Factors Affecting the Uptake of COVID-19 Vaccine amongDubai Airport's Professionals

Aim: Comprehending the elements that influence COVID-19 vaccination acceptability and recognizing expediters for vaccination decisions are critical components of developing effective ways to increase vaccine coverage in the general population. This study aims to investigate the main factors affecting COVID-19 vaccination uptake among Dubai 'Airport's employees. In addition, it seeks to explore the main signs and symptoms that appeared on vaccinated employees after taking the COVID-19 vaccination, hence, track the vaccine's safety. Methods: Employees at Dubai's airport in the United Arab Emirates (UAE), mainly in Dubai, provided data. To gather data online utilising the Google Forms platform, a questionnaire was used as the main quantitative tool. As 2000 questionnaires got distributed, 1007 employees participated in the survey, yielding a 50.4% response rate. Results: The results show that employees overwhelmingly agree with the assertion that the factors of accessibility and affordability have a significant effect on their decision to receive the COVID-19 vaccine, followed by a trust in vaccine, knowledge, vaccine safety, advice and information, and beliefs on the vaccine. In this study, the agreement level on factors affecting the COVID-19 vaccine uptake was found significantly to be higher in females (88.6%) who were married (91.6%) and those aged over 60 years (89.2%) at P <.05. In addition, the results show that 53.7% of vaccinated staff was found to have one or more side effects of the vaccine, where none of them was hospitalized after immunization. The binary logistic regression analysis in this study shows that females were two times more likely to have 'vaccine's symptoms after vaccination than males (Exp (B): 1.6; 95%CI: 1.127 - 2.351, P< .01). It further reveals that participants in the age group over 50 were three times more likely to have 'vaccine's symptoms after vaccination than participants in the age group 20-29 (Exp (B): 2.9; 95%CI: 2.497-9.681, P< .001). Finally, it indicates that individuals with previous SARS-CoV-2 infection were 2 times more likely to have 'vaccine's symptoms after vaccination than those without known past infection (Exp (B): 1.9; 95%CI: 1.272 - 2.542, P< .01). Conclusion: There are several factors that playing a significant role in population’s decision to receive the COVID-19 vaccine, where the accessibility and affordability factors were found to have the greatest effect on their decision to uptake the vaccine. The current study concluded that COVID-19 vaccination is safe and that adverse effects from a vaccine are usually modest and affected by several factors such as age, gender, and COVID-19 infection history. &nbsp

A systematic review on sequence-to-sequence learning with neural network and its models

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications

New Media and Crisis Management in Jordan: COVID 19 Perspective

The study aimed at identifying the relationship between the most effective digital media in managing the Corona crisis in Jordan; and the contribution of digital media in managing the (Covid 19) crisis. To achieve the aims of the study, the researcher adopted the quantitative survey method using an electronic questionnaire designed to collect data. It was distributed to an intentional sample of 50 employees (males and females) from the communication and media staff in the Jordanian government institutions. The study concluded that the most effective digital communication technologies in managing the Corona crisis were press conferences (media briefings), press coverage through digital platforms, electronic news, video reports and community initiatives through social media platforms. The results also showed that digital media contributed to managing the Corona crisis in Jordan by relying on effective and organized digital crisis communication, which helped to coordinate efforts with the relevant health and security authorities in the country about the disease developments and the decisions related, besides the immediate responses to inquiries and questions of reviewers and callers about preventive health procedures and measures of safety from the Corona virus. In light of these results, the study recommended that media briefings in crises through digital media should be adopted due to their effectiveness in managing health crises facing countries, as one of the most effective methods of digital communication technologies

A systematic review of text classification research based on deep learning models in Arabic language

