Iraqi Journal for Computer Science and Mathematics最新文献

{"title":"Two-Stage Shrinkage Bayesian Estimators For The Shape Parameter of Pareto Distribution Dependent on Katti’s Regions","authors":"Marwa Hashem Abd Ali, Alaa. K. Jiheel, Z. Al-Hemyari","doi":"10.52866/ijcsm.2022.02.01.005","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.02.01.005","url":null,"abstract":"This study proposes a two-stage shrinkage Bayesian estimation of the shape parameter of Pareto\u0000distribution. Additional information from the past and considered presently in new estimation processes has been receiving considerable attention in the last few decades, especially when a sample unit is costly or difficult to obtain. The proposed two-stage pooling estimation procedure assumes that the prior knowledge of θ can take the form of an initial estimate θ0 of θ. The expressions for bias, bias ratio, mean square error, expected sample size, and relative efficiency are derived based on the two regions of R1 and R2. Certain values of the constants are considered, and the R language is used for statistical programming. The numerical results and conclusion suggest that the proposed estimators have higher relative efficiency compared with the classical Bayesian estimator with respect to a guess value. The effective region of the estimator dependent on R2 is better than that of the estimator dependent dependent on R1.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124534870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradient Techniques To Predict Distributed Denial-Of-Service Attack","authors":"Roheen Qamar","doi":"10.52866/ijcsm.2022.02.01.006","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.02.01.006","url":null,"abstract":"A distributed denial-of-service (DDoS) attack attempts to prevent people from accessing a server. A\u0000website may become inaccessible due to a DDoS attack because the server is inundated with fake requests and cannot handle real ones. A DDoS attack affects a large number of computers. Attackers employ a zombie network, which is a collection of infected machines on which the attacker has hidden the denial-of-service attacking application to carry out a DDoS attack. The MATLAB 2018a simulator was used in this study for training. Additionally, during design, the knowledge discovery dataset (KDD) was cleaned and the values of attacks were incorporated. A neural network model was subsequently developed, and the KDD was trained using a recursive artificial neural network. This network was developed using five distinct training algorithms: 1) Fletcher–Powell conjugate gradient, 2) Polak–Ribiére conjugate gradient of, 3) resilient backpropagation, 4) gradient conjugation with Powell/Beale restarts, and 5) gradient descent algorithm with variable learning rate. The artificial neural network toolset in MATLAB was used to investigate the detection of DDoS attacks. The conjugate gradient with Powell/Beale restart algorithm had a success rate of 99.9% and a training time of 00:53. This inquiry uses the KDD-CUP99 dataset. Has a better level of accuracy, according to the results","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117055722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizations of sf-simple extension of topologies","authors":"Mohammed Yaseen, R. Hussain","doi":"10.52866/ijcsm.2022.02.01.003","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.02.01.003","url":null,"abstract":"The main aim of this study is to create a new type of topology called “ -simple extension” and investigate its properties. We introduce a new definition for -open sets and consider this aspect as the basis of our main definition. Furthermore, we investigate the properties of the proposed concept to allow us to provide new examples of explicit descriptions of topological spaces and certain types of -covering for topological spaces, such as ( -Lindelof and -paracompact spaces). The use of the tool offers important results for topological spaces. Other findings related to the proposed approach have also been identified.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"50 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy C means Based Evaluation Algorithms For Cancer Gene Expression Data Clustering","authors":"Omar Al-Janabee, Basad Al-Sarray","doi":"10.52866/ijcsm.2022.02.01.004","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.02.01.004","url":null,"abstract":"The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, which is used to cluster genes. FCM allows an object to belong to two or more clusters with a membership grade between zero and one and the sum of belonging to all clusters of each gene is equal to one. This paradigm is useful when dealing with microarray data. The total time required to implement the first model is 22.2589 s. The second model combines FCM and particle swarm optimization (PSO) to obtain better results. The hybrid algorithm, i.e., FCM–PSO, uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–PSO method is effective. The total time of implementation of this model is 89.6087 s. The third model combines FCM with a genetic algorithm (GA) to obtain better results. This hybrid algorithm also uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–GA method is effective. Its total time of implementation is 50.8021 s. In addition, this study uses cluster validity indexes to determine the best partitioning for the underlying data. Internal validity indexes include the Jaccard, Davies Bouldin, Dunn, Xie–Beni, and silhouette. Meanwhile, external validity indexes include Minkowski, adjusted Rand, and percentage of correctly categorized pairings. Experiments conducted on brain tumor gene expression data demonstrate that the techniques used in this study outperform traditional models in terms of stability and biological significance.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123515399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chaotic Dynamics in the 2D System of Nonsmooth Ordinary Differential Equations","authors":"Zain-Aldeen S. A. Rahman, Basil H. Jasim, Yasir Al‐Yasir","doi":"10.52866/ijcsm.2022.02.01.002","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.02.01.002","url":null,"abstract":"Over the last decade, the chaotic behaviors of dynamical systems have been extensively explored. Recently, discovering or developing a 2D system of ordinary differential equations (ODEs) capable of exhibiting\u0000chaotic dynamical behaviors is an attractive research topic. In this study, a chaotic system with a 2D system of\u0000nonsmooth ODEs has been developed. This system is can exhibit chaotic dynamical behaviors. Its main dynamical behaviors, including time-series trajectories, phase portraits of attractors, and equilibria and their stability, have been investigated. The developed system has been verified by an excessive variety of fascinating chaotic behaviors, such as