Kafrelsheikh Journal of Information Sciences最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"A Survey Paper of Information Hiding by Using Steganography Techniques","authors":"Abdelmgeid A. Ali, Waled T. A. Mohamed, Mentllah Sayed","doi":"10.21608/kjis.2022.280155","DOIUrl":"https://doi.org/10.21608/kjis.2022.280155","url":null,"abstract":"","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"11 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":"129065613","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}