An Enhanced Multipath TCP: E-Adoption of Emerging Technology for Better Internet Bandwidth

Since 2020, the coronavirus disease 2019 (COVID-19) pandemic and lockdown policy have changed our life drastically. Due to this, companies, offices, schools, etc. are running in online mode. Therefore, the demand for internet bandwidth is very popular nowadays. Due to an increase in demand for internet bandwidth and the availability of multihomed devices, multipath TCP (MPTCP), and e-adoption of emerging technology enhanced bandwidth by utilizing the bandwidth of all available networks simultaneously. This paper analyzes the working principle of the MPTCP so that each internet device can easily use MPTCP to achieve high bandwidth. Load distribution and its performance mainly depend on the congestion control algorithm and the packet scheduler. MPTCP has various existing packet scheduling algorithms and congestion control algorithms. Here, the authors compare the performance of MPTCP with each congestion control algorithm and packet scheduler one by one and try to find out the best-performing MPTCP. This paper also shows that the MPTCP helps COVID-19 patients in various aspects.

Machine Learning-Based COVID-19 Classification Using E-Adopted CT Scans

In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

Machine Learning-Based COVID-19 Classification Using E-Adopted CT Scans

In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

A Study on the Impact of COVID-19 on Students and their Perception of e-Learning via MyCaptain App

The educational system across the country has been adversely affected due to the COVID-19 pandemic. This forced several educational institutions to be closed, affecting students across the country. Because of the pandemic, people have been advised to maintain a safe distance of at least 6 feet, which clearly doesn't support the whole premise of educational institutions. This has brought in a huge change in the interactions that take place between teachers and students. This has brought a huge difference in students' lives as traditional classroom teaching has had to take a back seat while computer-based learning is prevalent now. This type of learning, termed online learning, is how educational goals have been achieved ever since the pandemic broke out. It is imperative to understand how students' lives have changed due to unforeseen circumstances and how their perception has changed toward the e-learning platform. In this research paper, an attempt has been made to focus on and understand the impact of COVID-19 on students and their perception of e-learning especially using MyCaptain app as a platform. This paper reveals that the students perceive the online learning platform to be the future mainly due to the availability of the study material all the time, and convenience. © 2022 IEEE.

Online teaching and its impact on self-monitoring of faculty members: learnings for the future from a pandemic

PurposeThis research aims to combine and extend the literature on the self-monitoring approach used by faculty members in online teaching during the COVID-19 pandemic using the Technology Acceptance Model (TAM) model. The study also highlights the challenges faced by faculty members in online teaching.Design/methodology/approachBased on a mixed methodology approach, the primary data was obtained from the faculty members of the post-graduate business schools. This data enabled the measurement of self-monitoring adopted by the faculty members and the relationship of the factors by using the TAM model. Multivariate regression was adopted to study the relationships between the elements in the TAM model and faculty members' self-monitoring. Secondly, a few exploratory questions were asked to the respondents about the challenges faced by them during online teaching.FindingsThe quantitative analysis conducted using multiple regression directed that the faculty's contentment with any digital platform influenced their engagement, attention and participation while taking an online class as a part of the self-monitoring process. The perception of the technology platforms used for online teaching affected the faculty members' self-monitoring dimensions: attention, participation and engagement. Based on the qualitative approach, the thematic analysis pointed out five major challenges for faculty members in conducting online classes: I.T. support, hesitation, interaction with peers and students, proficiency with an online platform and evaluation challenges.Research limitations/implicationsThis study was conducted during the complete lockdown of the COVID-19 pandemic;many faculty members were initially trained to get familiarized with the online teaching platforms and educate students. Hence, this study enriches the literature on online teaching during pandemic times.Practical implicationsTo ensure that the faculty impacts quality online education and the students obtain the knowledge and skills required, faculty need to alter their pedagogy based on the technology they use to focus on their students' teaching, learning and needs.Originality/valueThis study measures self-monitoring and its dimensions for faculty members, which is unique in nature. This was the first time the faculty members were imposed with the responsibility of online teaching and ensuring that the learning-teaching process was fruitful. This study has both-theoretical and practical implications as the paper focuses on various insights which can make online teaching-learning more effective.

Hybrid Learning Challenges and Knowledge Management: Role of Information Technology

Coronavirus disease of 2019 (COVID-19) has made the situation challenging for higher education institutions. Students have been given the option to study either in offline or online mode. This has forced higher education institutions to conduct classes in a hybrid mode. Teaching in a hybrid mode has its unique challenges. Apart from the issues in delivering lectures, it has made Knowledge Management difficult. This study examines the problems faced by the educators from higher education institutions in delivering lectures in the hybrid mode and the problems faced by the institutions in Knowledge Management. The role of Information Technology in facilitating Knowledge Management is studied. This study uses a qualitative approach. The findings show that educators are facing challenging to deliver lecturers in a hybrid mode dually at the same time assimilation and dissemination of knowledge has become a challenge. It is also observed that Information Technology tool like Internet of Things (IoT) can play a significant role in capturing data and Knowledge Management. © 2022 Bharati Vidyapeeth, New Delhi.