<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, comes in second place with a gross ratio of 91%. Furthermore, Bayesian ridge (BR), linear regressor (LR), and stochastic gradient descent (SGD), with mean square error and with accuracy ratios of 84.365%, 84.363%, and 79%. As a result, the performance precision of these regression models yields. The interaction framework was designed to be a straightforward tool for working with this paradigm. This model is a valuable tool for establishing strategies to counter the swiftness of climate change in the area under study.</span>
The present study aims at empirically investigating the effect of vocabulary learning strategies on Iraqi intermediate school students’vocabulary performance and reading comprehension. The population of the present study includes all the 1st year male students of Al-Wark’a intermediate school of Al-Risafa 1/ General Directorate of Education for the first course of the academic year (2015-2016). To achieve the aim of the study ,a pre-test and post-test after (5) weeks of experiment are administrated .The sample of the present study consists of (100) subjects :(50) students as an experimental group and other (50) students as a control group . The subj
... Show MoreThe study aims to identify the impact of the implementation of the integrated education strategy in the curriculum of the Arabic language for the seventh grade on the academic achievement in the schools of the capital Amman. The researcher adopted the experimental method, where two divisions of the seventh grade students were chosen from the secondary school for girls. The sample of the study was 60 students divided into two equal groups: 30 students represented the experimental group (A) and (30) students represented the control group. To collect the needed data, a test of (40) Multiple Choices was used. The results showed statistically significant differences between the mean scores of the experimental group who were taught acc
... Show MoreDeep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing
... Show MoreThe exchanges in various fields,like economics, science, culture, etc., have been enhanced unceasingly among different countries around the world in the twenty-first century, thus, the university graduate who masters one foreign language does not meet the need of the labor market in most countries.So, many universities began to develop new programs to cultivate students who can use more foreign languages to serve the intercultural communication. At the same time, there is more scientific research emerged which is related to the relationship between the second and third languages. This humble research seeks to explain the relevant concepts and analyze the real data collected from Shanghai International Studies University in China, to expl
... Show MoreEuphemisms are advantageous in people’s social life by turning sensitive into a more acceptable ones so that resentful feelings and embarrassment can be avoided. This study investigates the ability of Iraqi English learners in using euphemistic expressions, meanwhile, raising their awareness and the faculty members in English teaching faculties regarding the relevance of discussing the topics that demand euphemisation. This study comprised three stages: initial test, explicit instruction with activities, and a final test for the students’ development in this domain. A test has been distributed among 50 respondents, who are at the fourth year of their undergraduate study at the University of Babylon/ College of Basic Education. The lo
... Show MoreDeep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec
... Show MoreRegarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
... Show MoreIn most manufacturing processes, and in spite of statistical control, several process capability indices refer to non conformance of the true mean (µc ) from the target mean ( µT ), and the variation is also high. In this paper, data have been analyzed and studied for a blow molded plastic product (Zahi Bottle) (ZB). WinQSB software was used to facilitate the statistical process control, and process capability analysis and some of capability indices. The relationship between different process capability indices and the true mean of the process were represented, and then with the standard deviation (σ ), of achievement of process capability value that can reduce the standard deviation value and improve production out of theoretical con
... Show MoreIn this study, active knife and fixed knife of single-row disc silage machine has three different clearance C1, C2 and C3 (1, 3 and 5 mm) and it is tried in three different working speed V1, V2 and V3 (1.8, 2.5 and 3.7 km / h) and PTO speed (540 min-1) and machine's fuel consumption (l/h), average power consumption (kW), field energy consumption (kW/da), product energy consumption (kW/t), field working capacity (da/h), product working capacity (t/h) and Chopping size distribution characteristics of the fragmented material were determined. It has been found that knife-counter knife clearances smaller than 3 mm (1 mm) and larger (5 mm) have a negative effect on machine performance in general. In terms of fuel and power consumptions, the m
... Show MoreThis research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB
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