The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019 . The usage of sophisticated artificial intelligence technology (AI) and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages. In this research, the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia, reported COVID-19 disease, and normal cases. The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures. Transfer Learning technique has been implemented in this work. Transfer learning is an ambitious task, but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images. The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection. Since all diagnostic measures show failure levels that pose questions, the scientific profession should determine the probability of integration of X-rays with the clinical treatment, utilizing the results. The proposed model achieved 96.73% accuracy outperforming the ResNet50 and traditional Resnet18 models. Based on our findings, the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.
A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.
Cloud computing electronic learning (CCEL) is a new technology that enables educators and students to extend the storage and access learning materials. The use of this technology is limited worldwide. However, in the time of COVID 19, this technology becomes essential for the educational processes. The purpose of this study is to examine the predictors of using CCEL among academic staff and students in Iraq. Based on literature, this study proposed that perceived ease of use (PEOU), perceived usefulness (PU), and subjective norms (SN) will affect the intention to use (IU) which in turn will affect the actual use (AU). Attitude (AT) is proposed to mediate the relationships while technology readiness (TR) is proposed as a moderator. The data is collected from 331 students and academic staff in three Iraqi universities using stratified sampling. Data analysis is conducted using SmartPLS. The results showed that PEOU, PU, and SN affect positively IU which in turn affected AU. AT mediated partially the effect of PEOU, PU, and SN on IU while TR moderated only the effect of PEOU on IU. Decision makers are recommended to simplify the usage of CCEL and to conduct workshops about the usage and benefits of CCEL.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.