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CT Image Segmentation Based on clustering Methods.
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Background: image processing of medical images is major method to increase reliability of cancer diagnosis.
Methods: The proposed system proceeded into two stages: First, enhancement stage which was performed using of median filter to reduce the noise and artifacts that present in a CT image of a human lung with a cancer, Second: implementation of k-means clustering algorithm.
Results: the result image of k-means algorithm compared with the image resulted from implementation of fuzzy c-means (FCM) algorithm.
Conclusion: We found that the time required for k-means algorithm implementation is less than that of FCM algorithm.MATLAB package (version 7.3) was used in writing the programming code of our work.

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Publication Date
Fri Dec 08 2023
Journal Name
Iraqi Journal Of Science
Intrusion Detection Approach Based on DNA Signature
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Intrusion-detection systems (IDSs) aim at detecting attacks against computer systems and networks or, in general, against information systems. Most of the diseases in human body are discovered through Deoxyribonucleic Acid (DNA) investigations. In this paper, the DNA sequence is utilized for intrusion detection by proposing an approach to detect attacks in network. The proposed approach is a misuse intrusion detection that consists of three stages. First, a DNA sequence for a network traffic taken from Knowledge Discovery and Data mining (KDD Cup 99) is generated. Then, Teiresias algorithm, which is used to detect sequences in human DNA and assist researchers in decoding the human genome, is used to discover the Shortest Tandem Repeat (S

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Publication Date
Wed Feb 16 2022
Journal Name
Iraqi Journal Of Science
New Arabic Stemming based on Arabic Patterns
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Algorithms for Arabic stemming available in two main types which are root-based approach and stem-based approach. Both types have problems which have been solved in the proposed stemmer which combined rules of both main types and based on Arabic patterns (Tafealat1) to find the added letters. The proposed stemmer achieved root exploration ratio (99.08) and fault ratio (0.9).

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Publication Date
Sat Aug 03 2024
Journal Name
Proceedings Of Ninth International Congress On Information And Communication Technology
Offline Signature Verification Based on Neural Network
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

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Publication Date
Thu Dec 30 2021
Journal Name
Iraqi Journal Of Science
3D Model Watermarking based on Wavelet Transform
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      In the last decade, 3D models gained interest in many applications, such as games, the medical field, and manufacture. It is necessary to protect these models from unauthorized copying, distribution, and editing. Digital watermarking is the best way to solve this problem. This paper introduces a robust watermarking method by embedding the watermark in the low-frequency domain, then selecting the coarsest level for embedding the watermark based on the strength factor. The invisibility of the watermark for the proposed algorithm is tested by using different measurements, such as HD and PSNR. The robustness was tested by using different types of attacks; the correlation coefficient was applied for the evaluati

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Publication Date
Fri Sep 22 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Design Active Filter Based on Genetic Algorithm
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The  lossy-FDNR  based  aclive  fil ter has an  important   property among  many  design  realizations. 'This includes  a significant reduction in component count particularly in the number  of OP-AMP which consumes   power.  However  the·  problem  of  this   type  is the  large component spreads  which affect the fdter performance.

In  this  paper   Genetic   Algorithm   is  applied   to  minimize   the component  spread   (capacitance  and  resistance  p,read). The minimization of these spreads allow the fil

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Publication Date
Thu Dec 29 2016
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Proposed Steganography Method Based on DCT Coefficients
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      In this paper an algorithm for Steganography using DCT for cover image and DWT for hidden image with an embedding order key is proposed. For more security and complexity the cover image convert from RGB to YIQ, Y plane is used and divided into four equally parts and then converted to DCT domain. The four coefficient of the DWT of the hidden image are embedded into each part of cover DCT, the embedding order based on the order key of which is stored with cover in a database table in both the sender and receiver sender. Experimental results show that the proposed algorithm gets successful hiding information into the cover image. We use Microsoft Office Access 2003 database as DBMS, the hiding, extracting algo

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Publication Date
Mon May 20 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Modeling Dynamic Background based on Linear Equation
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     Detection moving car in front view is difficult operation because of the dynamic background due to the movement of moving car and the complex environment that surround the car, to solve that, this paper proposed new method based on linear equation to determine the region of interest by building more effective background model to deal with dynamic background scenes. This method exploited the permitted region between cars according to traffic law to determine the region (road) that in front the moving car which the moving cars move on. The experimental results show that the proposed method can define the region that represents the lane in front of moving car successfully with precision over 94%and detection rate 86

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Publication Date
Thu Aug 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Emotion Recognition System Based on Hybrid Techniques
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Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

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Scopus (8)
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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Network Traffic Prediction Based on Boosting Learning
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Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traff

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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Engineering
Intelligent Dust Monitoring System Based on IoT
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Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

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