Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks
Existing communication networks have inherent limitations in translation theory and adapt to address the complexity of repairing new remote applications at the highest possible level. For further investigation, you are more likely to pass this test using a data-driven program and increasing the expo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8013640 |
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| _version_ | 1832562357205729280 |
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| author | Ajay. P Nagaraj. B Ruihang Huang |
| author_facet | Ajay. P Nagaraj. B Ruihang Huang |
| author_sort | Ajay. P |
| collection | DOAJ |
| description | Existing communication networks have inherent limitations in translation theory and adapt to address the complexity of repairing new remote applications at the highest possible level. For further investigation, you are more likely to pass this test using a data-driven program and increasing the exposure of your wireless network with limited distance resources. This study focuses on various deep learning strategies used in peer-to-peer communication networks. It discusses autoencoders, productive enemy networks, deep emotional networks, common neural networks, and long-term memory, all of which show promise in all aspects of a wireless communication network. In social networks, all of these strategies provide significant reliability, robustness, and cost-effective solutions. In-depth learning enhances test-based performance that helps design, develop, and adapt wireless communication networks. |
| format | Article |
| id | doaj-art-661c9b44bace4e3c8ee85d5ea91acfa3 |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-661c9b44bace4e3c8ee85d5ea91acfa32025-02-03T01:22:51ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/8013640Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication NetworksAjay. P0Nagaraj. B1Ruihang Huang2Faculty of Information and Communication EngineeringDepartment of ECEDonghua UniversityExisting communication networks have inherent limitations in translation theory and adapt to address the complexity of repairing new remote applications at the highest possible level. For further investigation, you are more likely to pass this test using a data-driven program and increasing the exposure of your wireless network with limited distance resources. This study focuses on various deep learning strategies used in peer-to-peer communication networks. It discusses autoencoders, productive enemy networks, deep emotional networks, common neural networks, and long-term memory, all of which show promise in all aspects of a wireless communication network. In social networks, all of these strategies provide significant reliability, robustness, and cost-effective solutions. In-depth learning enhances test-based performance that helps design, develop, and adapt wireless communication networks.http://dx.doi.org/10.1155/2022/8013640 |
| spellingShingle | Ajay. P Nagaraj. B Ruihang Huang Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks Journal of Control Science and Engineering |
| title | Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks |
| title_full | Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks |
| title_fullStr | Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks |
| title_full_unstemmed | Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks |
| title_short | Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks |
| title_sort | deep learning techniques for peer to peer physical systems based on communication networks |
| url | http://dx.doi.org/10.1155/2022/8013640 |
| work_keys_str_mv | AT ajayp deeplearningtechniquesforpeertopeerphysicalsystemsbasedoncommunicationnetworks AT nagarajb deeplearningtechniquesforpeertopeerphysicalsystemsbasedoncommunicationnetworks AT ruihanghuang deeplearningtechniquesforpeertopeerphysicalsystemsbasedoncommunicationnetworks |