Pub Date : 2023-11-01DOI: 10.21608/kjis.2023.220945.1014
Sara shehab, Eman Shehab, AbdulRahman Khawaga
The extraordinary developments in the health sector have resulted in the substantial production of data in daily life. To get valuable information out of this data—infor-mation that can be used for analysis, forecasting, making suggestions, and making decisions—it must be processed. Accessible data is converted into useful information using data mining and machine learning approaches. The first challenge for medical practitioners in developing a preventative strategy and successful treatment plan is the timely diagnosis of diseases. Sometimes, this can result in death if accuracy is lacking. In this study, we examine supervised machine learning methods (Decision Tree, Multilayer Perceptron “MLP”, K-nearest neighbors “ KNN”, Logistic Regression, Random Forest, and Support Vector Machine “SVC”) for anemia prediction utilizing CBC (Complete Blood Count) data gathered from pathology labs. The outcomes demonstrate that the Random Forest, Multilayer Perceptron “MLP”, Decision Tree, and Logistic Regression techniques outperform KNN and SVC in terms of accuracy of 99.94%.
{"title":"Anemia Diagnosis And Prediction Based On Machine Learning","authors":"Sara shehab, Eman Shehab, AbdulRahman Khawaga","doi":"10.21608/kjis.2023.220945.1014","DOIUrl":"https://doi.org/10.21608/kjis.2023.220945.1014","url":null,"abstract":"The extraordinary developments in the health sector have resulted in the substantial production of data in daily life. To get valuable information out of this data—infor-mation that can be used for analysis, forecasting, making suggestions, and making decisions—it must be processed. Accessible data is converted into useful information using data mining and machine learning approaches. The first challenge for medical practitioners in developing a preventative strategy and successful treatment plan is the timely diagnosis of diseases. Sometimes, this can result in death if accuracy is lacking. In this study, we examine supervised machine learning methods (Decision Tree, Multilayer Perceptron “MLP”, K-nearest neighbors “ KNN”, Logistic Regression, Random Forest, and Support Vector Machine “SVC”) for anemia prediction utilizing CBC (Complete Blood Count) data gathered from pathology labs. The outcomes demonstrate that the Random Forest, Multilayer Perceptron “MLP”, Decision Tree, and Logistic Regression techniques outperform KNN and SVC in terms of accuracy of 99.94%.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292136","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}
Pub Date : 2023-11-01DOI: 10.21608/kjis.2023.220954.1015
Sara shehab, Eman Shehab, aya morsi
Chronic kidney failure is one of the most common diseases that threaten the lives of many people and cause death for many. By using artificial intelligence, we predict the disease and classify people into infected and non-infected people. One of the goals is to reduce non-communicable disease-related premature death by a third by 2030. 10-15% of the world's population may have chronic kidney disease (CKD), which is one of the major causes of non-communicable disease morbidity and mortality. In order to reduce the effects of patient health complications like hypertension, anaemia (low blood count), mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications with timely intervention through appropriate medications, early and accurate detection of the stages of CKD is thought to be essential. Several studies on the early identification of CKD have been conducted utilising machine learning approaches. They weren't primarily concerned with predicting the exact stages. In this work classification methods are used like support vector classifier, random forest, logistic regression, and decision tree. The results detect that Linear SVC Support Vector Machine achieved high accuracy and Random Forest and Decision tree (100%) and logistic regression achieved (96.8%). A data set with 24 feature and 401 records are used for testing the algorithms. 20% of data set will be used in testing and 80% for training. The proposed work achieves high accuracy when compared with the previous works.
