Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network
Cloud Computing has drastically simplified the management of IT resources by introducing the concept of resource pooling. It has led to a tremendous improvement in infrastructure planning. The major goals of cloud computing include maximization of computing resources with minimization of cost. But t...
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| Format: | Article |
| Language: | fas |
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University of Tehran
2023-08-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_93627_f902c014eba26928412cf5a7bfad28f8.pdf |
| _version_ | 1797647775496667136 |
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| author | Nandita Goyal Munesh Chandra Trivedi |
| author_facet | Nandita Goyal Munesh Chandra Trivedi |
| author_sort | Nandita Goyal |
| collection | DOAJ |
| description | Cloud Computing has drastically simplified the management of IT resources by introducing the concept of resource pooling. It has led to a tremendous improvement in infrastructure planning. The major goals of cloud computing include maximization of computing resources with minimization of cost. But the truth is that everything has a price and cloud computing is no different. With Cloud computing there comes a number of security concerns which need to be addressed. Cloud forensics plays a vital role to address the security issues related to cloud computing by identifying, collecting and studying digital evidence in cloud environment.The aim of the research paper is to explore the concept of cloud forensic by applying optimization for feature selection before classification of data on cloud side. The data is classified as malicious and non-malicious using convolutional neural network. The proposed system makes a comparison of models with and without feature selection algorithms before applying the data to CNN. A comparison of different metaheuristics algorithms- Particle Swarm Optimization, Shuffled Frog Leap Optimization and Fire fly algorithm for feature optimization is done based on convergence rate and efficiency. |
| first_indexed | 2024-03-11T15:21:24Z |
| format | Article |
| id | doaj.art-ab1cb01a0ccd47079e5b9329e4917ccb |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-03-11T15:21:24Z |
| publishDate | 2023-08-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-ab1cb01a0ccd47079e5b9329e4917ccb2023-10-28T07:17:14ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592023-08-011539911210.22059/jitm.2023.9362793627Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural NetworkNandita Goyal0Munesh Chandra Trivedi1M.Tech., Assistant Professor, Department of Information Technology, Ajay Kumar Garg Engineering College, Ghaziabad, India.Associate Professor, Department of Computer Science and Engineering National Institute of Technology, Agartala, India.Cloud Computing has drastically simplified the management of IT resources by introducing the concept of resource pooling. It has led to a tremendous improvement in infrastructure planning. The major goals of cloud computing include maximization of computing resources with minimization of cost. But the truth is that everything has a price and cloud computing is no different. With Cloud computing there comes a number of security concerns which need to be addressed. Cloud forensics plays a vital role to address the security issues related to cloud computing by identifying, collecting and studying digital evidence in cloud environment.The aim of the research paper is to explore the concept of cloud forensic by applying optimization for feature selection before classification of data on cloud side. The data is classified as malicious and non-malicious using convolutional neural network. The proposed system makes a comparison of models with and without feature selection algorithms before applying the data to CNN. A comparison of different metaheuristics algorithms- Particle Swarm Optimization, Shuffled Frog Leap Optimization and Fire fly algorithm for feature optimization is done based on convergence rate and efficiency.https://jitm.ut.ac.ir/article_93627_f902c014eba26928412cf5a7bfad28f8.pdffeature selectionclassificationcloud computingmetaheuristic algorithmconvolution neural network |
| spellingShingle | Nandita Goyal Munesh Chandra Trivedi Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network Journal of Information Technology Management feature selection classification cloud computing metaheuristic algorithm convolution neural network |
| title | Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network |
| title_full | Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network |
| title_fullStr | Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network |
| title_full_unstemmed | Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network |
| title_short | Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network |
| title_sort | metaheuristic algorithms for optimization and feature selection in cloud data classification using convolutional neural network |
| topic | feature selection classification cloud computing metaheuristic algorithm convolution neural network |
| url | https://jitm.ut.ac.ir/article_93627_f902c014eba26928412cf5a7bfad28f8.pdf |
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