The research aims to know the question asking skills in terms of levels, conditions, classification, and types. The research limited to the literature that dealt with the importance of questioning for students and teachers. The most important term used in the research is the skill (Ryan defined it as "the ability to perform with great efficiency, accuracy, and ease). The results of the research are as follows: 1. the questions asked by the schoolteacher within the assessment of students' learning. 2. Teachers should focus on the lower levels of learning (remembering, understanding and comprehension) and then evaluating students at the higher levels (synthesis and evaluation). 3. Teacher with good knowledge can skillfully use the questioning inside the class. The main research recommendations are: 1. Encouraging students to ask questions through procedural patterns that help them. 2. Motivating teachers to employ all types of the question-asking skills. The most important suggestion is to conduct a questionnaire to measure the question-asking skills among teacher who teaching social subjects.
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A study of taxonomic quality of soil algae was conducted with some environmental variables in three sites of local gardens (Kadhimiya, Adhamiya and Dora) within the governorate of Baghdad for the period from October 2016 to March 2017. The study identified 28 species belonging to 16 species in which the predominance of blue green algae (18 species) Followed by Bacillarophyta algae (7 species) and three types of Chlorophyta. The study showed an increase in species of Oscillatoria. The results showed no significant differences between sites in temperature, pH and relative humidity, while there were clear differences between sites for salinity and nutrient The study showed a difference of irrigation water quality and use of different fertilize
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<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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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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To evaluate and compare serum Leptin hormone level between Iraqi male & female and the relation between this hormone & BMI in these two groups.
A total of 44 normal male & female subjects were included in this study
{Group 1 : 22 female } , { Group 2 : 22 male}.
Serum Leptin hormone ,BMI &fasting blood glucose were measured for both groups.
Serum Leptin level in group 1 was (8.82 + 2.9 μg/L) where as in group 2 it was (4.65 + 3.2 μg/L) . These changes were statistically significant. Fasting blood glucose levels were technically within the normal value (
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Ovarian cancer has a high mortality and delayed diagnosis. Several immunological alterations take place during ovarian carcinogenesis, and can be of value in the surveillance of the diseases. This research was conducted to evaluate serum immunoglobulin levels in women with ovarian cancer and to assess their role in disease process. The present study is composed of 85 women (mean age = 62.03±12.4 yrs) with clinically and pathologically confirmed ovarian cancer and 65 healthy females as a control group (mean age = 61±12.1 yrs). ELISA test was achieved for the determination of serum [IgG, IgA, IgM]. The findings of current study illustrated significant (P=0.001) increase in serum IgG, IgA, and IgM levels as compared to co
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