A not-for-financial gain Group, IEEE is the whole world's major complex Skilled Corporation committed to advancing know-how for the advantage of humanity.
The following posts are merged in Scholar. Their mixed citations are counted only for the main short article.
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下面的文章将在学术搜索中合并。它们的合并引用次数只会计入第一篇文章。
The worldwide financial system is going through probably the most complicated era of climate change over and above what's obvious inside the pre-industrial age. Although Africa's share of worldwide greenhouse gas (GHG) is minimal, the…
The next articles are merged in Scholar. Their combined citations are counted just for the first report.
Toward assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Finding out approach
A not-for-financial gain organization, IEEE is the earth's biggest complex Expert Group devoted to advancing technological know-how for the advantage Jiahao Shen King's College London of humanity.
A QoE-pushed multicast technique with section routing—A novel multimedia traffic engineering paradigm
The current period is going through a Predicament relating to engaging the essentialities of Electrical power sources to achieve financial prosperity as a result of the following environmental complications. As a result, sectors…
An optimized quantum minimum amount looking algorithm with absolutely sure-success likelihood and its experiment simulation with Cirq
Monitoring environmental sustainability pathways in Africa: Do all-natural source dependence, renewable Electrical power, and technological improvements amplify or lessen the pollution noises?
Scanning radar ahead-on the lookout superresolution imaging determined by the Weibull distribution for any sea-floor target
This "Cited by" rely contains citations to the subsequent articles in Scholar. The ones marked * might be various from your write-up inside the profile.
此“引用引文的出版物数量”计数包含了对学术搜索中以下文章的引用次数。带有*标记的引文可能与个人学术档案中的文章有所区别。
Tips on how to disturb community reconnaissance: a relocating goal defense method depending on deep reinforcement Understanding