{"title":"Chronic Kidney Disease Classification Using ML Algorithms","authors":"Sara shehab, Eman Shehab, aya morsi","doi":"10.21608/kjis.2023.220954.1015","DOIUrl":"https://doi.org/10.21608/kjis.2023.220954.1015","url":null,"abstract":"Chronic kidney failure is one of the most common diseases that threaten the lives of many people and cause death for many. By using artificial intelligence, we predict the disease and classify people into infected and non-infected people. One of the goals is to reduce non-communicable disease-related premature death by a third by 2030. 10-15% of the world's population may have chronic kidney disease (CKD), which is one of the major causes of non-communicable disease morbidity and mortality. In order to reduce the effects of patient health complications like hypertension, anaemia (low blood count), mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications with timely intervention through appropriate medications, early and accurate detection of the stages of CKD is thought to be essential. Several studies on the early identification of CKD have been conducted utilising machine learning approaches. They weren't primarily concerned with predicting the exact stages. In this work classification methods are used like support vector classifier, random forest, logistic regression, and decision tree. The results detect that Linear SVC Support Vector Machine achieved high accuracy and Random Forest and Decision tree (100%) and logistic regression achieved (96.8%). A data set with 24 feature and 401 records are used for testing the algorithms. 20% of data set will be used in testing and 80% for training. The proposed work achieves high accuracy when compared with the previous works.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"167 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139294810","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}
Pub Date : 2023-11-01DOI: 10.21608/kjis.2023.251235.1018
Alaa Allakany
Software-defined networks (SDN) provide a centralized administration programming interface for managing the network infrastructure. This new approach replaced traditional networks by establishing a flexible connection between the control and data planes, managing network operations through a centralized controller. As a result, prioritizing the security of the SDN controller becomes imperative in SDN networks. In the recent wave of distributed denial-of-service (DDoS) attacks, attackers have shifted their strategy from directly targeting the SDN controller to concentrating on specific links or area, causing disruptions in connectivity. This attack, known as Link-flooding attack (LFA), represent a novel form of DDoS attack. LFA targets the SDN control channel, which transmits control traffic from the SDN controller to switches, taking advantage of shared links in both control and data traffic paths. This sharing exposes a vulnerability that attackers can exploit to disrupt the control channel, using malicious data traffic to execute LFA. Considering the control channel's responsibility for granting centralized control to the controller over each network switch, it becomes relatively easy for an attacker to compromise all network functions. To handle this problem, in this paper, we develop a novel approach based on SDN designed for security solutions against DDoS and LFA. Our proposed scheme utilizes hop-by-hop network measurement to identify and capture abnormal link performance, enabling effective detection of such attacks. Subsequently, a Machine Learning (ML) model is employed to determine whether the congested links indicate the presence of such attacks. Unlike conventional approaches in the literature that solely rely on automatic ML models, our method begins by measuring congestion in each link. If abnormalities are detected, the ML model is then executed to identify whether it is an attack or not. By adopting this approach, we achieve optimized utilization of controller resources. Our proposed scheme will be implemented as an application at the application layer of the Ryu controller. Through our evaluation, we have demonstrated that this approach can efficiently optimize the process of measuring link performance, optimizing the utilization of SDN controller resources, and detecting DDoS and LFA.
软件定义网络（SDN）为管理网络基础设施提供了一个集中管理编程界面。这种新方法在控制平面和数据平面之间建立了灵活的连接，通过集中式控制器管理网络运行，从而取代了传统网络。因此，SDN 网络必须优先考虑 SDN 控制器的安全性。在最近的分布式拒绝服务（DDoS）攻击浪潮中，攻击者已将策略从直接针对 SDN 控制器转向集中攻击特定链路或区域，从而造成连接中断。这种攻击被称为链路泛洪攻击（LFA），是一种新型的 DDoS 攻击。LFA 针对的是 SDN 控制通道，该通道利用控制和数据流量路径中的共享链路，将控制流量从 SDN 控制器传输到交换机。这种共享暴露了一个漏洞，攻击者可以利用这个漏洞破坏控制通道，使用恶意数据流量执行 LFA。考虑到控制通道负责向控制器授予对每个网络交换机的集中控制，攻击者相对容易入侵所有网络功能。为解决这一问题，我们在本文中开发了一种基于 SDN 的新方法，旨在提供针对 DDoS 和 LFA 的安全解决方案。我们提出的方案利用逐跳网络测量来识别和捕获异常链路性能，从而有效检测此类攻击。随后，采用机器学习（ML）模型来确定拥塞链路是否表明存在此类攻击。与文献中仅依赖自动 ML 模型的传统方法不同，我们的方法首先测量每个链路的拥塞情况。如果检测到异常，则执行 ML 模型来确定是否是攻击。通过采用这种方法，我们实现了控制器资源的优化利用。我们提出的方案将作为 Ryu 控制器应用层的一个应用程序来实施。通过评估，我们证明这种方法可以有效优化测量链路性能、优化 SDN 控制器资源利用以及检测 DDoS 和 LFA 的过程。
