Analysis of Attack Patterns and Cyber Threats in Healthcare Sector

Information Technology (IT) has become the integral part of majority of businesses. Healthcare sector is also one such sector where IT adoption is increased in recent times. This adoption of IT has increased the internet exposure and hence increased the attack surface of the organisations working in healthcare sector. During covid outbreak, we have observed various cyber-attack and threats on organisations operating in healthcare sector. This paper focuses on cyber threat pattern in healthcare sector during covid-19 outbreak and post-covid-19 period. This research paper also aims to generate basic cyber awareness through generic cyber sanity checks to secure healthcare sector from malicious threat actors. The adaptation of proactive measures required to enhance the cyber hygiene of organisations becomes very essential in this sector. © 2023 IEEE.

We Need More Praxis: A Case for Praxis Assignments in Psychology Courses

Introduction: This paper explores what praxis is and its importance for catalyzing social justice. Statement of the Problem: At times, psychologists have articulated the importance of bridging the researcher-activist divide via praxis, but progress in creating these bridges has been slow. Literature Review: We examine how praxis can be rooted in decolonial pedagogical approaches and a tool that can bridge scholarship and activism. Building on previous work by teachers of psychology, we review small, medium, and large-scale praxis assignments that have been used in university courses. Teaching Implications: We discuss our own versions of praxis assignments used in four different psychology courses (three of which took place during the pandemic). We reflect on the ways we see students motivated by an assignment with relevance to the real world and potential for creating social change, the ways that students are able to integrate course material more deeply through action, and some of the challenges with these assignments. Conclusion: We conclude by providing recommendations for educators interested in assigning praxis projects in their psychology courses.

Impact of COVID-19 on the Factors Influencing on-Time Software Project Delivery: An Empirical Study

The objective of this research paper is to investigate the impact of COVID-19 on the factors influencing on-time software project delivery in different Software Development Life Cycle (SDLC) models such as Agile, Incremental, Waterfall, and Prototype models. Also to identify the change of crucial factors with respect to different demographic information that influences on-time software project delivery. This study has been conducted using a quantitative approach. We surveyed Software Developers, Project Managers, Software Architect, QA Engineer and other roles using a Google form. Python has been used for data analysis purposes. We received 72 responses from 11 different software companies of Bangladesh, based on that we find that Attentional Focus, Team Stability, Communication, Team Maturity, and User Involvement are the most important factors for on-time software project delivery in different SDLC models during COVID-19. On the contrary, before COVID-19 Team Capabilities, Infrastructure, Team Commitment, Team Stability and Team Maturity are found as the most crucial factors. Team Maturity and Team Stability are found as common important factors for both before and during the COVID-19 scenario. We also identified the change in the impact level of factors with respect to demographic information such as experience, company size, and different SDLC models used by participants. Attentional focus is the most important factor for experienced developers while for freshers all factors are almost equally important. This study finds that there is a significant change among factors for on-time software project delivery before and during the COVID-19 scenario. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.

COVID-19 Contact-Tracing Networks Datasets

In this research paper, COVID-19 tracing data are utilized to form two dataset networks, one is based on the virus transition between the world countries, as the dataset consists of 36 countries and 75 relationships between them. Whereas the other dataset is an attributed network based on the virus transition among the contact tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease or virus was not formed based on COVID-19 virus transmission. © 2023 IEEE.

Accurate Machine Learning Algorithm for Monkey Pox Based on Covid-19

The Covid-19 virus is still marching all over the world. Many people are getting infected and a few are fatal to death. This research paper expressed that supervised learning has revealed supreme results than unsupervised learning in machine learning. Within supervised learning, random forest regression outplays all other algorithms like logistic regression (LR), support vector machine (SVM), decision tree (DT), etc. Now monkeypox is escalating in other countries at present. This virus is allied to human orthopox viruses. It can expand from one to one through contact person having rash or body fluids etc. The symptoms of monkeypox are much similar to covid19 virus-like fever, cold, fatigue, and body pains. Herewith we concluded that random forest regression shows possible foremost (97.15%) accuracy. © 2023 IEEE.

Social Media and Healthcare Technology in 2022: Macro analytics, text and data mining, social media platforms and COVID-19

Social media has changed the way individuals and institutions approach healthcare and health information and offers opportunities to understand health-related interactions at all levels, from the micro to the macro. The Social Media and Healthcare Technology mini-track presents research papers that address a diverse array of social media and associated technology within healthcare and healthcare research;including macro analytics, text and data mining and the role of social media platforms and influencers in health care and health-related decision making. © 2022 IEEE Computer Society. All rights reserved.

