Playing the video games during COVID-19 pandemic and its effects on player's well-being

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players' well-being and additionally on the effects of gamers' health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life1. © 2023 IEEE.

Classification of COVID-19 Tweets

Classification is a vital work to human beings in day today life as it breaks down complex subjects. In the same way, text classification is very important to understand and realize the subject of the text. © 2021 Copyright for this paper by its authors.

The impact of the COVID-19 Pandemic on Higher Education-A Case of Success of ICT Educational Programs

The COVID-19 pandemic has had a big impact in all human being activities;education context was especially affected. A disrupted shift from face-to-face teaching-learning practices to learning activities and/or pedagogical activities supported by digital platforms or social networks was lived. Limitations in Internet access and mastering of technological media impacted the pedagogical, psychological, and affective side of the students. This paper presents a study on the changes caused by the COVID-19 pandemic in terms of school dropout;an analysis was carried out on the percentages of the indicator of non-enrollment of each one of the educational programs at a Mexican university. The results showed that educational programs with an ICT orientation beat this contingency in a better way. This encourages us to continue this research to investigate how new professionals are getting ready for the fashion of working from home. © 2022 IEEE.

Evaluation of Various Machine Learning Based Existing Stress Prediction Support Systems (SPSSs) for COVID-19 Pandemic

COVID-19 profoundly impacts human beings in various ways, i.e., psychological, socioeconomic, fear, social isolation, etc., augmenting the prevailing inequalities in mental health. The role of machine learning (ML) can be understood through its various potential applications in Stress Prediction in mental health. This literature survey uncovered various related articles, which were utilized to determine the essential structure for analysis. The gathered information helped in providing the new ideas and the concepts, which were incorporated with the support of literature and classified under broad themes based on mental health during the pandemic COVID-19. This study emphasized assessing various existing "Stress Prediction Support Systems” based on machine learning. This article also addresses the mental health issues that were emerged due to COVID-19 pandemic, further;also analysed the previously available stress prediction Machine Learning based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

GeauxTrace: A Scalable Privacy-Protecting Contact Tracing App Design Using Blockchain

Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second. © 2022 IEEE.

Smart doors - A Machine Learning Approach to monitor body temperature and detect face mask

In recent years Internet of Things(IoT) plays a vital role in automation. Nearly millions of people have been affected by the threatening disease COVID-19 (coronavirus), who are either sick or being killed due to the spread of the disease. The densely populated world possesses a threat of spreading such infectious diseases rapidly. So, there is a need for supervision of people's health status working in large organizations/institutions. This paper emphasizes the automation in monitoring the temperature of human beings and face mask detection so that spreading of infectious disease like COVID-19 can be brought down. The proposed solution aids the institutions/organizations to find out the infected person and take necessary precaution at an earlier stage to avoid spreading of the disease to the other healthy persons. This prototype overcomes the drawback of existing ideas in which affected individuals are frequently exposed to high radiation devices. The idea comes with the provision of ensuring the operation of the system only in the presence of human beings and also it paves the way to install a low cost set-up. The system makes use of sensor technology to spot the common symptoms of the disease and machine learning algorithm to ensure people are wearing masks. The obtained data gets stored on the cloud and analyzed by the organizations/institution's authorities. The lid present in the entrance is opened for the people with normal constraints. The whole scheme helps the larger organizations/institutions to avoid spreading of infectious diseases. © 2022 IEEE.

Design and implementation of IoT enabled ultraviolet C(UVC) irradiation-based sanitizer system for public transport with remote monitoring

