Evaluation of digital competence level among educators in UAE Higher Education Institutions using Digital Competence of Educators (DigComEdu) framework.

Digital competence has become an important issue in academia owing to the advent of various communication tools. This study used a standardized questionnaire based on the Digital Competence of Educators (DigCompEdu) framework, which is a validated comprehensive framework, and its associated assessment tool. This tool was designed to assess professional skills of faculties in higher education institutes in United Arab Emirates (UAE) in terms of their abilities to use information and communication technology (ICT) and current digital competences in their teaching and educational practices. We conducted an online survey to help participants reflect on their strengths and weaknesses while using digital technologies in education to find correlation between them, thus helping teachers determine their relative strengths and identify areas that need more attention.

Concatenated Xception-ResNet50 - A novel hybrid approach for accurate skin cancer prediction.

Skin cancer is a malignant disease that affects millions of people around the world every year. It is an invasive disease characterised by an abnormal proliferation of skin cells in the body that multiply and spread through the lymph nodes, killing the surrounding tissue. The number of skin cancer cases is on the rise due to lifestyle changes and sun-seeking behaviour. As skin cancer is a deadly disease, early diagnosis and grading are crucial to save lives. In this work, state-of-the-art AI approaches are applied to develop a unique deep learning model that integrates Xception and ResNet50. This network achieves maximum accuracy by combining the properties of two robust networks. The proposed concatenated Xception-ResNet50 (X-R50) model can classify skin tumours as basal cell carcinoma, melanoma, melanocytic nevi, dermatofibroma, actinic keratoses and intraepithelial carcinoma, vascular and non-cancerous benign keratosis-like lesions. The performance of the proposed method is compared with a DeepCNN and other state-of-the-art transfer learning models. The Human Against Machine (HAM10000) dataset assesses the suggested method's performance. For this study, 10,500 skin images were used. The model is trained and tested with the sliding window technique. The proposed concatenated X-R50 model is cutting-edge, with a 97.8% prediction accuracy. The performance of the model is also validated by a statistical hypothesis test using analysis of variance (ANOVA). The reported approach is both accurate and efficient and can help dermatologists and clinicians detect skin cancer at an early stage of the clinical process.

A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients.

The novel Coronavirus (COVID-19) disease has disrupted human life worldwide and put the entire planet on standby. A resurgence of coronavirus infections has been confirmed in most countries, resulting in a second wave of the deadly virus. The infectious virus has symptoms ranging from an itchy throat to Pneumonia, resulting in the loss of thousands of human lives while globally infecting millions. Detecting the presence of COVID-19 as early as possible is critical, as it helps prevent further spread of disease and helps isolate and provide treatment to the infected patients. Recent radiological imaging findings confirm that lung X-ray and CT scans provide an excellent indication of the progression of COVID-19 infection in acute symptomatic carriers. This investigation aims to rapidly detect COVID-19 progression and non-COVID Pneumonia from lung X-ray images of heavily symptomatic patients. A novel and highly efficient COVID-DeepNet model is presented for the accurate and rapid prediction of COVID-19 infection using state-of-the-art Artificial Intelligence techniques. The proposed model provides a multi-class classification of lung X-ray images into COVID-19, non-COVID Pneumonia, and normal (healthy). The proposed systems' performance is assessed based on the evaluation metrics such as accuracy, sensitivity, precision, and f1 score. The current research employed a dataset size of 7500 X-ray samples. The high recognition accuracy of 99.67% was observed for the proposed COVID-DeepNet model, and it complies with the most recent state-of-the-art. The proposed COVID-DeepNet model is highly efficient and accurate, and it can assist radiologists and doctors in the early clinical diagnosis of COVID-19 infection for symptomatic patients.