Reviewing the critical challenges that influence the adoption of the e-learning system in higher educational institutions in the era of the COVID-19 pandemic

Purpose: This paper aims to review the critical challenges and factors influencing the successful adoption of electronic learning (e-learning) systems in higher educational institutions before and during the current propagation of the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach: This study undertook a literature review concerning the in-depth revision of previous studies published in 2020 and 2021. A total of 100 out of 170 selected research papers were adopted to identify and recognise the factors restricting the application of e-learning systems. Findings: The findings determine and illuminate the most challenging factors that impact the successful application of online learning, particularly during the wide propagation of the COVID-19 pandemic. The review of the literature provides evidence that technological, organisational and behavioural issues constitute significant drivers that frontier the facilitation of the e-learning process in higher educational institutions. Practical implications: The current paper suggests a guide for managers and scholars in educational institutions and acts as a roadmap for practitioners and academics in the educational field and policymakers as this research spotlights the significant factors challenging the e-learning process before and during the pandemic crisis. Originality/value: The provided in-depth literature review in this research will support the researchers and system designers with a comprehensive review and recent studies conducted before and during the COVID-19 pandemic considering the factors limiting the e-learning process. This paper formulates a valuable contribution to the body of knowledge that will assist the stakeholders in the higher educational institutions' context. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2022-0085. © 2023, Emerald Publishing Limited.

A Knowledge-driven Domain Adaptive Approach to Early Misinformation Detection in an Emergent Health Domain on Social Media

This paper focuses on an important problem of early misinformation detection in an emergent health domain on social media. Current misinformation detection solutions often suffer from the lack of resources (e.g., labeled datasets, sufficient medical knowledge) in the emerging health domain to accurately identify online misinformation at an early stage. To address such a limitation, we develop a knowledge-driven domain adaptive approach that explores a good set of annotated data and reliable knowledge facts in a source domain (e.g., COVID-19) to learn the domain-invariant features that can be adapted to detect misinformation in the emergent target domain with little ground truth labels (e.g., Monkeypox). Two critical challenges exist in developing our solution: i) how to leverage the noisy knowledge facts in the source domain to obtain the medical knowledge related to the target domain? ii) How to adapt the domain discrepancy between the source and target domains to accurately assess the truthfulness of the social media posts in the target domain? To address the above challenges, we develop KAdapt, a knowledge-driven domain adaptive early misinformation detection framework that explicitly extracts rel-evant knowledge facts from the source domain and jointly learns the domain-invariant representation of the social media posts and their relevant knowledge facts to accurately identify misleading posts in the target domain. Evaluation results on five real-world datasets demonstrate that KAdapt significantly outperforms state-of-the-art baselines in terms of accurately detecting misleading Monkeypox posts on social media. © 2022 IEEE.

Smart Health Monitoring System Using Robotics

The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.

A Machine Learning Approach for Diagnosing and Identifying Symptoms of COVID-19

Coronavirus 2019 (COVID-19) is a pandemic that hit the world and was responsible for the death of millions and the life disruption of billions of people. One of the most critical challenges faced during the earlier breakthrough of the diseases was identifying symptoms confused with colds, flu, and other common infections. Nevertheless, despite all the effort and research conducted for this purpose, this challenge continues as more strains, variants, and mutations appear. This work presents a solution for this problem based on machine learning classification and variable importance algorithms. A public dataset of 274,957 cases has been classified into typical and COVID-19 cases based on the reported symptoms and other variables. The dataset was used for classifying the reported cases using K-nearest neighbor (KNN), Naïve Bayes, and Decision Trees (DT) algorithms and identifying the significant symptoms that were decisive in classifying the patients using Gini, Information Gain, and Information Gain Ratio algorithms. Naïve Bayes and Decision Trees performed best with a Classification Accuracy (CA) score of 95.2% and 96.3%, respectively. The Naïve Bayes classifier scored an Area Under the Curve (AUC) of 88.75%. In addition, the applied variable importance algorithms identified headache, fever, and sore throat as the most important symptoms. © 2022 IEEE.

"Sounds like a Cheesy Radio Ad": Using User Perspectives for Enhancing Digital COVID Vaccine Communication Strategies for Public Health Agencies

The World Health Organization (WHO) and other public health agencies have identified vaccine hesitancy as a critical challenge in reducing future cases and deaths from COVID-19. The current study has investigated ways to improve a widely circulated vaccine infographic video by Centers for Disease Control and Prevention. After gathering qualitative feedback on properties of the message that could be improved (from online crowdworkers), we conducted a randomized experiment to investigate different combinations of these attributes. Our results suggest participants were more likely to share the video which was: (1) played more slowly;(2) had a female speaker;(3) did not have background music. The study demonstrates potential of user studies for improving existing communication strategies for encouraging vaccinations and alleviating vaccine hesitancy on social media platforms. Our contribution also includes a repository of messages to encourage vaccination, generated by online crowdworkers, which could be utilized by future studies. © 2022 ACM.

A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok

This paper studies an emerging and important problem of identifying misleading COVID-19 short videos where the misleading content is jointly expressed in the visual, audio, and textual content of videos. Existing solutions for misleading video detection mainly focus on the authenticity of videos or audios against AI algorithms (e.g., deepfake) or video manipulation, and are insufficient to address our problem where most videos are user-generated and intentionally edited. Two critical challenges exist in solving our problem: i) how to effectively extract information from the distractive and manipulated visual content in TikTok videos? ii) How to efficiently aggregate heterogeneous information across different modalities in short videos? To address the above challenges, we develop TikTec, a multimodal misinformation detection framework that explicitly exploits the captions to accurately capture the key information from the distractive video content, and effectively learns the composed misinformation that is jointly conveyed by the visual and audio content. We evaluate TikTec on a real-world COVID- 19 video dataset collected from TikTok. Evaluation results show that TikTec achieves significant performance gains compared to state-of-the-art baselines in accurately detecting misleading COVID-19 short videos. © 2021 IEEE.

Fleet management with real-time data analytics

Significant effort has to be devoted to surviving the businesses relying on fleet vehicles in the year 2020 and ahead as the novel coronavirus (COVID-19) epidemic became pandemic. Executing profitable business while keeping the staff safe and productive is a critical challenge to deal with. To find a solution, we focus on driver management out of major functions in fleet management such as vehicle, driver, and operation management. We were unable to identify a study conducted to capture real-time data on a ride in a fleet. Therefore, to fill that gap we implemented a cost-effective real-time Fleet Management System (FMS) using data analytics with the use of ESP32 SIM800L with reprogrammable capabilities. Fleet can use this system to monitor real-time data on vehicle location, remaining time to the destination, vehicle speed, and distance traveled. Moreover, the system can be personalized as it has reprogrammable features to be enabled or disabled based on the customer's preference. Once the data is captured through the Global Positioning System (GPS) receiver, data will be transmitted via General Packet Radio Service (GPRS) to two remote servers. One server is hosted locally with SQL and where the other is hosted in a cloud environment with a Firebase realtime database. The vehicle location is tracked using GPS. For fast data transfer, 3G Global System for Mobile communications (GSM) with ESP32 800L microprocessor was used. A web-based graphical user interface is developed to analyse and present the transmitted data. Vehicle information can be viewed and located on the web application in form of google maps. Real-time data analytics is used with Firebase's real-time database. Furthermore, Short Message Service (SMS) facility is made available for the driver to communicate with configured mobile numbers © 2021 IEEE.