Titlebook: Advances in Computing and Network Communications; Proceedings of CoCoN Sabu M. Thampi,Erol Gelenbe,Kuan-Ching Li Conference proceedings 202

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書目名稱Advances in Computing and Network Communications影響因子(影響力)




書目名稱Advances in Computing and Network Communications影響因子(影響力)學(xué)科排名




書目名稱Advances in Computing and Network Communications網(wǎng)絡(luò)公開度




書目名稱Advances in Computing and Network Communications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Computing and Network Communications被引頻次




書目名稱Advances in Computing and Network Communications被引頻次學(xué)科排名




書目名稱Advances in Computing and Network Communications年度引用




書目名稱Advances in Computing and Network Communications年度引用學(xué)科排名




書目名稱Advances in Computing and Network Communications讀者反饋




書目名稱Advances in Computing and Network Communications讀者反饋學(xué)科排名

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絕緣

Agnieszka Marek,Grzegorz Zasuwa validate the prediction models, smart meter data from our campus is used. The results show that the long short-term memory (LSTM) model is more suitable for energy demand prediction. The LSTM model is then used to predict the energy demand in students’ hostels during conditions such as climate and holidays.

昏迷狀態(tài)

Conference proceedings 2021 (CoCoNet‘20), October 14–17, 2020, Chennai, India. The papers?presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical?sections on Signal, Image and Speech Processing, Wireless and Mobile Communication, Internet of Things, Cloud and?Edge C

Brain-Imaging

pacifist

https://doi.org/10.1007/978-3-662-63004-4alization in the exploratory methodology toward Karawitan musicians (N?=?20), the outcomes demonstrated higher brain activities in tuning into recognizable music, ., Javanese traditional music. In addition, the dominant brain activities happened in the temporal lobe while Karawitan musicians listened to ., Javanese traditional music.

遭受

Zukunftsf?hige Industrie in Hessen gestalten classifier and it also performs optimally even in the presence of varying noises. All colored noise samples gave superior classification accuracy with KNN classifier and all electronic and natural noises gave best accuracy with Extreme Gradient Boost classifier.

Mammal

Maxi Schneider,Christopher Feldhese is compared with existing models of comparable complexity. The proposed methods have an average LSF performance index of 0.4082 and 0.4008, respectively, which is higher than existing similar work reported.

anthesis

Early Detection of COVID-19 from CT Scans Using Deep Learning Techniques

不適

Towards Protein Tertiary Structure Prediction Using LSTM/BLSTM978-1-349-19655-5