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Educational Data Mining For Predicting Academic Student Performance Using Active Classification
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     The increasing amount of educational data has rapidly in the latest few years. The Educational Data Mining (EDM) techniques are utilized to detect the valuable pattern so that improves the educational process and to obtain high performance of all educational elements. The proposed work contains three stages: preprocessing, features selection, and an active classification stage. The dataset was collected using EDM that had a lack in the label data, it contained 2050 records collected by using questionnaires and by using the students’ academic records. There are twenty-five features that were combined from the following five factors: (curriculum, teacher, student, the environment of education, and the family). Active learning had been utilized in the classification. Four techniques had been applied for classifying the features: Random Forest (RF) algorithm, Label Propagation (LP), Logistic Regression (LR), and Multilayer Perceptron (MLP). The accuracies of prediction were 95.121%, 92.195%, 92.292%, and 93.951% respectively. Also, the RF algorithm has been utilized for assorting the features depending on their importance.

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Publication Date
Thu May 25 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Physiochemical and Spectral Characterization For a Biological Active, Solanidine&Solasodine Partial Purification Compounds From (Solanum nigrum L)
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 This study includes a physiochemical and a spectrocpical characterization to some alkaloid compounds in the (ANAB AL- THEAB) plant (Solanum nigrun L.). It’s the most important medicinal herb belonging to the family (Solanaceae). Acid  hydrolysis was performed by using limited conc. of  Hcl and H2SO4, to obtain the aglycon  part of previously separated steroidal componants as (A, B and C).   The characterization of the(A,B and C) compounds indicates that they varied between them as the separated steroidal like-alkaloids, carried by using melting  point (m.p.), thin layer chromatography (TLC), Infra -Red  spectroscopy (IR) and Ultra violet-Visible  spectroscopy (UV - Visible).High perfor

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Publication Date
Thu Apr 29 2021
Journal Name
Iraqi Journal Of Science
The use of Vitally Active Cellulose Membranes for the Reduction of Pathogenic Bacterial Count in White Cheese
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This study was designed to evaluate the effects of cellulose membranes produced by Acetobacter xylinum bacteria, after enrichment of the growth media with Alzahdy palm dates syrup to enhance cellulose production for reducing the contamination of locally-produced white cheese with pathogenic bacteria. Cellulose was vitally activated by incubation with both probiotics Lactobacillus acidophilus and Lactobacillus plantarum and the effectiveness of the produced cellulose membranes was measured by studying six characteristics: elongation, tensile strength, membrane rupture, permeability to oxygen, permeability to water vapor, and thickness (mm). The produced membranes showed remarkable functionality and characteristi

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Educational And Psychological Researches
Educational and Psychological perceptions of Ibn Khaldun Thought Educational and psychological view s in The Thought of Ibn Khaldun Descriptive
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The research aims to identify the effect of Ibn Khaldun educational perceptions on modern science and comparing it with the modern-educational thought as well as exploring the educational perceptions of ibn Khldun by coming cross (the introduction of ibn Khaldun and some other references). In addition to identifying the effect of psychological perceptions of geographical context on people habits and behaviors, the researcher intends to identify the thoughts that relate to methods of teaching and the requirements of learning and teaching, and finally, identifying the psychological thoughts that relate to educational psychology and parapsychology. The study concluded that the purposes of educational aims of Ibn Khaldun are to give a chance

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Publication Date
Wed May 25 2022
Journal Name
Iraqi Journal Of Science
Using Persistence Barcode to Show the Impact of Data Complexity on the Neural Network Architecture
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    It is so much noticeable that initialization of architectural parameters has a great impact on whole learnability stream so that knowing  mathematical properties of dataset results in providing neural network architecture a better expressivity and capacity. In this paper, five random samples of the Volve field dataset were taken. Then a training set was specified and the persistent homology of the dataset was calculated to show impact of data complexity on selection of multilayer perceptron regressor (MLPR) architecture. By using the proposed method that provides a well-rounded strategy to compute data complexity. Our method is a compound algorithm composed of the t-SNE method, alpha-complexity algorithm, and a persistence barcod

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Publication Date
Wed Jun 26 2019
Journal Name
Iraqi Journal Of Science
Random Noise Attenuation by using FDNAT Filter on Seismic Data in East Diwaniya, South Eastern – Iraq
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The frequency dependent noise attenuation (FDNAT) filter was applied on 2D seismic data line DE21 in east Diwaniya, south eastern Iraq to improve the signal to noise ratio. After applied FDNAT on the seismic data, it gives good results and caused to remove a lot of random noise. This processing is helpful in enhancement the picking of the signal of the reflectors and therefore the interpretation of data will be easy later. The quality control by using spectrum analysis is used as a quality factor in proving the effects of FDNAT filter to remove the random noise.

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Publication Date
Sat Dec 01 2018
Journal Name
Indian Journal Of Ecology
Classification of al-hammar marshes satellite images in Iraq using artificial neural network based on coding representation
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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Classification of Arabic Alphabets Using a Combination of a Convolutional Neural Network and the Morphological Gradient Method
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The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

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Publication Date
Wed Aug 17 2022
Journal Name
Aip Conference Proceedings
The effect of using Gaussian, Kurtosis and LogCosh as kernels in ICA on the satellite classification accuracy
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This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)
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    Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA usin

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Publication Date
Mon Feb 25 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Using Classification of Brown risks in Evaluation of the internal control system: Application Research in Karbala University
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Internal control system is a safety valve that preserves economic units assets and ensure the accuracy of financial data, as well as to obligation in the laws, regulations, administrative policies ,and improve the efficiency, effectiveness and economic of operation, so it has become imperative for these units attention to internal and developed control system The research problem in exposure the economic units when the exercise of their business to many of the risks to growth or hinder the achievement of its objectives and the risks (financial, operational, strategy, risk) and not it rely on risk Assessment according to modern scientific methods, as in Brown's risk Classification, Which led to the weakness of the internal control identif

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