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.
The study aims to identify the students’ attitudes toward recent techniques’ use in teaching; using power point software and data show to facilitate teaching approaches as well as following the approach of lecture in giving subjects, do these techniques facilitate, increase, and tackle the difficulties of subjects, do these attitudes positive or negative?, and to what extent these techniques raise up students’ motivation.
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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Education specialists have differed about determining the best ways to detect the
talented. Since the appearance of the mental and psychological measurement movement, some
scholars adopted intelligence ratios as a criterion to identify the talented and others went to
rely on the degree of academic achievement. Each of these two methods has its own flaws and
mistakes and a large number of talented children were victims of these two methods.
Therefore the need to use other scales for the purpose of detection of talented children
appeared because they provide valuable information which may not be obtained easily
through objective tests and these scales are derived from consecutive studies of gifted andtalented children
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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Active Learning And Creative Thinking
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ABSTRACT Background: Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders affecting women in their reproductive age.It is characterized by anovulation or oligo-ovulation and hyperandrogensim.Androgen excess is the central defect in polycystic ovary syndrome. It is a complex disorder affects general health in addition to oral health.This study aimed to assess the gingival health status among a group of women with polycystic ovary syndrome as well as to estimate the levels of salivaryfree testosterone in unstimulated saliva in relation to gingival health condition. Materials and methods: Sixty two women with an age range 20-25 years old and with a body mass index range18.5-24.9 (normal weight) were included in this s
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This study was conducted in Baghdad, Iraq from December 2021 to May 2022. The goal was to determine the effect of Toxoplasma gondii on liver function by examining the relationship between Toxoplasma infection and hormones. One hundred and twenty male patients with Chronic liver disease (CLD) (age:14-75 years) and 120 control males (age: 24-70 years) participated in this study. Serum samples were taken from all individuals and were then analysed for anti-Toxoplasma antibodies. Hormonal tests were conducted for all participants which included (Cortisol, testosterone, prolactin, insulin, and thyroid-stimulating hormone TSH). Biochemical tests included (Prothrombin time PT, international normalized ratio INR and albumin); liver enzymes
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