During the ongoing Covid19 Pandemic, it is a need of the hour to have a fully sanitized public transport system, free from Covid19 virus. Public transport is one of the major segments responsible for the spreading of covid19 like pandemic infections. It is required to sanitize public transport before every new trip. During the Covid19 pandemic, human beings are forced to live with viruses, hence making disinfection a routine work and making disinfection more user-friendly and efficient is the main objective of this research work. Spraying alcohol-based solution inside public transport is not suitable due to fire safety and other reasons. Ultraviolet C (UVC) based disinfection is more suitable in such applications, as disinfection can be done anytime, anywhere without damaging the interiors of the vehicle. It can kill viruses or bacteria in less than 20 seconds and can disinfect any surfaces, seats, or any point of public contact inside any public transport by effectively killing bacteria, fungi, dust mites, viruses, etc. This research paper aims to offer the design and implementation of an Ultraviolet C irradiation-based sanitizer system for the public transport system, which can disinfect the public contact surfaces inside the public transport, to make our travel safe from Covid19 like viruses. The sanitization system is developed using a NODEMCU microcontroller, UVC led arrays, switching circuit, PIR sensor, and mobile app. Ultraviolet sensor is used to read UVC irradiation index inside the transport to measure the effectiveness of the developed system and real-time data is linked with internet cloud for remote monitoring and control. © 2022 IEEE.

Analysis of Quality Management and Sustainability in Sports Services During the Covid-19 Pandemic

With the arrival of the pandemic, the management of sports services was affected, and the sports infrastructure closed its doors to the community in general, which has produced great losses in material resources;therefore, the objective of the study was to determine the difficulties and benefits offered by post-pandemic sports services, through the application of a test of perceived quality in sports services in the province of Cotopaxi. Within the methodology used, qualitative–quantitative research stands out, with exploratory-descriptive levels in a sample of 125 users of sports centers;the results obtained drive the strengthening of sports spaces towards the provision of quality services, even more since it is necessary for the human being to have a strengthened immune system through the practice of physical activities, it is concluded that quality sports services depend directly on the budget allocated for an adequate sport management for the benefit of the community. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Privacy-Preserving Approach to Identify COVID-19 Infection Origins via Volunteered Share of Health Data Records by Mobile Users

Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of 'heavy centre' to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small. © 2001-2012 IEEE.

Impact of human'human virus transmission in an air-conditioned room with proper ventilation system

As we are probably aware of certain infectious diseases that transmit from body to body because of perspiration or respiration of air from a human being containing strains of the infection, the goal of this investigation is to see how the infection is getting spread from a human residing in a closed area provided with air conditioner and with an appropriate ventilation framework that need to be involved to diminish infection dissemination in this enclosed area. Considering the present COVID-19 situation, it is important to discover the effect of infection spread to an individual contagion source. An appropriate CFD-model giving analysis of infection transmission from individual to individual in an air-conditioned room would give results to understand such situations. Likewise, this examination would help in determining the velocity, temperature, and particle contours in a characterized walled area. Besides, we have displayed various nooks utilizing different ventilation frameworks to discover which framework would give better outcomes to decrease infection transmission. Our investigation would provide how varying flow rates in a room at an outlet could be effective in reducing virus dissemination, as this model could be applied to cafes, cinemas, inns, and above all emergency clinics where individuals remain in an enclosed air-conditioned room. © C. Manas et al., Published by EDP Sciences, 2022.

The Best of Both Worlds: Mixed Systems with ML and Humans in the Loop to Combat Fake Information

Dealing adequately with misinformation is one of the societal challenges of our times, since misinformation has been proven to be harmful for people, societies, and democracy. Improving Artificial Intelligence algorithms underlying information retrieval and recommendation systems is a path that must be encouraged;however, this is not the only path ahead and, above all, it can be combined with other approaches. In addition to the known limitations of Machine Learning model results, which cannot be guaranteed to be 100% accurate, ethical issues are raised when an algorithm acts as a censor of what information a human being can and cannot access. This paper discusses some recent initiatives during the COVID-19 pandemic to improve the quality of information delivered to users that have two characteristics in common: firstly, they are technically simple, hard coded, and do not involve any AI;secondly, they represent a preliminary step in a broader perspective that goes beyond technical improvements to promote critical thinking among those receiving the information. Although they can be seen as preliminary cases of how to deal with misinformation, they seem to be effective and they point towards more interdisciplinary solutions to the contemporary issue of misinformation, possibly bringing other developments to ethical, Human-Centered AI. © 2022, Springer Nature Switzerland AG.

