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The Effect of Endosulfan Pesticide in Some Biochemical Parameters of white Mice
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The present study aimed to examine the effect of endosulfan insecticide on some molecular and biochemical parameters in white mice. Thirty mice were separated randomly into three groups for treatment with endosulfan. One group (G1) served as the control, while the other two groups received intraperitoneal injections of endosulfan G2 (3 mg/kg) and G3 (17 mg/kg) twice a week for 21 and 45 days, respectively. A biochemical study by measuring liver  function parameters, including (alanine aminotransferase (ALT) and aspartate aminotransferase (AST)) and kidney function parameters, including (Blood Urea and Creatinine) and malondialdehyde (MDA), catalase activity (CAT). This study also tested DNA damage by comet assay (normal%, low%, medium%, high%). The results of renal function parameters (Blood Urea and Creatinine) were significantly increased in all treated groups after 21 and 45 days exposed to endosulfan compared with control groups. The highest value of blood urea recorded was (49.33 ±0.88 mg/dl) at 17 mg/kg for 45 days compared with the control group, and the highest value of Creatinine recorded was (1.81 ±0.13 mg/dl) at 17 mg/kg for 45 days compared with the control group. Liver function parameters (ALT and AST) significantly increased in all treated groups compared with control groups. The results of MDA, CAT enzyme, were significantly increased in all treated groups after 21 and 45 days compared with control groups. The highest value of MDA recorded was (3.93 ±0.07 μM) at 17 mg/kg for 45 days compared with the control group. Tail DNA (%) showed a significant increase at high concentrations, and the results showed a considerable increase in the severe damage of DNA in the treated group 17 mg\kg b.wt. (25.00 ±1.00)% for 45 days, compared with the control group (3.00 ±1.00) %.

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Publication Date
Wed Oct 09 2024
Journal Name
Engineering, Technology & Applied Science Research
Improving Pre-trained CNN-LSTM Models for Image Captioning with Hyper-Parameter Optimization
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The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

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Publication Date
Wed Jul 29 2020
Journal Name
Iraqi Journal Of Science
Fractal Image Compression Using Block Indexing Technique: A Review
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Fractal image compression depends on representing an image using affine transformations. The main concern for researches in the discipline of fractal image compression (FIC) algorithm is to decrease encoding time needed to compress image data. The basic technique is that each portion of the image is similar to other portions of the same image. In this process, there are many models that were developed. The presence of fractals was initially noticed and handled using Iterated Function System (IFS); that is used for encoding images. In this paper, a review of fractal image compression is discussed with its variants along with other techniques. A summarized review of contributions is achieved to determine the fulfillment of fractal image co

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Publication Date
Sat Jul 31 2021
Journal Name
Iraqi Journal Of Science
On Sandwich Theorems Results for Certain Univalent Functions Defined by Generalized Operators
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In this present paper, we obtain some differential subordination and superordination results, by using generalized operators for certain subclass of analytic functions in the open unit disk. Also, we derive some sandwich results.

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Publication Date
Mon Sep 23 2019
Journal Name
Baghdad Science Journal
Hazard Rate Estimation Using Varying Kernel Function for Censored Data Type I
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     In this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used:  local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the

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Publication Date
Sun Aug 01 2021
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Novel Heuristic Approach for Solving Multi-objective Scheduling Problems
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    In this paper, we studied the scheduling of  jobs on a single machine.  Each of n jobs is to be processed without interruption and becomes available for processing at time zero. The objective is to find a processing order of the jobs, minimizing the sum of maximum earliness and maximum tardiness. This problem is to minimize the earliness and tardiness values, so this model is equivalent to the just-in-time production system. Our lower bound depended on the decomposition of the problem into two subprograms. We presented a novel heuristic approach to find a near-optimal solution for the problem. This approach depends on finding efficient solutions for two problems. The first problem is minimizing total completi

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Publication Date
Sun Mar 04 2018
Journal Name
Baghdad Science Journal
Solving Fuzzy Games Problems by Using Ranking Functions
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In this paper, we deal with games of fuzzy payoffs problem while there is uncertainty in data. We use the trapezoidal membership function to transform the data into fuzzy numbers and utilize the three different ranking function algorithms. Then we compare between these three ranking algorithms by using trapezoidal fuzzy numbers for the decision maker to get the best gains

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Publication Date
Thu Jan 14 2021
Journal Name
Iraqi Journal Of Science
Identifying Digital Forensic Frameworks Based on Processes Models
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Digital forensic is part of forensic science that implicitly covers crime related to computer and other digital devices. It‟s being for a while that academic studies are interested in digital forensics. The researchers aim to find out a discipline based on scientific structures that defines a model reflecting their observations. This paper suggests a model to improve the whole investigation process and obtaining an accurate and complete evidence and adopts securing the digital evidence by cryptography algorithms presenting a reliable evidence in a court of law. This paper presents the main and basic concepts of the frameworks and models used in digital forensics investigation.

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Publication Date
Sun Jul 31 2022
Journal Name
Iraqi Journal Of Science
Deep Learning and Machine Learning via a Genetic Algorithm to Classify Breast Cancer DNA Data
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       This paper uses Artificial Intelligence (AI) based algorithm analysis to classify breast cancer Deoxyribonucleic (DNA). Main idea is to focus on application of machine and deep learning techniques. Furthermore, a genetic algorithm is used to diagnose gene expression to reduce the number of misclassified cancers. After patients' genetic data are entered, processing operations that require filling the missing values using different techniques are used. The best data for the classification process are chosen by combining each technique using the genetic algorithm and comparing them  in terms of accuracy.

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Publication Date
Sat May 07 2016
Journal Name
International Journal Of Science And Research
Synthesis, Characterization and Antimicrobial Activity Studies of Mixed-1,10-phenanthroline- Mn(II),Co(II), Cu(II), Ni(II) and Hg(II) Complexes with Schiff Base[2,2'-(1Z,1'Z)-(biphenyl-4,4'- diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1- ylidene)diphenol]
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Abstract: The M(II) complexes [M2(phen)2(L)(H2O)2Cl2] in (2:1:2 (M:L:phen) molar ratio, (where M(II) =Mn(II), Co(II), Cu(II), Ni(II) and Hg(II), phen = 1,10-phenanthroline; L = 2,2'-(1Z,1'Z)-(biphenyl-4,4'-diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1- ylidene)diphenol] were synthesized. The mixed complexes have been prepared and characterized using 1H and13C NMR, UV/Visible, FTIR spectra methods and elemental microanalysis, as well as magnetic susceptibility and conductivity measurements. The metal complexes were tested in vitro against three types of pathogenic bacteria microorganisms: Staphylococcus aurous, Escherichia coli, Bacillussubtilis and Pseudomonasaeroginosa to assess their antimicrobial properties. From this study shows that a

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Publication Date
Tue Mar 14 2023
Journal Name
Iraqi Journal Of Science
Improve PGP Cryptography Protocol Using Genetic NTRU Technique
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The strong cryptography employed by PGP (Pretty Good Privacy) is one of the best available today. The PGP protocol is a hybrid cryptosystem that combines some of the best features of both conventional and public-key cryptography. This paper aim to improve PGP protocol by combined between the Random Genetic algorithm, NTRU (N-th degree Truncated polynomial Ring Unit) algorithm with PGP protocol stages in order to increase PGP protocol speed, security, and make it more difficult in front of the counterfeiter. This can be achieved by use the Genetic algorithm that only generates the keys according to the Random Genetic equations. The final keys that obtained from Genetic algorithm were observed to be purely random (according to the randomne

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