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Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

Computers, Materials & Continua 2022年第4期71卷 457-471页

作者： Milos Mravik T.Vetriselvi K.Venkatachalam marko sarac Nebojsa Bacanin Sasa Adamovic Department of Computer Science Singidunum UniversityBelgrade11000Serbia Department of Computer science and Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

At present,the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level.Accurate prediction of diabetes patients is an important research area.Many researchers have proposed techniques to predict this disease through data mining and machine learning methods.In prediction,feature selection is a key concept in preprocessing.Thus,the features that are relevant to the disease are used for prediction.This condition improves the prediction accuracy.Selecting the right features in the whole feature set is a complicated process,and many researchers are concentrating on it to produce a predictive model with high accuracy.In this work,a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression(L2)to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data set.Overfitting is a major problem in feature selection,where the new data are unfit to the model because the training data are small.Ridge regression is mainly used to overcome the overfitting problem.The features are selected by using the proposed feature selection method,and random forest classifier is used to classify the data on the basis of the selected features.This work uses the Pima Indians Diabetes data set,and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed algorithm.The accuracy of the proposed algorithm in predicting diabetes is 100%,and its area under the curve is 97%.The proposed algorithm outperforms existing algorithms.

关键词： Ridge regression recursive feature elimination random forest machine learning feature selection

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Protecting Data Mobility in Cloud Networks Using Metadata Security

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Computer Systems Science & Engineering 2022年第7期42卷 105-120页

作者： R.Punithavathi M.Kowsigan R.Shanthakumari Miodrag Zivkovic Nebojsa Bacanin marko sarac Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Engineering School of ComputingSRM Institute of TechnologyKattankulathur CampusChennai603203India Department of Information Technology Kongu Engineering CollegeErode638060India Singidunum University Belgrade11000Serbia

At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data.Sometimes,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are not.Threats to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data environments.At present,several security solu-tions support cloud systems.Some of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security inter-actions.Thus,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing security.In our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud environments.By analyzing metadata,data are classiﬁed as secured and open data based on their importance.Secured data are sensitive user data,whereas open data are open to the public.On the basis of data classiﬁcation,secured data are applied to the DMoS algorithm to achieve high security in HDFS.The pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated.

关键词： Data mobility data security cloud computing big data DMoS algorithm

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Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

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Computer Systems Science & Engineering 2022年第5期41卷 751-765页

作者： G.R.Sreekanth S.Ahmed Najat Ahmed marko sarac Ivana Strumberger Nebojsa Bacanin Miodrag Zivkovic Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India Department of Computer Engineering Lebanese French UniversityErbil44001Iraq Singidunum University Belgrade11000Serbia

Abstract:Fog computing provides quality of service for cloud infrastructure.As the data computation intensifies,edge computing becomes difficult.Therefore,mobile fog computing is used for reducing traffic and the time for data computation in the network.In previous studies,software-defined networking(SDN)and network functions virtualization(NFV)were used separately in edge computing.Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance,reliability,and scalability.SDN/NFV is still in development.The traditional Internet of things(IoT)data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model.This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing.SDN provides a generalization structure of the forwarding plane,which is separated from the control plane.Meanwhile,NFV concentrates on virtualization by combining the forwarding model with virtual network functions(VNFs)as a single or chain of VNFs,which leads to interoperability and consistency.The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions:task creation,modification,operation,and completion.Our proposed architecture is simulated on the EstiNet simulator,and total time delay,reliability,and satisfaction are used as evaluation parameters.The simulation results are compared with the results of existing architectures,such as software-defined unified virtual monitoring function and ASTP,to analyze the performance of the proposed architecture.The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay(1800 s for 200 IoT devices),reliability(90%),and satisfaction(90%).

关键词： Mobile fog computing edge computing edge node IoT softwaredefined networking network functions virtualization orchestrator

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