TWMS Journal of Applied and Engineering Mathematics | Volume: 16 Issue: 9

As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

في البداية اود الاشارة الى ان فهم حقيقة الازمة هو ذو جانب فني يتعلق بالجينات الوراثية لنظام يملك في احيناته قدرة عالية على تفريخ المشتقات. هذا النظام الذي يزداد عقما وتدميرا يزداد قدرة على خلق النقود الائتمانية/المشتقات، وكلما اقتربنا اكثر من فهم هذا الجانب كلما اسقطت في ايدينا تلك التوصيفات الاكاديمية الجاهزة في نقص الرقابة والاشراف، تركيز المخاطر،....الخ التي تناولتها الكتابات الشائعة في معظم طروحات

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

This study proposes a pioneering Ethical Artificial Intelligence (EAI) framework for advancing sustainable development in Iraq by integrating eight multidimensional sustainability indicators—administrative, technological, economic, environmental, social, legal, security, and governance. Utilizing data from 60 completed development projects, the framework combines SPSS statistical analysis, the SMART-AI model, and Artificial Neural Networks (ANN) to identify key determinants of project success and failure. Results reveal a 37% project failure rate, with administrative and technological deficiencies emerging as the most influential predictors. The SMART-AI model achieved an accuracy of 91.3% using stratified k-fold cross-validation. A bilin

In this paper all possible regressions procedure as well as stepwise regression procedure were applied to select the best regression equation that explain the effect of human capital represented by different levels of human cadres on the productivity of the processing industries sector in Iraq by employing the data of a time series consisting of 21 years period. The statistical program SPSS was used to perform the required calculations.

The current research aims to identify the effect of the Cornell note-taking strategy on the achievement of chemistry among second-grade female students in government daytime middle schools. Al-Fadhila School was chosen intentionally as its student sample, affiliated with the First Karkh Directorate for the academic year (2024-2025). One of the two classes was then chosen to represent the experimental group, taught according to the Cornell note-taking strategy, and the other class, the control group, was taught according to the traditional method. The equivalence of the two research groups was verified using a set of variables, including chronological age calculated in months, the Raven Intelligence Test, previous achievement in chemistry, a