Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Knowledge and perception about COVID-19 among undergraduate MBBS students of Assam Medical College Dibrugarh

Background: COVID-19 have made the people more susceptible to get affected due to insufficient knowledge and unhealthy practices. Medical students can play a pivotal role as reliable information providers. This study aimed to assess the knowledge and perception about COVID-19 among medical students of a Tertiary care centre. Method(s): An online cross-sectional survey was conducted using Google Forms, which the participants completed between November 2020 January, 2021.The study was conducted among MBBS undergraduate. Descriptive statistics was applied to represent participant characteristics. Result(s): 100 percent participants completed the survey. All are MBBS students. Majority of participants were having adequate knowledge while about 6.2% had partial knowledge (selected either RT-PCR or Immunofluorescent antigen detection assay) for Covid 19 detection. Students have shown a positive perception of COVID-19 prevention. More than half of the students (62.5%) were found to have a correct perception that antibiotics are not effective in COVID-19 treatment as well as 15.8% stated that vaccines are not sufficient to prevent COVID-19 transmission at present. Conclusion(s): As the COVID-19 is showing on and off waves of cases worldwide, it is important to impart the knowledge and beliefs among common man to further prevent a pandemic of such concern. Medical students with their education background and basic understanding about COVID-19 can play a substantial role by making community people aware about the true facts of this pandemic situation. Copyright © 2022, Anka Publishers. All rights reserved.

Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Deep Learning Model for Classification of COVID-19 Related Fake News

The health crisis caused by COVID-19 throws the whole world into the biggest emergency of the century. Moreover, the pandemic has become awful because of the spread of inadequate and fake news or information among common people. Fake news, gossip and misleading information are on the rise due to the popularity of web-based information sources among people, such as social media, news feeds, online blogs and e-news articles. Monitoring and identifying such fake stories is a prerequisite to cease unwanted panic in this pandemic. But carrying out this task manually is challenging and labour intensive. Computer-assisted pattern recognition can now be used to replace human contact thanks to developments in machine learning, deep learning models and natural language processing. This is also essential for accurately distinguishing between true and false information automatically. A hybrid deep learning classification model has been proposed here to identify and classify the fake news and misleading information on the ‘COVID-19 Fake News Dataset’ (taken from Mendeley) which is a collection of news or web article related to COVID-19. The proposed classification model has achieved an accuracy of 75.34% and outperforms the existing LSTM and BiLSTM techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effect of cognition on e-learners as compared to traditional learners during Covid19

From the past several year's educational institutions at every level was practicing conventional teaching-learning process. It seems to be the only way of imparting education to the learners. However, due to the onset of Coronavirus, i.e., Covid19, everything came to a halt for a while all over the world. The complete ecosystem of human society gets affected by this. Educational institutions also hit hard by this pandemic, and the complete layout of the teaching-learning process over the globe changed. It brings challenges for both students and teachers to withstand in this tough situation and to restore the backbone of learning. E-learning activities slowly-slowly gripped the complete educational ecosystem and the way of imparting education also changed. The primary purpose of this study is to examine the level of cognition faced by e-learners as compared to traditional learners at higher education level during Covid19. For this study, 120 students from Chitkara University have voluntarily participated and submitted their responses through the Google Form. It deduces from this study that learners using E-learning mode feel more cognitive load as compared to traditional learners. The cohen's d-value which represents the effect size comes to be 0.97 which means that the effect of cognitive load is significantly large on e-learners than the traditional learners. In the future, the reasons behind the rise of the cognition level of e-learners need to evaluate and improve the learning gain of e-learners. © 2022 Author(s).

Attention-based bidirectional LSTM with embedding technique for classification of COVID-19 articles

The epidemic of COVID-19 has thrown the planet into an awfully tricky situation putting a terrifying end to thousands of lives;the global health infrastructure continues to be in significant danger. Several machine learning techniques and pre-defined models have been demonstrated to accomplish the classification of COVID-19 articles. These delineate strategies to extract information from structured and unstructured data sources which form the article repository for physicians and researchers. Expanding the knowledge of diagnosis and treatment of COVID-19 virus is the key benefit of these researches. A multi-label Deep Learning classification model has been proposed here on the LitCovid dataset which is a collection of research articles on coronavirus. Relevant prior articles are explored to select appropriate network parameters that could promote the achievement of a stable Artificial Neural Network mechanism for COVID-19 virus-related challenges. We have noticed that the proposed classification model achieves accuracy and micro-F1 score of 75.95% and 85.2, respectively. The experimental result also indicates that the propound technique outperforms the surviving methods like BioBERT and Longformer.

