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Background: COVID-19 has become a global concern as it has ended the lives of lakhs of people. The study done by. Lin proposed the results of an analysis of a theoretical computational model of epidemiology taking into account the crucial components such as response of an individual behavior, measures taken by government, rapid transmission and emigration of fever virus to a huge extent of human population. Simple model framework has been used by them to examine the requirements of essential elements.Problem: We claim the perfect validation of the assumptions stated for the purpose of modeling and its analysis, however, the analytical solution of the presented model is not discussed. And thus have a poor potential to make computational estimates.Methodology adopted: We have tried to present and validate the mathematical/analytic solution of the developed model, considering their assumptions.Conclusion: We have reached to the outcome that the work presented by Q. Lin should be demonstrated adequately with a cautionary advice of not to consider the results and analysis of such numerical models.The current study proposed the results of an analysis of a theoretical computational model of epidemiology taking into account the crucial components such as response of an individual behavior, measures taken by government, rapid transmission and emigration of fever virus to a huge extent of human population. Simple model framework has been used by them to examine the requirements of essential elements. We claim the perfect validation of the assumptions stated for the purpose of modeling and its analysis, however, the analytical solution of the presented model is not discussed. The decision point of this work is to adequately demonstrate along with the cautionary advice of not to use the outcomes and analysis of such numerical models. [ABSTRACT FROM AUTHOR]

The objective of this work is, to identify Corona virus contact using Internet of things. To monitor the temperature by using a non-contact temperature sensor. The main symptom of the virus is fever. So scan the temperature of all humans is not possible. This paper is done manually. It is very difficult to all over the state. In this paper, the device detects the temperature and generates the all details in the database automatically. Expecting to this issue, the paper presents a sort of internal heat level disseminated screen framework. Multi-temperature sensors DS18B20 were associated with acknowledge internal heat level sign assortment, SCM AT89C52 processes estimation sign and drives field show and disturbing hardware work. The main symptom of the virus is fever. So scan the temperature of all humans is not possible. This paper is done manually. It is very difficult to all over the state. In this paper, the device detects the temperature and generates the all details in the database automatically. Intending to this issue, the paper presents a sort of internal heat level conveyed screen framework. Multi-temperature sensors were associated with acknowledge internal heat level sign assortment, SCM AT89C52 processes evaluation sign and drives field show and disturbing stuffwork. The objective of this paper is to plan and cultivate an interior hotness level assessment contraption that should be visible to the expert ceaselessly similarly as credible data through the web with an alert/sign if there ought to be an event of abnormalities. This paper is just zeroing in on inside heat level remote seeing frameworks. The temperature sensors will send the readings to a microcontroller utilizing remote. To send the steady data to the prosperity noticing informational collection, a distant area (WLAN) has been used. Arduino with Ethernet defend reliant upon IEEE 802.11 standard has been used hence. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic causes devastating impacts globally, including Surabaya. One of the significant impacts is that the increasing number of death cases, forcing every activity to comply with the health protocol for people safety. DISPENDUKCAPIL Surabaya (City Council for Civil Registration) faces the condition of increasing demand for the death certificate while the health protocol is obliged. To overcome this condition, DISPENDUKCAPIL transforms their service system from KLAMPID to KLAMPID COVID and the latest one into KLAMPID ONLINE. These transformations intend to reduce the crowd and direct human contact by shifting the activity into online activity. These transformations are proven in reducing transportation and human contact, however, increasing electricity and internet consumption. This study tries to assess the environmental impact change from each transformation. The SimaPro software calculation results for KLAMPID, KLAMPID COVID-offline submission, KLAMPID COVID-online submission, KLAMPID ONLINE-offline submission, and KLAMPID ONLINE-online submission generate 77.67 mPt, 169.31 mPt, 153.69 mPt, 61.74 mPt, and 54.53 mPt of environmental impact. It shows that there's an environmental impact jump during the KLAMPID COVID system because of the COVID-19 pandemic even though there is a reduction in transportation and paper use. [ABSTRACT FROM AUTHOR]

This paper discusses a data acquisition system (DAQ) development of a low-cost health monitoring system for a person with suspected COVID-19, named CovWatch. The system's design is based on the overall function of the CovWatch and the design requirements for low-cost and widely available devices that support extensive data collection using IoT. Two phases of the development process started from a DAQ of a single smartphone collecting data from a single smartband (point-to-point device) to a DAQ of a single smartphone for multiple smartbands (multipoint-to-point device) data. Changes in the system adopted the clinical protocol needs from self-isolation to inpatient care. Each type of data acquisition device (DAD) has a unique DAQ that requires customizing the data acquisition tools. This paper emphasized reporting each customization process. It involves analyzing the validity, reliability, and limitation of the DAD so that the CovWatch shall function according to the design requirements. From this paper, we know that CovWatch can acquire, collect, and send heart rate, blood oxygen saturation, and blood pressure data from 1-4 patients every 62 seconds with a data acquisition's success rate of 99.04%. CovWatch can be used to monitor patients for 72 hours. [ABSTRACT FROM AUTHOR]

In this paper, we develop a risk based predictive analytics model utilizing the available dataset to train the model. To predict the likelihood of a new strain of Covid-19 syndrome in patients, the researchers used a deep learning classifier called Recurrent Neural Network (RNN). The study considers diabetes patients as the respondents and the data is collected from the diabetes patients. The risk of covid-19 effects on diabetes patients are deeply analyzed using RNN. The collected datasets are initially pre-processed and then the features are extracted with final classification using RNN. The experimental analysis is conducted to validate the efficacy of the predictive analytics using RNN. The findings indicate that the suggested RNN outperforms other approaches for forecasting covid-19 risk in diabetic patients. [ABSTRACT FROM AUTHOR]

