer and gyroscope data from the smartwatch to detect hand transition to face and sends a quick haptic alert to the users. CovidAlert is highly energy efficient as it employs STA/LTA algorithm as a gatekeeper to curtail the usage of Random Forest model on the watch when user is inactive. The overall accuracy of system is 88.4 % with low false negatives and false positives. We also demonstrated the system viability by implementing it on a commercial Fossil Gen 5 smartwatch. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

IoT-based Covid-19 Patient Service Robot Design

Various innovations have emerged since the Covid-19 Pandemic. Innovations that arise are mainly to reduce the rate of disease spread. Studies on the development of robots are still rare when viewed from various studies. This study enriches the conceptual research related to designing a patient care robot that can be controlled remotely. The technology developed is based on the Internet of Things (IoT). This robot can deliver patient needs and communicate with patients through the LCD screen available on the robot. With the design of this Covid-19 patient service robot, patient care can be carried out remotely. This technology has the potential to be developed and optimized according to the needs in the field.

Prediction of COVID-19 Victims with Chest X-Ray using COVIDNet and Deep Convolutional Neural Network

The economy of numerous nations has been influenced a great deal because of a viral sickness Corona infection. The virus has spread all over the world and many people have lost their sustenance. Many researches are carried out to find the vaccine for the virus but still there is a problem in prediction of COVID affected people. Most victims don't have any symptoms and they adopt their usual lifestyle, which in turn affects the surroundings by the spread of virus from the victim. The work focuses on foreseeing the COVID influenced casualty from the chest X beam picture. The profound Convolutional Neural Network calculation is utilized to foresee something similar. The accuracy of the algorithm clearly highlights the efficiency of prediction of the disease. © 2021 IEEE.

Mask Effectiveness: A Project to Connect Air Pollution and Materials Science

The global COVID-19 pandemic promoted the world community to use face masks to reduce viral transmission. This practice has again raised interest in the effectiveness of masks in preventing the spread of virus particles. This theme provided a unique, timely subject to enhance learning in the field of air pollution control, while enabling distinct connections to the fields of material science as well as human health and air quality. A collaborative “Mask Effectiveness” class project was developed with the objectives of enabling students (a) to identify the types, sizes, and movement of particles that are found in air, particularly those that are expelled during normal human activity, and (b) to characterize the material properties that influence the control of these different particles. A specific focus was placed on the use of face masks made from common textile materials. The “Mask Effectiveness” project required the development of Excel-based animations and tools that encourage students to explore relationships between air pollutants and materials science. The tool was developed such that it provides a solution to the limitations of a student design project for online and hybrid courses. By engaging with the computer-based Excel tool, students are able to evaluate alternative scenarios that include the collection efficiency of particles that arise from different sources (talking, coughing, and sneezing), and the relationships between mask “breathability”, porosity, and collection efficiency of a mask. The project was designed to be implemented initially with undergraduate engineering students across two universities- Arizona State University and the University of New Mexico. One specific goal at Arizona State University was to reinforce concepts consistent with entrepreneurial mindset learning approaches. An additional goal was to provide a learning experience which allowed students to connect environmental engineering and material science topics to a design challenge that addressed a global community need. This paper describes the specific activities that were undertaken, and connects these activities to ways in which teaching methods may be altered by using an Excel-based module. © American Society for Engineering Education, 2021

Forecasting the lung diseases from Rediography scans with hybrid Transfer Learning Techniques

Lung related issues are rapidly increasing day by day as it is very important to identify the disease and get treated earliest possible as lungs are part of very complex system, expanding and relaxing thousands of times each day allow us to breathe by bringing oxygen into our bodies and sending carbon dioxide out. Lung related issues are directly preoperational to breathing problems. X-rays are one of the important ways of identifying the status of lungs. As there are many communicable diseases like Covid-19, the person should be identified early and should be treated to control the spread of virus. Lung Opacity is one of the major problem faced by many people and also a very serious problem if not treated early it will spread entire lungs and which leads to cancer similarly Pneumonia is another disease which is an infection to one's lungs caused by spread of virus. All these diseases directly affect Respiratory system of human. The paper aims to lung diseases classification among Pneumonia, Lung opacity, Normal and Covid-19 using the proposed hybrid model. The Deep Transfer Learning model helps to extract good features which helps for better learning and greater results. The Ensembled model of Deep Transfer Learning is used in this paper, which is a combination of VGG, EfficientNet and DenseNet. Considering the output of image augmentation as input for Ensembled model and classification of lung disease. The accuracy of the proposed hybrid model is very much accurate when compared to individual base models. © 2021 IEEE.