Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson correlation coefficient (PCC) are chosen from 10: Sex, Speech delay, Jaundice, Genetic disorders, and family history. Next, chosen ASD feature dataset through its paces using five ML techniques: Naive Bayes (NB), K-Nearest Neighbor (k-NN), Decision Tree (DT), Support Vector Machine (SVM), and AdaBoostM1 (ABM1). The proposed framework is assessed in the third phase utilizing five measurements such as accuracy, precision, predicting time, recall, and F1-score,. The findings revealed that: The NB and K-NN approaches exhibit superior accuracy rates of 99.2% and 97.2%, with minimal prediction times of approximately 0.3 seconds and 0.45 seconds, correspondingly. Conversely, the DT and AdBM1 methods demonstrate a minor decline in accuracy, achieving 94.8% and 87.6%, respectively, along with increased prediction times. Nonetheless, the SVM approach exhibits the least performance, achieving an accuracy of 80.4% with a highest prediction time of 0.84 seconds.
Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar
... Show MoreAbstract: The aim of the research identify the effect of using the five-finger strategy in learning a movement chain on the balance beam apparatus for students in the third stage in the College of Physical Education and Sports Science, as well as to identify which groups (experimental and controlling) are better in learning the kinematic chain on the balance beam device, has been used The experimental approach is to design the experimental and control groups with pre-and post-test. The research sample was represented by third-graders, as the third division (j) was chosen by lot to represent the experimental group, and a division Third (i) to represent the control group, after which (10) students from each division were tested by lot to repr
... Show MoreThe 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 wa
... Show MoreDiabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed
... Show MorePoverty phenomenon is very substantial topic that determines the future of societies and governments and the way that they deals with education, health and economy. Sometimes poverty takes multidimensional trends through education and health. The research aims at studying multidimensional poverty in Iraq by using panelized regression methods, to analyze Big Data sets from demographical surveys collected by the Central Statistical Organization in Iraq. We choose classical penalized regression method represented by The Ridge Regression, Moreover; we choose another penalized method which is the Smooth Integration of Counting and Absolute Deviation (SICA) to analyze Big Data sets related to the different poverty forms in Iraq. Euclidian Distanc
... Show MoreThis paper presents a point multiplication processor over the binary field GF (2233) with internal registers integrated within the point-addition architecture to enhance the Performance Index (PI) of scalar multiplication. The proposed design uses one of two types of finite field multipliers, either the Montgomery multiplier or the interleaved multiplier supported by the additional layer of internal registers. Lopez Dahab coordinates are used for the computation of point multiplication on Koblitz Curve (K-233bit). In contrast, the metric used for comparison of the implementations of the design on different types of FPGA platforms is the Performance Index.
The first approach attains a performance index
... Show MoreVisceral leishmaniasis (VL) is a parasitic infection caused by an intracellular growth of Leishmania spp. in macrophage cells. The autoimmune disorder is a condition takes place when the immune system produces antibodies which incorrectly attacked its own body tissues. VL has been involved as an effect or on the autoimmune aspect. This study was conducted to identify the auto antibodies profile in patients infected with VL. The presences of auto antibodies in 21 Iraqi children infected with VL were tested for laboratory autoimmune aspect. The highest percentage of seropositive in Leishmania patients was observed for anti-ds DNA, anti-Mi-2, anti-Ku and anti-PCNA antibodies (90.5%, 90.5%, 90.5% and 61.9%) respectively, while the lowest percen
... Show MoreAcquires this research importance of addressing the subject (environmental problems) with
age group task, a category that children pre-school, and also reflected the importance of
research, because the (environmental problems) constitute a major threat to the continuation
of human life, particularly the children, so the environment is Bmchkladtha within
kindergarten programs represent the basis of a hub of learning where the axis, where the
kindergarten took into account included in the programs in order to help the development of
environmental awareness among children and get them used to the sound practices and
behaviors since childhood .
The research also detected problem-solving skills creative with kids Riyad
W Tarik A, AW Ali T, Journal of the Faculty of Medicine, 2015 - Cited by 2
Background: Obesity has become one of the most important public heath problems all over the world.An epidemic of obesity is affecting children and adolescents across the developed and developing countries in recent years. As the prevalence of obesity increased, so did the prevalence of co morbidities like metabolic and endocrine diseases.Objectives: To overview obesity clinical features and the prevalence of associated co morbidities in children and adolescents attended the obesity researches and therapy center in Alkindy medical collage.Type of study: This is a cohort observational studyMethods : Obese child and adolescents aged 4-15year attended the obesity research and therapy unit in AL Kindy medical collage from the 1st of September
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