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Matching Algorithms for Intrusion Detection System based on DNA Encoding
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Pattern matching algorithms are usually used as detecting process in intrusion detection system. The efficiency of these algorithms is affected by the performance of the intrusion detection system which reflects the requirement of a new investigation in this field. Four matching algorithms and a combined of two algorithms, for intrusion detection system based on new DNA encoding, are applied for evaluation of their achievements. These algorithms are Brute-force algorithm, Boyer-Moore algorithm, Horspool algorithm, Knuth-Morris-Pratt algorithm, and the combined of Boyer-Moore algorithm and Knuth–Morris– Pratt algorithm. The performance of the proposed approach is calculated based on the executed time, where these algorithms are applied on NSL-KDD dataset. The obtained results showed that the average time for matching for all NSL-KDD dataset records, based on Brute-force algorithm, Boyer-Moore algorithm, Horspool algorithm, Knuth-Morris-Pratt algorithm, and the combined of Boyer-Moore algorithm and Knuth–Morris–Pratt algorithm are equal to 18.4, 11.5, 9.23, 7.5, and 23.2 seconds respectively. These results demonstrated that using single algorithm achieved better time than combined algorithms, and Knuth-Morris-Pratt algorithm gives the best result than the rest of the other three algorithms. The results are reasonable and acceptable when they are compared with previous systems.

Scopus
Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
Diagnosis of Malaria Infected Blood Cell Digital Images using Deep Convolutional Neural Networks
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     Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN

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Publication Date
Mon Nov 29 2021
Journal Name
Iraqi Journal Of Science
Determining and Estimating the Creep of Tigris Riverbanks at the Baghdad University Camp Using GIS Analytical Symmetrical Difference Method
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The Tigris River in Iraq is of highly meandering in several of its parts. So, the largest meandering inside Baghdad City, is in Al-Jadriyah. During its course, the Tigris Riverbanks are facing erosion frequently due to alteration in the geomorphological and hydrological characteristics affecting the river channel. The entire length of Tigris River from the northern entrance of Baghdad to the convergence with Diyala River at southern of Baghdad is about 49 km length. The Tigris River is suffering from the erosion, deposition, and migration conditions. The river migration was found as maximum in the left bank at the side of the University, and lesser in the right bank in the opposite side, Dora. The aim of this study is to measure the magn

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