Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

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.

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

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.

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

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%).