Learning Disabilities are described as a hidden and puzzling disability. Children with these difficulties have the potential to hide weaknesses in their performance because they are a homogenous group of disorders that consist of obvious difficulties in acquiring and using reading, writing, Mathematical inference. Thus, the research aims to identify the disabilities of academic learning in (reading, writing, mathematics), identify the problems of behavior (general, motor, social). Identify the relationship among behaviour problems. The research also aims to identify the counseling needs to reduce the behavioral problems. The researcher adopted the analytical descriptive method by preparing two main tools for measuring learning disabilities and behavioral problems, which were administered to a sample of sixth-grade pupils in (16) primary schools in four governorates in central and southern Iraq. The results of the study showed that the sample has academic learning difficulties and behavioral problems in all fields. Moreover, the study revealed a number of necessary guidance needs. The researcher came out with some recommendations.
This research aims at identifying problems that the Iraqi widow woman suffers from. The research is limited to a Sample of terrorism victims widows which is up to (70) widows from Al-Amal center for widows and orphans care in Baghdad. <
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A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
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Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte
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Active Learning And Creative Thinking
Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
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The aim of this paper is to determine the effect of nostalgia marketing on consumers’ purchase intention and demographic factors. Nostalgia marketing is one of the marketing ideas that some organizations use it to attract customers by evoking memories or heritage in their minds. This method would affect the emotions and feelings of people, which may raise their desire to buy. The questionnaire was used as a tool for data collection, and it was distributed to a random sample of 512 individuals. A sample is a group of individuals who have seen small sculptures displayed in shops inside Babylon Mall in Baghdad. The small sculptures show the life of Baghdadis in the fifties and sixties of the last century. Statistical software was used for
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The current research aims to identify the effect of the program to develop the skill of friendship among kindergarten children, as well as the scope of the impact of the program on the sample. To achieve the objectives of the research, the researcher hypothesizes there is no significant difference between the average scores of the sample members on the friendship skill scale for the dimensional scale according to the experimental and control group. The research sample consisted of (60) girl and boy with age ranges (4-6) who were randomly selected from the Kindergarten Unity at Baghdad city/ Rusafa 1. The children were distributed into an experimental and control group, each group consists of (30) girl and boy. The two groups were chosen
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In this paper, the topic of forecasting the changes in the value of Iraqi crude oil exports for the period from 2019 to 2025, using the Markov transitional series based on the data of the time series for the period from January 2011 to November 2018, is real data obtained from the published data of the Central Agency Of the Iraqi statistics and the Iraqi Ministry of Oil that the results reached indicate stability in the value of crude oil exports according to the data analyzed and listed in the annex to the research.
Keywords: Using Markov chains