Factors influencing small and medium-sized enterprise (SME) resilience during the COVID-19 outbreak

Purpose: This research paper highlights the economic impact on small and medium-sized enterprises (SMEs) due to Coronavirus outbreaks. It proposes factors that influence the strengthening and survival of SMEs. Design/methodology/approach: In this research, resilience is reflected in the following aspects hope, problem resolution and persistence. This quantitative study analyses a purposive sample of 120 small and medium-sized firms in India. The study's primary data are the responses to questionnaires issued to respondents, analyzed and hypotheses formed and tested using the structural equation modeling (SEM) technique. Findings: The study results show that all the variables significantly reduce the impact of COVID-19 on SMEs. The presented model is expected to help researchers, business modelers, analysts and real professionals with further studies in the SME context. Originality/value: This new approach adds to the business resilience knowledge of SMEs and has practical implications for manufacturing organizations seeking to become robust during and after COVID-19. © 2023, Emerald Publishing Limited.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Early Detection of Covid Using Spectral Analysis of Cough and Deep Convolutional Neural Network

Now-a-days, there are numerous techniques and ICT tools for the detection of Covid-19. But, these techniques are working with the help;of culminated or peak of symptoms. However, there is a demanding need for the early detection of Covid with self-reported symptoms or even without any symptoms, which makes it easier for further diagnosis or treatment. This research paper proposes a novel approach for the early detection of Covid with the spectral analysis of Cough sound using discrete wavelet transform (DWT), followed by deep convolution neural network (DCNN) based classification. The proposed method with the cough spectral analysis and Deep Learning based algorithm returns the covid infection probability. The empirical results show that the proposed method of covid detection using cough spectral analysis using DWT and deep learning achieves better accuracy, while compared to the conventional methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Role of IoT to Avoid Spreading of Covid19

To determine the capabilities of this technology, we refer to different research papers related to this topic. This literature-based research could assist practitioners in devising responses to relevant issues and combating the COVID-19 pandemic. This paper examines the position of IoT-based technology in COVID-19. It looks at state-of-the-art architectures, networks, implementations, and industrial IoT-based solutions for combating COVID-19 in three stages: early detection, quarantine, and recovery. Since 2020 was a challenging year for all of us, and during this pandemic, we all realized that social gatherings had to be avoided, and the serious issue was to handle it. So to tackle this and ease the handling of the Corona Virus, we developed an automatic door that monitors an individual's temperature and whether the person is wearing a mask. In the absence of a mask, it clicks a picture of the person and stores it in the database for future reference. © 2023 The authors and IOS Press.

INCREMENTAL TEXT CLUSTERING ALGORITHM FOR CLOUD-BASED DATA MANAGEMENT IN SCIENTIFIC RESEARCH PAPERS

This study aims to build clusters of similar research papers. Text clustering for research articles is challenging because re-clustering is necessary to handle newly added papers. An incremental clustering algorithm is presented to find similar research papers for COVID-19 related literature. The proposed approach uses an incremental word embedding generation technique to extract feature vectors of the papers. The initial clustering is done by using the K-means algorithm by two NLP feature extraction models;TF-IDF and Word2vec. The clustering results show that the Word2vec outperforms the TF-IDF model. With increasing COVID-19 literature continuously, the ultimate focus is to add the newly published papers to the existing clusters without re-clustering. Title, , and full body of papers are considered for testing the proposed incremental algorithm. Clustering quality is evaluated by the Microsoft language similarity package, which shows clustering of the full-text body outperforms the and title of papers. © 2022 Society for Modeling & Simulation International (SCS)

Development of a Covid-19 Information Dashboard to Access the Health Care Resources and Requirements Online

With the emergence of coronavirus and the rapidly increasing number of cases, we observed that people were suffering from a lack of information about where to source critical requirements such as oxygen cylinders, beds, ambulance service, and ventilators. In this research paper and project, the authors have designed and developed an interactive information dashboard to access the availability of these resources and requirements at different locations across India from myriad sources. The information dashboard will show information for all the states across India. The dashboard visualizes the number of corona cases, hospital beds, blood bank, etc. The end-users and the COVID-19 support team can imagine all the requirements in the dashboard that is factual and credible within their vicinity from multiple information sources. The user need not search various information sites to search for different conditions. The project collates the data of other requirements from primary sources of information like Twitter and government websites and lists them on the dashboard. The authors used open-source programming and database technologies to display the data per user requirements. The dashboard was helpful to the end-users during COVID-19 times in terms of convenient accessibility and efficiency. © 2022 IEEE.

Voting Classification Approach for Covid-19 Prediction with K-mean and PCA

Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers. © 2022 IEEE.

The Positive Impact of Lockdown on University Electricity and Water Consumption

This research paper has shed light on the adverse effects of the covid-19 pandemic on our environment, which led us to lockdown at home and work or study remotely. Thus, American University of Ras-al-Khaimah was also closed, forcing all students to attend classes online. As a result of the online study model, the university environment has gradually changed. In actuality, this has impacted the environment significantly. Waste production has been drastically reduced, and electricity and water usage have been reduced significantly. Therefore, this paper focuses on electricity and water consumption, where a comparison study was conducted between electricity consumption in KWHs and water consumption in gallons before and after the pandemic. Furthermore, the fact that there are fewer faculty members and fewer students on campus will reflect a reduction in the amount of waste produced on campus. According to the study, the online study model of education uses less electricity and water, protecting the environment, so it is recommended that it be adopted in the future © 2022 IEEE.