Coronavirus Disease-2019 (COVID-19) pandemic discovered in 2019 was caused through Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). It is spreading rapidly as a communicable disease which has been declared as a pandemic by WHO. This virus was firstly reported in people of China who were associated with the Huanan seafood wholesale market of Wuhan. COVID-19 pandemic has challenged the lives of millions of people in India butdiabetic patients are at high risk of hospitalization and mortality during the COVID-19 pandemic. So, they need to deal effectively with the difficulty of its treatment as diabetes acts as a risk factor for the severity of this disease. This paper presents a statistical analysis of the information given by 125 diabetic patients in India. The effect of this information was determined through analysis of variance (ANOVA) using SPSS. Further, a bibliometric analysis of 51 Indian publications indexed in Scopus related to diabetic patients having COVID-19 was also conducted. The aim of this paper is to briefly study the effect of coronavirus on diabetic people as well as to study the recent research trends of present researches togive a better knowledge of COVID-19 in diabetic patients. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak impacted drastically to education and most of educational institutions started preferring online education for students. However, after the settlement of the pandemic there is uncertainty among people about whether they should prefer online education for furthermore or start in offline mode to make it more interactive, so this paper is about an analysis of people's sentiments and emotions through Tweets about COVID-19 Education. This paper aims to study the reaction of people around the world toward online education during COVID-19. This study is conducted on the basis of the responses of students, teachers, parents, college professors, etc. We started with labeling the data into three sentiments namely positive, neutral, and negative and for validation then we used Machine learning (ML) classifiers namely, Logistic regression, Decision tree, Random Forest, Multilayer Perceptron (MLP), Naïve Bayes, Support vector machine (SVM), K-nearest neighbors (KNN), and XG-Boost. Then we performed emotion detection by considering 5 emotions namely happy, surprise, sad, fear, and angry and for validation we used ML classifiers. After applying all these ML approaches, the XG Boost ML classifier achieved the highest accuracy of 94% in classifying the tweets as positive, neutral, or negative, and 96% accuracy in classifying the tweets as happy, surprised, sad, fearful, or angry. [ABSTRACT FROM AUTHOR]

COVID 19 disease has spread worldwide, disrupting the economy, society, education, and health. It became crucial to understand the transmission curve and identify the best action in combating this highly transmissible epidemic. Epidemiological parameters of the deterministic Susceptible-Infected-Removed (SIR) model are widely used in explaining the characteristics and behavior of the disease spreading. Disease like COVID-19 is highly influenced by the uncontrolled factors of randomness, hence in this paper, the deterministic susceptible-infected-recovered-death (SIRD) model is extended to the stochastic SIRD (SSIRD) model. SSIRD model takes into consideration the noisy behavior of the process which explains the uncontrolled effects of the COVID-19 outbreak. The epidemiological parameter of the model changes throughout the epidemic due to external factors such as enforcement and public obedience to the control measures as well as the changes in healthcare facilities. These parameters need to be estimated. This paper estimates the epidemiological parameters of the SSIRD model using the Markov Chain Monte Carlo (MCMC) method of the Metropolis Hasting algorithm. The COVID-19 data from Malaysia and Indonesia are used and the dynamical behavior of the COVID-19 outbreak in both countries is simulated. The results show the parameter changing due to the uncontrolled factors influencing the trend of the pandemic curve. [ABSTRACT FROM AUTHOR]

The extensive existence about COVID-19 diseases has led to global initiatives to regulate and manage the virus with the goal of eradicating it. Machine Learning (ML) is one key for analyzing and combating COVID-19 in line with research. This is a hotly debated topic right now. Even though numerous studies are in line with medical literature, there is a requirement to follow maintain with fast-increasing quantity of papers on ML applications connected to COVID-19. Day-to-day information on the COVID-19 virus's transmission is critical for assessing the virus's global behavior. As a result, in the state of COVID-19, this paper examines forecasting methods for COVID-19 affected instances utilizing existing machine learning methods. Most of the ML algorithms used in the early detection and diagnosis of contagion are guided learning methods. The prognosis features reported by ML models are in line with medical literature findings. Many of the relevant studies are still in their early phases. One of the limitations of machine learning approaches is the use of unbalanced datasets exposed to bias in selection. [ABSTRACT FROM AUTHOR]

The Electric Vehicle (EV) industry is growing in India despite Covid 19 challenges. The scope of this paper is to understand the need for approaches towards range anxiety solutions. It highlights the current development in the standardization principles using lean techniques and how this can help standardization in-vehicle platform architecture and battery of an EV. This review paper analyses the four different case studies in the EV context and justifies how the EV battery pack is the right candidate for standardization. The study highlights the benefits, challenges, and associated factors to address these challenges. The paper concludes how the standardized EV battery pack helps manage the Range Anxiety for many stakeholders in the EV ecosystem in Business to Business (B2B) and Business to Customer (B2C) segments. Finally, the paper highlights the future research scope towards EV Battery Pack Geometry Form Factor standardization. [ABSTRACT FROM AUTHOR]

