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Color-based for tree yield fruits image counting
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Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the colour space transform (i.e., YUV, YIQ, or YCbCr). The resultant image was then applied to the algorithm for fruit segmentation. After it is applied Morphological Operations which is enhanced image then execute Blob counting method which identify the object and count the number of it. Accuracy of this algorithm used in this thesis is 82.21% for images that have been scanned.

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Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

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Publication Date
Fri Jun 24 2022
Journal Name
Iraqi Journal Of Science
EfficientEfficientEfficientEfficientEfficientEfficientEfficientEfficientEfficient text in image hidingtext in image hidingtext in image hidingtext in image hidingtext in image hidingtext in image hidingtext in image hidingtext in image hidingtext in image
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The steganography (text in image hiding) methods still considered important issues to the researchers at the present time. The steganography methods were varied in its hiding styles from a simple to complex techniques that are resistant to potential attacks. In current research the attack on the host's secret text problem didn’t considered, but an improved text hiding within the image have highly confidential was proposed and implemented companied with a strong password method, so as to ensure no change will be made in the pixel values of the host image after text hiding. The phrase “highly confidential” denoted to the low suspicious it has been performed may be found in the covered image. The Experimental results show that the cov

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Publication Date
Wed Aug 31 2022
Journal Name
Iraqi Journal Of Science
Generating Streams of Random Key Based on Image Chaos and Genetic Algorithm
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    Today the Genetic Algorithm (GA) tops all the standard algorithms in solving complex nonlinear equations based on the laws of nature. However, permute convergence is considered one of the most significant drawbacks of GA, which is known as increasing the number of iterations needed to achieve a global optimum. To address this shortcoming, this paper proposes a new GA based on chaotic systems. In GA processes, we use the logistic map and the Linear Feedback Shift Register (LFSR) to generate chaotic values to use instead of each step requiring random values. The Chaos Genetic Algorithm (CGA) avoids local convergence more frequently than the traditional GA due to its diversity. The concept is using chaotic sequences with LFSR to gene

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Publication Date
Thu Oct 25 2018
Journal Name
Al–bahith Al–a'alami
Image of relief organizations for displaced Iraqis
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This research aims to know the intellectual picture the displaced people formed about aid organizations and determine whether they were positive or negative, the researchers used survey tool as standard to study the society represented by displaced people living in Baghdad camps from Shiites, Sunnis, Shabak, Turkmen, Christians, and Ezidis.
The researcher reached to important results and the most important thing he found is that displaced people living in camps included in this survey hold a positive opinion about organizations working to meet their demands but they complain about the shortfall in the health care side.
The research also found that displaced people from (Shabak, Turkmen, and Ezidi) minorities see that internati

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Publication Date
Tue Jan 01 2013
Journal Name
International Journal Of Advanced Research In Computer Science And Software Engineering
Boundary & Geometric Region Features Image Segmentation for Quadtree Partitioning Scheme
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In this paper, an efficient image segmentation scheme is proposed of boundary based & geometric region features as an alternative way of utilizing statistical base only. The test results vary according to partitioning control parameters values and image details or characteristics, with preserving the segmented image edges.

Publication Date
Sun Apr 26 2020
Journal Name
Iraqi Journal Of Science
Selective Image Encryption Based on DCT, Hybrid Shift Coding and Randomly Generated Secret Key
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Most of today’s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and the
computational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.
The results showed a significant reduct

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Scopus (14)
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Publication Date
Wed Dec 20 2017
Journal Name
New Visions In Plant Science
Improving Nitrogen and Phosphorus Efficiency for Optimal Plant Growth and Yield
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Nitrogen (N) and phosphorus (P) are the most important nutrients for crop production. The N contributes to the structural component, generic, and metabolic compounds in a plant cell. N is mainly an essential part of chlorophyll, the compound in the plants that is responsible for photosynthesis process. The plant can get its available nitrogen from the soil by mineralizing organic materials, fixed-N by bacteria, and nitrogen can be released from plant as residue decay. Soil minerals do not release an enough amount of nitrogen to support plant; therefore, fertilizing is necessary for high production. Phosphorous contributes in the complex of the nucleic acid structure of plants. The nucleic acid is essential in protein synthesis regulation; t

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges
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The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

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Scopus (9)
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Publication Date
Thu Jul 20 2023
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Applying Ensemble Classifier, K-Nearest Neighbor and Decision Tree for Predicting Oral Reading Rate Levels
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For many years, reading rate as word correct per minute (WCPM) has been investigated by many researchers as an indicator of learners’ level of oral reading speed, accuracy, and comprehension. The aim of the study is to predict the levels of WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), and K- Nearest Neighbor (KNN). The data of this study were collected from 100 Kurdish EFL students in the 2nd-year, English language department, at the University of Duhok in 2021. The outcomes showed that the ensemble classifier (EC) obtained the highest accuracy of testing results with a value of 94%. Also, EC recorded the highest precision, recall, and F1 scores with values of 0.92 for

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Publication Date
Mon Nov 29 2021
Journal Name
Iraqi Journal Of Science
Application of Surfactant for Enhancing the Adsorption of Azo Dye Onto Buckthorn Tree Wood Surface
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Cetyl trimethyl ammonium bromide (CTAB), was used as surfactant to enhance
the removal of Congo- red (C-R) dye from its solution by employing a wood of
buckthorn tree as a low cost and eco-friendly adsorbent. Different factors affecting
the sorption process like, time of adsorption, wood dose, initial (C-R) concentration
and hydrogen ion concentration have been studied for both adsorbent wood (W) and
modified wood (MW). The suitability of the two isotherm models Langmuir and
frundlich to the equilibrium results was mentioned for the adsorption of (C-R) on
both wood and modified wood. From the value of correlation - factor, frundlich
model give a better fit than Langmuir isotherm model. Thermodynamic function
su

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