The coronavirus disease 2019 (COVID-19) pandemic and the infection escalation around the globe encourage the implementation of the global protocol for standard care patients aiming to cease the infection spread. Evaluating the potency of these therapy courses has drawn particular attention in health practice. This observational study aimed to assess the efficacy of Remdesivir and Favipiravir drugs compared to the standard care patients in COVID-19 confirmed patients. One hundred twenty-seven patients showed the disease at different stages, and one hundred and fifty patients received only standard care as a control group were included in this study. Patients under the Remdesivir therapy protocol were (62.20%); meanwhile, there (30.71

              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breat

Background: The SARS-CoV-2 virus causes COVID-19, a respiratory syndrome. It causes inflammation and damages several organs in the body. miRNAs play a role in regulating the infection resulting from SARS-CoV-2. MicroRNA-155, a kind of microRNA linked to viral defences, can affect the immune responses during COVID-19. Objectives: Examination of the involvement of microRNA-155 in the development and severity of COVID-19, as well as finding the correlation between microRNA-155 and viral load (copies/mL) in severe cases of the disease. Materials and Method: A case-control research study was performed between October 2022 and June 2023. It included a cohort of 120 hospitalised individuals with severe cases of COVID-19, together with 115 individu

Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

The study aims to measure the level of academic stress in the e-learning environment in three areas, students and their dealing with classmates, dealing with the professor and technical skills, and the nature and content of the curriculum among graduate students in the College of Education at King Khalid University during COVID-19 pandemic. This study was descriptive in nature (survey, comparative). The sample consisted of (512) male and female graduate students in the master's and doctoral programs. The Academic Stress Scale in the E-learning Environment designed by Amer (2021) was used. The results indicated a high level of academic stress among graduate students in the e-learning environment. The study also found that there were stati