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Optimized Deep Learning-Based E-Waste Management in IoT Application via Energy-Aware Routing.

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Title: Optimized Deep Learning-Based E-Waste Management in IoT Application via Energy-Aware Routing.
Authors: Ramya, Puppala1 (AUTHOR) puppalaramya24@gmail.com, Ramya, V.1 (AUTHOR), Babu Rao, M.2 (AUTHOR)
Source: Cybernetics & Systems. 2024, Vol. 55 Issue 8, p2041-2070. 30p.
Subjects: Convolutional neural networks, Data augmentation, Routing algorithms, Electronic waste, Feature extraction, Deep learning
Abstract: Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for managing E-waste in use today. In essence, there is no ideal method of managing E-waste. This process required more labor and resources. E-waste classification is fulfilled using fractional Henry gas optimization-based deep convolutional neural network (FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste images are conversed through the energy-aware FHGO routing algorithm. The feature extraction procedure is performed to cut out the features for example the gray level co-occurrence matrix (GLCM) feature, local Gabor binary pattern (LGBP) and histogram of oriented gradient (HOG) and the pre-processing phase is concluded with a median filter. To supplement the extracted feature size the data augmentation is fulfilled. Moreover, the E-waste classification is done based on deep CNN, which is trained using a FHGO algorithm. FHGO is exhibited by the merging of Henry gas solubility optimization (HGSO) algorithm, fractional calculus (FC). Comparing to the existing approaches like deep learning, tensor flow deep learning, Cuckoo search-based neural network and machine learning the accuracy of the proposed method is 19.49%, 18.05%, 12.77%, and 7.89% privileged. [ABSTRACT FROM AUTHOR]
Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Ramya%2C+Puppala%22">Ramya, Puppala</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> puppalaramya24@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ramya%2C+V%2E%22">Ramya, V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Babu+Rao%2C+M%2E%22">Babu Rao, M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Cybernetics+%26+Systems%22">Cybernetics & Systems</searchLink>. 2024, Vol. 55 Issue 8, p2041-2070. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Routing+algorithms%22">Routing algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+waste%22">Electronic waste</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Label: Abstract
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  Data: Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for managing E-waste in use today. In essence, there is no ideal method of managing E-waste. This process required more labor and resources. E-waste classification is fulfilled using fractional Henry gas optimization-based deep convolutional neural network (FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste images are conversed through the energy-aware FHGO routing algorithm. The feature extraction procedure is performed to cut out the features for example the gray level co-occurrence matrix (GLCM) feature, local Gabor binary pattern (LGBP) and histogram of oriented gradient (HOG) and the pre-processing phase is concluded with a median filter. To supplement the extracted feature size the data augmentation is fulfilled. Moreover, the E-waste classification is done based on deep CNN, which is trained using a FHGO algorithm. FHGO is exhibited by the merging of Henry gas solubility optimization (HGSO) algorithm, fractional calculus (FC). Comparing to the existing approaches like deep learning, tensor flow deep learning, Cuckoo search-based neural network and machine learning the accuracy of the proposed method is 19.49%, 18.05%, 12.77%, and 7.89% privileged. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/01969722.2023.2175119
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      – Code: eng
        Text: English
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        PageCount: 30
        StartPage: 2041
    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Data augmentation
        Type: general
      – SubjectFull: Routing algorithms
        Type: general
      – SubjectFull: Electronic waste
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Deep learning
        Type: general
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      – TitleFull: Optimized Deep Learning-Based E-Waste Management in IoT Application via Energy-Aware Routing.
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            NameFull: Ramya, Puppala
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            NameFull: Ramya, V.
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            NameFull: Babu Rao, M.
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            – D: 01
              M: 11
              Text: 2024
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              Y: 2024
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            – TitleFull: Cybernetics & Systems
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