Arabic fake news detection based on deep contextualized embedding models.

Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Arabic language is scarce. Our contribution is twofold: first, we have constructed a large and diverse Arabic fake news dataset. Second, we have developed and evaluated transformer-based classifiers to identify fake news while utilizing eight state-of-the-art Arabic contextualized embedding models. The majority of these models had not been previously used for Arabic fake news detection. We conduct a thorough analysis of the state-of-the-art Arabic contextualized embedding models as well as comparison with similar fake news detection systems. Experimental results confirm that these state-of-the-art models are robust, with accuracy exceeding 98%.

COVID-19 vaccine uptake and its associated factors among Palestinian healthcare workers: Expectations beaten by reality.

BACKGROUND: In response to this extraordinary outbreak, many countries and companies rush to develop an effective vaccine, authorize, and deliver it to all people across the world. Despite these extensive efforts, curbing this pandemic relies highly upon vaccination coverage. This study aimed to determine SARS-COV-2 vaccine uptake among Palestinian healthcare workers, the factors that influence vaccination uptake, and the motivators and barriers to vaccination. METHODS: A cross-sectional study was conducted using an online anonymous self-administered questionnaire during April and May 2021, after the Palestinian Ministry of Health launched the COVID-19 vaccination campaign. The questionnaire collected socio-demographic characteristics, vaccination attitude and vaccination uptake status, and motivators and barriers towards vaccination. In addition, multivariate logistic regression was performed to identify the influencing factors of vaccination uptake. RESULTS: The study included 1018 participants from different professions, including 560 (55.0%) females. Of the participants, 677 (66.5%; 95% CI: 63.5-69.4%) received the vaccine. Higher uptake was observed among males (aOR = 1.5; 95 %CI: 1.1-2.1), single HCWs (aOR = 1.3; 95 %CI: 1.1-1.8), HCWs working in the non-governmental sector (aOR = 1.6; 95 %CI: 1.2-2.4), higher monthly income (aOR = 1.9; 95 %CI: 1.4-2.8) and smoking (aOR = 1.5; 95 %CI: 1.1-3.5). The lower level of negative vaccination attitudes predicted higher intake; mistrust of vaccine belief (aOR = 1.6; 95 %CI: 1.4-1.7) and worries over unforeseen future effects (aOR = 1.2; 95 %CI: 1.1-1.3). CONCLUSION: In conclusion, the COVID-19 vaccination uptake was comparable to other studies worldwide but still needs to be improved, especially in the context of this ongoing global pandemic. It is imperative to invest resources to promote vaccination uptake and target all the vaccine misconceptions and fears.

COVID-19 vaccine uptake and its associated factors among Palestinian healthcare workers: Expectations beaten by reality

Background In response to this extraordinary outbreak, many countries and companies rush to develop an effective vaccine, authorize, and deliver it to all people across the world. Despite these extensive efforts, curbing this pandemic relies highly upon vaccination coverage. This study aimed to determine SARS-COV-2 vaccine uptake among Palestinian healthcare workers, the factors that influence vaccination uptake, and the motivators and barriers to vaccination. Methods A cross-sectional study was conducted using an online anonymous self-administered questionnaire during April and May 2021, after the Palestinian Ministry of Health launched the COVID-19 vaccination campaign. The questionnaire collected socio-demographic characteristics, vaccination attitude and vaccination uptake status, and motivators and barriers towards vaccination. In addition, multivariate logistic regression was performed to identify the influencing factors of vaccination uptake. Results The study included 1018 participants from different professions, including 560 (55.0%) females. Of the participants, 677 (66.5%;95% CI: 63.5%- 69.4%) received the vaccine. Higher uptake was observed among males (aOR=1.5;95%CI: 1.1-2.1), single HCWs (aOR=1.3;95%CI: 1.1-1.8), HCWs working in the non-governmental sector (aOR=1.6;95%CI: 1.2-2.4), higher monthly income (aOR=1.9;95%CI: 1.4-2.8) and smoking (aOR=1.5;95%CI: 1.1-3.5). The lower level of negative vaccination attitudes predicted higher intake;mistrust of vaccine belief (aOR=1.6;95%CI: 1.4-1.7) and worries over unforeseen future effects (aOR=1.2;95%CI: 1.1-1.3). Conclusion In conclusion, the COVID-19 vaccination uptake was comparable to other studies worldwide but still needs to be improved, especially in the context of this ongoing global pandemic. It is imperative to invest resources to promote vaccination uptake and target all the vaccine misconceptions and fears.

COVID-19 Detection Systems Using Deep-Learning Algorithms Based on Speech and Image Data

The global epidemic caused by COVID-19 has had a severe impact on the health of human beings. The virus has wreaked havoc throughout the world since its declaration as a worldwide pandemic and has affected an expanding number of nations in numerous countries around the world. Recently, a substantial amount of work has been done by doctors, scientists, and many others working on the frontlines to battle the effects of the spreading virus. The integration of artificial intelligence, specifically deep- and machine-learning applications, in the health sector has contributed substantially to the fight against COVID-19 by providing a modern innovative approach for detecting, diagnosing, treating, and preventing the virus. In this proposed work, we focus mainly on the role of the speech signal and/or image processing in detecting the presence of COVID-19. Three types of experiments have been conducted, utilizing speech-based, image-based, and speech and image-based models. Long short-term memory (LSTM) has been utilized for the speech classification of the patient’s cough, voice, and breathing, obtaining an accuracy that exceeds 98%. Moreover, CNN models VGG16, VGG19, Densnet201, ResNet50, Inceptionv3, InceptionResNetV2, and Xception have been benchmarked for the classification of chest X-ray images. The VGG16 model outperforms all other CNN models, achieving an accuracy of 85.25% without fine-tuning and 89.64% after performing fine-tuning techniques. Furthermore, the speech–image-based model has been evaluated using the same seven models, attaining an accuracy of 82.22% by the InceptionResNetV2 model. Accordingly, it is inessential for the combined speech–image-based model to be employed for diagnosis purposes since the speech-based and image-based models have each shown higher terms of accuracy than the combined model.

Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19.

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users' daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either "potentially COVID-19 infected" or "no evident signs of infection". We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).

COVID-19 and Dentistry in 72 Questions: An Overview of the Literature.

The outbreak of Coronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has significantly affected the dental care sector. Dental professionals are at high risk of being infected, and therefore transmitting SARS-CoV-2, due to the nature of their profession, with close proximity to the patient's oropharyngeal and nasal regions and the use of aerosol-generating procedures. The aim of this article is to provide an update on different issues regarding SARS-CoV-2 and COVID-19 that may be relevant for dentists. Members of the French National College of Oral Biology Lecturers ("Collège National des EnseignantS en Biologie Orale"; CNESBO-COVID19 Task Force) answered seventy-two questions related to various topics, including epidemiology, virology, immunology, diagnosis and testing, SARS-CoV-2 transmission and oral cavity, COVID-19 clinical presentation, current treatment options, vaccine strategies, as well as infection prevention and control in dental practice. The questions were selected based on their relevance for dental practitioners. Authors independently extracted and gathered scientific data related to COVID-19, SARS-CoV-2 and the specific topics using scientific databases. With this review, the dental practitioners will have a general overview of the COVID-19 pandemic and its impact on their practice.