Surveillance Rover to Maintain Social Distancing in Crowded Areas

COVID-19 is making a huge impact both in terms of the economy and human lives. Many lost their lives due to COVID-19 which is found in most of the nations. The number of positive symptoms is increasing rapidly all over the world. To safeguard us from the virus, some protocols have been addressed by WHO in which people has to wear a mask and make a social distancing when moved in public. Therefore, social distancing places an important role in preventing us from the spread of the diseases. The minimum distance between to be maintained is informed at 6 feet informed by the health organizations. When people gathered on a group social distancing could not be maintained even if manual or any kind of technology implemented. Temperature measurement on mass gathering was also a tedious process where the monitoring is essential. Multiple methods such as thermal cameras, temperature sensors for monitoring the personnel has not been efficient. In the proposed work to monitor the social distancing between the persons an ultrasonic sensor is placed to detect the obstacle and an IR sensor to make the rover move. An encoder is used to calculate the distance based on the rpm of the wheel. Based on this input the distance is checked within this limit the obstacle is detected, an alert signal is made using the buzzer. A thermal sensor is used to measure the temperature of the person and an LCD display shows the temperature of the person and distance between obstacles. The proposed system has resulted in identifying the distance and helps in reducing the spread during the pandemic situation. © 2023 IEEE.

CoviIS: A Real-Time Covid Help Information System Using Digital Media

In this paper, we present CoviIS, an emergency Covid Information System that utilizes digital media to provide helpful information in uncertain times of the Covid pandemic. Since people require different types of information during times of crisis, the findings obtained from this work integrate various pieces of information into a form of coherency, thereby aiding people during an emergency and reducing further damage. The study brings together real-time Covid informatics employing multiple methods such as general search, social media search, and geographical analysis. To assist people in this emergency, we also conduct a comprehensive analysis of news articles and social media activities to provide an economically feasible solution. CoviIS helps locate the nearest hospitals and Covid isolation centers for seeking medical attention during an emergency. CoviIS also provides emergency information through news articles and social media posts, thereby serving as an important Covid emergency tool. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Chunk Learning Media for Cognitive Load Optimization on Science Learning

The sudden shift in the education world due to the pandemic of Covid-19 bring both challenge and opportunity at the same time. Since decades ago, the understanding of the importance to manage cognitive load for effective learning had been applied in multiple methods. Having said that, only a few addresses the opportunity to combine it with the latest trend attractive for today's young learners to minimize more extraneous cognitive load. This research discusses the matter by proposing the adoption of the combination of chunk learning, animation, and super short video in social media platforms to convey learning materials on nervous system science, which has been stamped as a hard subject for high school students. The adaptation of super short video animation on nervous system science successfully helps students cope with the daunting pile of materials align with the cognitive load theory. © 2022 IEEE.

Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays

Since the beginning of the COVID-19 pandemic, more than 350 million cases and 5 million deaths have occurred. Since day one, multiple methods have been provided to diagnose patients who have been infected. Alongside the gold standard of laboratory analyses, deep learning algorithms on chest X-rays (CXR) have been developed to support the COVID-19 diagnosis. The literature reports that convolutional neural networks (CNNs) have obtained excellent results on image datasets when the tests are performed in cross-validation, but such models fail to generalize to unseen data. To overcome this limitation, we exploit the strength of multiple CNNs by building an ensemble of classifiers via an optimized late fusion approach. To demonstrate the system’s robustness, we present different experiments on open source CXR datasets to simulate a real-world scenario, where scans of patients affected by various lung pathologies and coming from external datasets are tested. Promising performances are obtained both in cross-validation and in external validation, obtaining an average accuracy of 93.02% and 91.02%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

When Will Immersive Virtual Reality Have Its Day? Challenges to IVR Adoption in the Home as Exposed in Studies with Teenagers, Parents, and Experts

In response to the pandemic, many countries have had multiple lockdowns punctuated by par tial freedoms limiting physically being together. In 2020–2021, during the COVID-19 pandemic, parents were stressed and exhausted by the challenges of work, home schooling, and barriers to typical childcare arrangements. Children were missing one another, their social lives, and the variety of experiences that the world beyond the home brings. Immersive Vir tual Reality (IVR) offers tried and tested ways to enable children to maintain beyond-household family activities and dynamics. However, it is not viewed as a solution. Instead, as demonstrated through a multiple method study involving a Rapid Evidence Assessment, workshops with 91 teenagers, interviews with 15 exper ts, a Delphi study with 21 exper ts, 402 parent questionnaires pre-pandemic, 232 parent questionnaires during the pandemic, and longitudinal interviews with 13 parents during the first UK lockdown in 2020, IVR is not viewed as having value in the home beyond gaming. Results highlight limited consideration of IVR as a way to enhance family life or the home, with a lack of evidence and direction from current research, innovation, and policy. The ar ticle empirically demonstrates that exper ts, teenagers, and parents have limited expectations for VR. Fur ther, with parental resistance to adoption and a lack of ideas or innovations in how IVR could be used, the likelihood of VR-headset adoption remains low as does its potential as a means of educating, enter taining, and socially engaging children and teenagers. © 2021 by the Massachusetts Institute of Technology.