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language

Acceptance determinants of 5G services

5G is a revolutionary development in network technologies which is gradually becoming very common among people contributing significantly in different fields such as education, industry, agriculture, health, tourism and military. Currently, 5G is an outbreak change as opposed to the traditional service of the Internet since it offers better quality, ultra-fast connection, low-cost, reduced latency, energy saving, which makes its great impact even greater in people’s life. The present study examines various factors that have a significant impact on the Use of 5G in the Gulf area. The study extended the TAM (Technology Acceptance Model) to include factors such as Perceived Enjoyment, Perceived Resources and Perceived Skills Readiness. The present research has adopted a hybrid model that incorporates TAM determinants with other external factors which have a direct relation with 5G as internet service. Previous studies have focused on the importance of 5G in different environments and countries. However, this study focuses on the newly spread Use of 5G in the gulf area by adopting a hybrid conceptual model. The findings suggest that 5G may help in promoting the usage of internet service more effectively with its low-cost, faster data transfer and better quality. Moreover, the findings indicate a positive effect of the gender as a mediator between the variables: Perceived Skills Readiness, Perceived Ease of use, and Perceived Resources

Acceptance determinants of 5G services

5G is a revolutionary development in network technologies which is gradually becoming very common among people contributing significantly in different fields such as education, industry, agriculture, health, tourism and military. Currently, 5G is an outbreak change as opposed to the traditional service of the Internet since it offers better quality, ultra-fast connection, low-cost, reduced latency, energy saving, which makes its great impact even greater in people’s life. The present study examines various factors that have a significant impact on the Use of 5G in the Gulf area. The study extended the TAM (Technology Acceptance Model) to include factors such as Perceived Enjoyment, Perceived Resources and Perceived Skills Readiness. The present research has adopted a hybrid model that incorporates TAM determinants with other external factors which have a direct relation with 5G as internet service. Previous studies have focused on the importance of 5G in different environments and countries. However, this study focuses on the newly spread Use of 5G in the gulf area by adopting a hybrid conceptual model. The findings suggest that 5G may help in promoting the usage of internet service more effectively with its low-cost, faster data transfer and better quality. Moreover, the findings indicate a positive effect of the gender as a mediator between the variables: Perceived Skills Readiness, Perceived Ease of use, and Perceived Resources

Sentiment analysis in English texts

The growing popularity of social media sites has generated a massive amount of data that attracted researchers, decision-makers, and companies to investigate people’s opinions and thoughts in various fields. Sentiment analysis is considered an emerging topic recently. Decision-makers, companies, and service providers as well-considered sentiment analysis as a valuable tool for improvement. This research paper aims to obtain a dataset of tweets and apply different machine learning algorithms to analyze and classify texts. This research paper explored text classification accuracy while using different classifiers for classifying balanced and unbalanced datasets. It was found that the performance of different classifiers varied depending on the size of the dataset. The results also revealed that the Naive Byes and ID3 gave a better accuracy level than other classifiers, and the performance was better with the balanced datasets. The different classifiers (K-NN, Decision Tree, Random Forest, and Random Tree) gave a better performance with the unbalanced datasets

Developing an educational framework for using mobile learning during the era of COVID-19

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally

The acceptance of social media video for knowledge acquisition, sharing and application: A comparative study among YouYube users and TikTok users’ for medical purposes

YouTube and TikTok have gained increasing recognition as social network sites to support online knowledge acquisition, sharing, and application via social media platforms in the medical field. This study examines which aspect of TikTok and YouTube stimulates doctors, nurses, and any other YouTube and TikTok in the medical setting, to rely on them as sources of knowledge acquisition and sharing to keep their medical repertoire updated. A hybrid model is designed to investigate users’ acceptance of YouTube and TikTok as social media platforms. The model focuses on four main external factors: Content richness, innovativeness, satisfaction, and enjoyment. These factors are connected with two TAM constructs which are perceived ease of use and perceived usefulness. The results have shown that both YouTube and TikTok are affected by PEOU, PU, personal innovativeness, flow theory, and content richness. Both social media networks provide up-to-date sources described as useful, enjoyable, and relevant. Nevertheless, the comparative results have shown that YouTube has deeply influenced users’ medical perception and knowledge compared to TikTok. It is created for the very mere purpose of socialization and self-expression. In contrast, YouTube is used for educational and non-educational purposes due to the type of uploaded content and time management. Therefore, TikTok developers and influencers should initiate highly specialized videos and create content that raises awareness of medical field issues