{"title":"Cost-Efficient Method for Detecting and Mitigating DDOS Attacks in SDN Based Networks","authors":"Alaa Allakany","doi":"10.21608/kjis.2023.251235.1018","DOIUrl":"https://doi.org/10.21608/kjis.2023.251235.1018","url":null,"abstract":"Software-defined networks (SDN) provide a centralized administration programming interface for managing the network infrastructure. This new approach replaced traditional networks by establishing a flexible connection between the control and data planes, managing network operations through a centralized controller. As a result, prioritizing the security of the SDN controller becomes imperative in SDN networks. In the recent wave of distributed denial-of-service (DDoS) attacks, attackers have shifted their strategy from directly targeting the SDN controller to concentrating on specific links or area, causing disruptions in connectivity. This attack, known as Link-flooding attack (LFA), represent a novel form of DDoS attack. LFA targets the SDN control channel, which transmits control traffic from the SDN controller to switches, taking advantage of shared links in both control and data traffic paths. This sharing exposes a vulnerability that attackers can exploit to disrupt the control channel, using malicious data traffic to execute LFA. Considering the control channel's responsibility for granting centralized control to the controller over each network switch, it becomes relatively easy for an attacker to compromise all network functions. To handle this problem, in this paper, we develop a novel approach based on SDN designed for security solutions against DDoS and LFA. Our proposed scheme utilizes hop-by-hop network measurement to identify and capture abnormal link performance, enabling effective detection of such attacks. Subsequently, a Machine Learning (ML) model is employed to determine whether the congested links indicate the presence of such attacks. Unlike conventional approaches in the literature that solely rely on automatic ML models, our method begins by measuring congestion in each link. If abnormalities are detected, the ML model is then executed to identify whether it is an attack or not. By adopting this approach, we achieve optimized utilization of controller resources. Our proposed scheme will be implemented as an application at the application layer of the Ryu controller. Through our evaluation, we have demonstrated that this approach can efficiently optimize the process of measuring link performance, optimizing the utilization of SDN controller resources, and detecting DDoS and LFA.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139300986","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}
Pub Date : 2023-11-01DOI: 10.21608/kjis.2023.250400.1017
Mahmoud nasef
The original rough set model was based on a special kind of topological structure whose partition resulted from an equivalence relation. We have shown that real-world problems can be dealt with using the modern topological structure induced by Pawlak’s rough approximation. In this research, actual information was collected for some patients in hospitals, health centers, isolation centers and some symptoms were recorded through “ the World Health Organization” website enabled us analyze their data. By establishing an information system in which data can be analyzed using rough topology in order to draw conclusion about the most important symptoms in disease conPirmation.
{"title":"Decision Making in an Information System Via Pawlak’s Rough Approximation","authors":"Mahmoud nasef","doi":"10.21608/kjis.2023.250400.1017","DOIUrl":"https://doi.org/10.21608/kjis.2023.250400.1017","url":null,"abstract":"The original rough set model was based on a special kind of topological structure whose partition resulted from an equivalence relation. We have shown that real-world problems can be dealt with using the modern topological structure induced by Pawlak’s rough approximation. In this research, actual information was collected for some patients in hospitals, health centers, isolation centers and some symptoms were recorded through “ the World Health Organization” website enabled us analyze their data. By establishing an information system in which data can be analyzed using rough topology in order to draw conclusion about the most important symptoms in disease conPirmation.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139301671","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}
Pub Date : 2023-11-01DOI: 10.21608/kjis.2023.221370.1016
Sara shehab, Eman Shehab, Rahma Nabil
The Mushroom is kind of fungi. Major health benefits of mushrooms include their ability to kill cancer cells. The goal of this research is to determine the most effective method for mushroom classification, with the categories of deadly and nonpoisonous mushrooms being used. Separate from plants and animals, they belong in their own realm. In terms of how they get nutrients, fungi are different from plants and mammals. Mushrooms are classified as edible and poisoned. To distinguish between two varieties of mushrooms, we can use machine learning, which is used in classification. There are numerous machine learning algorithms that perform classification, but in our model, I utilize random forest, MLP, Linear Regression and decision tree on the features of the mushroom to categorize it into edible and poisonous. Random Forest achieves high accuracy 98.70%. from these results, we can use Ml to differentiate between two varieties of mushrooms because it is used in classification efficiently.
