A not-for-income Corporation, IEEE is the planet's biggest technical Qualified Corporation devoted to advancing technology for the good thing about humanity.
The next content articles are merged in Scholar. Their mixed citations are counted just 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 financial system is encountering essentially the most challenging era of local weather modify outside of precisely what is evident during the pre-industrial age. While Africa's share of global greenhouse fuel (GHG) is nominal, the…
The following content are merged in Scholar. Their blended citations are counted just for the primary post.
Towards attack-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Finding out approach
A not-for-financial gain Firm, IEEE is the world's premier technical Qualified Business devoted to advancing engineering for the good thing about humanity.
A QoE-driven multicast system with Jiahao Shen King's College London phase routing—A novel multimedia targeted traffic engineering paradigm
The present era is experiencing a dilemma regarding engaging the essentialities of Electricity resources to achieve financial prosperity as a result of the following environmental problems. For that reason, sectors…
An optimized quantum minimal hunting algorithm with certain-accomplishment probability and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do purely natural resource dependence, renewable Electricity, and technological innovations amplify or decrease the air pollution noises?
Scanning radar forward-hunting superresolution imaging according to the Weibull distribution to get a sea-floor target
This "Cited by" rely features citations to the subsequent article content in Scholar. Those marked * could be different through the report 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。
Ways to disturb network reconnaissance: a relocating focus on protection strategy based upon deep reinforcement learning