مطالب مرتبط با کلیدواژه

determination


۱.

Factors Underlying Characteristics of Acquisition of English Language in EFL Classrooms(مقاله علمی وزارت علوم)

نویسنده:
منبع: English Language Teaching and Learning. No.۱۹, Spring & Summer ۲۰۱۷

کلیدواژه‌ها: teaching methodology Learning boosters Facilitation determination voluntary affective factors

حوزه‌های تخصصی:
تعداد بازدید : ۱۰۹۸ تعداد دانلود : ۸۰۵
This study tried to find out what factors underline the characteristics of acquisition of English language in EFL classrooms. To this end, the Characteristics of English Language Acquisition Scale (ELAS) consisting of 41 items was designed by the researchers of this study and administered to 388 pre University Iranian EFL students at various private and public schools in Neyshabour and Zebarkhan. Factor analysis was run to determine the number of factors underlying the scale. The application of Principle Axis Factoring and rotating the extracted factors showed that the characteristics loaded acceptably on twelve factors underlying the learners’ EFL acquisition, i.e., learning boosters, facilitation, determination, voluntary, teaching methodology, affective factors, attitudes toward foreign speakers and their culture, learner engagement, adjustment, enhancement, teachers' output and individual differences. The results are discussed and suggestions are made for future research.
۲.

Determination of Audit Fees Using Support Vector Machine: Evidence from the Tehran Stock Market(مقاله پژوهشی دانشگاه آزاد)

نویسنده:
منبع: Journal of Emerging Technologies in Accounting, Auditing and Finance,Vol. ۱, No ۴, Winter ۲۰۲۳ 11-20

کلیدواژه‌ها: Audit Fee determination Tehran Stock Market SVR

حوزه‌های تخصصی:
تعداد بازدید : ۱۰ تعداد دانلود : ۱۱
Objective: This study explores the determination of audit fees (AF) using Support Vector Regression (SVR) among companies listed on the Iranian stock market from 2017 to 2021. It investigates the relationship between financial variables like financial leverage (DA), current assets ratio (CA), quick ratio (QUICK), ASSETS, current ratio to current liabilities (CR), and long-term debt (DE), with AF as the target. Methodology: Data from 60 listed companies during this period, totaling 279 year-observations, are employed. SVR models are trained on this dataset using Google Colab. Results: The SVR model achieves a 90.5% R2 value and a 3.7 Mean Squared Error (MSE) on training data, indicating high explained variance and reasonable error levels. However, on new data, the model's performance diminishes, with an R2 of 67% and an MSE of 8.1, implying reduced accuracy and intermediate predictive accuracy. Innovation: This study advances the understanding of AF determination using SVR, highlighting the importance of considering various financial variables.