Blockchain Application in Incentivizing Students on Participation in Online Classes

The world we know has changed over a brief period, with the ascent and spread of Covid-19. This affected the education sector resulting in offline classes to online classes. Technologies made it easy with numerous websites, materials, video lectures, courses, and techniques for the students. In this situation, the main problem arises with students not participating in the class having many reasons such as illness, being introverted, and feeling that they may be wrong. If we are not interested in something to talk about or are shy, we must face it so that we will make the best out of it. People remember the things they listen to carefully, so we can probably study less if we listen. In this research paper, we proposed a Blockchain based application, so students who are going to attend online classes would be able to participate more in the class. They will be able to gain incentives that are based on crypto currency and by using those cryptocurrencies they can spend it on fees or any other resources in the university which would be beneficial for students. They will be able to gain incentives that are based on cryptocurrency and by using those crypto currencies they can spend it on fees or any other resources in the university. We are using the most popular technology which is Block chain technology to make sure that students who are attending online classes will be able to pay more attention. Also, we are using the most famous functionality of Blockchain which is incentivization. To give rewards to the students we will be using incentivization so they can pay more attention to the classes. This design is beneficial for teachers also to look at the status of each student and get in contact with them. © 2022 IEEE.

The effectiveness of social media for inclusion of women in computing

Despite the projection of an increase in the number of jobs in the computer science (CS) field by 13% from 2020 to 2030 in the United States (as reported by the Bureau of Labor Statistics), the representation of women, especially women of color, in the field remains low. Lack of representation for women in computer science negatively impacts the growth of this demographic as it becomes harder for prospective individuals to envision themselves in the field when they do not see others like them already succeeding in CS. Studies have found that the retention of women in the field is stronger when the representation of women is evident in their environment, however, it is hard to come by considering the low population of women computer scientists. While new prospects may find fewer women in their CS departments in their college experience, or at their workplaces, there is a plethora of social media personalities and communities for them to engage in and find like-minded individuals.This full research paper investigates the experiences of women, or lack thereof, in CS communities centered around social media and how it contributes to their sense of belonging in the CS field at large. It is evident that there is limited scope in the existing literature that studies the impact social media participation has on CS women. This literature review distinguishes the narrow scope of literature focused on women's experiences with open-source software communities in CS from women's experiences with more generic widespread platforms such as Twitter, or Instagram. It argues for the expansion of knowledge for the effects of CS women's participation on such platforms and provides insight into approaches, such as photovoice, that may be utilized to study this space. The outcomes of this review reveal the potential of utilizing online platforms in retaining women in the CS workforce effectively. Considering the current status of many organizations that have switched from in-person to remote engagement due to COVID, this review contributes to the analysis of the effective use of technology and its impact at a critical time. © 2022 IEEE.

SWARM: Sanitizer With Attendance through Remote Monitoring

With Covid19 being endemic, it is very essential to continue proper physical hygiene protocols even today to avoid escalation. To ensure hygiene inside educational institutions, many governing bodies-imposed protocols to insist students wear hand gloves and facemasks. Such an implementation, however, has increased surgical waste in and around educational institutions, and also there is a rise in allergies due to the constant use of hand gloves by the students. Hence, a prototype of a hand sanitization-based attendance monitoring system has been proposed in the current research paper. This proposed sanitizer with attendance through remote monitoring (SWARM) uses Raspberry Pi devices to capture the image of a student's identity card holding the registration number and through a bar code analysis module of computer vision, the ID number is extracted. This ID number is compared with a master attendance file to mark the students' presence and then the updated file is shared with the concerned teacher via email. Such a setup is installed in the laboratory premise, thereby reducing the unnecessary use and disposal of surgical waste within the educational premise. © 2022 IEEE.

Deep Learning-Based COVID-19 Screening Using Photographs of Chest X-Rays Displayed in Computer Monitors

Several recent research papers have shown the usefulness of Deep Learning (DL) techniques for COVID-19 screening in Chest X-Rays (CXRs). To make this technology accessible and easy to use, a natural path is to leverage the widespread use of smartphones. In these cases, the DL models will inevitably be presented with photographs taken with such devices from a computer monitor. Thus, in this work, a dataset of CXR digital photographs taken from computer monitors with smartphones is built and DL models are evaluated on it. The results show that the current models are not able to correctly classify this kind of input. As an alternative, we build a model that discards pictures of monitors such that the COVID-19 screening module does not have to cope with them. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.