Evolutionary game theory with deep learning-based target detection and tracking in sensor networks.

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Title: Evolutionary game theory with deep learning-based target detection and tracking in sensor networks.
Authors: Sun, Lili1,2, Zhou, Yang3, Wu, Yue4, Cai, Helen5, Zhang, Ying6, Liu, Yang7, yangliuevent@outlook.com
Source: Annals of Operations Research; Jan2026, Vol. 356 Issue 1, p499-519, 21p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190856550
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  Data: Evolutionary game theory with deep learning-based target detection and tracking in sensor networks.
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  Data: <searchLink fieldCode="AU" term="%22Sun%2C+Lili%22">Sun, Lili</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AU" term="%22Zhou%2C+Yang%22">Zhou, Yang</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AU" term="%22Wu%2C+Yue%22">Wu, Yue</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AU" term="%22Cai%2C+Helen%22">Cai, Helen</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AU" term="%22Zhang%2C+Ying%22">Zhang, Ying</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AU" term="%22Liu%2C+Yang%22">Liu, Yang</searchLink><relatesTo>7</relatesTo>, <i>yangliuevent@outlook.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Operations+Research%22">Annals of Operations Research</searchLink>; Jan2026, Vol. 356 Issue 1, p499-519, 21p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=190856550
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10479-024-06379-6
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 499
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      – TitleFull: Evolutionary game theory with deep learning-based target detection and tracking in sensor networks.
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            NameFull: Sun, Lili
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            NameFull: Zhou, Yang
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            NameFull: Wu, Yue
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            NameFull: Cai, Helen
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            NameFull: Zhang, Ying
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            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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              Value: 356
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