In this paper, the spread of the Covid-19 in.Indonesia is described by the SIRD epidemiological mathematical model. The mathematical model used in this paper is Susceptible, Infected, Recovered, Death (SIRD). The modified extended Kalman filter algorithm is applied to predict the spread of Covid-19 in.the future. We modified the algorithm by generating real data based on the previous estimation results. The real data generated from the generation is used at the correction stage to obtain prediction results in a fairly long period. Simulations were carried out with three types of mobility restrictions, namely mobility 100%, mobility 75%, and mobility 50%. Based on the simulation results, it can be concluded that mobility restrictions in Indonesia, which starts on September 4, 2020, can reduce the number of infected and death individuals and can increase the number of individuals who recover from Covid-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been going on for more than a year. COVID-19 causes asymptomatic infection, severe infection, until death. Diagnosis of COVID-19 infection is made by blood tests, antigen swabs, PCR, as well as radiology and CT-scan examinations. Severe COVID-19 infection shows similar symptoms to acute radiation injury. The paper aims to describe the similarity of severe infection COVID-19 and syndrome of acute radiation injury. This paper is a literature review. Literature was searched in the Science Direct and PubMed databases. The keywords were severe COVID-19 infection, acute radiation syndrome, similarity. The similarities between the pathophysiology of severe COVID-19 infection and acute radiation injury are cytokine storms. Symptoms of severe COVID-19 infection are acute respiratory distress syndrome (ARDS), hypoxia, and damage to the cardiovascular system. Researchers are trying to look for symptoms similar to this severe COVID-19 infection so they can find better prevention and therapeutic measures. Study of biological radiation has shown complex organ failure after exposure to high doses of radiation. This condition is called the acute radiation syndrome (ARS). Severe inflammation accompanied by cytokine storms causes severe symptoms of COVID-19 or ARS infection. ARS is diagnosed with a variety of biomarkers of radiation exposure. The similarity of symptoms and pathophysiology may be used to plan better treatment for patients with severe COVID-19 infection in the future. The treatment would be the same. [ABSTRACT FROM AUTHOR]

What has changed in the concept of resilient/sustainable cities and human settlements with Covid-19? When we talk about "adequate housing and basic service" today, do we have in mind the same housing and services we were thinking about before the pandemic? Nowadays, what indicators do we need to develop and monitor sustainable urban policies? The paper seeks to answer these questions by focusing on some dimensions of Sustainable Development Goal 11 (SDG 11) linked to sustainability in the urban environment: accessibility to adequate housing and basic services (with reference to target 11.1, Affordable housing), land consumption and direct participation in urban planning (both dimensions in reference to target 11.3, Inclusive and Sustainable Urbanization). The objective of this paper is twofold: first, an updated picture of the indicators used to monitor target 11.1 and 11.3 to global, European and national level, in Italy is explored; second, assuming a field of analysis limited to a single European country (Italy), we identify some gaps in the existing monitoring according to sustainable issues emerging during the pandemic and scaling-up issues at urban and suburban level, proposing three potential pathways of innovation and implementation of the existing indicators framework. In this sense, the paper proposes a detailed state of the art of the measurement of the aforementioned targets, punctually investigating the concepts at the basis of the existing indicators, the methodologies used for the quantification of the indicators themselves and the different declinations according to the global, European and Italian national scale of reference, providing a complete synthesis framework useful and necessary to open a review of these issues. In line with the aim, the different pathways for the development and implementation of the new indicators are proposed contextually to the Piedmont region and, in particular, to the city of Turin and its metropolitan area. [ABSTRACT FROM AUTHOR]

Fake news is a fabrication of the original news intentionally to deceive readers. Internet and social media help such news to spread widely and affect individuals and society negatively. Because of the lack of control over writing the posts on social media. The spread of this type of news has become much more than before. We present one of the most societal severe affairs for misinformation, especially in the presidential elections and fake news related to health like COVID-19. Therefore, there is a need for machine learning algorithms to detect and classify all types of fake news that is difficult to be detected by a human and experts. In this paper, Covid-19 FNs are detected using the Term Frequency-Inverse Document Frequency (TF-IDF) as features extraction and two machine learning algorithms (SVM, Multinomial Naive Bayes) as a classifier. The results show that the accuracy of the proposed algorithms is equal to 94.83% and 91.38%, respectively. We conclude that using machine learning algorithms can help detect such fake news based on good achieved accuracy. [ABSTRACT FROM AUTHOR]

Quantum computing and its advancement seem to be dominating technology in building the future of the world. The increasing need to extract information from images and videos led to quantum image processing which emerged from a branch of science to a mature research field. Here we provide an overview, how quantum image processing advances can be made in health applications like C.T. scans to enhance methods for obtaining faster results. We have focused on building a concept of using quantum image processing for enhancing the C.T. scans technique used for detection of the COVID 19 virus and lead to its contribution in the field of the health industry. Along with the detection of the virus, it is relevant to capture and store all the data for further research work, and thus building a management system with quantum use is suggested in the paper. The quantum image processing, with its high speed and capabilities, can help in reducing the time required to detect the virus and help in storing the data with its mechanism. This paper specifies how quantum image processing can be seen as a path towards a solution in this time of the present state of the COVID situation and the use of this technology for future enhancement in the health industry. [ABSTRACT FROM AUTHOR]

Coronavirus (Covid-19) appeared at the end of 2019 in the Chinese city of Wuhan, and it should be mentioned that there are research centers in both America and China that have worked on these viruses for many years ago before the appearance of this virus. At the beginning of the year 2020, this virus has spread widely in most countries worldwide and has led to great human and material losses, and the World Health Organization considered it a pandemic. A review analysis is conducted to be focused on many factors affecting the published papers on Covid-19 that applied to Science Direct library on 7/4/2020. We observed that most research studies have shown that this virus attacks the human respiratory system, which leads to shortness of breath and also leads to death in some cases. The obtained results showed that most of these research papers are focusing on statistics (41%), medicine (40%), management (17%), and healthcare (2%) respectively. Statistical analysis is helpful to understand the orientation of research papers published on Covid-19. Statistics and medicine respectively, occupy a large part of the research side of the Covid-19. It is a short time since the spread of the Corona virus, so research results cannot reach maturity. [ABSTRACT FROM AUTHOR]

