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Automatic Segmentation and Identification of Abnormal Breast Region in Mammogram Images Based on Statistical Features
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Breast cancer is one of the most common malignant diseases among women;
Mammography is at present one of the available method for early detection of
abnormalities which is related to breast cancer. There are different lesions that are
breast cancer characteristic such as masses and calcifications which can be detected
trough this technique. This paper proposes a computer aided diagnostic system for
the extraction of features like masses and calcifications lesions in mammograms for
early detection of breast cancer. The proposed technique is based on a two-step
procedure: (a) unsupervised segmentation method includes two stages performed
using the minimum distance (MD) criterion, (b) feature extraction based on Gray
level Co-occurrence matrices GLCM for the identification of masses and
calcifications lesions. The method suggested for the detection of abnormal lesions
from mammogram image segmentation and analysis was tested over several images
taken from National Center for Early Detection of cancer in Baghdad.

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Publication Date
Tue Feb 01 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Finger Vein Recognition Based on PCA and Fusion Convolutional Neural Network
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Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

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Publication Date
Thu Aug 30 2018
Journal Name
Iraqi Journal Of Science
Towards Generating Robust Key Based on Neural Networks and Chaos Theory
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There are large numbers of weakness in the generated keys of security algorithms. This paper includes a new algorithm to generate key of 5120 bits for a new proposed cryptography algorithm for 10 rounds that combine neural networks and chaos theory (1D logistic map). Two methods of neural networks (NN) are employed as Adaline and Hopfield and the results are combined through several sequential operation. Carefully integrating high quality random number generators from neural networks and chaos theory to obtain suitable key for randomness and complexity.

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Publication Date
Sat Sep 30 2023
Journal Name
Iraqi Journal Of Science
Novel Dynamic S-Box Based on Password Key and Circle Map
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     Many cryptosystems and security techniques use substitution boxes to ensure the data’s secure communication. A new technique is presented for generating a robust S-box to fulfill security requirements. The AES algorithm represents a block cipher cryptographic algorithm. It was selected by the National Institute of Science and Technology as the optimal cryptographic algorithm in 2011. Through the study of the properties of original S-BOX, this algorithm has been subjected to a number of attacks (linear, differential, statistical, and interpolation), and original S-BOX has been static, which makes the attack strong and shows a weakness in the algorithm.  It is necessary to make this algorithm more efficient and powerful through

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Publication Date
Tue Jun 01 2021
Journal Name
Ieee Systems Journal
Intelligent Traffic Management and Load Balance Based on Spike ISDN-IoT
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An intelligent software defined network (ISDN) based on an intelligent controller can manage and control the network in a remarkable way. In this article, a methodology is proposed to estimate the packet flow at the sensing plane in the software defined network-Internet of Things based on a partial recurrent spike neural network (PRSNN) congestion controller, to predict the next step ahead of packet flow and thus, reduce the congestion that may occur. That is, the proposed model (spike ISDN-IoT) is enhanced with a congestion controller. This controller works as a proactive controller in the proposed model. In addition, we propose another intelligent clustering controller based on an artificial neural network, which operates as a reactive co

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Publication Date
Tue Jan 01 2019
Journal Name
Ieee Access
Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials
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Publication Date
Sun Feb 27 2022
Journal Name
Iraqi Journal Of Science
Digital Speech Files Encryption based on Hénon and Gingerbread Chaotic Maps
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Speech encryption approaches are used to prevent eavesdropping, tracking, and other security concerns in speech communication. In this paper, a new cryptography algorithm is proposed to encrypt digital speech files. Initially, the digital speech files are rearranged as a cubic model with six sides to scatter speech data. Furthermore, each side is encrypted by random keys that are created by using two chaotic maps (Hénon and Gingerbread chaotic maps). Encryption for each side of the cube is achieved, using the based map vector that is generated randomly by using a simple random function. Map vector that consists of six bits, each bit refers to one of the specific chaotic maps that generate a random key to encrypt each face of the cube. R

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Scopus (12)
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Publication Date
Mon Nov 29 2021
Journal Name
Iraqi Journal Of Science
An Accurate Handwritten Digits Recognition system Based on DWT and FCT
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In this paper an accurate Indian handwritten digits recognition system is
proposed. The system used three proposed method for extracting the most effecting
features to represent the characteristic of each digit. Discrete Wavelet Transform
(DWT) at level one and Fast Cosine Transform (FCT) is used for features extraction
from the thinned image. Besides that, the system used a standard database which is
ADBase database for evaluation. The extracted features were classified with KNearest
Neighbor (KNN) classifier based on cityblock distance function and the
experimental results show that the proposed system achieved 98.2% recognition
rate.

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Publication Date
Wed Dec 14 2022
Journal Name
Journal Of The College Of Languages (jcl)
Images of ruin in "Throne" and " Water stream" by Maysaloon Hadi and "War" by Glezio: A Compartive Study: Les images du débris dans "Le trône et le ruisseau " de Maysaloon Hadi et "La guerre" de Le Glézio (étude comparative)
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Our research comes to shed light on Iraqi literature as literature that arose in special circumstances alongside foreign literature. Using comparative research methods, we chose to highlight two distinguished writers, who have their mark in the world of literature. The first is the Iraqi writer Maysaloun Hadi, who is considered an icon of Iraqi feminist literature, and the second is the French writer Le Clézieu, who won the Nobel in 2008. We will see through the research how the two authors expressed their views of modernity and urbanism. And how each of them separately portrayed the psychological and moral projections that formed the essence of man today. 

Résumé

Notre recherche abord un des points inc

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Publication Date
Sun Dec 01 2013
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
THE FAUNISTIC SURVEY OF LADY BEETLES (COLEOPTERA, COCCINELLIDAE) IN THE MEHRIZ REGION(YAZD PROVINCE), IRAN
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  In this study the faunistic of lady beetles (Coleoptera, Coccinellidae) was studied in Mehriz region (Yazd province) during 2009-2010. The total number of specimens of coccinellid beetles were collected from 6 different localities having altitudes from 1420-2420 m. Altogether 11 species from 8 genera, 3 tribes and 3 subfamilies were collected and identified. External characters plus characteristics male and female genitalia were used in order to diagnose species. Seven species were recorded for the first time from Yazd province (marked*). Many species were predacious, preying on various species of aphids, mites and coccids. Some species were also sent to Dr. Helmut Fursch in Germany for identification or confirmation. T

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Publication Date
Fri Jul 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Determine the best model to predict the consumption of electric energy in the southern region
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Abstract:          

                Interest in the topic of prediction has increased in recent years and appeared modern methods such as Artificial Neural Networks models, if these methods are able to learn and adapt self with any model, and does not require assumptions on the nature of the time series. On the other hand, the methods currently used to predict the classic method such as Box-Jenkins may be difficult to diagnose chain and modeling because they assume strict conditions.

  

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