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Classification of Cardiac Arrhythmia using ID3 Classifier Based on Wavelet Transform
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Accurate detection of Electro Cardio Graphic (ECG) features is an important demand for medical purposes, therefore an accurate algorithm is required to detect these features. This paper proposes an approach to classify the cardiac arrhythmia from a normal ECG signal based on wavelet decomposition and ID3 classification algorithm. First, ECG signals are denoised using the Discrete Wavelet Transform (DWT) and the second step is extract the ECG features from the processed signal. Interactive Dichotomizer 3 (ID3) algorithm is applied to classify the different arrhythmias including normal case. Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database is used to evaluate the ID3 algorithm. The experimental result shows that the accuracy of ID3 is 92% in the case of Haar transform and 94% with Daubeshies4 transform.

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Publication Date
Wed Nov 24 2021
Journal Name
Iraqi Journal Of Science
Classification of soil infiltration rate depending on the Hydrological soil group map South East Iraq
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The study area is located in the East of Missan governorate, southeast of Iraq between (32°'29.52" – 32°37'30") latitude and (46°46'21.16" – 47°58'53.52")longitude. It encompasses an area of (1858 ) with elevation ranges from 8 to 165m. Soil is a natural body that exists as part of the pedosphere and which performs four important functions. It is a medium for plant growth and a means of water storage, supply and purification. The spatial mapping of soil usually involves delineating soil types that have identifiable characteristics. The delineation is based on many factors such as geomorphologic origin and conditions under which the soil is formed. Hydrologic soil group (HSG) refers to the classification of soils based on their ru

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Publication Date
Sun Apr 30 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Classification and Construction of (k,3)-Arcs on Projective Plane Over Galois Field GF(9)
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  In this work, we construct and classify the projectively distinct (k,3)-arcs in PG(2,9), where k ≥ 5, and prove that the complete (k,3)-arcs do not exist, where 5 ≤ k ≤ 13. We found that the maximum complete (k,3)-arc in PG(2,q) is the (16,3)-arc and the minimum complete (k,3)-arc in PG(2,q) is the (14,3)-arc. Moreover, we found the complete (k,3)-arcs between them.

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Publication Date
Sun Apr 30 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Classification and Construction of (k,3)-Arcs on Projective Plane Over Galois Field GF(7)
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  The purpose of this work is to study the classification and construction of (k,3)-arcs in the projective plane PG(2,7). We found that there are two (5,3)-arcs, four (6,3)-arcs, six (7,3)arcs, six (8,3)-arcs, seven (9,3)-arcs, six (10,3)-arcs and six (11,3)-arcs.         All of these arcs are incomplete.         The number of distinct (12,3)-arcs are six, two of them are complete.         There are four distinct (13,3)-arcs, two of them are complete and one (14,3)-arc which is incomplete.         There exists one complete (15,3)-arc.
 

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Publication Date
Fri Jan 01 2016
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
Genetic--Based Face Retrieval Using Statistical Features
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Publication Date
Tue Jan 29 2019
Journal Name
Journal Of The College Of Education For Women
Object Filling Using Table Based Boundary Tracking
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The feature extraction step plays major role for proper object classification and recognition, this step depends mainly on correct object detection in the given scene, the object detection algorithms may result with some noises that affect the final object shape, a novel approach is introduced in this paper for filling the holes in that object for better object detection and for correct feature extraction, this method is based on the hole definition which is the black pixel surrounded by a connected boundary region, and hence trying to find a connected contour region that surrounds the background pixel using roadmap racing algorithm, the method shows a good results in 2D space objects.
Keywords: object filling, object detection, objec

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Publication Date
Fri Nov 24 2023
Journal Name
Iraqi Journal Of Science
Characterization and Classification of Radioactive Wastes from Disposal Silo
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In the present work, classification of radioactive wastes based on Annual Intake (AI) values is studied. Where the characterization of radionuclides was done by hand held GeLi detector with an overall efficiency better than 42%. It was noted the most predominant contaminant are Cs-137, Co-60 and Pa-234.The radioactive waste in disposal silo has been divided into five categories according to the harmful effect of radionuclides.For the purpose of storageradioactive wastein a safe manner, it wassuggesteda new method by shielding radioactive waste in each category with concrete;where the thickness of shielding is the time required to reduce the annual dose to 10%.

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
Gait Recognition Based on Deep Learning
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      In current generation of technology, a robust security system is required based on biometric trait such as human gait, which is a smooth biometric feature to understand humans via their taking walks pattern. In this paper, a person is recognized based on his gait's style that is captured from a video motion previously recorded with a digital camera. The video package is handled via more than one phase after splitting it into a successive image (called frames), which are passes through a preprocessing step earlier than classification procedure operation. The pre-processing steps encompass converting each image into a gray image, cast off all undesirable components and ridding it from noise, discover differen

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Publication Date
Thu Mar 30 2023
Journal Name
Iraqi Journal Of Science
Detection and Mitigation of Cache Pollution Attack Using Popularity Variation in Information Centric Networking Based on SDN
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      Information centric networking (ICN) is the next generation of internet architecture with its ability to provide in-network caching that make users retrieve their data efficiently regardless of their location. In ICN, security is applied to data itself rather than communication channels or devices. In-network caches are vulnerable to many types of attacks, such as cache poisoning attacks, cache privacy attacks, and cache pollution attacks (CPA). An attacker floods non-popular content to the network and makes the caches evict popular ones. As a result, the cache hit ratio for legitimate users will suffer from a performance degradation and an increase in the content’s retrieval latency. In this paper, a popularity variation me

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
Operational assessment of biological wastewater treatment using advanced return-mass reactors based on principal component cluster analysis
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Abstract<p>In this study, field results data were conducted, implemented in 64 biofilm reactors to analyses extract organic matter nutrients from wastewater through a laboratory level nutrient removal process, biofilm layer moving process using anaerobic aerobic units. The kinetic layer biofilm reactors were continuously operating in Turbo 4BIO for BOD COD with nitrogen phosphorous. The Barakia plant is designed to serve 200,000 resident works on biological treatment through merge two process (activated sludge process, moving bed bio reactio MBBR) with an average wastewater flow of 50,000 m3/day the data were collected annually from 2017-2020. The water samples were analysis in the central labor</p> ... Show More
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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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