Table of content

Journal of Al-Qadisiyah for Computer Science and Mathematics

مجلة القادسية لعلوم الحاسوب والرياضيات

ISSN: 20740204 / 25213504
Publisher: Al-Qadisiyah University
Faculty: Computer Science and Mathematics
Language: English

This journal is Open Access

About

The journal of Al-Qadisiyah of computer science and mathematics is refereed journal holding ISSN (print)2074 – 0204, ISSN(online)2521-3504 . The journal accept original research papers to the editor , this journal covers subjects that include computer science, mathematics and Statistics & Information , is two issues per year, contributions in English and Arabic languages are accepted for submission provided that they conform to the universally accepted rules of scientific research.

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Journalcm@qu.edu.iq
qhaq2010@gmail.com

Table of content: 2019 volume:11 issue:2

Article
L(wc) -spaces and Some of its Weak Forms

Authors: Rasha N. Majeed --- Haider J. Ali --- Nadia A. Nadhim
Pages: Math Page 20-29
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Abstract

In this paper, we provide a new generalization of LC-spaces which is LC-spaces, -spaces also another weak forms of LC-spaces which is called -spaces,(i=1,2,3,4). In addition, we give the relationships between these new types and studied the heredity property for each type.


Article
Solving Job Scheduling Problem Using Fireworks Algorithm

Authors: Rehab Hassan --- Jamal N. Hasoon
Pages: Comp Page 1-8
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Abstract

Scheduling is critical part in most creation frameworks and information processing as sequencing of tasks or jobs framework executed on a grouping of processors. One of the NP-hard problem is “Job Shop Scheduling Problem”. In this work, a method of optimization proposed called “Fireworks Algorithm”. The solutions divided into fireworks and each one applied sparks to find the best solution. For some selected spark applied Gaussian mutation to find enhanced solution and find optimum solution. FWA tested on dataset to improve performance and it do well with respect to some other algorithm like Meerkat Clan Algorithm (MCA), Camel Herds Algorithm) CHA (, and Cukoo Search Algorithm (CSA).


Article
Anemia Blood Cell localization Using Modified K- Means Algorithm

Authors: Loay E.George --- Huda M.Rada --- Mela.G.Abdul-Haleem
Pages: Comp Page 9-21
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Abstract

In this project segmentation of image strategy based on K-means clustering calculation is displayed. The proposed strategy utilizes clustering to allocate the dominant colors in medical tissue images for purpose of segmentation with high performance. The initialization step of the system is the selection of suitable color model used for segmentation. A set of inter and intra-class measures are used to evaluate the degree of model suitability. The method is able to make segmentation at different classification resolutions. For purpose of performance evaluation the comes about of the proposed strategy, standard K-Means and as of late altered K-Means are compared. The exploratory comes about appeared that the proposed strategy gives superior result.


Article
Proposed Aspect Based Sentiment Analysis system for English reviews

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Abstract

Reviews are a crucial source of opinions that may influence the decision in many areas. So there is a need for an algorithm that is efficient in understanding the aspects that the reviewers have focused on in their reviews and comments on social networks or other web applications. This paper submits a proposed approach for aspect-based sentiment analysis that consists of two steps; the first step is by a proposed p_chunker algorithm for aspect extraction using Latent Dirchilet Analysis and noun phrase chunking, the second step is sentiment analysis using a proposed hybrid algorithm that depending on both lexicon and supervised sentiment analysis to specify the sentiment for extracted aspects. The proposed paradigm is tested using standard datasets from kaggle for both aspect extraction and sentiment analysis, the result show efficacy in the proposed method.


Article
Prediction Model for Financial Distress Using Proposed Data Mining Approach By

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Abstract

The problem of financial distress researches are the lack of awareness of banks about the risks of financial failure and its impact on the continuity of its activity in the future, as the traditional methods used to predict financial failure through financial analysis based on financial ratios in a single result gives misleading results cannot be relied upon to judge the continuity of the activity of banks, With an increase in the number of failed banks and their inability to continue. Which requires the discovery of modern techniques that serve as an early warning of the possibility of failure and lack of continuity. The research aims to apply data mining technology to predict the financial failure of banks, and how it can provide information that helps to judge the extent to which banks continue to operate. This effort suggested founded back propagation artificial neural network to build predict system. The proposed module evaluated with banks from Free Iraq Stock Exchange dataset the investigational outcomes displays capable method to identify failure banks with great discovery rate and small wrong terror rate.


