Metaverse Skills for Executives and Senior Managers: The Pros and Cons

The COVID-19 pandemic has accelerated the shift from traditional office setups to remote working, driven by information and communication technology advancements. As a result, the metaverse concept is gaining popularity in modern organizations, allowing users to create avatars for virtual work, socializing, and other activities. While its corporate adoption is rising, managers must acquire the necessary skills to integrate and utilize the technology successfully. However, technological progress can be disruptive, making it essential to weigh the benefits and drawbacks.Methodology: This proposal aims to investigate metaverse skills that managers require for remote working using virtual realities, assessing the positive and negative risks for employees and management within business organizations. Using secondary data from reliable online databases, a qualitative research approach was used to understand the pros and cons of the metaverse and remote work.Purpose: The study examines the essential skills managers need to adopt metaverse virtual realities for remote working and how employees and organizations can implement it while maintaining a positive work environment. Keywords such as metaverse, remote working, virtual reality, and information and communication technology are critical. As technology evolves, managers and organizations must consider the metaverse's inherent advantages and disadvantages to ensure a successful transition to remote.Research Questions: What are the necessary skills needed by managers towards the adoption of metaverse virtual realities for remote working? How can employees and organizations adapt to implementing metaverse for remote work and sustain a positive work environment? © 2023 IEEE.

Mucormycosis of the Mandible Following Sars-Cov- 2 Infection - a Case Report with a Brief Review of Literature

Mucormycosis is a rare fungal infection of the craniofacial region and lungs. An upsurge in the cases of mucormycosis was observed in the patients who had a history of SARS-CoV-2 infection.Infact in India, mucormycosis was declared an epidemic during the second wave of the COVID-19 pandemic. Rhino-orbital and cerebral regions were most commonly involved and very few cases of mandibular involvement have been reported in Post-COVID-19 Mucormycosis in India. Herewith, we report a case of isolated mandibular mucormycosis in a COVID-19 patient. A 47-year-old patient who recently recovered from COVID-19 presented with typical symptoms of osteomyelitis which was confirmed by radiological findings. An incisional biopsy followed by histopathologic examination confirmed mucormycosis of the mandible. Mucormycosis is an aggressive fungal infection thatrequires prompt diagnosis and treatment. Judicious management of osteomyelitis with secondary fungal infections involving the maxilla or mandible in patients with a history of SARS-CoV-19 infection can improve prognosis.

A systematic comparison of transfer learning models for COVID-19 prediction

The pandemic COVID-19 is already in its third year and there is no sign of ebbing. The world continues to be in a never-ending cycle of disease outbreaks. Since the introduction of Omicron-the most mutated and transmissible of the five variants of COVID-19-fear and instability have grown. Many papers have been written on this topic, as early detection of COVID-19 infection is crucial. Most studies have used X-rays and CT images as these are highly sensitive to detect early lung changes. However, for privacy reasons, large databases of these images are not publicly available, making it difficult to obtain very accurate AI Deep Learning models. To address this shortcoming, transfer learning (pre-trained) models are used. The current study aims to provide a thorough comparison of known AI Deep Transfer Learning models for classifying lung radiographs into COVID-19, non COVID pneumonia and normal (healthy). The VGG-19, Inception-ResNet, EfficientNet-B0, ResNet-50, Xception and Inception models were trained and tested on 3568 radiographs. The performance of the models was evaluated using accuracy, sensitivity, precision and F1 score. High detection accuracy scores of 98% and 97% were found for the VGG-19 and Inception-ResNet models, respectively. © 2022-IOS Press. All rights reserved.

A HYBRID REXCEPTION NETWORK FOR COVID-19 CLASSIFICATION FROM CHEST X-RAY IMAGES

Nowadays, with the rapid spread of Coronavirus disease (COVID-19) across the globe, the necessity to develop an intelligent system for early diagnosis and detection the COVID-19 infectious disease increases. In recent researches, Chest X-ray (CXR) of individual lungs became a common method to identify COVID-19 virus. Manual interpretation of the CXR images can be a lengthy process and subjective to human errors. In this paper, a hybrid Deep Learning model called ReXception is implemented, trained, and evaluated using two types of datasets;Mutliclass and Binary. The network is evaluated based on its overall accuracy, loss, precision, and recall, in addition to the running time and network size. The results show positive indications of the network's performance, especially when compared to other state-of-the-art networks.

Explorative predictive analysis of Covid-19 in US and its impact on US Presidential Election

Coronavirus is a large family of viruses, and it is declared as a global pandemic by World Health Organization. Millions of people have been affected and lost their lives due to COVID-19. In our work, we are showing the analysis and forecasting studies of corona cases in the United States from 22nd Jan 2020 to 27th Nov 2020, with the top 10 affected provinces of confirmed and deaths cases. A detailed comparison study was carried out to observe the confirmed and death cases with presidential election results in the US. This work is showing how the result become favorable to Mr. Joe Biden. The model building is done using the Time Series Algorithm of Autoregressive Integrated Moving Average (ARIMA) and evaluation is based on mean absolute percentage error (MAPE) © 2021 IEEE.

Accurate Prediction of COVID-19 (+) Using AI Deep VGG16 Model

Current research aims at the efficient prediction of COVID-19 (+) by employing advanced machine intelligence techniques by means of lung X-rays. In this paper, we have presented the promising VGG16 transfer learning model for the accurate and faster diagnosis of COVID-19 (+). The system provides a binary classification of the lung X-ray image into COVID-19 (+) and Normal. The effectiveness of the system being proposed is appraised by means of the performance metrics such as accuracy, precision, recall, and f1 score. Experiments were performed with 2000 X-ray specimens. For the two-class classification of the reported sample size, the proposed VGG16 model provides an outstanding recognition accuracy of 99.5%, which is loftier to all the contemporary methods provided in the literature. The suggested approach is extremely efficient and precise, for that reason, it can be used to aid and support radiologists and healthcare professionals to identify COVID-19 (+) utilizing the lung X-rays. © 2020 IEEE.