As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent predictive method using stacked-based ensemble learning (GA-Stacking) optimized by a Genetic Algorithm (GA) for outbreak prediction and health decision aided processes. GA-Stacking utilizes five well-known classifiers, including Decision Tree (DT), Random Forest (RF), RIGID regression, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost), at its first level. It also introduces GA to identify comparisons to forecast the number, combination, and trust of these base classifiers based on the Mean Squared Error (MSE) as a fitness function. At the second level of the stacked ensemble model, a Linear Regression (LR) classifier is used to produce the final prediction. The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering, Johns Hopkins University, which consisted of 10,722 data samples. The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99% for the three selected countries. Furthermore, the proposed model achieved good performance when compared with existing baggingbased approaches. The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC, 2023, vol.74, no.2 to predict the epidemic trend for other countries when comparing preventive and control measures.
To understand the link between some nutritional diseases and different natural sources of carbohydrates, formulated pan bread (control) was prepared and investigated in a comparative study with other formulae in which an acceptable amount of foodstuffs was introduced. These suggested foodstuffs, 3% of individual dried orange albedo layer, dried apple, dried carrot and pure citrus pectin or 1.5% of pure apple pectin, were fed to experimental rat groups, in a feeding period consisting of two stages, in addition to the other three groups. The first group was fed on basal diet and the second was fed on a hypercholesterolemic diet all around the assay period. The third group and the other six groups were fed on a hypercholesterolmic diet for 2 wk (the first stage) then completed in the second period (4 wk) by feeding the former groups on the basal diet and the latter groups on the suggested diets. Assaying some biological parameters showed that consumption of the tested diets was associated with a significant effect on the biological tests compared to the basal diet, the hypercholesterolmic diet, control pan bread and that containing pure pectin. In general, the daily body weight gain and organs weight seemed to be lower than those of the rats fed on the hypercholesterolmic diets. Blood cholesterol fractions, TC, LDL and vLDL, blood glucose, triglycerides (TG) and liver function (GPT and GOT) were also reduced at the mid and end of the second stage. On contrary, there was a significant increment in both of HDL and feces weight around the same stage, confirming the dietary benefits achieved by the suggested diets in the current study.
Background:The COVID-19 is known as the foremost threat to humankind since the Second World War, and the most important global health catastrophe of the century. Aim: the aim of this study was assessment of knowledge of nursing students regarding COVID-19. Design: Cross-sectional descriptive study A descriptive study design was used. Sample: A stratified simple random sample of 334 nursing students. Setting: Sohag faculty of nursing. Tools: An electronic questionnaire was used it included two parts, Part I: Demographic data of the nursing students; this part is composed of 5 questions covering: name, gender, residence and academic level/year. (Q1-Q5). Part II: Assessment of nursing students' knowledge regarding COVID-19(Q6-Q17): it included information about concept of COVID-19, modes of transmission, clinical symptoms, risk groups, vaccine and treatment. Results: 91.6% of them reported that COVID-19 can be transmitted through close contact, eating wild and drinking. 69.2% of them were unable to differentiate between COVID-19 and common cold signs and symptoms. Total satisfactory knowledge of nursing students regarding COVID-19 was 64%. Conclusion: More than one-third of nursing students had unsatisfactory knowledge about COVID-19. Recommendation: posters about difference between clinical symptoms of COVID-19 and common cold should be placed at placed at university.
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