Determination of the Factors Influencing Proper Face Recognition in Faces Protected by Face Masks, an Analysis of Their Algorithms and the Factors Affecting Recognition Success

One of the many collateral effects that the entire planet has suffered with the appearance of covid-19 and the declaration of this as a pandemic, has been evidenced in the treatment of facial recognition algorithms and the variety of applications, both commercial and for exclusive use in research for this same purpose. For the time being, there are already reports of effectiveness with respect to the analysis of these algorithms and that are paving the way to understand the degree of affectation that the use of face masks can have on facial recognition processes. In this context, it is important to determine how it is possible that throughout these almost two years of confinement and use of face shields and masks, the human being, regardless of his age, has been able to maintain its advantage over artificial intelligence systems when recognizing the face of a relative, friend or simply an acquaintance;that is why, the present study aims to evaluate some face recognition systems in order to determine the main problems faced by these algorithms when recognizing a face protected with a mask. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Precise Epidemic Control based on GeoHash

Coronavirus Disease 2019 (COVID-19) is a highly transmissible and pathogenic coronavirus that emerged in late 2019 and has caused a pandemic of acute respiratory disease. Before vaccines are widely used or the invention of specific medication, many measures have been taken by human beings to prevent the spread of the epidemic. Quarantining infected groups and locking down high-risk regions are common means used by the latest medical experience. As such measures are generally carried out under administrative divisions, issues of imprecise epidemic control and unquantifiable risk warning are exposed gradually. In order to better achieve the purpose of precise epidemic prevention and control, we propose a kind of dynamic block division technology based on GeoHash which can be used to monitor, mark out and control the epidemic regions. By using GeoHash, we divide the earth map into connected dynamic blocks. Dynamic blocks are easily visualized in geographic information systems (GIS) equipped in electronic devices. GeoHash blocks are dynamically overlaid on the map as grids. Each block contains essential epidemic-related data and important features which are concerned by professional medical work. Quantitative analysis of epidemic data is carried out on each block. Our research shows the analysis results can support decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. Such research can be applied not only to COVID-19 but also to other infectious diseases. © 2022 IEEE.

Analytical Approach Toward Face Mask Detection Using Artificial Intelligence Techniques

By continuous hike of the deadly COVID-19 pandemic, the lifestyle of an individual has switched and changed all over the globe. Every individual has found it necessary to use a face mask in these situations. Identifying individual is wearing a face mask is very challenging due to wave of the deadly COVID-19 pandemic. The author proposed an approach in this study review work that would limit the evolution of the COVID-19 virus by personal identification who is not covering up any face mask. Many pieces of research have showed that wearing a mask reduces the possible chance of viral transmission of this life-threatening coronavirus and provides a sense of protection. The research during this zone has hiked over the past years. A typical review of the literature is studied to evaluate whether or not human beings are wearing masks, and based on these reviews, a modified analysis is done to detect which approach is feasible. This review included various search methodologies, too many research papers were recognized out of which seventeen are relevant papers. This paper will assess the research progresses related to the facial masks of an individual. It also helps the author to review out the ongoing and the forthcoming scenario of this research which have been working on facial mask detection using artificial intelligence. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Privacy-Preserving Approach to Identify COVID-19 Infection Origins via Volunteered Share of Health Data Records by Mobile Users

Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of ’heavy centre’to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small. IEEE

The crisis of the metaphysical foundations of human existence as a global problem of post-modernity and the ways of managerial solutions

The relevance of the study of the crisis of the metaphysical foundations of human existence is due to the fact that humanity is in a crisis of global development of the world associated with the Fourth Industrial Revolution 4.0, the technological development of the world, the demographic and environmental crisis, the epidemic of the coronavirus COVID-19. The purpose of the study is to conceptualize the socio-philosophical reflection of the crisis of the metaphysical foundations of human existence, which is a global problem of our time, which threatens our civilization, is associated with economic and social development, with many problems, including the problem of COVID-19. The objectives of the research are to identify the problems of the metaphysical dimensions of human existence and find ways that promise to lead to great achievements - to raise living standards, create new economic opportunities, solve anthropological, existential and ontological problems of human being associated with the real de-objectification of human existence, loss of the integrity of self-determination.The scientific novelty of the research is that it presents a solution to new problems of the crisis of the metaphysical foundations of human existence and ways to solve them.

