Demystifying and Analysing Metaverse Towards Education 4.0

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word 'Metaverse' especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education. © 2023 IEEE.

An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing;however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases. © 2022 IGI Global. All rights reserved.

Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPADLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work's novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches. © 2023 Tech Science Press. All rights reserved.

Study of the role of mindfulness intervention based on stress reduction in psychological distress and self-efficacy among the health industry staff during COVID-19 pandemic

The present study aimed to evaluate the effect of mindfulness intervention based on stress reduction in psychological distress and self-efficacy of health industry staff in Russia in 2021 during the COVID-19 pandemic. The statistical population included 600 physicians and nurses working in COVID-19 wards of hospitals in Moscow. Data were collected using standard questionnaires. Data analysis was performed in SPSS. According to the results, mindfulness intervention based on stress reduction improved psychological distress and self-efficacy in nurses of the test group, compared to the control group, during the COVID-19 pandemic (P < 0.05). According to the results, mindfulness treatment based on stress reduction reduced three components of psychological distress, stress (F = 24.03, effect size = 0.78, P < 0.001), anxiety (F = 32.12, effect size = 0.69, P = 0.001), and depression (F = 22.31, effect size = 0.72, P = 0.001) while increasing self-efficacy (F = 44.52, effect size = 0.84, P = 0.001). Copyright © 2022 Inderscience Enterprises Ltd.

Forecasting of Individuals' Movement in Society After Corona Pandemic Using RNNs with LSTM and GRUs

The health crisis that attributed to the quick spread of the COVID-19 has impacted the globe negatively in terms of economy, education, and transport and led to the global lockdown. The risk of the COVID-19 infection has been increased due to a lack of a successful cure for the disease. Thus, social distancing is considered the most appropriate precaution measure to control the viral spread throughout the world. In this study, a model was proposed for deep learning capable of predicting the movement of people in the pandemic in the short term (one day) to take precautions and control the COVID-19 infection. The proposed model consists of four phases: data collection, pre-processing phase, prediction stage, and evaluation and Comparison phase. The dataset is obtained from 428 mobility reports, collected based on data from users that have been selected for their Google Account location history for a country such as Iraq for 428 days. A deep learning algorithm such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and hybrid model (GRU&LSTM) is applied to pre-processed data to predict the movement of people. They are compared using statistical measures: Mean absolute error (MAE) and root mean square error (RMSE) for performance measurement of these machine learning algorithms. The results of the GRU are the sum of MAE 0.4277 and sum of RMSE 0.6470 for predict person path and movement with training time equal to 33.189 sec, while the results of the hybrid model are the sum of MAE 0.4355 and sum of RMSE 0.6563 for prediction and the training time equal to 53.144 sec, and the results of the LSTM are the sum of MAE 0.4395 and sum of RMSE 0.6612 for prediction and the training time equal to 100.752 sec. These statistical measurement values indicate proposed model GRU outperformed all other models, it showed a solid performance to predict person path and movement in coronavirus pandemic and took little time to train compared to other algorithms, while the hybrid algorithm showed good performance and a short period in training compared with the LSTM model. © 2021 IEEE.

An improved WBSN key-agreement protocol based on static parameters and hash functions

Wireless telecommunications systems have expanded rapidly over the past few years. Wireless Body Sensor Network (WBSN) is a relatively novel area of research and development in healthcare systems. However, it has multiple constraints and challenges regarding human health, social interactions, coverage radius, energy consumption, and communication reliability. In addition, communications between nodes can contain highly sensitive personal information, while hostile environments will impose a wide range of security risks. Therefore, designing authenticated key agreement (AKA) protocols is a crucial challenge in these networks. The current study, considering the security issues of the Li et al. scheme and some of their new extensions, proposes an improved AKA protocol with anonymity and unlinkability of the sensor node sessions. The results of theoretical analysis compared with similar schemes indicated that the proposed scheme could reduce average energy consumption and communication cost by 41 percent. It also reduced the average computation time by 61 percent. Furthermore, it was shown by formal/informal analyses that, on top of unlinkability and anonymity features, other central security features in the current scheme were similar and comparable to those in the recent and similar schemes. CCBY