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Titlebook: Advances in Information Communication Technology and Computing; Proceedings of AICTC Vishal Goar,Manoj Kuri,Tomonobu Senjyu Conference proc

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發(fā)表于 2025-3-21 19:49:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Information Communication Technology and Computing
期刊簡稱Proceedings of AICTC
影響因子2023Vishal Goar,Manoj Kuri,Tomonobu Senjyu
視頻videohttp://file.papertrans.cn/149/148342/148342.mp4
發(fā)行地址Recent research on ICT for Sustainability.Results of the AICTC 2022 conference held in Bikaner, India.Serves as a reference for researchers and practitioners in academia and industry
學(xué)科分類Lecture Notes in Networks and Systems
圖書封面Titlebook: Advances in Information Communication Technology and Computing; Proceedings of AICTC Vishal Goar,Manoj Kuri,Tomonobu Senjyu Conference proc
影響因子The book is a collection of best selected research papers presented at the International Conference on Advances in Information Communication Technology and Computing (AICTC 2022), held in Government Engineering College Bikaner, Bikaner, India during 17 – 18 December 2022. The book covers ICT-based approaches in the areas of ICT for Energy Efficiency, Life Cycle Assessment of ICT, Green IT, Green Information Systems, Environmental Informatics, Energy Informatics, Sustainable HCI, or Computational Sustainability..
Pindex Conference proceedings 2023
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書目名稱Advances in Information Communication Technology and Computing影響因子(影響力)




書目名稱Advances in Information Communication Technology and Computing影響因子(影響力)學(xué)科排名




書目名稱Advances in Information Communication Technology and Computing網(wǎng)絡(luò)公開度




書目名稱Advances in Information Communication Technology and Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Information Communication Technology and Computing被引頻次




書目名稱Advances in Information Communication Technology and Computing被引頻次學(xué)科排名




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Comparative Analysis of Convolutional and Long Term Short Memory Architectures in Machine Learning,978-3-662-32615-2
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The Contribution of the Internet of Things to Enhance the Brands of Small and Medium-Sized Enterpri978-3-319-72694-6
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https://doi.org/10.1007/978-3-662-39894-4ve in various daylight circumstances, including sunny, snowy, and rainy days, as well as inside tunnels. The proposed approach processes frame on average in 28 ms and have a detection accuracy of 96.78%, as shown by implementation results. This article aims to provide a simple technique for identify
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Aus Wirthschaft und Wissenschaft,s to use a machine-learning algorithm to detect and categorize e-mail messages of university faculty into spam and non-spam with the identification of the most occurring attributes that are contained in the messages. These attributes helped in the generation of a classification model based on the Ra
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