Article
Internet of Things Security using New Chaotic System and Lightweight AES

Authors: Jolan Rokan Naif --- Ghassan H. Abdul-majeed --- Alaa K. Farhan
Pages: Comp Page 45-52
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Abstract

The Internet of Things (IoT)services and application were increasing during the last years in several life fields causes need to provide a secure identifier for protecting sensing data passing between IoT sensors/devices and embedded-subsystem connected by networks. This paper was proposed an algorithm for helping in IoT communication security can used in different IOT entities used in unofficial industrial machine to machine (M2M) communications, smart energy-grids, home or buildings and other computing devices. This paper proposed a secure system using new proposed 4D chaotic system combined with the modified lightweight Advanced Encryption Standard (AES). The proposed 4-dimension (4D) chaos system Lyapunov was tested and pass for many initial periods and get a super chaos system (4 positive Lyapunov). Generated chaos keys (used JORN) were used in the lightweight AES and the Secure Hash Algorithm version 3 (SHA3-256). The Lightweight AES was design in case to reduce CPU computation cycles and complexity of AES. Results show that computation time for proposed system decreased (has 145% speedup more). The output of modified lightweight AES encryption System has the good statistical tests near to original AES that can avoid many attacks.

Keywords

IoT --- IoT security --- AES --- chaos and AES.


Article
Bayesian Lasso Tobit regression

Authors: Haider Kadhim Abbas
Pages: Stat Page 1-13
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Abstract

In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced. We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.


Article
Modeling the Rainfall Count data Using Some Zero Type models with application

Authors: Luay Habeeb Hashim --- Ahmad Naeem Flaih
Pages: Stat Page 14-27
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Abstract

Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

Keywords


Article
Selecting the best model to fit the Rainfall Count data Using Some Zero Type models with application

Authors: Luay Habeeb Hashim --- Ahmad Naeem Flaih
Pages: Stat Page 28-41
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Abstract

Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regression model to analysis count data is likely to bias the results, under these limitations, Poisson regression model and “Negative binomial regression” are likely the appropriate models to analysis count data. Sometimes researchers may Counts more zeros than the expected. Count data with many Zeros leads to a concept called “Zero-inflation”. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the “zero- altered Poisson” (ZAP) model and the “negative binomial” model. In this paper, the models, Poisson, Negative Binomial, ZAP, and ZANB were been used to analyze rainfall data.

Keywords


Article
Comparison of Some Robust Wilks’ Statistics for the One-Way Multivariate Analysis of Variance (MANOVA (

Authors: Abdullah A. Ameen --- Osama H. Abbas
Pages: Stat Page 42-58
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Abstract

The classical Wilks' statistic is mostly used to test hypotheses in the one-way multivariate analysis of variance (MANOVA), which is highly sensitive to the effects of outliers. The non-robustness of the test statistics based on normal theory has led many authors to examine various options. In this paper, we presented a robust version of the Wilks' statistic and constructed its approximate distribution. A comparison was made between the proposed statistics and some Wilks' statistics. The Monte Carlo studies are used to obtain performance assessment of test statistics in different data sets. Moreover, the results of the type I error rate and the power of test were considered as statistical tools to compare test statistics. The study reveals that, under normally distributed, the type I error rates for the classical and the proposed Wilks' statistics are close to the true significance levels, and the power of the test statistics are so close. In addition, in the case of contaminated distribution, the proposed statistic is the best.


Article
On Fuzzy differential equation

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Abstract

In this paper, we introduce a hybrid method to use fuzzy differential equation, and Genetic Turing Machine developed for solving nth order fuzzy differential equation under Seikkala differentiability concept [14]. The Errors between the exact solutions and the approximate solutions were computed by fitness function and the Genetic Turing Machine results are obtained. After comparing the approximate solution obtained by the GTM method with approximate to the exact solution, the approximate results by Genetic Turing Machine demonstrate the efficiency of hybrid methods for solving fuzzy differential equations (FDE).