Systematic Review on Various Techniques of Android Malware Detection

Smartphone has become the 4th basic necessity of human being after Food, Cloths and Home. It has become an integral part of the life that most of the business and office work can be operated by mobile phone and the demand for online classes demand for all class of students have become a compulsion without any alternate due to the COVID-19 pandemic. Android is considered as the most prevailing and used operating system for the mobile phone on this planet and for the same reason it is the most targeted mobile operating system by the hackers. Android malware has been increasing every quarter and every year. An android malware is installed and executed on the smartphones quietly without any indication and user’s acceptance, that possess threats to the consumer’s personal and/or classified information stored. To address these threats, varieties of techniques have been proposed by the researchers like Static, Dynamic and Hybrid. In this paper a systematic review has been carried out on the relevant studies from 2017 to 2020. Assessment of the malware detection capabilities of various techniques used by different researchers has been carried out with comparison of the performance of different machine learning models for the detection of android malwares by assessing the results of empirical evidences such as datasets, features, tools, etc. However the android malware detection still faces several challenges and the possible solution with some novel approach or technique to improve the detection capabilities is discussed in the discussion and conclusion. © 2022, Springer Nature Switzerland AG.

An Ensemble Method for Covid-19 Positive Cases Detection using Machine Learning Algorithms

In recent days, people from all over the world is facing a severe issue related to their survival of life. Due to Covid-19, many people lost their loved ones. It was a major threat not only to human beings but also to all the living creatures. Many Organizations are trying to find out the solution and at the same time, the corona affected people's rate is also increasing tremendously. Thousands and thousands of people lost their lives. The reason behind the increased death rate is that, many people were not aware of whether they are infected with the Covid virus or not. So, to solve the issue and to take remedies before it is too late, a method is proposed which helps people to identify whether they were affected with the Covid virus or not. The data in the dataset contains 152 Covid-19 positive cases and 1143 negative or healthy cases in India. In order to identify the positive and healthy cases efficiently, preprocessing is done on the collected data and features are extracted using MFCC before classification. Using several classifiers, the level of accuracy has been predicted. The classifier which gives highest level of accuracy is considered as the best classifier and using the classifier, Covid-19 positive cases are identified. Oversampling is performed on the extracted features in order to provide good accuracy. Several metrics like precision, recall, confusion matrix, Mathew's Correlation coefficient, F1-score and accuracy has been calculated to produce efficient results. Finally, K-NN classifies the Covid affected patients with 92% of accuracy, also scored good results in all the metrics calculated. © 2022 IEEE.

MISOhungry: A Platform that Encourages Easy Access to Recipe Search, Management, and Traditional Recipes

Cooking has played an essential role in the growth of culture and civilization for the past 1.8 million years. However, the lockdown in various countries, including Germany, has prompted people to improve their health and well-being due to the coronavirus pandemic. While doing this, searching for recipes becomes one of the popular and essential activities as it allows people worldwide to prepare dishes from various countries. But finding recipes on the internet is like searching in the wild with thousands of recipes available for a single dish. Traditional recipes are essential in a human being’s life. However, for students away from home or working young people who have little time to cook, many recipes have been forgotten for a long time. Therefore, MISOhungry gives solutions to both the user groups through this platform. The recipes provided are by scraping data from online food blogs to create recipes complete with ingredients nutritional information. On the same site, youngsters may also access traditional recipes provided by the elderly. Studies show that sharing recipes linked with memories stimulates generative activity in older adults and makes them happy later. The study demonstrates that the platform is accessible to both user groups, young people are interested in receiving traditional recipes, and they would like to use this platform which directly bridges the generation gap in recipe sharing, search, and management. MISOhungry promotes the idea of “Happiness is Homemade” by making cooking more accessible to both user groups. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.