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Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm
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Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different resolutions. By considering features from multiple levels, the detection algorithm can better capture both global and local characteristics of the manipulated regions, enhancing the accuracy of forgery detection. To achieve a high accuracy rate, this paper presents a variety of scenarios based on a machine-learning approach. In Copy-Move detection, artifacts and their properties are used as image features and support Vector Machine (SVM) to determine whether an image is tampered with. The dataset is manipulated to train and test each classifier; the target is to learn the discriminative patterns that detect instances of copy-move forgery. Media Integration and Call Center Forgery (MICC-F2000) were utilized in this paper. Experimental evaluations demonstrate the effectiveness of the proposed methodology in detecting copy-move. The implementation phases in the proposed work have produced encouraging outcomes. In the case of the best-implemented scenario involving multiple trials, the detection stage achieved a copy-move accuracy of 97.8 %. 

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Publication Date
Thu Oct 01 2026
Journal Name
Mustansiria Dental Journal
Soft tissue measurements of Iraqi individuals with Cl I and Cl III skeletal pattern: a comparative cephalometric study
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Publication Date
Thu Oct 01 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Assessment of tongue space area in a sample of Iraqi adults with class I dental and skeletal pattern
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Background: Lateral cephalometric radiography is commonly used as a standard tool in orthodontic assessment and treatment planning. This study aimed to determine the tongue and surrounding space area in a sample of Iraqi adults with class I dental and skeletal pattern. Materials and methods: The study included thirty healthy subjects (15 males and 15 females) with an age ranged between 23-34 years and class I dental and skeletal pattern with no history of any sleep related disorders. The assessed cephalometric measurement included length and height of the tongue and position of hyoid bone from cervical line. Descriptive statistics were obtained for the data. Genders difference was evaluated by independent sample t-test. Results: There wer

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Publication Date
Tue Mar 28 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Comparative Analysis of the Fatty Acid Pattern of Silybum marianum with Nigella sativa by Gas Chromatography-Mass Spectrometry
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Many studied were conducted to evaluate the antihepatotoxic and antioxidant activities of Silybum marianum and proved these actions. The Naturally grown seed in Iraqi-Kurdistan Region also were studied for its chemical contents and biological activities. Vegetable oils occur in various plant parts mainly concentrated in the seeds.

In this study comparison was made between the fatty acid patterns of two plant seeds, Silybum marianum and Nigella sativa. Seed sample of Silybum marianum and Nigella sativa were exposed for extraction and isolation of the fatty acid contents using two different solvents (petroleum ether and n-hexane) at 60-80oC using soxhlet apparatus and the oily extract

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Publication Date
Thu Apr 01 2021
Journal Name
Basra Journal Of Science
Preparation and Characterization of Polyvinylpyrrolidone/Multi-walled Carbon Nanotubes Nanocomposite hybrid with Graphene
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In this work, polyvinylpyrrolidone (PVP), Multi-walled carbon nanotubes (MWCNTs) nanocomposite was prepared and hybrid with Graphene (Gr) by casting method. The morphological and optical properties were investigated. Fourier Transformer-Infrared (FT-IR) indicates the presence of primary distinctive peaks belonging to vibration groups that describe the prepared samples. Scanning Electron Microscopy (SEM) images showed a uniform dispersion of graphene within the PVP-MWCNT nanocomposite. The results of the optical study show decrease in the energy gap with increasing MWCNT and graphene concentration. The absorption coefficient spectra indicate the presence of two absorption peaks at 282 and 287 nm attributed to the π-π* electronic tr

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Improving Fractal Image Compression Scheme through Quantization Operation
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We explore the transform coefficients of fractal and exploit new method to improve the compression capabilities of these schemes. In most of the standard encoder/ decoder systems the quantization/ de-quantization managed as a separate step, here we introduce new way (method) to work (managed) simultaneously. Additional compression is achieved by this method with high image quality as you will see later.

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Publication Date
Sat Nov 02 2019
Journal Name
Advances In Intelligent Systems And Computing
Spin-Image Descriptors for Text-Independent Speaker Recognition
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Building a system to identify individuals through their speech recording can find its application in diverse areas, such as telephone shopping, voice mail and security control. However, building such systems is a tricky task because of the vast range of differences in the human voice. Thus, selecting strong features becomes very crucial for the recognition system. Therefore, a speaker recognition system based on new spin-image descriptors (SISR) is proposed in this paper. In the proposed system, circular windows (spins) are extracted from the frequency domain of the spectrogram image of the sound, and then a run length matrix is built for each spin, to work as a base for feature extraction tasks. Five different descriptors are generated fro

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Publication Date
Mon Sep 30 2024
Journal Name
Al-mustansiriyah Journal Of Science
A Transfer Learning Approach for Arabic Image Captions
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Self-Localization of Guide Robots Through Image Classification
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The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

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Publication Date
Sat Sep 30 2023
Journal Name
حوليات أداب عين شمس
Ethnology and globalization of image in cinematographic discourse
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Publication Date
Sat Jun 01 2019
Journal Name
International Journal Of Computer Science And Mobile Computing
Hierarchal Polynomial Coding of Grayscale Lossless Image Compression
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