Article
Certain Types of Groupoids

Authors: Taghreed Hur Majeed --- Deyaa Hussain Ali
Pages: Math Page 10-19
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Abstract

In this work, we try to construct anew types of groupoids and disucss their properties.


Article
On Semiprime Gamma Near-Rings with Perpendicular Generalized 3-Derivations

Authors: Ikram A. Saed
Pages: Math Page 30-37
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Abstract

In this paper ,we introduce the notion of perpendicular generalized 3-derivations in semiprime gamma near-rings and present several necessary and sufficient conditions for generalized 3-derivations on semiprime gamma near-rings to be perpendicular.


Article
Some Properties of Topology Fuzzy Modular Space

Authors: and Al-ham S . Nief --- Noori F. Al-Mayahi
Pages: Math Page 38-45
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Abstract

In the present paper , the authors have introduced and studied fuzzy modular space. they have investigated some properties of this space in the open and closed balls. Also the authors discussed the convex set and the locally convex in fuzzy modular space. The result obtained are correct and the methods used are interesting .


Article
Differential Subordination Results for Holomorphic Functions Related to Differential Operator

Authors: Abbas Kareem Wanas --- S R Swamy
Pages: Math Page 46-53
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Abstract

,In the present work, we introduce and study a certain class of holomorphic functions defined by differential operator in the open unit disk U. Also, we derive some important geometric properties for this class such as integral representation, inclusion relationship and argument estimate.


Article
Approximaitly Quasi-Prime Submodules and Some Related Concepts

Authors: Haibat K. Mohammadali --- Ali Sh. Ajeel
Pages: Math Page 54-62
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Abstract

“Let R be a commutative ring with identity and B is a left unitary R-module. A proper submodule E of B is called a quasi-prime submodule, if whenever rsb∈E, where r,s∈R, b∈B implies that either rb∈E or sb∈E”. As a generalization of a quasi-prime submodules, in this paper we introduce the concept of approximaitly quasi-prime submodules, where a proper submodule E of B is an approximaitly quasi-prime submodule, if whenever rsb∈E, where r,s∈R, b∈B implies that either rb∈E+soc(B) or sb∈E+soc(B), where soc(B) is the intersection of all essential submodules of B. Many basic properties, characterization and examples of this concept are given. Furthermore, we study the behavior of approximaitly quasi-prime submodules under R-homomorphisms. Finally, we introduced characterizations of approximaitly quasi-prime submodule in class of multiplication modules.


Article
EXISTENCE OF NONOSCILLATORY RELATIVELY BOUNDED SOLUTIONS OF SECOND ORDER NEUTRAL DIFFERENTIAL EQUATIONS

Authors: Hussain Ali mohamad --- Bashar Ahmed Jawad Sharba
Pages: Math Page 63-71
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Abstract

In this paper some sufficient conditions are obtained to insure the existence of positive solutions which is relatively bounded from one side for nonlinear neutral differential equations of second order. We used the Krasnoselskii’s fixed point theorem and Lebesgue’s dominated convergence theorem to obtain new sufficient conditions for the existence of a Nonoscillatory one side relatively bounded solutions. These conditions are more applicable than some known results in the references. Three examples included to illustrate the results obtained.


Article
Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study

Authors: Hanaa Mohsin Ahmed --- Halah Hasan Mahmoud
Pages: Comp Page 53-64
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Abstract

Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields. In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations. Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.


