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Seasonal Variations Of Attached Cyanophyceae Algae on Cocreate Brideges In Tigress Rivere From Baghdad

The attached Cyanophyta community is a model for monitoring aquatic systems and interpreting environmental change in aquatic systems. The present study aims to conduct a seasonal study on Cyanophyta attached to the concrete abutments of three bridges (Al-Jadriya Bridge, Bab Al-Moadam Bridge, and Al-Muthanna Bridge) in the Tigris River within the city of Baghdad, central Iraq (from October 2021 to June 2022). The study included the quality of Cyanophyta and, the eleven environmental parameters (water temperature, electric conductivity, salinity, pH, total alkalinity, Calcium, Magnesium, dissolved oxygen, total dissolved solids, total phosphorus, and total nitrogen). Monthly samples were taken from the water under each bridge from three sampling sites in the river, and the results were presented as four seasons. A total of 45 Cyanophyta taxa were identified in the three orders. Order: Chroococcales 11%; Order: Oscillatoriales 64%; Order: Nostocales 25% dominance of Oscillatoriales from the second site, and the highest number of species recorded in  the autumn. The study showed clear differences in the total number of algal species attached to different sites. The study was able to add a new record species to the Iraqi algae flora, which is Lyngbya sordida Gomont.

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Publication Date
Mon Feb 28 2022
Journal Name
Journal Of Educational And Psychological Researches
A Suggested Proposal to Activate Educational Supervision Based on Professional Learning Societies

Professional learning societies (PLS) are a systematic method for improving teaching and learning performance through designing and building professional learning societies. This leads to overcoming a culture of isolation and fragmenting the work of educational supervisors. Many studies show that constructing and developing strong professional learning societies - focused on improving education, curriculum and evaluation will lead to increased cooperation and participation of educational supervisors and teachers, as well as increases the application of effective educational practices in the classroom.

The roles of the educational supervisor to ensure the best and optimal implementation and activation of professional learning soci

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Publication Date
Wed Sep 07 2022
Journal Name
2022 Iraqi International Conference On Communication And Information Technologies (iiccit)
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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

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Publication Date
Tue Oct 25 2022
Journal Name
Minar Congress 6
HANDWRITTEN DIGITS CLASSIFICATION BASED ON DISCRETE WAVELET TRANSFORM AND SPIKE NEURAL NETWORK

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

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Publication Date
Wed Jan 12 2022
Journal Name
Iraqi Journal Of Science
Face Detection by Using OpenCV’s Viola-Jones Algorithm based on coding eyes

Facial identification is one of the biometrical approaches implemented for identifying any facial image with the use of the basic properties of that face. In this paper we proposes a new improved approach for face detection based on coding eyes by using Open CV's Viola-Jones algorithm which removes the falsely detected faces depending on coding eyes. The Haar training module in Open CV is an implementation of the Viola-Jones framework, the training algorithm takes as input a training group of positive and negative images, and generates strong features in the format of an XML file which is capable of subsequently being utilized for detecting the wanted face and eyes in images, the integral image is used to speed up Haar-like features calc

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Publication Date
Sun Dec 31 2023
Journal Name
Iraqi Journal Of Information And Communication Technology
EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

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Publication Date
Mon May 22 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Proposed Hybrid Algorithm for Generate Database Index Key Based on Image Contents

  This paper deals with proposing new lifting scheme (HYBRID Algorithm) that is capable of preventing images and documents which are fraud through decomposing there in to the real colors value arrays (red, blue and green) to create retrieval keys for its properties and store it in the database and then check the document originality by retrieve the query image or document through the decomposition described above and compare the predicted color values (retrieval keys)  of the query document with those stored in the database. The proposed algorithm has been developed from the two known lifting schemes (Haar and D4) by merging them to find out HYBRID lifting scheme. The validity and accuracy of the proposed algorithm have been ev

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Publication Date
Thu Apr 18 2019
Journal Name
Iraqi Journal Of Science
Secondary Emission Effect on SomePre-Equilibrium Nuclear Reactions Spectra at Different Energies

The nuclear pre-equilibrium emission spectra have been studied and calculated using the exciton model with different reactions and incident energiesfor the target nuclei: . The secondary emissioncomponent has been inserted to the final emission spectrum and its effectshave been studied for only reactions with primary nucleons emission because the restrictions introduced by primary clusters emission reactions. It revealed a big contributioninenhancing the calculated energy spectra atincident energies more than

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Publication Date
Tue Nov 30 2021
Journal Name
Iraqi Journal Of Science
Quantifying Suicidal Ideation on Social Media using Machine Learning: A Critical Review

Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took th

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