A not-for-profit organization, IEEE is the planet's largest technological Specialist Group devoted to advancing know-how for the advantage of humanity.
The following article content are merged in Scholar. Their merged citations are counted only for the first posting.
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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 economic system is experiencing quite possibly the most challenging period of climate change outside of what exactly is obvious inside the pre-industrial age. Although Africa's share of worldwide greenhouse gas (GHG) is negligible, the…
The following article content are merged in Scholar. Their mixed citations are counted just for the initial posting.
Toward assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Studying solution
A not-for-profit Corporation, IEEE Jiahao Shen King's College London is the globe's greatest technological Skilled Corporation focused on advancing engineering for the benefit of humanity.
A QoE-pushed multicast strategy with segment routing—A novel multimedia site visitors engineering paradigm
The existing era is experiencing a dilemma associated with partaking the essentialities of Power methods to achieve economic prosperity on account of the following environmental problems. For that reason, sectors…
An optimized quantum minimum amount browsing algorithm with absolutely sure-success chance and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do pure source dependence, renewable Power, and technological improvements amplify or decrease the pollution noises?
Scanning radar ahead-wanting superresolution imaging dependant on the Weibull distribution for the sea-area concentrate on
This "Cited by" rely features citations to the following content articles in Scholar. The ones marked * may be distinct within the post in 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。
How you can disturb community reconnaissance: a transferring concentrate on defense strategy based upon deep reinforcement learning