Article
CHOOSING APPROPRIATE IMPUTATION METHODS FOR MISSING DATA: A DECISION ALGORITHM ON METHODS FOR MISSING DATA

Authors: Wisam A. Mahmood --- Mohammed S. Rashid --- Teaba wala Aldeen
Pages: Comp Page 65-73
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Abstract

Missing values commonly happen in the realm of medical research, which is regarded creating a lot of bias in case it is neglected with poor handling. However, while dealing with such challenges, some standard statistical methods have been already developed and available, yet no credible method is available so far to infer credible estimates. The existing data size gets lowered, apart from a decrease in efficiency happens when missing values is found in a dataset. A number of imputation methods have addressed such challenges in early scholarly works for handling missing values. Some of the regular methods include complete case method, mean imputation method, Last Observation Carried Forward (LOCF) method, Expectation-Maximization (EM) algorithm, and Markov Chain Monte Carlo (MCMC), Mean Imputation (Mean), Hot Deck (HOT), Regression Imputation (Regress), K-nearest neighbor (KNN),K-Mean Clustering, Fuzzy K-Mean Clustering, Support Vector Machine, and Multiple Imputation (MI) method. In the present paper, a simulation study is attempted for carrying out an investigative exploration into the efficacy of the above mentioned archetypal imputation methods along with longitudinal data setting under missing completely at random (MCAR). We took out missingness from three cases in a block having low missingness of 5% as well as higher levels at 30% and 50%. With this simulation study, we concluded LOCF method having more bias than the other methods in most of the situations after carrying out a comparison through simulation study.


Article
Jaccard Coefficients based Clustering of XML Web Messages for Network Traffic Aggregation

Authors: Dhiah Al-Shammary
Pages: Comp Page 82-91
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Abstract

This paper provides static efficient clustering model based simple Jaccard coefficients that supports XML messages aggregator in order to potentially reduce network traffic. The proposed model works by grouping only highly similar messages with the aim to provide messages with high redundancy for web aggregators. Web messages aggregation has become a significant solution to overcome network bottlenecks and congestions by efficiently reducing network volume by aggregating messages together removing their redundant information. The proposed model performance is compared to both K-Means and Principle Component Analysis (PCA) combined with K-Means. Jaccard based clustering model has shown potential performance as it only consumes around %32 and %25 processing time in comparison with K-Means and PCA combined with K-Means respectively. Quality measure (Aggregator Compression Ratio) has overcome both benchmark models.


Article
Traffic signs recognition using cuckoo search algorithm and Curvelet transform with image processing methods

Authors: Ahmed saadi Abdullah --- Majida Ali Abed --- Ahmed Naser Ismael
Pages: Comp Page 74-81
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Abstract

Compliance with traffic signs is one of the most important things to follow to avoid traffic accidents as well as compliance with traffic rules in terms of parking, speed control, and other traffic sings. Progress in different areas, such as self-propelled car manufacturing or the production of devices that help the visually impaired, require values to find a way to determine traffic signals with high precision in this research, The first step is to take a picture of the traffic sign and apply some digital image processing techniques to increase image contrast and eliminate noise in the image, the second step resize of origin image , the third step convert color to(YCbCr, HSB) or stay on RGB, the fourth step image is disassembled using curvelet transform and get coefficients , and the last step using cuckoo search algorithm to recognition sings traffics , the MATLAB (2011b) program was used to implement the proposed algorithm . After applying this method to a set of traffic the percentage of discrimination of traffic signs was yellow 93%, green 94%, blue 94.5%, red 96%.


Article
On Sandwich Theorems for Certain Univalent Functions Defined by a New operator

Authors: Elaf Ibrahim Badawi --- Waggas Galib Atshan
Pages: Math Page 72-80
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Abstract

In this paper, we study some differential subordination and superordination results for certain univalent functions in the open unit disc U by using a new operator f_(s,a,μ)^λ. Also, we derive some sandwich theorems.


Article
Coefficient estimates for some subclasses of bi-univalent functions related to m-fold symmetry

Authors: Salwa Kalf Kazim --- Waggas Galib Atshan
Pages: Math Page 81-86
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Abstract: The purpose of present paper is to introduce and investigate two new subclasses N_(∑_m ) (τ,γ,α) and N_(∑_m ) (τ,γ,β) of analytic and m-fold symmetric bi- univalent functions in the open unit disk . Among other results belonging to these subclasses upper coefficients bounds |a_(m+1) | and |a_(2m+1) | are obtained in this study. Certain special cases are also indicated .

Table of content: volume:11 issue:2