Today, the world is exposed to many widespread health crises. One of these crises is the emergence and spread of the covid-19 virus, where any person becomes infected with this disease through the rapid transmission of infection between people, either by palpation or by inhaling droplets of carriers of this virus. Hence, there is a need to build a tracking system to respond quickly to problems. This paper proposes using one of the unsupervised machine learning algorithms, the K-Means Clustering Data, to determine the areas with the most significant percentage of infections by dividing them into infected and uninfected human groups. Where the infected persons are divided into three high groups (C1), medium (C2), and low (C3), and follow the path of the infected persons using Google Maps. The contribution to this paper is to support appropriate decision-making by public health authorities by predicting endemic areas and how to address problems that occur as they can use this system to reduce other infectious diseases. [ABSTRACT FROM AUTHOR]

The domino effect caused by the COVID-19 pandemic has become a serious problem. One of these problems is the increasing volume of infectious waste and household waste from handling COVID-19. The form of handling is often destroyed in incinerators, namely machines used to burn waste involving high temperatures. This paper will try to apply the Internet of Things (IoT) on an android-based incinerator. The stages include design, making tools, designing software and testing tools. The Android-based Incinerator is useful for minimizing the inhalation of particulates by the user, besides that the user also avoids the transmission of diseases or viruses that exist in infectious waste because the incineration process does not need to touch the incinerator. The results of the incinerator test also show that the longer the combustion time and the higher the temperature, the less residue produced, the longer the combustion time and the higher the temperature, causing the less ability to remove ash residue. [ABSTRACT FROM AUTHOR]

Investing in the stock market involves inevitable risk, especially due to the Coronavirus disease (Covid 19) and regional quarantine (lockdown) policies. Experiencing pandemic conditions, some investors might be worried and sold their stocks. Any action would make some loss or miss potential significant gain. Even though time the market is considered a hard and complicated process, understanding the risk can be one way to quantitatively measure the risk. In this paper, the risk is quantified using Monte Carlo simulation by considering Covid 19 outbreak where the expected returns and the highest values were observed. Furthermore, Analyzing Process use data from three different coal mining companies in Indonesia stock exchange. As a result, the three stocks presented which stock was the most, moderate, and least risky. [ABSTRACT FROM AUTHOR]

The operation of a university is strongly influenced by the availability of electrical energy. So that currently, electrical energy is one of the absolute and unavoidable basic needs of universities such as Hasanuddin University (Unhas). This is because at Unhas, there are modern learning and practicum facilities, both for the learning process, academic administration needs, and teaching media that generally use electrical energy-based equipment. Prior to the Covid-19 pandemic, Unhas's electricity consumption was relatively high. From 2018 to 2019, the average amount of electrical energy consumption was 14.3 GWh per year. Since entering 2020, the consumption of electrical energy has decreased to an average of 4.3 GWh. The decrease in usage is because at Unhas, there is often a policy of working from home for employees and lecturers, and studying at home for students. The decrease of 4.3 GWh was due to the absence of student activities at Unhas. So that it can be illustrated that the use of electrical energy of 4.3 GWh is an academic administrative activity. This paper aims to deliver an overview of the impact of covid 19 on the decline in electricity consumption at Unhas. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, major changes have been observed in travel behavior. With the occurrence of multiple waves of this pandemic, Bangladesh as a least-developed country needs to deal with these changes, particularly changes in mode choice behavior. As Dhaka has been marked as a hotspot for the virus contagion, this paper has focused on the changes in mode choice behavior of Dhaka people while they are on the road. A web-based questionnaire survey was conducted for more than ten weeks to capture the information on mode preferences and perspectives on travel characteristics for commute and discretionary trips before and during COVID-19. After a careful, manual screening of the data, 571 responses were produced. Multinomial Logit (MNL) model based on a utility function has been used to investigate the significance of the socio-demographic attributes and travel characteristics of the trips on the mode choice behavior. The utility function contains gender, age, net monthly income (NMI), occupation, vehicle ownership, reliability, availability, and cost-effectiveness of the trip to calculate the maximum utility of the mode choice. The parameter estimates of the models identify the independent variables which are significant for mode choice behavior for this study. The MNL model of this study maintained overall prediction accuracies of 67.3% and 55% for commute trips and with an accuracy of 68.7% and 61.6% for discretionary trips before and during COVID-19 respectively. In addition, temporal comparison of trip characteristics perspective using stacked graphs show that public transports are the least reliable modes during the pandemic and NMVs are the most cost-effective modes in commute trips. Furthermore, this paper suggested some viable policy-making implications to cope with the current pandemic and relatable future national and global crises. Finally, the paper concludes by suggesting some future research insights. [ABSTRACT FROM AUTHOR]

Using a Learning Management System to generate, share, monitor, and maintain various kinds of education and creation content has proven a milestone in improving online learning. Since the first Learning Management System (LMS) emerged, significant technical developments made this platform a vital technology for curriculum management, rich content management, appraisal and assessment, and complex cooperation. The future expects several improvements in its development, processes, and execution, with many emerging fields of the study exploring different innovations relevant to the LMS. Online learning has been on the increase globally due to the rapid growth in technology in education. The 2019 Coronavirus disease (COVID-19) pandemic has implemented online classes for students in all colleges and universities. However, students are unknown about their ability to consider online learning. This paper presents how machine learning could support the Learning Management System's controlling to resolve student's feedback and queries associated with technical issues and problems when they have engaged in online classes. In this paper, a web-based online survey is conducted. Two hundred fifteen (215) students who have enrolled in various streams of Ph.D., Master, and Bachelor level programs in Colleges and University of Asian countries participated in this survey. Based on students' feedback, results are analyzed. A detailed analysis of possible solutions or suggestions is stated and proposed a Machine Learning-based LMS Model to handle students' problems and challenges efficiently during the online classes. [ABSTRACT FROM AUTHOR]

