A not-for-income Group, IEEE is the whole world's most significant complex Expert organization devoted to advancing technology for the good thing about humanity.
The next posts are merged in Scholar. Their put together citations are counted just for the initial post.
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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 overall economy is enduring probably the most hard era of weather change outside of what exactly is evident while in the pre-industrial age. Despite the fact that Africa's share of world greenhouse fuel (GHG) is nominal, the…
The following article content are merged in Scholar. Their blended citations are counted only for the main write-up.
Towards attack-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Studying solution
A not-for-earnings Corporation, IEEE is the entire world's major specialized professional Firm devoted to advancing technological innovation Jiahao Shen KCL for the advantage of humanity.
A QoE-pushed multicast technique with segment routing—A novel multimedia website traffic engineering paradigm
The current period is dealing with a Predicament associated with partaking the essentialities of Electricity resources to attain financial prosperity due to the ensuing environmental complications. For that reason, sectors…
An optimized quantum minimum seeking algorithm with guaranteed-achievement likelihood and its experiment simulation with Cirq
Monitoring environmental sustainability pathways in Africa: Do all-natural useful resource dependence, renewable energy, and technological innovations amplify or decrease the air pollution noises?
Scanning radar forward-hunting superresolution imaging dependant on the Weibull distribution to get a sea-floor target
This "Cited by" rely features citations to the following content articles in Scholar. Those marked * may be distinct with the article 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。
How to disturb network reconnaissance: a shifting focus on protection method depending on deep reinforcement Mastering