Comparative Predictive Analysis of Mortality Rate after Covid-19 Vaccination Using Various Machine Learning Approaches

This work has mainly targeted in performing comparative real time predictive analysis of mortality rate after having COVID-19 vaccination using different machine learning approaches. In this paper various deep learning models viz. RNN, LSTM and CNN have been utilized to make future prediction on mortality rate on the basis of administered vaccine doses. Firstly, the dataset of confirmed active cases, death cases and administered vaccine doses have been converted from time-series format to supervised learning format, and secondly different deep learning models have been trained and compared based on the transformed dataset. The prediction analysis is performed strictly based on the newest COVID-19 Delta Variant infected cases. The predictive analysis has resulted 15.53% of reduction in mortality rate and 24.67% of reduction in confirmed active cases with increase in vaccination rate. © 2022 IEEE.

Lessons Learned in the Implementation of a Training Knowledge Management System for the SERVIR Network

The SERVIR program is a unique partnership between NASA, the U.S. Agency for International Development (USAID), focusing on building capacity to use Earth observations for addressing development challenges. In that context, between 2004 and 2020, the program delivered approximately 365 trainings to almost 10,000 professionals. More recently, between November 2020 and August 2021, the SERVIR network executed some 55 training events addressing SERVIR’s 4 priority thematic areas, and roughly a quarter of SERVIR trainings overall have focused on themes related to Ecological Forecasting. Due to the ongoing COVID-19 pandemic, almost four-fifths of recent training events have been virtual, with the remainder being in-person under limited circumstances. The large number of training events delivered represents both an opportunity and a challenge in terms of knowledge management. While the training materials developed can later be reused in other parts of the SERVIR network, prior to recently, the lack of a central repository for those materials has prevented wider dissemination and use. The recently developed Training Knowledge Management System (TKMS) is now becoming an integral part of the SERVIR Capacity Building Framework, supporting the exchange of resources and methods for conducting training activities across the network. This presentation focuses on the structure of this system, as well as on the anticipated benefits for the User Communities for Earth Observations of Terrestrial Systems.

Patient satisfaction following implementation of a nursing preparation protocol for telehealth visits

Objective: The Covid-19 pandemic prompted broad adoption of telehealth platforms. Our goals were to determine effectiveness of a telemedicine nursing protocol in patient engagement, preparation, and satisfaction. Methods: We implemented a standardized telemedicine nursing protocol prior to a scheduled telehealth visit with a urologic provider at a tertiary care center. Demographic data, telehealth platform and smart device preference, requirement of set up assistance, and rate of success were reviewed. We prospectively administered the Telehealth Usefulness Questionnaire (TUQ), a validated 21-item survey assessing patient satisfaction in 6 domains: Usefulness, Ease of use, Interface quality, Interaction quality, Reliability, and Future use. Scores >105 (>5 for individual items) correlate with high satisfaction. Results: From April - May 2020, 265 patients were included. Demographic data is provided in Table 1. The most commonly used platform for audiovisual visits was Doximity Dialer (85.7%) via Android (50.2%) or Apple (43.0%) smartphone. Eighteen (6.8%) patients reported setup assistance from family/friends. Only 4 (1.8%) were unsuccessful and required conversion to a non-visual phone visit (3 for lack of access to a compatible device;1 for inability to understand instructions). Of these, 186 (70.1%) patients completed the post-visit questionnaire. Mean TUQ scores were 118.31 ± 23.44. Nineteen of 21 individual items had mean scores >5.0. The Usefulness (5.936 ± 1.231) and Interaction Quality (5.89 ± 1.412) subdomains had the highest mean scores. The Reliability subdomain had the lowest mean score (4.715 ± 1.593). Increased TUQ scores were associated with decreased age (P = 0.02) and female gender (P = 0.02). Patients reported high satisfaction with their telemedicine experience regardless of race, marital status, annual income, education level, employment status, or physical distance from clinic but younger age and female gender were associated with greater satisfaction. Conclusions: A standardized nursing protocol designed to maximize patient engagement with telehealth was successful in achieving patient-provider connectivity in 98% of subjects with high patient satisfaction. A team approach to telehealth is recommended.

