This research aims to identify the level of Voluntary work among university students, and explore the statistical differences of voluntary work among students due the gender and major. A total of (400) male and female student from morning study were selected as a sample to achieve the research's objectives. Al-Malaki (2010) scale was adopted to collect the required data. The results revealed that men take massive part in voluntary work than women, and students of human sciences showed significant differences than those of other majors.   

Results: The results summarized two goals, the first goal stipulates (to identify the degree of cyberbullying among academically outstanding students in the middle school stage). To achieve this goal, the researchers applied the research tool (electronic bullying scale) and then extracted the arithmetic mean for the sample of the current research, which amounted to (6.28) with a standard deviation of (4.03). Then the researchers applied the t-test for one sample to identify the significance of the differences between the means. The arithmetic mean for the sample and the hypothetical (theoretical) mean, which amounted to (11.5) degrees, and after applying the T-test for one sample, it was found that the calculated T-value, which amounted to

The research aims at constructing a Scale of Kindness phenomenon among university female students and elicit criteria to it, It also recognizes the differences in kindness levels among female students according to variables (specialization, academic grade, social status, and the age). The sample consists of 534 female students who were selected randomly. The two researchers rely on experience and the results of questionnaire, The questionnaire is given to 130 female university students from different colleges as well as their acquaintance with literary works witch dealt with kindness , The scale consists of 39 items , It has psychometric characteristics (Validity and Reliability) . The criterion (Z) is extracted from it and throu

The current study aims to identify the introspective awareness of the study sample, as well as to identify the introspective awareness of the study sample in terms of gender. The researcher adopted the viewpoint of Mehling (2002) as a theoretical framework for Introspective awareness. A sample of (239) male students and (331) female students were chosen randomly from two universities (Baghdad University and Al- Mustansiriyah University). To achieve the objectives of the research, the researcher adopted a vulgar scale (Mehling, 2012), which in its final form consisted of (32) items distributed into eight domains. As for the reliability coefficient of the scale, it reached (0.896) in the Cronbach alpha equation. The study revealed that the

The current research aimed to explore the practicing of cyber bullying among college students as well as the differences of practicing cyber bullying among university students based on gender variable. The finding revealed that there was a high prevalence rate for cyber bullying among research sample who was students from Baghdad University. It assured clearly that this group participated in cyber bullying effectively, due to the biological and psychological factors which make them super sensitive toward the social and economic problems. Moreover, the results showed that there were significant differences between male and female in practicing cyber bullying. The study proved that women use cyber bulling more than men.  

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

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims