درخت حوزه‌های تخصصی

Evaluating the performance of countries in international markets and measuring how they utilize allocated resources provides a lucid picture of the state of business management in countries also assessing performance sustainability in international markets, is one of the important issues in predicting international trade. In international markets knowledge of countries' performance leads to the recognition of their economic and non-economic management statuses even more it is used to allocate financial support of global organization. In the present article, an attempt was made to design a model capable of examining the performance of nations in international markets by both economic and non-economic dimensions in the presence of undesirable factors and two preparation and exploitation stages of existing platforms and also assessing the sustainability of countries' performance in the studied period which is quite innovative. Then, data from 21 countries, from 2010 to 2017 were evaluated to test the model. The results indicated that the model is capable of examining the performance of countries and assess the sustainability of the countries over a period of time. According to the results of this model, an attempt was made to evaluate Iran's performance status among homogeneous countries and provide a roadmap for enhancing Iran's performance

In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.