Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313328086> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W4313328086 endingPage "5661" @default.
- W4313328086 startingPage "5641" @default.
- W4313328086 abstract "In computer vision, convolutional neural networks have a wide range of uses. Images represent most of today’s data, so it’s important to know how to handle these large amounts of data efficiently. Convolutional neural networks have been shown to solve image processing problems effectively. However, when designing the network structure for a particular problem, you need to adjust the hyperparameters for higher accuracy. This technique is time consuming and requires a lot of work and domain knowledge. Designing a convolutional neural network architecture is a classic NP-hard optimization challenge. On the other hand, different datasets require different combinations of models or hyperparameters, which can be time consuming and inconvenient. Various approaches have been proposed to overcome this problem, such as grid search limited to low-dimensional space and queuing by random selection. To address this issue, we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks (CNNs) using optimized hyperparameters. This study proposes a method using Non-dominated sorted genetic algorithms (NSGA) to improve the hyperparameters of the CNN model. In addition, different types and parameter ranges of existing genetic algorithms are used. A comparative study was conducted with various state-of-the-art methodologies and algorithms. Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy, and the results are published in modern computing literature." @default.
- W4313328086 created "2023-01-06" @default.
- W4313328086 creator A5006730304 @default.
- W4313328086 creator A5013338296 @default.
- W4313328086 creator A5035960451 @default.
- W4313328086 creator A5041114324 @default.
- W4313328086 creator A5041632027 @default.
- W4313328086 creator A5050269424 @default.
- W4313328086 creator A5055683496 @default.
- W4313328086 creator A5070050410 @default.
- W4313328086 creator A5080602119 @default.
- W4313328086 date "2023-01-01" @default.
- W4313328086 modified "2023-09-30" @default.
- W4313328086 title "An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II" @default.
- W4313328086 cites W2116661285 @default.
- W4313328086 cites W2884675507 @default.
- W4313328086 cites W2906697496 @default.
- W4313328086 cites W2921458329 @default.
- W4313328086 cites W2934581799 @default.
- W4313328086 cites W2945150844 @default.
- W4313328086 cites W2962949934 @default.
- W4313328086 cites W3000182889 @default.
- W4313328086 cites W3028852288 @default.
- W4313328086 cites W3046220160 @default.
- W4313328086 cites W3106064248 @default.
- W4313328086 cites W3126121953 @default.
- W4313328086 cites W3130245971 @default.
- W4313328086 cites W3140854437 @default.
- W4313328086 cites W3155121415 @default.
- W4313328086 cites W3155756093 @default.
- W4313328086 cites W3194150683 @default.
- W4313328086 cites W3200104413 @default.
- W4313328086 cites W3207750801 @default.
- W4313328086 cites W4205161115 @default.
- W4313328086 cites W4210681034 @default.
- W4313328086 doi "https://doi.org/10.32604/cmc.2023.033733" @default.
- W4313328086 hasPublicationYear "2023" @default.
- W4313328086 type Work @default.
- W4313328086 citedByCount "0" @default.
- W4313328086 crossrefType "journal-article" @default.
- W4313328086 hasAuthorship W4313328086A5006730304 @default.
- W4313328086 hasAuthorship W4313328086A5013338296 @default.
- W4313328086 hasAuthorship W4313328086A5035960451 @default.
- W4313328086 hasAuthorship W4313328086A5041114324 @default.
- W4313328086 hasAuthorship W4313328086A5041632027 @default.
- W4313328086 hasAuthorship W4313328086A5050269424 @default.
- W4313328086 hasAuthorship W4313328086A5055683496 @default.
- W4313328086 hasAuthorship W4313328086A5070050410 @default.
- W4313328086 hasAuthorship W4313328086A5080602119 @default.
- W4313328086 hasBestOaLocation W43133280861 @default.
- W4313328086 hasConcept C10485038 @default.
- W4313328086 hasConcept C11413529 @default.
- W4313328086 hasConcept C119857082 @default.
- W4313328086 hasConcept C12267149 @default.
- W4313328086 hasConcept C124101348 @default.
- W4313328086 hasConcept C154945302 @default.
- W4313328086 hasConcept C41008148 @default.
- W4313328086 hasConcept C50644808 @default.
- W4313328086 hasConcept C81363708 @default.
- W4313328086 hasConcept C8642999 @default.
- W4313328086 hasConcept C8880873 @default.
- W4313328086 hasConceptScore W4313328086C10485038 @default.
- W4313328086 hasConceptScore W4313328086C11413529 @default.
- W4313328086 hasConceptScore W4313328086C119857082 @default.
- W4313328086 hasConceptScore W4313328086C12267149 @default.
- W4313328086 hasConceptScore W4313328086C124101348 @default.
- W4313328086 hasConceptScore W4313328086C154945302 @default.
- W4313328086 hasConceptScore W4313328086C41008148 @default.
- W4313328086 hasConceptScore W4313328086C50644808 @default.
- W4313328086 hasConceptScore W4313328086C81363708 @default.
- W4313328086 hasConceptScore W4313328086C8642999 @default.
- W4313328086 hasConceptScore W4313328086C8880873 @default.
- W4313328086 hasIssue "3" @default.
- W4313328086 hasLocation W43133280861 @default.
- W4313328086 hasOpenAccess W4313328086 @default.
- W4313328086 hasPrimaryLocation W43133280861 @default.
- W4313328086 hasRelatedWork W3014750173 @default.
- W4313328086 hasRelatedWork W3199608561 @default.
- W4313328086 hasRelatedWork W4281646320 @default.
- W4313328086 hasRelatedWork W4283697347 @default.
- W4313328086 hasRelatedWork W4287818966 @default.
- W4313328086 hasRelatedWork W4295309597 @default.
- W4313328086 hasRelatedWork W4298144215 @default.
- W4313328086 hasRelatedWork W4312224733 @default.
- W4313328086 hasRelatedWork W4323894855 @default.
- W4313328086 hasRelatedWork W4381737452 @default.
- W4313328086 hasVolume "74" @default.
- W4313328086 isParatext "false" @default.
- W4313328086 isRetracted "false" @default.
- W4313328086 workType "article" @default.