Detection of COVID-19 for image dataset by using Deep learning algorithm

This COVID-19 study uses a new way of looking at data to shed light on important topics and societal problems. After digesting specific interpretations, experts' points of view are looked at: We'll study and categorize these subfields based on their importance and influence in the academic world. Web-based education, cutting-edge technologies, AI, dashboards, social networking, network security, industry titans (including blockchain), safety, and inventions will be discussed. By combining chest X-ray images with machine learning, the article views provide element breadth, ideal understanding, critical issue detection, and hypothesis and practice concepts. We've used machine learning techniques in COVID-19 to help manage the pandemic flow and stop infections. Statistics show that the hybrid strategy is better than traditional ones. © 2022 IEEE.

FRD: Few-shot rumor detection for the novel coronavirus pneumonia epidemic

With the full arrival of the digital era, fueled by both information technology and business marketing, rumors are produced and spread endlessly on social networks. During the recent novel coronavirus pneumonia epidemic, online rumors have continued to flourish. Most existing studies on traditional rumor detection rely on a large number of features in practical applications. However, the current severe epidemic scenarios have limited rumor information features, and it remains a challenging problem to detect epidemic rumors with high accuracy using only limited information. As a result, we propose a novel Few-shot Rumor Detection model (FRD) for the novel coronavirus pneumonia, which is combined with meta-learning to be able to accurately identify rumors as soon as possible in crises. Specifically, we started by using the BERT+BiLSTM combination for rumor text feature extraction and representation to generate the historical rumor sample-wise vector and epidemic rumor sample-wise vector;secondly, the prototypical network was introduced to summarize the historical rumor data, and the feature vectors of samples belonging to the same category were averaged to obtain the prototype representation of historical rumor category;finally, we utilize the modified cosine similarity measure function to calculate the distance between the class-wise vector of historical rumor text and the sample-wise vector of epidemic rumor, and complete the rumor detection according to the nearest neighbor method. Our experimental results on English datasets show that the FRD rumor detection model proposed in this paper is superior to other baseline algorithms in terms of accuracy, precision, recall and macro F1 value. From the comparison of experimental results, the FRD model can effectively improve conventional rumor detection methods, and better realize the early detection of sudden epidemic rumors. © 2022 IEEE.

The Impact of Online Banking Services on Customer Spending Behavior

This paper is aimed on examining and testing the effect of mobile banking services on customer spending behavior and the changes caused by this influence of the COVID-19 pandemic in The Kingdom of Bahrain. In this study, the online banking services is the independent variable, where customer spending behavior is the dependent variable, COVID-19 pandemic is the moderator variable of the study. The study is focused on examining and testing the impact of the online banking services toward the consumer spending and saving behavior on making decision either to buy or save. The data will be collected in a primary form where the questionnaire survey method will be adopted to gather responses from bank consumers in the Kingdom of Bahrain and will be analyzed through the Statistical Package for the Social Sciences software (SPSS) tool by using the built-in functions such as regression, mediation, scale, correlation, coefficient, significant, and moderation analysis. The results of the study will show the acceptance and rejection of the hypotheses of the study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Integrated Neutrosophic SNA-MABAC Approach for Blockchain Applicability Evaluation in Sustainable Supply Chains

The outbreak of the novel coronavirus pneumonia and the turbulent international situation in recent years have seriously disrupted the normal operation of the entire supply chain (SC). As an emerging technology, blockchain is characterized by decentralization, reliability, transparency and traceability, which can be effectively applied to solve social, environmental and economic concerns and achieve sustainability of supply chain. However, whether blockchain is suitable for every function of a sustainable supply chain (SSC), or what function is best suited for the application of a set of blockchain criteria, can be viewed as a multi-criteria group decision-making (MCGDM) problem. This paper presents a combined MCGDM technique utilizing the social network analysis (SNA) and Multi-Attributive Border Approximation Area Comparison (MABAC), for selecting an appropriate function of SSCs to implement blockchain technology with Neutrosophic information. The framework gives quantitative consideration to the weight of relevant blockchain criteria and decision makers under high uncertainty. This study can also facilitate the effective allocation of resources and enhance the competitiveness of SSCs in the coordinated planning of various blockchain deployments. © 2022 IEEE.

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal;nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model © 2013 IEEE.

