The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

The current research aimed to identify psychological stability and its relationship to university integration and spiritual intelligence among university students. The research sample consisted of (158) students from the College of Education - Al-Mustansiriya University.

A scale was applied: psychological stability, university integration, and spiritual intelligence,   and by using the (Pearson) correlation coefficient, and the t-test, the results showed: the sample members enjoy psychological stability, university integration, and spiritual intelligence, and there is a positive, statistically significant correlation between the research variables, and the results resulted in some recommendations and proposals.

The aim of the present study is to identify the level of prosoical behavior of Baghdad University's students and to recognize the differences between male and female students. Moreover, it also aims to identify the level of openness to experience for these students. A random sample of (123) students has been selected; 77 males and 46 females. Two scales have been used in the study. The Prosocialness scale for adults by Caprara. Et al (2005) has been translated into the Arabic language and relies on four types of actions (Helping, Sharing, Taking care, and feeling Empathetic with others) and the other scale is the Openness to Experience Scale, which is one of the Big Five Inventory by John and Srivastava (1999). The main results showed a

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The

The purpose of the study is to identify the teaching techniques that mathematics' teachers use due to the Brain-based learning theory. The sample is composed of (90) teacher: (50) male, (40) female. The results have shown no significant differences between male and female responses' mean. Additionally, through the observation of author, he found a lack of using Brain-based learning techniques. Thus, the researcher recommend that it is necessary to involve teachers in remedial courses to enhance their ability to create a classroom that raise up brain-based learning skills.  

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

The research aims to identify the digital repository of the University of Baghdad, explain its features, how to register, explaining the mechanism for adding and retrieving research. In addition to identifying the number of people registered in and the number of intellectual outputs stored in it. The descriptive approach and survey methods were adopted by visiting the repository's website and the websites of the University of Baghdad's formations during the period (1-6/4/2024). The theoretical framework included an explanation of the concept of the digital repository, the importance of repositories, their features, and types, with reference to models of Iraqi, Arab, and foreign university digital repositories. The practical framework of the

The study aims to identify the cognitive bias and the level of emotional thinking among university students, besides, identifying the significant differences between male and female students regarding those two variables, and determine if there is a correlation between cognitive bias and emotional thinking. To this end, two scales were adopted to collect needed data: cognitive bias scale designed by (Al-any, 2015), composed of (14) items, and emotional thinking scale designed by (Abdu Allah, 2017), consisted of (27) items. These two scales were administered to (140) students composed the study sample. They were chosen from four different colleges at Al-mustansiriyah University for the academic year (2017-1018). The findings revealed that