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Image Compression Using 3-D Two-Level Techniques
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In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent root-mean-square difference (PRD %), energy retained (Er) and Peak Signal to Noise Ratio (PSNR). Based on testing results, a comparison between the three techniques is presented. CR in the three techniques is the same and has the largest value in the 2nd level of 3-D. The hybrid technique has the highest PSNR values in the 1st and 2nd level of 3-D and has the lowest values of (PRD %). so, the 3-D 2-level hybrid is the best technique for image compression.

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Publication Date
Mon Feb 19 2018
Journal Name
Al-academy
Directing Techniques to Process the Radio Drama Script ( Khata'a Play as a Sample)
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     The radio drama is considered to be one of the arts that is discovered after a long period of theater's discovery. Initially , it was the broad framework of the theater's work when radio was broadcasting the shows on the huge theaters. This beginning encouraged many of the radio specialists to correlate plays with radio and make a novice and distinctive type of art. Thus, radio drama made its first step including the following   ( plays, short and long series drama as well as other types of radio arts). Because of the above mentioned , the researcher is stimulating  to study directing techniques to process the radio drama script ( Khata'a play as a sample).

The first chapter deals with the

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Publication Date
Sun Mar 15 2020
Journal Name
Journal Of The College Of Education For Women
A Stylistic Study of Cohesion in Relation to Narrative Techniques in Religious Discourse
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Cohesion is well known as the study of the relationships, whether grammatical and/or lexical, between the different elements of a particular text by the use of what are commonly called 'cohesive devices'. These devices bring connectivity and bind a text together. Besides, the nature and the amount of such cohesive devices usually affect the understanding of that text in the sense of making it easier to comprehend.  The present study is intendedto examine the use of grammatical cohesive devicesin relation to narrative techniques. The story of Joseph from the Holy Quran has been selected to be examined by using Halliday and Hasan's Model of Cohesion (1976, 1989). The aim of the study is to comparatively examine to what extent the type

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Publication Date
Sun Jan 01 2017
Journal Name
Research Journal Of Pharmacy And Technology
Estimating the plain and negative tendonography techniques for evaluating injured tendon in rabbit
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Publication Date
Tue Jan 08 2019
Journal Name
Lubricants
Influence of Sample Mixing Techniques on Engine Oil Contamination Analysis by Infrared Spectroscopy
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For the most reliable and reproducible results for calibration or general testing purposes of two immiscible liquids, such as water in engine oil, good emulsification is vital. This study explores the impact of emulsion quality on the Fourier transform infrared (FT-IR) spectroscopy calibration standards for measuring water contamination in used or in-service engine oil, in an attempt to strengthen the specific guidelines of ASTM International standards for sample preparation. By using different emulsification techniques and readily available laboratory equipment, this work is an attempt to establish the ideal sample preparation technique for reliability, repeatability, and reproducibility for FT-IR analysis while still considering t

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Publication Date
Wed Mar 08 2023
Journal Name
Sensors
A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology
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To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

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Publication Date
Thu Mar 02 2023
Journal Name
Applied Sciences
Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
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The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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Publication Date
Fri Sep 01 2017
Journal Name
Journal Of Language Teaching And Research
The Effectiveness of Role Play Techniques in Teaching Speaking for EFL College Students
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The paper investigates the impact of role-playing as a classroom technique on Iraqi EFL students’ speaking skill on Iraqi EFL students at the college level. The students are 40 college language students in University of Baghdad, College of Education Ibn-Rushd randomly chosen. Then, they were divided into two groups, experimental and control groups. Thirty questions were applied to both groups as a pre-test of speaking and the students asked to answer them orally. The experimental group was taught speaking skill of the targeted role-play technique while the control group was taught in traditional method. After 20 lessons of the teaching, the post-test of speaking was conducted in which the students in both groups were asked to answ

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Simplified Novel Approach for Accurate Employee Churn Categorization using MCDM, De-Pareto Principle Approach, and Machine Learning
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Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

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Publication Date
Thu Dec 01 2016
Journal Name
Iraqi Journal Of Physics, 2016
Energy transfer process between two laser compounds coumarin 334 & rhodamine 590
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Abstract: In the current research the absorption and fluorescence spectrum of Coumarin (334) and Rhodamine (590) in ethanol solvent at different concentration (10-3, 10-4, 10-5) M had been studied. The absorption intensity of these dyes increases as the Concentration increase in addition to that the spectrum was shifted towards the longer wavelength (red shift). The energy transfer process has been investigated after achievement this condition. The fluorescence peak intensity of donor molecule was decrease and its bandwidth will increases on the contrary of the acceptor molecule its intensity increase gradually and its bandwidth decreases as the acceptor concentration increase.

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