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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 Feb 25 2024
Journal Name
Baghdad Science Journal
Self-Localization of Guide Robots Through Image Classification
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The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

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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
Wed Aug 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Performance Evaluation Based on Multi-UAV in Airborne Computer Network System
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network
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Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

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Publication Date
Thu Nov 17 2016
Journal Name
Plos One
Efficient and Stable Routing Algorithm Based on User Mobility and Node Density in Urban Vehicular Network
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Vehicular ad hoc networks (VANETs) are considered an emerging technology in the industrial and educational fields. This technology is essential in the deployment of the intelligent transportation system, which is targeted to improve safety and efficiency of traffic. The implementation of VANETs can be effectively executed by transmitting data among vehicles with the use of multiple hops. However, the intrinsic characteristics of VANETs, such as its dynamic network topology and intermittent connectivity, limit data delivery. One particular challenge of this network is the possibility that the contributing node may only remain in the network for a limited time. Hence, to prevent data loss from that node, the information must reach the destina

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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
Wed Apr 28 2021
Journal Name
2021 1st Babylon International Conference On Information Technology And Science (bicits)
An Efficient Method for Stamps Verification Using Haar Wavelet Sub-bands with Histogram and Moment
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Publication Date
Fri Jul 01 2016
Journal Name
Journal Of Engineering
Buckling Analysis of Edge Cracked Sandwich Plate
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This work presents mainly the buckling load of sandwich plates with or without crack for different cases. The buckling loads are analyzed experimentally and numerically by using ANSYS 15. The experimental investigation was to fabricate the cracked sandwich plate from stainless steel and PVC to find mechanical properties of stainless steel and PVC such as young modulus. The buckling load for different aspect ratio, crack length, cracked location and plate without crack found. The experimental results were compared with that found from ANSYS program. Present of crack is decreased the buckling load and that depends on crack size, crack location and aspect ratio.

 

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Publication Date
Wed Aug 07 2019
Journal Name
Iraqi National Journal Of Nursing Specialties
Body satisfaction and Depression Symptoms among Children with Precocious Puberty in Baghdad City
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Objective: to evaluate body image and depression symptoms of children with precocious puberty, and find out association between children`s sociodemographic characteristics and their body image and depression signs. Methodology: A cross sectional study, sample of (80) child from both gender, > 7 years were included due to their ability to express their own feeling, diagnosed with precocious puberty, attending out-patient endocrine clinics at pediatric hospitals in Baghdad city. Data collected, during the period from May to November 2018. Consent form has taken from children and their guardians to participate in study. Child body image scale (CBIS) was used to evaluate children body satisfaction (1) and Mood and feeling questionnaire (M

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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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