A not-for-gain Corporation, IEEE is the planet's biggest technological Qualified Firm dedicated to advancing engineering for the benefit of humanity.

The subsequent content are merged in Scholar. Their blended citations are counted only for the primary 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 financial system is encountering the most demanding era of weather alter further than what on earth is evident during the pre-industrial age. Despite the fact that Africa's share of global greenhouse gasoline (GHG) is minimum, the…

The subsequent content articles are merged in Scholar. Their put together citations are counted only for the first report.

Toward assault-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Finding out approach

A not-for-income organization, IEEE is the globe's largest complex Experienced Firm dedicated to advancing technological innovation 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 present era is experiencing a Problem relating to partaking the essentialities of Power methods to attain economic prosperity on account of the following environmental problems. Therefore, sectors…

An optimized quantum minimum browsing algorithm with guaranteed-good results probability and its experiment simulation with Cirq

Tracking environmental sustainability pathways in Africa: Do pure resource dependence, renewable Vitality, and technological improvements amplify or reduce the pollution noises?

Scanning radar ahead-on the lookout superresolution imaging depending on the Weibull distribution for your sea-surface goal

This "Cited by" depend includes citations to the following content in Scholar. The ones marked * could possibly be different from the write-up from 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 community reconnaissance: a transferring focus on defense strategy depending on deep reinforcement Understanding