蘑菇是一种真菌。蘑菇对健康的主要益处包括能够杀死癌细胞。这项研究的目标是确定最有效的蘑菇分类方法，并将蘑菇分为致命蘑菇和无毒蘑菇。蘑菇有别于动植物，属于自己的领域。就获取营养的方式而言，真菌与植物和哺乳动物不同。蘑菇分为食用蘑菇和毒蘑菇。为了区分两种蘑菇，我们可以使用机器学习来进行分类。进行分类的机器学习算法有很多，但在我们的模型中，我利用随机森林、MLP、线性回归和决策树对蘑菇的特征进行分类，将其分为可食用的和有毒的。随机森林的准确率高达 98.70%。从这些结果来看，我们可以使用 Ml 来区分两种蘑菇，因为它可以有效地用于分类。
{"title":"The classification of mushroom using ML","authors":"Sara shehab, Eman Shehab, Rahma Nabil","doi":"10.21608/kjis.2023.221370.1016","DOIUrl":"https://doi.org/10.21608/kjis.2023.221370.1016","url":null,"abstract":"The Mushroom is kind of fungi. Major health benefits of mushrooms include their ability to kill cancer cells. The goal of this research is to determine the most effective method for mushroom classification, with the categories of deadly and nonpoisonous mushrooms being used. Separate from plants and animals, they belong in their own realm. In terms of how they get nutrients, fungi are different from plants and mammals. Mushrooms are classified as edible and poisoned. To distinguish between two varieties of mushrooms, we can use machine learning, which is used in classification. There are numerous machine learning algorithms that perform classification, but in our model, I utilize random forest, MLP, Linear Regression and decision tree on the features of the mushroom to categorize it into edible and poisonous. Random Forest achieves high accuracy 98.70%. from these results, we can use Ml to differentiate between two varieties of mushrooms because it is used in classification efficiently.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305488","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}
Pub Date : 2023-11-01DOI: 10.21608/kjis.2023.329059
A. Salama, M. Darwish, M. Shokry, M.A.Nasef
Fuzzy set Theory of Lotfi A. Zadeh (1965) [1] has been one of the most important area for researches due to its advanced applications in many fields which has the ability to deal with non-linearity and independence of plant modeling, especially in Electrical machines and its control techniques to reach optimum Dynamic response with load variations. In this paper control of direct current (DC) motor with conventional controls proportional–integral–derivative (PID) and fuzzy logic control (FLC) has been investigated and compared with each others for different operating conditions. The mathematical model of Dc motor was modeled and simulated in Matlab Simulink (Mathworks) with illustrated graphs and plots. The performance of the model is expected to show a great results for the fuzzy logic control (FLC) over the PID control [2].
{"title":"Dynamic Responce of DC Motor Via Fuzzy Logic and PID Controllers","authors":"A. Salama, M. Darwish, M. Shokry, M.A.Nasef","doi":"10.21608/kjis.2023.329059","DOIUrl":"https://doi.org/10.21608/kjis.2023.329059","url":null,"abstract":"Fuzzy set Theory of Lotfi A. Zadeh (1965) [1] has been one of the most important area for researches due to its advanced applications in many fields which has the ability to deal with non-linearity and independence of plant modeling, especially in Electrical machines and its control techniques to reach optimum Dynamic response with load variations. In this paper control of direct current (DC) motor with conventional controls proportional–integral–derivative (PID) and fuzzy logic control (FLC) has been investigated and compared with each others for different operating conditions. The mathematical model of Dc motor was modeled and simulated in Matlab Simulink (Mathworks) with illustrated graphs and plots. The performance of the model is expected to show a great results for the fuzzy logic control (FLC) over the PID control [2].","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305740","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}
Pub Date : 2023-06-01DOI: 10.21608/kjis.2023.216661.1013
Sarah Osama, A. Ali, Hassan Shaban
Gene expression data has become an essen2al tool for cancer classifica2on because it provides substan2al insights into the underlying mechanisms of cancer progression. However, the high-dimensional nature of microarray gene expression data presents a significant challenge. This paper introduces a new method called IG-COA, which combines Informa2on Gain (IG) approach and Coa2 Op2miza2on Algorithm (COA), to iden2fy the biomarkers genes. COA is a recent algorithm that has not been previously examined for feature or gene selec2on, to the best of our knowledge. Firstly, the IG method is used because using COA directly on microarray datasets is ineffec2ve and can make it challenging to train a classifier accurately. Secondly, the COA algorithm is u2lized to select the op2mal subset of genes from the previously selected ones. The effec2veness of the suggested IG-COA method with a Support Vector Machine is tested on several microarray gene expression datasets, and it exceeds other state-of-the-art methods.
