The most important topic for psychologist generally is factor of education and it's active tools because learning needs active perception for stimulus that recived by the educator and give it avalue and meaning , Need for cognition is
very important in the various daily fields of life , especially in learning and teaching and the academy work , it help with shifting the learning level for people , and icreas the intense and challenge between them
The research endeavored to achieve the following aim :
1- Measuring the level of peripheral perception for the university student .
2- Measuring the level of need for cognition for the university student .
3- Measuring the level of peripheral perception for the university student

The aimed of the research was recognize the Big five personality factors and Academic procrastination among Baghdad university students, recognized differences between the gender according to   Big five personality factors and Academic procrastination , to recognized differences between specialization (scientific, human), and to recognize the relationship Between the variables of the research, and the extend of contribution Big five personality factors in Academic procrastination , to achieve these aims , Adopt scale to measure the Big five personality factors for (John Danahue & Kentle) , As we as the preparation of scale Based on An amber of previous scales to measure Academic procrastination, After processing the data st

This study aimed to measure the innovative thinking and cognitive cessation among university students. The sample consisted of (400) male and female students at al Mustansiriya University for the academic year (2018/2019). The results of the study showed that there are differences in innovative thinking and cognitive inhibition according to the gender variable in favor of males. There is a positive relationship between innovative thinking and cognitive inhibition. In light of these findings, the researcher presented a set of conclusions and recommendations.

Background: This study aimed to evaluate the effect addition of polyester fibers on the some mechanical properties of heat cured acrylic resin (implant strength, flexural strength and hardness) Materials and methods: Ninety specimens were used in the study. Thirty specimens were used for impact strength measurements (80mm X 10mm X 4mm) length, width and thickness respectively. The specimens divided into three test groups (n=10), first group formed from heat cure acrylic resin without fiber reinforcement. Second group was formed from heat cure acrylic resin was reinforced with 2 mm length polyester fiber and third group was formed from heat cure acrylic resin reinforced with 4mm length polyester fiber, impact strength measured by impact test

The problem of the current paper is embodied in the weakness of the female students
of the department of the Quran sciences in the college of Education for Women in the
University of Baghdad in the subject of reciting and memorizing the Holy Quran. This is what
the professors and the scientific and educational supervisors stress equally through their visits
to the students applicants during the period of their practical application of teaching in the
schools; especially that the subject is thought for four years during their study in the college.
That weakness is so explicit with a quite large number of the students-applicants, who are
supposed to be the future teachers in the subject of the Holy Quran and Islamic Ed

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

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