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1.
2022 International Conference on Data Science and Intelligent Computing, ICDSIC 2022 ; : 164-169, 2022.
Artículo en Inglés | Scopus | ID: covidwho-2296961

RESUMEN

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200 × 200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score. © 2022 IEEE.

2.
Sustainability (Switzerland) ; 15(5), 2023.
Artículo en Inglés | Scopus | ID: covidwho-2262347

RESUMEN

The COVID-19 pandemic profoundly affected global patterns, and the period of the declared virus pandemic has had a negative influence on all aspects of life. This research focuses on categorizing and empirically investigating the role of digital platforms in learning and business processes during the COVID-19 pandemic outbreak. The main purpose of this paper is to investigate to what extent the use of electronic learning (EL) has been boosted by COVID-19's spread, and EL's effectiveness on the sustainable development of electronic commerce due to the demand for a variety of electronic devices. For this purpose, the information has been collected through an online questionnaire applied to 430 participants from the Kurdistan Region of Iraq (KRI). The results indicate that participant usage and skills with electronic devices and online software programs are increasing, as the ratio indicated a level of 68% for both genders. Thus, the significance of EL concerning electronic commercial enterprises has been openly acknowledged and influenced by numerous factors. In addition, several suggestions and steps to be undertaken by the government are highlighted. Finally, this research mentions the current limitations of EL and suggests future works to build sustainable online experiences. © 2023 by the authors.

3.
International journal of online and biomedical engineering ; 18(13):113-130, 2022.
Artículo en Inglés | Scopus | ID: covidwho-2099977

RESUMEN

Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO’s performance using the Salp algorithm’s power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN © 2022, International journal of online and biomedical engineering.All Rights Reserved.

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