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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
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset
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Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

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Publication Date
Tue Feb 01 2022
Journal Name
Journal Of Engineering Science And Technology (jestec)
ADAPTIVE D2D COMMUNICATION WITH INTEGRATED IN-BAND AND OUT-BAND SPECTRUM BY EMPLOYING CHANNEL QUALITY INDICATOR
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Publication Date
Sun Sep 30 2012
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
The Control of Non Isothermal CSTR Using Different Controller Strategies
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In all process industries, the process variables like flow, pressure, level, concentration
and temperature are the main parameters that need to be controlled in both set point
and load changes.
A control system of propylene glycol production in a non isothermal (CSTR) was
developed in this work where the dynamic and control system based on basic mass
and energy balance were carried out.
Inlet concentration and temperature are the two disturbances, while the inlet
volumetric flow rate and the coolant temperature are the two manipulations. The
objective is to maintain constant temperature and concentration within the CSTR.
A dynamic model for non isothermal CSTR is described by a first order plus dead
time (FO

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Publication Date
Tue Oct 24 2017
Journal Name
International Journal Of Science And Research
Incidence Rate of Infectious Diseases in Hospitalized Children
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Objective: The incidence of infectious diseases among hospitalized children remains high. This study describes the environmental health indicator diseases and differences between gender and age of the affected children. Patients & Methods: A review of hospitalization and death causes among children admitted to the Child Teaching Hospital for Pediatrics in Baghdad, Iraq was performed. Medical records of children admitted throughout three years period were collected for demographics data and statistical analysis using Chi-square test. Results: A total of 4,272 admissions and 309 deaths were recorded. Infectious diseases remain the primary cause of hospitalization among children and frequently lead to death; which recorded (56%) followed by ac

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Publication Date
Tue Dec 01 2020
Journal Name
International Journal Of Research In Pharmaceutical Sciences
Iraqi Experience of Factor VII use in Children
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Bleeding disorders in pediatrics is an important issue and can be lifethreatening if not diagnosed and treated appropriately. We aimed to evaluate Iraqi pediatric practice (as an example of resource-limited settings) about the use of Recombinant Activated Factor VII (RFVIIa) in bleeding disorders, with emphasis on its effectiveness and safety, in comparison with adjuvant therapy. Budget restrictions may affect the availability of even lifesaving drugs such as (RFVIIa). Therefore, we tried to investigate the local experience of pediatric bleeding, with the evaluation of the potential ability of adjuvant therapy of blood products and vitamin K to substitute RFVIIa in case of non-availability. During a complete one year‘s period, 35 patients

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Scopus
Publication Date
Sat Jun 06 2020
Journal Name
Journal Of The College Of Education For Women
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

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Publication Date
Sat Nov 03 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Prevalence of Hypochondriasis Disorder among Iraqi Former Prisoners of Iraq-Iran War, 1980-1988
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Objective : To find out the prevalence of Hypochondriasis among Iraqi repatriated prisoners of
Iraq-Iran war, and the relationship with some variables.
Methodology: A descriptive study was carried out from Jan. 2nd , 2006 through May 4th , 2006. A
non-probability accidental sample of 400 repatriates who had visited; Ministry of Human Rights,
Ministry of Health, and Ministry of Defense. A questionnaire was constructed for this purpose, which
consisted of 6 items for demographic data, and 14 items for measuring Hypochondriasis. Reliability
and validity of the questionnaire had been determined through the pilot study (Test and retest) and the
experts panel. Data were collected with using the constructed questionnaire an

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Publication Date
Mon Dec 11 2017
Journal Name
Al-khwarizmi Engineering Journal
Proposed Hybrid Sparse Adaptive Algorithms for System Identification
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Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Physics: Conference Series
Improve topic modeling algorithms based on Twitter hashtags
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Abstract<p>Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned</p> ... Show More
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Publication Date
Thu Jun 16 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Optimization algorithms for transportation problems with stochastic demand
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The purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems of unbalanced transport, And the possibility of applying the model approved by the oil products distribution company in the Iraqi Ministry of Oil to m

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