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COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

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Computers, Materials & Continua 2022年第7期72卷 1685-1698页

作者： nebojsa Budimirovic E.Prabhu Milos Antonijevic Miodrag Zivkovic nebojsa bacanin Ivana Strumberger K.Venkatachalam Singidunum University Belgrade11000Serbia Department of Electronics and Communication Engineering Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatore641112India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic

Computerized tomography(CT)scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia.On the basis of the image analysis results of chest CT and X-rays,the severity of lung infection is monitored using a tool.Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient.To overcome these issues,our proposed study implements four cascaded stages.First,for pre-processing,a mean filter is used.Second,texture feature extraction uses principal component analysis(PCA).Third,a modified whale optimization algorithm is used(MWOA)for a feature selection algorithm.The severity of lung infection is detected on the basis of age group.Fourth,image classification is done by using the proposed MWOAwith the salp swarm algorithm(MWOA-SSA).MWOA-SSA has an accuracy of 97%,whereas PCA and MWOA have accuracies of 81%and 86%.The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA(84.4%)and MWOA(95.2%).MWOA-SSA outperforms other algorithms with a specificity of 97.8%.This proposed method improves the effective classification of lung affected images from large datasets.

关键词： PCA WOA CT-image lung infection COVID-19

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

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Computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： nebojsa bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

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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

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

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

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Computer Systems Science & Engineering 2022年第10期43卷 317-332页

作者： R.S.Latha B.Saravana Balaji nebojsa bacanin Ivana Strumberger Miodrag Zivkovic Milos Kabiljo Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia Department of Information Technology Lebanese French UniversityErbilIraq Singidunum University Belgrade160622Serbia

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.

关键词： Feature selection data optimization supervised learning swarm intelligence decomposed random differential grouping grey wolf optimization

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

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Computer Systems Science & Engineering 2022年第5期41卷 735-749页

作者： S.Prakash M.Vinoth Kumar R.Saravana Ram Miodrag Zivkovic nebojsa bacanin Milos Antonijevic Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering Dindigul624622India Singidunum University Belgrade11000Serbia

Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.

关键词： Breast cancer Gabor filter Legendre filter GLFIL algorithm animal migration optimization neural networks

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