EXERT
发表于 2025-3-21 16:48:49
书目名称Broadband Communications, Networks, and Systems影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0191227<br><br> <br><br>书目名称Broadband Communications, Networks, and Systems读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0191227<br><br> <br><br>
HAWK
发表于 2025-3-21 20:17:22
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发怨言
发表于 2025-3-22 02:13:54
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埋葬
发表于 2025-3-22 05:57:36
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过度
发表于 2025-3-22 11:44:08
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Interregnum
发表于 2025-3-22 14:16:03
Decision Making Under Uncertainty for the Deployment of Future Networks in IoT Scenarios such as smart spaces, and industry 4.0, through a systematic literature review. According to our research results, a new paradigm for computationally capturing and modeling human behavior context must be developed with the purpose of improving resource management.
indifferent
发表于 2025-3-22 17:51:18
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kyphoplasty
发表于 2025-3-23 00:22:48
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闲逛
发表于 2025-3-23 02:28:25
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GREG
发表于 2025-3-23 05:39:59
Identification of Significant Permissions for Efficient Android Malware Detectionon engine which consumes less train and test time without significant compromise on the model accuracy. Our experimental results show that the Android malware detection model based on the random forest classifier is most balanced and achieves the highest area under curve score of ., which is better