chaotic attractor, symmetry, sensitivity to initial conditions (ICs), fractal dimension, autocorrelation, power spectrum, Lyapunov exponent, and bifurcation diagram. Analytical and numerical simulations are used to study the dynamical behaviors of such a system. The developed system has extreme sensitivity to ICs, a fractal dimension of more than 1.8 and less than 2.05, an autocorrelation fluctuating randomly about an average of zero, a broadband power spectrum, and one positive Lyapunov exponent. The obtained numerical simulation results have proven the capability of the developed 2D system for exciting chaotic dynamical behaviors","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification Method of Power Internet Attack Information Based\u0000on Machine Learning","authors":"Yi-Ning Niu, Korneev Andrei","doi":"10.52866/ijcsm.2022.02.01.001","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.02.01.001","url":null,"abstract":"To solve the problem of large recognition errors in traditional attack information identification\u0000methods, we propose a machine learning (ML)-based identification method for electric power Internet attack\u0000information. Based on the Internet attack information, an Internet attack information model is constructed, the\u0000identification principle of the power Internet attack information is analysed based on ML, hash fixing is conducted to\u0000ensure that the same attack information will be assigned to the same thread and that the deviation generated by noise\u0000can be avoided so that the real-time lossless processing of the power Internet attack information can be ensured. The\u0000vulnerability adjacency matrix is constructed, and the vulnerability is quantitatively evaluated to complete the design\u0000of the optimal identification scheme for power Internet attack information. The experimental results show that the\u0000identification accuracy of the method can reach 98%, which can effectively reduce the risk of power Internet network\u0000attacks and ensure the safe and stable operation of the network.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129535883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning","authors":"Maad M. Mijwil","doi":"10.52866/ijcsm.2022.01.01.013","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.01.01.013","url":null,"abstract":"In the modern era, many terms related to artificial intelligence, machine learning, and deep learning are widely used in domains such as business, healthcare, industries, and military. In these fields, the accurate prediction and analysis of data are crucial, regardless of how large the data are. However, using big data is confusing due to the rapid growth and massive development in public life, which requires a tremendous human effort in order to deal with such type of data and extract worthy information from it. Thus, the role of artificial intelligence begins in analyzing big data based on scientific techniques, especially in machine learning, whereby it can identify patterns of decision-making and reduce human intervention. In this regard, the significance role of artificial intelligence, machine learning and deep learning is growing rapidly. In this article, the authors decide to highlight these sciences by discussing how to develop and apply them in many decision-making domains. In addition, the influence of artificial intelligence in healthcare and the gains this science provides in the face of the COVID-19 pandemic are highlighted. This article concludes that these sciences have a significant impact, especially in healthcare, as well as the ability to grow and improve their methodology in decision-making. Additionally, artificial intelligence is a vital science, especially in the face of COVID-19.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124642694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of Intrusion Detection Using Deep Learning in Internet of Things","authors":"baraa I. Farhan, Ammar D.Jasim","doi":"10.52866/ijcsm.2022.01.01.009","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.01.01.009","url":null,"abstract":"The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"164 2-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122745078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Properties of Chmielinski-orthogonality using Kadets-Klee property","authors":"saied Johnny, Buthainah A. A. Ahmed","doi":"10.52866/ijcsm.2022.01.01.010","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.01.01.010","url":null,"abstract":"The aim of this paper is to study new results of an approximate orthogonality of Birkhoff-James techniques in real Banach space , namely Chiemelinski orthogonality (even there is no ambiguity between the concepts symbolized by orthogonality) and provide some new geometric characterizations which is considered as the basis of our main definitions. Also, we explore relation between two different types of orthogonalities. First of them orthogonality in a real Banach space and the other orthogonality in the space of bounded linear operator . We obtain a complete characterizations of these two orthogonalities in some types of Banach spaces such as strictly convex space, smooth space and reflexive space. The study is designed to give different results about the concept symmetry of Chmielinski-orthogonality for a compact linear operator on a reflexive, strictly convex Banach space having Kadets-Klee property by exploring a new type of a generalized some results with Birkhoff James orthogonality in the space of bounded linear operators. We also exhibit a smooth compact linear operator with a spectral value that is defined on a reflexive, strictly convex Banach space having Kadets-Klee property either having zero nullity or not -right-symmetric.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134518389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Economic Order Quantity in a Fuzzy Environment for a Periodic Inventory Model with Variable Demand","authors":"K. Kalaiarasi, MARY HENRIETTA H, M. Sumathi, A. Stanley Raj","doi":"10.52866/ijcsm.2022.01.01.011","DOIUrl":"https://doi.org/10.52866/ijcsm.2022.01.01.011","url":null,"abstract":"The technique of limiting expenditure plays a critical part in an organization's ability to govern the smooth operation of its management system. The economic order quantity (EOQ) is calculated by solving a nonlinear problem, and the best solution is investigated in a fuzzy and intuitionistic fuzzy environment. The overall cost is made up of several factors, such as demand, holding, and ordering costs. The demand and stock-out characteristics were both fuzzified using fuzzy and intuitionistic fuzzy numbers. The numerical analysis shows the comparison between the two fuzzy numbers through sensitivity analysis.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123959501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}