Implementation of an early recoveryafter surgery (ERAS) protocol in patients undergoing female pelvic reconstructive Surgery: Impact on complications, hospital stayand cost

Objective: Minimizing hospital admission and maximizing utilization of outpatient surgery facilities are critical for patients undergoing elective surgery during the COVID-19 pandemic in order to prevent viral spread within healthcare facilities and maximize inpatient hospital bed availability. Methods: We implemented an early recovery after surgery (ERAS) protocol for all patients undergoing female pelvic reconstructive surgery starting on June 1st, 2020 by a single surgeon. The protocol included pre-op hydration, a urinary anesthetic, pre- and post-op acetaminophen and ibuprofen, postop perineal ice and bowel regimen, identification and enrollment of family members to assist with care, and communication regarding planned sameday discharge. We compared demographic, operative, hospital stay, complications, and cost data in patients pre (PRE) and post (POST) ERAS implementation. Results: In all, 173 patients (82 PRE Nov 2019 - Feb 2020, 91 POST June - Sept 2020) were included. There were no differences in age, body mass index, ASA score, smoking history, surgery type, operative time, intra-op complications, and post-op complications between the PRE and POST groups (P > 0.05). POST patients had a higher mean Charlson Comorbidity Index (2.6 vs 1.9, P = 0.0132). Significantly more surgeries were done in an outpatient setting in the POST group (73.6% vs 48.8%, P = 0.0008), and significantly more patients were discharged on the day of surgery in the POST group (80.2% vs 50.0%, P = 0.0003). There were no differences in the rates of unexpected emergency room or clinic visits (P > 0.05). Both peri-op and discharge opiate requirements did not significantly differ but trended towards being reduced in POST patients (P = 0.0782 and 0.0926, respectively). Post-op opiate requirement was significantly reduced in the POST group (P < 0.0001). There were no significant differences between revenues, expenses, and margins between the two groups (P > 0.05);however, there was a trend towards an increased operating margin in the POST group ($4,554 vs $2,151, p = 0.1163). Bed unit cost was significantly lower in the POST group ($210 vs $533, P < 0.0001). Conclusions: In patients undergoing female pelvic reconstructive surgery, an early recovery after surgery protocol facilitated transfer of procedures to an outpatient surgical site and permitted same-day discharge without increasing complications, clinic visits, or emergency room visits. It may also reduce cost and improve operating margins to hospital systems.

National board for wildlife and the illusion of wildlife protection

The recent approvals granted by the National Board for Wildlife permitting ecologically destructive activities within national parks and sanctuaries have generated a lot of concern. A signifi cant part of the concern is with respect to the timing, and whether it is appropriate to approve projects during the COVID-19 lockdown. Other larger issues of concern point to the fact that the NBWL has become a "clearing house" for projects, where, irrespective of its impact on wildlife, projects are approved and that the decisions of the board are guided more by economic, strategic, political and other considerations and rarely in terms of wildlife conservation. The NBWL is the apex body for conservation of wildlife and its habitat, and the NBWL's role is of critical importance to ensure the long-term protection of India's biodiversity. © 2021 Economic and Political Weekly. All rights reserved.

Paradigm Shift in Engineering Education during COVID 19: From Chalkboards to Talk Boards

Traditional teaching got a blow during lockdown because of pandemic condition and all of a sudden a need for transformation from traditional teaching to technology oriented teaching was realized. The paper aims at elaborating the paradigm shift in engineering education teaching and learning methods using online tools and focuses on evaluating the usability of proposed online learning models by the students. Data is collected through a survey questionnaire responded by 60 students of the engineering of Chitkara University. The findings are limited to only one mode of platform that is gotowebinar so they cannot be generalized beyond this concept. Future research should be considered using all possible platforms, which are available for higher education teaching. Originality- This research explores the determinants of education's acceptance of online mode of education and also the adoption from chalkboards to talk boards. © 2020 IEEE.