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials; the entrant may otherwise enter the premises after his/her hand has been sanitized. [ABSTRACT FROM AUTHOR]

Domestic violence against women, questions in a new way, the historicity of theory in its social, cultural, and political definitions. A common understanding of the causes of domestic violence can help communities develop more effective responses to the violence; such an understanding helps to avoid conflicting responses that could undermine efforts to protect victims and hold batterers accountable. This research paper tries to highlight the link between domestic violence that occurred to women during the COVID-19 lockdown, tries to expose the main reason for the increase of domestic violence against women these days, and increase in domestic violence will lead to the social and economic crisis. The data for this research was collected from the secondary data source through statistical reports regarding domestic violence against women during COVID-19, the reason behind the increase of violence and the nature of domestic violence against women across the world will be discussed in this paper. There exist a clear link between Covid-19 and domestic violence, which also impacts the economic and social crisis. [ABSTRACT FROM AUTHOR]

The epidemic of COVID19 has been unexpected for the global companies, leaving them in a challenging time trying to navigate the impact. It has changed the daily routine to a crisis response situation. It has been demonstrated that the Global Human Resource Management are prominence in such critical situations. The objective of this paper is to review the functions, management methods, management perspectives and management complexity of the Global HRM of organisations during the pandemic COVID-19. The paper shows that it is harder for the Global companies to navigate COVID-19 than the local enterprises with regard to managing their human resources. Besides, it has shown that Global HRM should consider the external factors such as cultural differences, economics and rules and regulations during this epidemic time. The contribution of this paper is its focus on the functions and strategies of global HRM to navigate the pandemic COVID-19. [ABSTRACT FROM AUTHOR]

This paper explores the disruption factor of practising WFH towards productivity that affected higher education in Malaysia during the outbreak of COVID 19. There is a disagreement with the WFH itself, but this impact can be mitigated or even reversed when people prefer integration. This research analyzed, reviewed and clarified in depth the technology readiness factor from previous studies. For the sake of a lack of understanding, this paper acknowledges and discusses the conceptual practice of working from home that contributes to productivity in higher education in a single setting in developing countries. This research examined the factor of the technology readiness of WFH that affect the productivity of Malaysia's higher education learning. The analysis findings will shed some light on our perception of the problematic technology readiness element in WFH practice. It also offers the current information corpus to researchers and a pledge to extend the study stream on WFH processes. It also provides researchers with the existing corpus of knowledge and promises to expand the research stream on WFH practices. The empirical findings will suggest the challenge of technology readiness during working from home and how to face it by the academician in possible ways to obtain considerable potential for improving productivity a higher education institution in Malaysia. [ABSTRACT FROM AUTHOR]

Understanding public responses to emergencies, including outbreaks of diseases, is necessary and significant. A demonstration of how to separate papers about the virus Covid-19 into different topics using topic modeling techniques in several studies is introduced in this research article. Inthe field of machine learning, topic modeling is a major topic. Though primarily, it is used to build models. It provides a quick and easy way to mine data from unstructured textual data, with samples representing documents.The most extensively utilized subject modeling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, model creation can be tedious and repetitious, requiring costly and methodical sensitivity analyses to determine the ideal collection of model parameters. Moreover, comparing models frequently require time-consuming subjective opinions. The topic models assign a probability to each word in the vocabulary corpus related to one or more themes (LSA, LDA). Several LDA and LSA models with varied degrees of coherence were generated, and the model with the greatest degree of coherence was selected. This experiment demonstrates that LDA outperforms LSA. [ABSTRACT FROM AUTHOR]

COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. [ABSTRACT FROM AUTHOR]

The majority of individuals on the earth have been affected by COVID-19 as a result of which researchers from across the psychological sciences are trying to document and examine the influence of COVID-19 in.a number of ways. This paper offers a rapid and comprehensive summary of high-quality studies that are available through early 2021 that examine the mental-health issues during COVID-19 pandemic. This is required because policy and practice aimed at assisting people live healthier and happier lives requires an insight into robust patterns of evidence. After the careful analysis of the available literature it was revealed that there was an increase in anxiety, depression, despair, and distress in the early stages of the epidemic. Throughout the first year of the epidemic, however, the rates of suicide, life satisfaction, and loneliness remained relatively steady. This paper also offers suggestions to support mental health both during and after the epidemic in response to these discoveries. [ABSTRACT FROM AUTHOR]

Coronavirus diseases 2021 (COVID-19) on going situation in Iraq is characterized in this paper. The pandemic handling by the government and the difficulties of public health measures enforcement in Iraq. Estimation of the COVID-19 data set was performed. Iraq is endangered to the pandemic, like the rest of the world besides sharing borders with hotspot neighbouring country Iran. The government of Iraq launched proactive measures in an attempt to prevent the viral spread. Nevertheless, reports of new cases keep escalating leaving the public health officials racing to take more firm constriction to face the pandemic. The paper bring forth the current COVID-19 scenario in Iraq, the government measures towards the public health challenges, and the future potential health risks that may emerge. [ABSTRACT FROM AUTHOR]

