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.
This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce
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A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification
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Abstract
This research is aimed at indicating the impact of business process reengineering on corporate performance in the Office of the Inspector General of the Ministry of Higher Education and Scientific Research of the Iraqi study has identified a problem in a number of the most important questions - what the impact of the Business Process Reengineering at the corporate office performance indicators respondent? What are the actual results of the analysis of paths Administrative Process Engineering and Corporate Performance respondent in the office? In order to achieve the goal of the research and answer the questions of the problem, the study applied to a sample of
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Abstract
The aim of this research is to concentrate on the of knowledge management activities, initial activities: (Acquisition, Selection, Generation, Assimilation, Emission) knowledge, and support activities: (Measurement, Control, Coordination, Leadership) that is manipulate and controlling in achieving knowledge management cases in organization, that’s is leads to knowledge chain model, then determining the level of membership for these activities to knowledge chain model in a sample of Iraqi organization pushed by knowledge (Universities). The research depends on check list for gaining the data required, theses check list designed by apparently in diagnosing research dimensions and measurem
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This study presents a mathematical model describing the interaction of gut bacteria in the participation of probiotics and antibiotics, assuming that some good bacteria become harmful through mutations due to antibiotic exposure. The qualitative analysis exposes twelve equilibrium points, such as a good-bacteria equilibrium, a bad-bacteria equilibrium, and a coexisting endemic equilibrium in which both bacteria exist while being exposed to antibiotics. The theory of the Sotomayor theorem is applied to study the local bifurcation around all possible equilibrium points. It’s noticed that the transcritical and saddle-node bifurcation could occur near some of the system’s equilibrium points, while pitchfork bifurcation cannot be accrued at
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The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there
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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
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In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.
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