The current study aims at identifying the impact of using learning acceleration model on the achievement of mathematics for third intermediategrade students. Forachieving this, the researchers chose the School (Al-Kholood Secondary School for Girls) affiliated to the General Directorate of Babylon Education / Hashemite Education Department for the academic year (2021/2021), The sample reached to (70) female students from the third intermediate grade, with (35) female students for each of the two research groups. The two researchers prepared an achievement test consisting of (25) objective items of multiple choice type, The psychometric properties of the test were confirmed, and after the completion of the experiment, the achievement test was applied to the two research groups at the same time, and after processing the data statistically using the statistical bag, the study concluded thatthat the experimental group students who studied according to learning acceleration model outperformed the students of the control group who studied according to the traditional method. First // Research problem: We live today in an open era, the most prominent characteristic of which is access to knowledge in many forms that are faster, more interesting and attractive, Perhaps what we really need is knowledge that is transformed into projects and works that contribute to changing reality and making the future;Through the study path of the two researchers and their visit to a number of mathematics teachers for the third intermediate grade, and through a questionnaire distributed to them, the researchers concluded that (85%) of the male and female teachers are not satisfied with their students achievement in mathematics, and this was confirmed by the success rates for the past year, which reached to (34.69%), and some studies indicated a decrease in the achievement level as a study ( Al-Ayoubi, 2007) and (Mizban, 2018) study. The two researchers noted that the reason for the low level of achievement is due to most male and female teachers adopting traditional methods of teaching, using few teaching methods, giving ready information to students, not benefiting from the students’ mental capabilities and abilities, and not knowing many teachers of modern strategies and models in teaching mathematics, which emphasizes the positive role for the learner and taking into account the individual differences among students. The two researchers believe that there is an urgent need to keep up with developments in teaching methods and means by relying on modern models and strategies in teaching, as it is no longer acceptable to maintain traditional methods because they are no longer sufficient to meet the educational process requirements, especially that the world is witnessing qualitative and quantitative leaps in all areas of life, and maintain the traditional methods of teaching will inevitably increase the gap between us and developed world countries. In order to address this problem, the two researchers believe that using learning acceleration model in teaching mathematics to the third intermediate grade female students is that it addresses low achievement problem.
The problem of Multicollinearity is one of the most common problems, which deal to a large extent with the internal correlation between explanatory variables. This problem is especially Appear in economics and applied research, The problem of Multicollinearity has a negative effect on the regression model, such as oversized variance degree and estimation of parameters that are unstable when we use the Least Square Method ( OLS), Therefore, other methods were used to estimate the parameters of the negative binomial model, including the estimated Ridge Regression Method and the Liu type estimator, The negative binomial regression model is a nonline
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The researcher tried to show the importance of obligation with the rules of professional conduct set by the Association of Accountants and Auditors of Iraqis when expressing an opinion the financial statements submitted tax administration and enhance the confidence of these lists, and adopted in the tax settling accounts process and its impact on tax revenues, and touched a researcher at the theoretical side of the search to the problem of research and of my Is there a trace of non- not to obligation by the rules of professional conduct in Iraq affect the tax revenue with the rules of professional conduct in tax revenue for companies research sample, the research aims to study the rules of professional conduct set by the Associatio
... Show MoreSymmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a rand
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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
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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
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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
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Measuring the efficiency of postgraduate and undergraduate programs is one of the essential elements in educational process. In this study, colleges of Baghdad University and data for the academic year (2011-2012) have been chosen to measure the relative efficiencies of postgraduate and undergraduate programs in terms of their inputs and outputs. A relevant method to conduct the analysis of this data is Data Envelopment Analysis (DEA). The effect of academic staff to the number of enrolled and alumni students to the postgraduate and undergraduate programs are the main focus of the study.
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