The spread of the Corona Virus epidemic (COVID-19)arouses the human normal life and seen a million of death around the world. Researchers are designing machine learning models to predict and judge the spread of COVID-19 epidemic. The significance of this paper is to predict the number of people who will be infected with COVID19 using machine learning algorithms. In this context, four models such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), Bayesian Ridge Regression(BRR), and Polynomial Regression (PR) have been employed to anticipate COVID-19 prediction in this paper. All models predict newly infected case's location wise including countries, states, cities etc. for the next 30 days. Furthermore, all models have applied on the time series dataset taken from WHO's official website and build a comparative study on five evaluation metrics such as RMSE, MAE, MSE, MAPE, and R2. The feature selection has been performed on the raw datasets via. Time series properties and demographic attributes. The results showed SVM outperformed the other models, while in the available dataset the Polynomial regression performs poorly in all the predictions. This model also scales on different geography, country and city. [ABSTRACT FROM AUTHOR]

The healthcare information dissemination on the diagnostic procedures and medicines is the need of the day to combat against Covid-19. The governments approved for the treatment protocol of Home Isolation for mild symptoms & no symptoms yet Covid-19 infected patients. The paper discusses the issues and concerns of an individual in treatment to Covid-19. Objective: To analyze with the existing research available on the issues and concerns in management of covid-19 among the patients. The science research databases revealed that self-medicated was exhibited among individuals with the Over-the-Counter (OTC) medicines. The information dissemination due to vast coverage brought in fear and ambiguity in the educated and uneducated population Methodology: The bibliometric analysis is carried to know the area of research undertaken by researchers across with the misuse of drugs, issues with home confinement and the concerns of public to get the vaccinations for the prevention of Covid-19. Conclusion: the paper concludes by stating the governments across have to formulate a strategy in disseminating information to public (an educative approach) but not misleading. Also, reveals the role of government, health-care providers, media-partners and the NGOs are high in disseminating information on drugs and their usage in prevention and management of covid-19. [ABSTRACT FROM AUTHOR]

The new coronavirus that is the source of the pandemic respiratory infection known as COVID-19 poses a major risk to the public's health. In this paper, we present a mathematical model of the spread of an infectious disease according to the characteristics of the COVID-19 pandemic. The proposed improvedmodel is developed on the AnyLogic system, which will be referred to as the SEAIR (Susceptible-Exposed-Asymptomatically Infected-Symptomatically infected-Recovered) model. The paper considers mathematical models that are used to analyze and predict the development of the COVID-19 pandemic, including time series analysis, equations of mathematical physics and simulation models, their various combinations and generalizations. Due to the novelty and complexity of the COVID-19 disease, the parameters of most mathematical models are usually unknown, and this leads to the need to consider and solve inverse problems. [ABSTRACT FROM AUTHOR]

In our paper discussed about the advantages and some difficult aspects that have impacted upon Uzbek universities and technical institutions (UzHEIs), which are continued to train students during of COVID-19 pandemic time. This reflects the impact of the unprecedented COVID-19 on the educational services of higher education institutions. Suddenly, there was an expectation that universities would comply with COVID-19 prevention measures, including social distancing and hygiene. As a result, many universities have switched to the remote transfer of knowledge and courses. This paper encourages university leaders to use online learning methods and distance education systems, which are support universities continue educational processes in the post-COVID-19. Paper summarizes by demonstrating main findings for university rectors and identifying directions for future research for academia. [ABSTRACT FROM AUTHOR]

This paper studies the impact of a covid-19 pandemic on the global economy. The empirical analysis of the work is based on data before and after (2019-2021) covid-19 and uses OECD data figures. The paper uses comparative analysis to find out changes during the pandemic and at the height of the pandemic, the global economy has passed the lowest point of the crisis, and it began economic growth in the leading countries of the world. This growth, like the crisis, developed in different ways in the economies of the US, EU and China. Our results show that during the pandemic, as a result of the lockdown, almost simultaneously, there was an abrupt increase in unemployment and a decline in production in all countries. In OECD countries, unemployment jumped from 5.2% in February to 8.5% in April, in the US - from 3.5% in February to 14.4% in April, in China - from 5.3% in January up to 6.2% in February. In China, both the epidemic and the economic crisis began earlier than in all other countries, respectively, the economy began to grow earlier. Our findings come to conclusion in a such way, despite the fact that, according to the forecast, the damage in the medium term will be less than after the global financial crisis, it is still significant. So, in 2024, the expected level of world production will be about 3 percent lower than before the pandemic. Countries differ in the degree of expected scarring, depending on the structure of the economy and the scale of the policy response. Emerging market and developing economies are predicted to face more palpable scars than advanced economies. [ABSTRACT FROM AUTHOR]

The sudden appearance of the extremely infectious COVID 19 virus has thrown the whole globe into disarray and compelled a transformation in the structure of the global economy so that it can adjust to the new conditions. The education industry, which formerly placed a significant emphasis on more conventional forms of contact such as face to face communication, has been obliged to embrace the concept of online learning. This shift, however, has not been an easy one, and the bulk of them are still having trouble converting into an e-learning platform that operates full-time. Both students and teachers need to make the transition from traditional classroom instruction to online learning platforms for the ecosystem of digital learning to exist. Parents, policymakers, and educational institutions need to play the role of facilitator to ensure that there is as little disruption as possible to the education process during this transition. Several educational establishments were first hesitant to abandon their time-honoured instructional approach in favour of the more modern approach of online learning, but with time, many were compelled to make the transition. The purpose of this essay is to suggest a developing model for efficient e-learning by including the important aspects that are necessary for online delivery under crisis conditions similar to those experienced by Covid. It also sheds light on the difficulties that important stakeholders are having in adjusting to the e-learning process as well as the potential solutions to those difficulties. Previous research has not concentrated on eLearning models that are applicable during times of emergency. Research Methodology – The authors want to conduct a comprehensive literature evaluation of a wide variety of publications and research papers related to Covid and its effect on online education in order to achieve their goal of developing a conceptual paper. In the event of a crisis comparable to a pandemic caused by the Coronavirus, an online learning paradigm is suggested for academic institutions. [ABSTRACT FROM AUTHOR]

In 2019, the TL0200559 project entitled "Safe Cities for Pedestrians and Seniors" was launched. Part of the solution of this project was also a survey and analysis of the use of pedestrian roads in selected cities. The town of Chotěboř was chosen for pilot processing. In the first half of 2019, a questionnaire was compiled for the questionnaire survey. At the same time, a local survey was conducted. In October and early November, data were collected by a questionnaire survey. In the following five months, the digitization of paper forms of the questionnaires took place. Their evaluation was interrupted by ongoing quarantine measures due to SARS-CoV-2 (Covid-19). In 2021, quarantine measures (especially the return of children and students to full-time school attendance) were relaxed to the extent that it was possible to return to the standard scientific research work and analyze and verify the obtained data by re-conducting local surveys. The procedure for compiling the questionnaire for this survey, the course of the actual data collection, and the results of this pilot survey are presented in this paper. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

The pandemic COVID19 that has been emerged around the world and Induced by a member from a family of corona viruses named (SARS COV-2) that has appeared in Wuhan in 2019 and can lead to sever acute respiratory syndrome with grave complication and even death of the infected person. The detection of persons infected with the virus is most important, as the virus as it can be easily transmitted from one to another and the person infected with the virus will also not know that he is infected until he has a number of symptoms. The detection of the virus is performed in this paper using deep learning as part of monitoring this outbreak, researchers began using software computing techniques to diagnose cases using chest CT-Scan images and X-Ray for the lungs, scan body temperatures and classify the severity of the disease. The research objective is to detect three classes: Covid-19 positive, Normal, and eumonia based on both X- Ray and CT-images. The importance of this research is to support the medical staff in Mosul city, in particular in the case of a heavy workload. The detection technique begins with some pre-processing tools for image processing to strengthen contrast, and then deep learning as the Convolution Neural Network (CNN) is used for detection. CNN will be based on the public dataset of COVID19 for training and forecasting other cases for the future. The programming language used in this paper is Matlab and the results of this study indicate that the best accuracy is obtained from the model with 99.55 %, 99.09 % sensitivity and 99.48 % precision of chest CT, but when the X-ray dataset is used The proposed model has achieved a classification accuracy of 85.58 %, 83.47% sensitivity and 87.33% precision. [ABSTRACT FROM AUTHOR]

Big data analytics is used to predict and analyze the data which is available in huge amount and having structured, unstructured and sometime semi structured values. Here in our research data analysis will be done on the behalf of data available which is nifty-50 stock market data. We are going to analyze the impact on nifty-50 due to covid-19. We have collected the dataset form Kaggle.com. The techniques used here is apache spark and language is Scala. In our research results will be shown on the basis of analysis done using the closing price and opening price of different stocks in different months and weeks. The results will be expressed in the form of graph using data visualization technique in tableau. [ABSTRACT FROM AUTHOR]

The sudden spread of the Covid-19 virus all over the globe and eruption of the infection requires an epidemiological examination of the malady in a short period and boost awareness of worthwhile interventions. In this paper, three machine-learning classification models that predict positive SARS-CoV-2 contamination based on symptoms like fever, cold, tiredness, etc are demonstrated. Covid dataset from Kaggle has been taken for the study. The comparison of different models based on the accuracy of the results given by various models is done. These models are SVM, KNN, and Decision Tree. The dataset has been randomly partitioned in which 80% data is reserved for training and 20% data is used for testing. For the validation purpose in KNN 10-fold cross-validation method is used. All three classifiers give efficient results. The reason for this much accuracy is that first of all the testing is done using supervised learning and secondly, only those attributes of the dataset are taken for model construction which has a high correlation with the target field. The symptoms such as nasal congestion, difficulty in breathing, tiredness are highly correlated with the target field. In this research work, Python programming language which is a very powerful language for machine learning is used. [ABSTRACT FROM AUTHOR]

Over one billion students from 150 countries are impacted due to the COVID-19 driven lockdown and educational institution closure as per the report shared by UNESCO. Innumerous efforts have been put up by the Governments of every country to control the impact of the COVID-19 but unfortunately, there is no vaccine available till now so social distancing is the only way to have an upper hand on it. The COVID-19 pandemic has affected the educational systems worldwide and led to the widespread closures of Educational Institutions across the globe. Numerous educational reformers have long-held out the expectation that information and computer technologies (ICTs) will play a crucial and integral role in bringing the long-needed revolution to teaching and learning pedagogies. The situation which took place due to COVID-19, the Online Education Systems and Platforms (OESP) are playing an important role to continue with the teaching processes in the educational institutions during the worldwide closure. Statistical analysis of the covid situation on the teaching-learning process has been depicted in this paper. The present paper focuses on the change of action in the education system and the exponential growth that happened in the Online Education Segment in the 21st century. [ABSTRACT FROM AUTHOR]

The recent outbreak of COVID-19 across the globe has been a challenge not just to the health & well-being of people. However, it has also led to a downturn in the economy throughout the world. The purpose of this study is to thoroughly investigate the possible use of emerging technologies such as robotic process automation (RPA) and artificial intelligence (AI) in order to find ways to tackle corona virus, its impact on the economy, and the people. The other technology involved is RPA which is a type of process that can easily and efficiently make it convenient for any machine to handle a repetitive task. When these two technologies are coupled, they can be used in a wide range of applications. This paper explains how RPA and AI can separately, used in various industries and sectors to tackle and effectively reduce the corona virus pandemic's negative impact. [ABSTRACT FROM AUTHOR]

