درخت حوزه‌های تخصصی

This paper gives a performance analysis of multiple vote classifiers based on meta-classification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%).

The objective of this study is to understand the customer journey in technology product adoption by exploring the experiences and perspectives of individuals who have recently adopted new technology products. This research aims to identify key themes and factors influencing the customer journey from initial awareness to ongoing engagement and retention, providing insights to enhance customer experiences and inform marketing strategies. This qualitative study employed semi-structured interviews with 20 participants selected through purposive sampling to ensure diverse representation. Participants included individuals who had recently adopted technology products within the past six months. Data collection continued until theoretical saturation was reached. The interview transcripts were analyzed using NVivo software, following an inductive approach to identify patterns and themes. The analysis revealed three main themes: Awareness and Decision-Making, First-Time Use and Learning, and Ongoing Engagement and Retention. Initial awareness was driven by advertising, social media, and word-of-mouth, while decision-making was influenced by peer recommendations, brand reputation, and price sensitivity. The first-time use phase highlighted the importance of intuitive design and support resources. Regular updates, community engagement, and product reliability emerged as critical factors in ongoing engagement and retention. Participants valued seamless omnichannel experiences, personalized marketing efforts, and robust support systems. The study underscores the significance of personalized, seamless customer journeys in technology product adoption. Businesses should focus on integrating physical and digital touchpoints, leveraging AI for personalized experiences, and maintaining active user communities to enhance customer satisfaction and loyalty. Addressing cultural nuances in global markets and managing digital signals effectively can further optimize the customer journey. These insights provide valuable guidance for businesses aiming to improve customer experiences and foster long-term engagement in the competitive technology market.

The general purpose this study aims to reach is to investigate the relationship between clerk’s job promotion and improvement in performance process in branches of Melli Bank in Bonab. In order to do so, studies, papers and books published in the field were examined to find proper framework for the study. Thus, the model used by Nekooyi Moghadam et.al. (2014) was taken for job promotion and the model used by Moghimi was utilized for improvement of performance. Moghimi’s model had six dimensions of credibility, attitude, work quality, innovation, cooperation and security. In this regard, one main and six secondary hypotheses were compiled. Statistical population of the study included all 67 clerks in branches of Melli Bank in Bonab. The data for the study were collected through two questionnaires of job promotion and performance. The reliability and validity of the questionnaires were approved by experts and then they were distributed among subjects. In order to analyze the data collected from questionnaires, descriptive and inferential statistics were utilized. In order to classify, summarize and interpret the data, descriptive statistics were used. Spearman and Regression tests were used to test research hypotheses. Results from these tests have approved all research hypotheses and demonstrated that there is a significant relationship between job promotion and improvement of performance in branches of Melli Bank in Bonab.