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Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms
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Abstract<p>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.</p>
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Publication Date
Sun Jan 01 2023
Journal Name
Aip Conference Proceedings
Mining categorical Covid-19 data using chi-square and logistic regression algorithms
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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science (ijeecs)
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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Publication Date
Fri Jul 01 2016
Journal Name
Agricultural Engineering
The Change in Spectrum of Drops in the Exploitation Process of Agricultural Nozzles
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Abstract<p>Agricultural nozzles usually produce a different drops size depending on the pressure and the physical condition (work life) of the nozzle besides producing a wide range of the drops spectrum in the spray cloud. In this paper the standard flat fan nozzles were investigated regarding the effect of the working pressure and the nozzle physical condition (new and worn nozzles). The size of drops and the spectrum of drops across the long axis of the spray pattern were examined by using Sympatec GmbH Laser Diffraction. Reducing the working pressure from 3 to 2 and then to 1 caused production of larger drops, also using worn nozzles (especially with lower pressure) changed the drops size whi</p> ... Show More
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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Early Baldness in Males and Hormonal Changes
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Androgenetic alopecia (AGA) or baldness is a common form of hair loss, affecting men more than women .The exact causes and pathogenesis of baldness are not well understood. A search for new drug is still needed. This study aims to evaluate the role of gonadotropins hormones in hair loss in males and its possibility to be used as treatment target. A total of 78 volunteers 43 with baldness and 35 healthy men were participated in this study. Their age ranged between (20-49) years, the studied groups were divided into two categories: firstly according to age (group1: 20-29 year), (group2: 30-39 year),and (group3: 40-49 year).Secondly according to baldness degree. LH and FSH were measured by using Enzyme Linked Immunosorbent Assay (ELISA) kits.

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Publication Date
Thu Jan 01 2026
Journal Name
Sustainable Civil Infrastructures
Using Artificial Intelligence Algorithms to Predict the Physical Fitness Level in Iraqi Premier League Football Players
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Publication Date
Mon Dec 31 2018
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Detection of Thiopurine S-Methyltransferase (TPMT) Polymorphisms TPMT*3A, TPMT*3B and TPMT*3C in Children with Acute Lymphoblastic Leukemia
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Publication Date
Sat Jan 02 2010
Journal Name
Journal Of Al-nahrain University
HIDDEN FEATURES DETECTION USING HISTOGRAM MODIFICATION IN MRI IMAGES
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Magnetic Resonance Imaging (MRI) uses magnetization and radio waves, rather than x-rays to make very detailed, cross- sectional pictures of the brain. In this work we are going to explain some procedures belongs contrast and brightness improvement which is very important in the improvement the image quality such as the manipulation with the image histogram. Its has been explained in this worked the histogram shrink i.e. reducing the size of the gray level gives a dim low contrast picture is produced, where, the histogram stretching of the gray level was distributed on a wide scale but there is no increase in the number of pixels in the bright region. The histogram equalization has also been discuss together with its effects of the improveme

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Publication Date
Thu Dec 01 2022
Journal Name
Iraqi Journal Of Science
PLAGIARISM DETECTION SYSTEM IN SCIENTIFIC PUBLICATION USING LSTM NETWORKS
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Scopus (3)
Scopus
Publication Date
Sun Sep 01 2013
Journal Name
International Journal Of Advanced Research In Computer Science And Software Engineering
Real Time Motion Detection in Surveillance Camera Using MATLAB
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Surveillance cameras are video cameras used for the purpose of observing an area. They are often connected to a recording device or IP network, and may be watched by a security guard or law enforcement officer. In case of location have less percentage of movement (like home courtyard during night); then we need to check whole recorded video to show where and when that motion occur which are wasting in time. So this paper aims at processing the real time video captured by a Webcam to detect motion in the Scene using MATLAB 2012a, with keeping in mind that camera still recorded which means real time detection. The results show accuracy and efficiency in detecting motion

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