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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.

In today's competitive markets, brands are always trying to create a desirable image of their products and services to elicit more favorable tendencies in customers. Beyond the marketing efforts of brands, there is an important factor that can either strengthen or weaken these marketing efforts. This factor is related to the country of origin or manufacturer of a brand, which can have an impact on consumer behavior and perceptions of products and brands from that country. Therefore, the aim of this research is to investigate the effect of the country of origin and manufacturer on consumer perceptions of the institutional environment of countries and purchasing decisions, taking into account the moderating role of beliefs arising from customer trust. This research is an applied study and, in terms of data collection method, is descriptive-correlational. A library approach was used to extract studies, and a field approach was used to collect statistical data, with the data collection tool being a questionnaire. The study population consisted of customers who own and purchase iPhones from the Apple brand in districts 1, 2, and 3 of Tehran. Descriptive statistics and inferential analysis were used to analyze the data, and the software used to analyze the data was SPSS and smart PLS. The results indicate that the country of origin of a brand has an impact on perceptions of the legitimacy and regulatory environment of that country. Additionally, the results show that the country of manufacture of a brand also has an impact on perceptions of the normative environment and regulatory environment of that country. On the other hand, perceptions of the legitimacy of the country of manufacture of a brand have an impact on customer preferences for a brand and a willingness to pay a higher price for a brand. However, the impact of the country of origin on customer purchasing decisions with the moderating role of beliefs in brand benevolence, honesty, and capability has not been confirmed.

The purpose of this study is to identify the effect of stock tendency to deal with fluctuations and price fluctuation thresholds in creating fluctuations. The statistical population of the present study is the companies listed on the Tehran Stock Exchange that were present in the stock exchange during the years 2005 to 2018. In order to confirm and reject the research hypothesis, Eviews software has been used. In Tehran Stock Exchange, following severe stock price fluctuations, the stock price fluctuation mechanism is used to limit severe stock price fluctuations, and according to certain periods, the range of stock price fluctuations changes and over time the range of fluctuations increases. The basis of trial and error has been determined and in a short period of time there have been many changes in the procedures related to the application of stock price fluctuations, without really measuring the impact of these decisions in the market and investors' reaction to changes in price fluctuations. Findings showed that the number of times each share hits the price fluctuation Yield fluctuation has a significant effect.slow efficiency fluctuations have a significant effect; And in line with the concepts of hyper-reactive theory, it can be justified, that is, after the price reaches the range of fluctuation, the trend reverses and the basis of the fluctuation limit changes; It has changed the volatility of stock returns based on jump and price continuity.

  Objective : Social networks have become widely popular among young people in recent years. This research was conducted with the aim of analyzing the content of young people's attitude towards the positive and negative effects of Instagram. Methods : The approach of the current research is qualitative and its method is qualitative content analysis. Among the students of Islamic Azad University of Shiraz, 13 students were selected in a purposeful way until the theoretical saturation of the data and were subjected to a semi-structured interview. Coding of the interviews was done in three steps and manually. Finding : The coding of the interviews shows 5 components of increasing awareness (with the subcomponents of enhancing awareness in various fields, learning techniques and skills, increasing media literacy), strengthening relationships (with the subcomponents of awareness of the conditions of friends and relatives, holding celebrations and occasions and strengthening group connection), addiction to Instagram (with the subcomponents of excessive use of Instagram, failure to handle other tasks and unpleasant mental states when not using Instagram), physical and psychological problems (with the subcomponents of negative effects on sleep, negative effects on attention and concentration, experiencing negative and unpleasant emotions and physical pains) and disruption in social ties (with the subcomponents of increased social comparisons, inequality and injustice, mistrust of others and social scandal). Conclusion : According to these findings, it can be concluded that the expansion of social networks, especially Instagram, in recent years has been associated with positive and negative individual and social consequences, which are necessary and vital to pay attention to by the relevant institutions.

The selection of knowledge workers in the organization is one of the main tasks of human resource management in the new era, and the lack of criteria and a coherent list of factors influencing the selection of these workers has been raised as a challenging issue in this field. The following article seeks to examine these influencing factors on the selection of Knowledge workers using a systematic review. The process of conducting a systematic research review is derived from the model of Okoli and Shabram (2017) and the selection steps of Silva (2015). Based on this, the number of 1976 articles published without time limit from the databases of Science Direct, Google Scholar, Normags, a comprehensive portal of humanities, the specialized database of Noor magazines, and the scientific information center of academic jihad were examined, and analyzed. In the final step, effective factors were extracted from 16 articles. The findings of the research were presented in the form of three categories of knowledge, skill, and ability, and finally, the key characteristics of knowledge workers were divided into two parts: some characteristics are non-developable and fixed, and others are developable and variable.

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. Matlab is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like, Mean, Median, Weighted Average, Guassian, and Bilateral in Python to verify their performance, a suitable filter can be selected for biomedical applications by comparing their performance.