The Role of the Russian Social Network Vkontakte During the Covid-19 Pandemic in Teaching Students of Agro-Industrial Faculties the First Foreign Language

This work is devoted to interactive methods of using the Russian social network VKontakte in the study of the first foreign language by students of technical universities of agro-industrial faculties. Currently, training is closely connected with modern Internet technologies and social networks. Social network is one of the forms of distance learning. In our article, we described an experiment conducted with the first-year full-time students of the Don State Technical University studying their first foreign language. The goal of our research was the identification and description of the most influencing forms and methods for the motivation of students in teaching the first foreign language. In the presented research work, we used the empirical method and the method of pedagogical research. We have explored a variety of social networks that are most often used by students, namely: Instagram, Odnoklassniki, Twitter, YouTube. We conducted an experiment and made an analysis of the obtained data. The material for our analysis was the social network VKontakte. We have created an open training group, in which students of the agro-industrial faculty of full-time education studying the first foreign language were signed. As a result of the inquest, we have identified the most relevant social network among first-year students. It has been established that Internet sources influence the interest and motivation of students in learning the first foreign language. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ICT in the university environment: Twitch and Instagram as learning tools

In recent days, Information and Communication Technologies (ICT) have been incorporated into the teaching-learning process. The study aims to achieve greater student engagement, and satisfaction using Instagram and Twitch platforms as methodological tools in teaching. The sample collects 50 students from the degree of a primary school teacher at the University of Valencia. The evaluation instrument has been based on direct observation and the use of the survey Barrado et al. (1999), evaluating the practical program, and the degree of satisfaction of the students. The results show the benefits of accessing learning at any time. Active participation in the platforms has made possible the involvement of students, receiving immediate feedback, and allowing them to build continuous learning. IEEE

Toward the Remote World in Which Life Is Many Times More Enjoyable—From "Remote by Necessity” to "Remote by Choice”

The novel coronavirus (COVID-19) pandemic has forced people to change their lifestyles and become remote. To enable people to choose a remote lifestyle or combine remote and real-world lifestyles so as to enrich their lives, NTT Human Informatics Laboratories is aiming to enable the Remote World unique to the post-pandemic era in two ways: (i) identifying and analyzing issues specific to new lifestyles from a wide range of perspectives including not only technology but also social science and humanities and (ii) promoting research and development regarding such issues. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions. © 2020 Tsinghua University Press.

Post-Pandemic Sentiment Analysis Based on Twitter Data Using Deep Learning

Since January 2020, COVID-19 has been spreading over the world and has been declared a pandemic. Nation and society are growing scared of it as fresh instances and mass deaths increase daily. India was one of the major countries to suffer the consequences of COVID-19 during that phase as multiple waves hit India. Many social media channels were being used by people from all over the country to discuss this pandemic and its aftereffects. One of the most popular ways to share opinions or judgments today is through social media. Therefore, machines are continuously being developed to analyze what people post on social networking sites like Twitter, Facebook, Instagram, and other platforms thanks to advancements in current computing technology. Based on their mood, these ideas or points of view can be grouped and examined. In this paper, we used tweets collected from Twitter to analyze the sentiment that people conveyed on social media after the second wave of Corona Virus. The sentiment of the tweets has been divided into five categories: "Strongly Negative", "Negative", "Neutral", "Positive"and "Strongly Positive". First, we classify data using Python's Vader. We have trained a model using our own labeled dataset and evaluated its performance using Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). © 2022 IEEE.

Knowledge Sharing in Private Social Solidarity Institutions during Pandemics

The COVID-19 pandemic period resulted in a global crisis, whether in the economy, personal or professional life. Because of the pandemic, people and institutions had to change the way they did things. Even though people are becoming more aware of the value of knowledge and it is becoming more common in some institutions, knowledge management methods are still not well known in the social sector and as a key tool for institutions in crisis. Considering the beneficial role that knowledge sharing (KS) practices play in organizations, the current study aims to investigate the impact of KS practices in Portuguese private social solidarity institutions in adapting to the COVID-19 pandemic period. To achieve the purpose and considering the exploratory nature of the research, semi-structured interviews were conducted with fifteen professionals from four private social solidarity institutions in northern Portugal. Nvivo processed the interviews. Because COVID-19 is new, there is no research on knowledge sharing in these institutions, so the study can be considered as original. Before and during pandemics, the presence of knowledge sharing practises, such as the integration of new employees, the proactivity of learning, the sharing of new ideas and mistakes, and the sharing relationship between peers and superiors and other institutions, was observed through the interviews. In this study, we discovered that trust, communication, technology, and social networks, as well as the role of leadership in creating an environment conducive to formal and informal sharing, were elements that facilitated knowledge sharing practises, even throughout the pandemics. During the interviews, both technical directors and employees acknowledged the following: the relationship between superiors and employees in decision-making processes;recognition, feedback and incentives from leaders and the presence of formal and informal communication networks. When it came to sharing, which could happen in a formal or informal setting, employees seemed to prefer informal interactions.To summarise, the institutions were able to adjust to the limits imposed by the pandemic, and the basic practises of KS are part of the daily routine of the organisations analysed. © 2022, Academic Conferences and Publishing International Limited. All rights reserved.

