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bsj-9740
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of Alzheimer's disease. The system employs MRI and feature extraction methods to categorize images. This paper adopts the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes functional MRI and Positron-Version Tomography scans for Alzheimer's patient identification, which were produced for people with Alzheimer's as well as typical individuals. The proposed technique uses MRI brain scans to discover and categorize traits utilizing the Histogram Features Extraction (HFE) technique to be combined with the Canny edge to representing the input image of the Convolutional Neural Networks (CNN) classification. This strategy keeps track of their instances of gradient orientation in an image. The experimental result provided an accuracy of 97.7% for classifying ADNI images.

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet atta</p> ... Show More
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Publication Date
Wed May 16 2018
Journal Name
Route Educational And Social Science Journal I
Graphing skills of Chemistry students at Ibn Al-Haytham Education college of pure sciences in Baghdad
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The aim of this research is to investigate the skills of the chemistry students from the Ibn Al-Haytham Education college of pure sciences in Baghdad in understanding and constructing graphical representations of data. The research sample consisted of (101) male and female students in their fourth year of study during the 2016-2017 academic year. This sample represents 71% of the total number of students in this group.The research methodology used consisted of two parts relating to 19 issues. The first part is an objective multi choice type of test to measure the student’s skill in selecting the right representation of specific subject graph amongst many provided. The second part concentrated on measuring the student’s skill in construc

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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
The Spatial Analysis for Bassia eriophora (Schrad.) Asch. Plant Distributed in all IRAQ by Using RS & GIS Techniques
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        Most vegetation’s are Land cover (LC) for the globe, and there is an increased attention to plants since they represent an element of balance to natural ecology and maintain the natural balance of rapid changes due to systematic and random human uses, including the subject of the current study (Bassia eriophora ) Which represent an essential part of the United Nations system for land cover classification (LCCS), developed by the World Food Organization (FAO) and the world Organization for environmental program (UNEP), to observe basic environmental elements with modern techniques. Although this plant is distributed all over Iraq, we found that this plant exists primarily in the middle

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Publication Date
Mon Nov 11 2019
Journal Name
Day 3 Wed, November 13, 2019
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
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Abstract<p>The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the</p> ... Show More
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Publication Date
Thu Jun 01 2023
Journal Name
Baghdad Science Journal
Mesenchymal and stemness transdifferentiation via in-vitro infection of T24 cell line with Klebsiella pneumoniae
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Klebsiella pneumoniae has been found in the urinary tract of some bladder cancer patients. Bacterial presence within tumor tissue may affect the tumor-microenvironment and consequently influence cancer behavior, development, and treatment response. This study investigated mesenchymal and stemness transdifferentiation of bladder cancer cell line due to environmental stress of K. pneumoniae. Cultures of urothelial bladder cancer cell line (T24) were infected with K. pneumoniae with different multiplicity of infection (MOI) for two and four days. Transdifferentiation-associated features were morphologically assessed.

Moreover, transdifferentiation markers were estimated using Q-PCR and immunohistoc

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Publication Date
Tue Dec 30 2008
Journal Name
Al-kindy College Medical Journal
Myomectomy During Early Pregnancy
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Myoma is a common benign uterine tumor; therefore
it is common in pregnancy. One in ten women will
have complications related to myoma in pregnancy.
Few treatment options are available during pregnancy,
conservative treatment with analgesia, reassurance and
supportive therapy is almost always adequate but in
carefully selected patients, myomectomy has been
performed successfully without jeopardizing
pregnancy outcome. The usual indications for surgery
during pregnancy include torsion of pedunculated
uterine myoma and obstructed labor, surgical
intervention during pregnancy is occasionally
necessary in uncommon cases of intractable pain.
19 years old lady presented with intractable lower
abdominal

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Publication Date
Mon Oct 01 2018
Journal Name
Ieee Transactions On Network Science And Engineering
A Resource Allocation Mechanism for Cloud Radio Access Network Based on Cell Differentiation and Integration Concept
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Publication Date
Wed Mar 01 2023
Journal Name
Baghdad Science Journal
Minimum Neighborhood Domination of Split Graph of Graphs
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Let  be a non-trivial simple graph. A dominating set in a graph is a set of vertices such that every vertex not in the set is adjacent to at least one vertex in the set. A subset  is a minimum neighborhood dominating set if  is a dominating set and if for every  holds. The minimum cardinality of the minimum neighborhood dominating set of a graph  is called as minimum neighborhood dominating number and it is denoted by  . A minimum neighborhood dominating set is a dominating set where the intersection of the neighborhoods of all vertices in the set is as small as possible, (i.e., ). The minimum neighborhood dominating number, denoted by , is the minimum cardinality of a minimum neighborhood dominating set. In other words, it is the

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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT
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Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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