基因表达数据已经成为癌症分类的重要工具，因为它为癌症进展的潜在机制提供了实质性的见解。然而，微阵列基因表达数据的高维性提出了一个重大挑战。本文介绍了一种结合Informa2on Gain (IG)法和Coa2 Op2miza2on算法(COA)的生物标记基因鉴定新方法IG-COA。据我们所知，COA是一种最近的算法，以前还没有对特征或基因选择进行过研究。首先，使用IG方法是因为直接在微阵列数据集上使用COA是无效的，并且会给准确训练分类器带来挑战。其次，利用COA算法从先前选择的基因中选择最优基因子集;基于支持向量机的igg - coa方法的有效性在多个微阵列基因表达数据集上进行了测试，并且超过了其他最先进的方法。
{"title":"A hybrid of Information gain and a Coati Optimization Algorithm for gene selection in microarray gene expression data classification.","authors":"Sarah Osama, A. Ali, Hassan Shaban","doi":"10.21608/kjis.2023.216661.1013","DOIUrl":"https://doi.org/10.21608/kjis.2023.216661.1013","url":null,"abstract":"Gene expression data has become an essen2al tool for cancer classifica2on because it provides substan2al insights into the underlying mechanisms of cancer progression. However, the high-dimensional nature of microarray gene expression data presents a significant challenge. This paper introduces a new method called IG-COA, which combines Informa2on Gain (IG) approach and Coa2 Op2miza2on Algorithm (COA), to iden2fy the biomarkers genes. COA is a recent algorithm that has not been previously examined for feature or gene selec2on, to the best of our knowledge. Firstly, the IG method is used because using COA directly on microarray datasets is ineffec2ve and can make it challenging to train a classifier accurately. Secondly, the COA algorithm is u2lized to select the op2mal subset of genes from the previously selected ones. The effec2veness of the suggested IG-COA method with a Support Vector Machine is tested on several microarray gene expression datasets, and it exceeds other state-of-the-art methods.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443948","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}
Pub Date : 2022-12-01DOI: 10.21608/kjis.2022.181419.1012
M. Hassaan
{"title":"Classification Event Sequences via Compact Big Sequence","authors":"M. Hassaan","doi":"10.21608/kjis.2022.181419.1012","DOIUrl":"https://doi.org/10.21608/kjis.2022.181419.1012","url":null,"abstract":"","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121329050","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}
Pub Date : 2022-12-01DOI: 10.21608/kjis.2022.170163.1011
M. Hassaan
: Graphs are widely used to model many real-world data in many application domains such as chemical compounds, protein structures, gene structures, metabolic pathways, communication networks, and images entities. Graph summarization is very important task which searching for a summary of the given graph. There are many benefits of the graph summarization task which are as follows. By graph summarization, we reduce the data volume and storage as much as possible, speedup the query processing algorithms, and apply the interactive analysis. In this paper, we propose a new graph summarization method based on the compactness of disjoint paths. Our algorithm called DJ_Paths. DJ_Paths is edge-grouping technique. The experimental results show that DJ_Path outperforms the state-of-the-art method, Slugger, with respect to compression ratio (It achieves up to 2x better compression), total response time (It outperforms Slugger by more than one order of magnitude), and memory usage (It is 8x less memory consumption).
{"title":"Summarizing Graph Data Via the Compactness of Disjoint Paths","authors":"M. Hassaan","doi":"10.21608/kjis.2022.170163.1011","DOIUrl":"https://doi.org/10.21608/kjis.2022.170163.1011","url":null,"abstract":": Graphs are widely used to model many real-world data in many application domains such as chemical compounds, protein structures, gene structures, metabolic pathways, communication networks, and images entities. Graph summarization is very important task which searching for a summary of the given graph. There are many benefits of the graph summarization task which are as follows. By graph summarization, we reduce the data volume and storage as much as possible, speedup the query processing algorithms, and apply the interactive analysis. In this paper, we propose a new graph summarization method based on the compactness of disjoint paths. Our algorithm called DJ_Paths. DJ_Paths is edge-grouping technique. The experimental results show that DJ_Path outperforms the state-of-the-art method, Slugger, with respect to compression ratio (It achieves up to 2x better compression), total response time (It outperforms Slugger by more than one order of magnitude), and memory usage (It is 8x less memory consumption).","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594257","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}
Pub Date : 2022-12-01DOI: 10.21608/kjis.2022.280155
Abdelmgeid A. Ali, Waled T. A. Mohamed, Mentllah Sayed
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