The presented paper deals with data integration, filtering, sorting and aggregation of Covid-19 data. The data file contains eight data fields and for the design of the proposed model for data integration and processing each of them is configured in data type, format and field length. Being designed in the Talend Open Studio, the model concerns the performance of six main tasks: data integration, data filtering, data sorting, data aggregation, data sorting after the process of aggregation and result display. [ABSTRACT FROM AUTHOR]

This paper is a study about COVID-19 cases in India to analyze and visualize the spread of COVID-19 cases in INDIA. For the analysis, the concept Exploratory Data Analysis (EDA) and some well know prediction models like Prophet Model, ARIMA Light GBM, Random Forest Regressor, XGBoost Regressor have been used to get the correct analyzed result. Furthermore, it has been observed that these prediction models and tools are helpful to analyze and visualize the COVID-19 situation in India and reason to spread it in optimal ways. Here, Matplotlib library has also been used to show the proper output in the form of graphs and charts. Analysis has been done on different Age/Gender Group. The Spike of Cases in India, State-wise Insights, the reason for the spread of COVID 19 cases. So, the main objective of this paper is to do analysis to know the reasonforCOVID-19 cases in India, State-wise Insight and State-Wise Testing, Prediction. To understand the optimal result of growth factors, the concept of Prophet Model ARIMA, LightGBM, Random Forest Regressor, XGBoost Regressor have been used on the data set. In addition, the data set includes the parameters of Age Group, Spike of Cases and Testing in India. From the literature survey, it has been observed that authors have used two and three techniques to analyze the result, but they have not used the methods we stated earlier for optimal result. This paper will provide the State-wise Insight and State-Wise Testing going in India with reason for spreading of COVID-19 cases. [ABSTRACT FROM AUTHOR]

The world has been living with the Coronavirus Disease 2019 (COVID-19) pandemic for more than two years and it has altered humanity in numerous ways, by the implementation of lockdowns, border closures, other movements, and social restrictions. The pandemic has also led to the deferral or termination of all events including sports, religion gathering, political elections, and social events. Industries, in particular oil and gas, entail to react within a short span of time to productively manage the stakeholders. As workforces began to work from home, business meetings, discussions, and engagements were swapped by video calls, and businesses around the globe began their contingency planning. The stakeholder management becomes critical when there is no standard procedure available under the circumstances, thus this paper aims to propose procedures on improving the stakeholder management in petrochemical projects during the COVID-19 pandemic. The paper assesses the impacts of the pandemic on stakeholder engagement in petrochemicals projects, examines current coping strategies in different projects, and recommends several practical actions to promote and maintain meaningful exchange with stakeholders in times of social distancing and lockdowns. The outcome of the paper enlists various improvement levers for a petrochemical company to adapt to ensure project stakeholders are satisfied and well-informed. [ABSTRACT FROM AUTHOR]

In the present times, Covid-19 is a great concern for every country as it is spread in multiple countries across the globe. In this paper, we will try to estimate potential consequences of SARS-CoV-2 transmission through a compartmental model. As many parameters may be observed to predict about number of deaths and covid-19 cases due to SARS-Cov2. We will use SEIRD model to predict basic reproduction number with the help of collected data of confirmed Covid-19 cases, deaths and the clinical parameters. In the next part of paper, we will try to make forecast about the number of deaths in India. [ABSTRACT FROM AUTHOR]

World Health Organization (WHO) recommend usage of mask as an operative prevention to avoid spread of Covid 19. Various reports present that wearing face masks top virus transmission and reduces the risk of transmission. This paper focus on cloud-based AI methodology for the detection of face mask. A mask detection data base with two categories of information includes with mask and without mask images that are stored in cloud data Centre via webcam. The proposed work uses Full Convolution Neural Network (FCNN) and Gradient Boost Machine Learning (GBML) model for classification and detection mask. This paper focus on the objective of to recognize whether the person on image or video stream is wearing a mask or not and not wear properly. [ABSTRACT FROM AUTHOR]

Several papers on the dynamics and early identification of COVID-19 by mathematical modelling have been published in the last two years. This review will look at the quantitative analysis and dynamical behaviors of novel coronavirus, with a focus on the Caputo fractional derivative. Currently many mathematical models are used to investigate the Novel coronavirus under Caputo fractional order system of differential equations. In this paper, we have focused on reviewing the Caputo fractional model of Covid 19, local stability and optimal control. The purpose of this work is to provide the research community a complete overview of the Caputo fractional derivative approach used in these investigations. [ABSTRACT FROM AUTHOR]

The social media and internet has sparked an unparalleled spread of incorrect information. The world relies on information. It becomes even more apparent during times of emergency or crisis, like the pandemic of COVID-19. COVID-19 outbreak is a continuing disaster that is predicted to now have a long-term impact on almost all countries throughout the world, including mixed results, thus it seems to dominate many other incidents. Many models and innovative techniques have been introduced for the purpose to deal with the detection of fake news. This paper firstly presents a clear picture of different categories of fake news including rumours (which can be either fake or real) in a tabular form and classifies them on the basis of parameters like its genuineness and intentions. Further the paper discusses several false news detection techniques proposed to track COVID-19 related fake news and compares them on the basis of parameters like learning technique, modal approach, dataset used etc. Hence the work presented in the paper provides adequate knowledge of the need, categories; different state-of-the-art technique used and recommends some effective countermeasures to deal with misleading information. [ABSTRACT FROM AUTHOR]