冠军
发表于 2025-3-21 16:52:59
书目名称Proceedings of International Conference on Recent Innovations in Computing影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0764515<br><br> <br><br>书目名称Proceedings of International Conference on Recent Innovations in Computing读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0764515<br><br> <br><br>
ostensible
发表于 2025-3-21 21:32:42
Proceedings of International Conference on Recent Innovations in Computing978-981-97-3442-9Series ISSN 1876-1100 Series E-ISSN 1876-1119
Coterminous
发表于 2025-3-22 00:29:24
https://doi.org/10.1007/978-981-97-3442-9Internet of Things; Machine Learning; Deep Learning; Big Data Analytics; Robotics Cloud Computing; Comput
Ingratiate
发表于 2025-3-22 06:06:19
Zoltán Illés,Chaman Verma,Pradeep Kumar SinghPresents research works in innovations in computing.Discusses results of ICRIC 2023 held in Jammu, India.Serves as a reference for researchers and practitioners in academia and industry
坚毅
发表于 2025-3-22 09:27:19
Anomaly Detection in Power Consumption: A Comprehensive Multi-technique Approachanomaly detection models. The LSTM autoencoder achieves balanced precision, recall, and high accuracy. These results emphasize the potential of the LSTM autoencoder as an effective approach for detecting anomalies in power consumption time series data, contributing to enhanced energy management practices.
期满
发表于 2025-3-22 15:08:10
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态学
发表于 2025-3-22 18:58:58
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小卷发
发表于 2025-3-23 00:46:18
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呼吸
发表于 2025-3-23 04:17:03
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臭了生气
发表于 2025-3-23 08:10:48
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