Journey on Flattening the Curve: The Actors and Networks of Indonesian Contact Tracing System

The contact tracing system implemented in Indonesia is called SILACAK, a modified version of DHIS2. SILACAK as a contact tracing system platform created a dashboard following national guidelines, which is a tool to monitor the COVID-19 cases in Indonesia and support decision making. The role of SILACAK has throughout several journeys and some changes that can be implemented by several actors involved such as the developer team, government, health workers, and also society. To analyze the connection between all aspects that affect the SILACAK system, A framework is needed to deeply review which can be a historical artifact on the system. Actor- network theory (ANT) is the best practice to know about how networks come into being, to trace what associations exist, how they move, how actors are enrolled into a network, how parts of a network form a whole network and how networks achieve temporary stability. © 2022, IFIP International Federation for Information Processing.

A Common First-Year Undergraduate Engineering Course in Manufacturing based on Industrial Robots and Flipped Classroom

The pedagogy of a first-year engineering course in manufacturing is presented. This course entitled Manufacturing and Society involves collaboration with social science, is based on industrial robots as the central theme to attract students’ interests and utilizes the flipped classroom approach for delivery. We hypothesize that, in one semester, recent high school graduates will be able to gain knowledge in manufacturing by learning the computer-aided engineering (CAD) software, applying CAD to design a penholder, fabricating the penholder using additive manufacturing and computer-aided manufacturing (CAM) software, programming the robot to create a toolpath for the pen, drawing using the pen on the penholder guided by a robot, and elaborating on impacts of robotic painting on society from a social science perspective. This course is designed to give students, regardless of their intended major in engineering, broad knowledge in manufacturing via 10 engineering, 3 social science, and 10 technical communication lectures;8 labs;and 4 projects. The social science lectures and discussions focus on how knowledge about society can be used to inform design and manufacturing decisions, social science research methods for understanding how engineers and technology can impact people's lives, and changing trends in work, the workplace, and the future workforce as it relates to manufacturing. This course aimed to give undergraduate first-year engineering students a positive view of advanced manufacturing and its impact on society. Student evaluations and comments were positive and affirmed the learning objective of teaching manufacturing to the first-year engineering students. The flipped classroom approach was demonstrated to be ideal during the COVID-19 pandemic with limited capacity for in-person lectures and labs. The use of flipped classrooms allowed students to learn at their own pace, review and reinforce knowledge, have a closer interaction with instructors, and reduce the number of technical errors using simulation tools. This course with the support of flipped classroom pedagogy can be successfully implemented in the post-pandemic era, devoting the time of the class to answer questions, expand upon the class content and have a closer in-person interaction with students. © 2022

Fake News Propagation in the era of Covid-19-Tracing and Combating Fake News using Block Chain Technology

News at the times of Covid-19 poses the threat to the health industry. This paper finds the study to understand the features and social sites that propagates fake news without any restriction. The analysis of this fake news has came to the conclusion that it has broadly effected the health, political, entertainment and religious themes. The health related fake news during pandemic tops the chart. The problem of 'fake news' has become the topic of concern and it plays with the sentiments of the people. This problem has spread rapidly during the pandemic due the easy accessibility of social sites. In this paper we are discussing the overview of fake news and using block chain technology and its framework which has served as an efficient tool to combat the spread of fake news and how the various features of fake news have design issues and how it is tackled. © 2021 IEEE.

