درخت حوزه‌های تخصصی

Diseases like diabetes are chronic and require long-term management. Inadequate production of insulin results in high blood sugar levels. Such diseases lead to serious health issues such as heart ailments, blood vessel complaints, eye ailments, kidney function disorders, and nerve ailments. Hence, accurate assessment and management of risk factors are crucial for the onset of diabetes. Our proposed approach combines fuzzy logic & machine learning algorithms for diabetes risk prediction. Three machine learning models were trained to classify patients into two categories of diabetes (Type-I and Type-II) based on their clinical dataset collected from Katihar Medical College & Hospital and Suvadhan Lab. The polynomial regression algorithm achieved a score of 0.947, while the support vector regression algorithm with the rbf kernel achieved a score of 0.954, with a linear kernel achieved a score of 0.73. Our proposed approach performed well with respect to the conventional approaches with improved accuracy by identifying the patients at diabetes risk. In future work, we further analyze the relationship between other ignored factors which contribute to diabetes risk.

The present study aimed to investigate the effects of institutional quality on the export ofintermediate goods 3 by developing countries according to good, moderate, and bad institutionalquality conditions. The institutional variable included the World Bank’s average goodgovernance index. The estimation of the model was carried out using panel data for 1997-2021on a sample of 131 developing countries by the application of the Estimated Generalized LeastSquares (EGLS) method. The results show that the institutional quality has an impact on bilateralexports of developing countries only among the pairs of countries with similar conditions ofmoderate institutional quality. Thus, the governments of developing countries having moderategovernance conditions need to prioritize development and extensive reforms concerninginstitutional development to increase their exports of intermediate goods.JFL Classification: F14, O14, O24, Q58

Purpose: In contemporary times, augmented reality is a cornerstone technology within the tourism sector. Nonetheless, the inclination towards embracing and utilizing augmented reality applications in tourism hinges upon multifaceted determinants, meriting examination through the integrated lens of technology acceptance and use theory. Concurrently, these determinants sway individuals' hedonistic motivations to engage with such apps. This juncture holds significance for managerial considerations, delineating pivotal factors influencing the adoption of this technology. Hence, this present inquiry explores the nexus between the technology acceptance and use theory and the hedonistic motivations for tourism augmented reality apps on mobile platforms within the Iranian context.Method: The current research's statistical population consists of users of tourism augmented reality apps. The sample size is 201 users. The data collection tool is a questionnaire. A structural equation test was used to analyze the data.Findings: The research findings indicate that four factors, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, have a positive and significant impact on the hedonic motivation to use tourism augmented reality apps. Moreover, all four factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) positively influenced the intention to use augmented reality applications on mobile phones. Additionally, hedonic motivation was shown to positively and significantly affect the intention to use tourism augmented reality apps on mobile phones. Notably, hedonic motivation is a crucial mediator between the Unified Theory of Acceptance and Use of Technology (UTAUT) dimensions and the intention to use augmented reality applications. However, the mediating role of hedonic motivation was not supported for performance expectancy and behavioral intention.Conclusion: This research investigates the factors influencing the motivation to use augmented reality (AR) apps in tourism. It finds that performance expectancy, effort expectancy, social influence, and facilitating conditions positively impact hedonic motivation for AR app usage. The study validates the Unified Theory of Acceptance and Use of Technology (UTAUT) for predicting AR app adoption. Notably, hedonic motivation mediates between UTAUT dimensions and intention to use AR technology, suggesting implications for enhancing AR app design in the tourism sector.