درخت حوزه‌های تخصصی

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.

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.

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 research is to design a model for evaluating the policies of proportionalization of administrative system formations in Iranian National Oil Company. In this regard, the research method was a combination of qualitative and quantitative methods and tools with an applied orientation along with survey strategy and inductive approach. The qualitative part of the research included a review of the records and background of the research and conducting library studies based on the content analysis and meta-composition methods and the Delphi technique, and in the quantitative part, several techniques and tools were used, including interpretive structural modeling and structural equations, factor analysis, pairwise comparison matrix, and questionnaire tools. By carrying out the qualitative stage and refining and screening and verifying the achievements in the quantitative stage, finally 89 indicators in the form of 11 dimensions and 35 components, under the four main concepts of "policy evaluation", "proportionalization", "administrative system formations" and "macro environment" were counted and classified. In the next step, by identifying the relationship and mutual effect of the identified factors, the evaluation model of the design policy and finally the validity and reliability of the model was put into the test and approved. The issue of the optimal size of the government and the public policies related to it has been discussed and debated among political and economic experts for about three decades in the documents of the government and the ruling space of the country with different expressions such as "downsizing", "privatization" and "rationalization". Following the studies conducted in this research, with the premise that on one hand, the correct and scientific evaluation of public policies has an undeniable role in the successful implementation of policies, and on the other hand, in our country, the issue of evaluating public policies has always been like a missing link in the space that governs this territory and better management of resources in Iranian National Oil Company.