Graph Convolutional Network-Based Rumor Blocking on Social Networks

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms. IEEE

Digital technologies in the activity of Portuguese University Tunas A case study at RaussTuna - Tuna Mista de Bragança

The covid-19 pandemic triggered the widespread use of digital technologies in practically all countries not only because of the need for organizations, companies and institutions to continue to produce, but also as a way for individuals - beings of a social nature - to communicate and interact with each other during this whole troubled period. Higher education institutions were also no exception and were forced to implement online teaching across the country so that students would not be left without access to knowledge and, consequently, regress in learning. The University Tunas that constitute themselves as musical groups and are normally assigned to these institutions (of higher education) are also an example of organizations or associations that have had to adapt to new contingencies through the latest digital technologies. This study focuses precisely on this issue of the use of digital technologies by University Tunas. However, the specific case study is the “RaussTuna - Tuna Mista de Bragança (TMB)” of the Polytechnic Institute of Bragança (Portugal). In general, we intend to understand how this group of young people use digital technologies within the scope of their activity as an associative group: What are the digital technologies adopted by the University Tunas? What restrictions do members of a Tuna have in the use of digital technologies? What suggestions can be implemented to improve the digital quality of Tuna's activities? The results point to the use of a wide range of digital technologies for different purposes, especially office tools, electronic presentation software, audio, video, management of conferences or events and social networks. Members assume that they have a set of restrictions regarding the use of technologies and because of this they need training in the area to improve their skills. Finally, Tuna members present a set of improvements both at the software and at the hardware level that eventually can be implemented to increase digital quality. © 2022 IEEE Computer Society. All rights reserved.

Analysis of SARS-CoV-2 High Infection Spread in India Based on SIRD Model and Structural Insights

SARS-CoV-2, the causative agent of COVID-19 first emerged in Wuhan, China, in 2019. With antigen drift of the RNA beta-coronavirus, a number of variants have appeared, especially, B.1.617 variant, which rapidly spread throughout India and caused a devastating global pandemic. However, the high infectious mechanism is still under discussion. In this paper, the Susceptible-Infectious-Recovered-Deceased (SIRD) model was used for the analysis of B.1.617 variant in India to estimate its higher infectivity than the wild one. With that in mind, animal contact, social network, technology detection and government deals are raised as important drivers of transmission. Furthermore, the paper also revealed that particular special mutations in B.1.617 variant such as T478K, L452R in the S protein might affect viral fitness [1], making it highly infectious, based on the structural and binding affinity comparison to wild-type and B.1.617 variant with human ACE2. © 2022 ACM.

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since most of people's daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT-enabled technologies in the health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated. © 2022

University degrees adapted to new social frameworks

For years, many studies has been stressing the importance of incorporating new technologies and tele-education in universities across the globe. However, the implementation of new methodologies that take advantage of educational innovation supported by new technological tools has been slow and gradual. The new reality to which we have been exposed due to the health emergency caused by COVID-19 has hastened the incorporation of these methodologies hastily and has highlighted the lack of resources or training that universities suffer from to face the changes that have occurred. There is a clear need to modernize educational methodologies in higher education to bring it closer to the new generations and their needs, implementing more flexible models that provide transversal competencies. This work proposes a methodology based on social networks as a tool for support and connection between teachers and students. In addition, we explored different combinations of accessible software that allowing us to conclude the steps to follow for the successful implementation of social networks in the classroom. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Cyberbullying detection system focusing on the isiXhosa language

Due to the Covid19 pandemic, and the restrictions placed in social interactions, there has been an upsurge in the use of social networks, such as Facebook, WhatsApp, Instagram, Telegram, Twitter, and others. As more people turn to the social networks for social interaction, there has been increased occurrences of cyberbullying. Cyberbullying is a type of bullying that occurs through online technology, whereby harmful texts and pictures are shared through social networks. This research project aimed to develop a system that can detect cyberbullying on social networks such as Twitter focusing on the IsiXhosa language. Machine learning algorithms were applied to Twitter feeds in order to detect cyberbullying. The project will help law enforcement to apprehend and prosecute cyberbullies that make threats using isiXhosa. The methodology used incorporated machine learning algorithms to fully implement the cyberbullying detection system. It starts with collecting the data from Twitter using Python, cleaning the data followed by testing the data. The results show that the implementation successfully collected the desired data from Twitter and the data was then pre-processed and prepared to be tested using the different algorithms mentioned in the paper. © 2022 IEEE.