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

dejected

书目名称Advances in Computing and Network Communications影响因子(影响力)




书目名称Advances in Computing and Network Communications影响因子(影响力)学科排名




书目名称Advances in Computing and Network Communications网络公开度




书目名称Advances in Computing and Network Communications网络公开度学科排名




书目名称Advances in Computing and Network Communications被引频次




书目名称Advances in Computing and Network Communications被引频次学科排名




书目名称Advances in Computing and Network Communications年度引用




书目名称Advances in Computing and Network Communications年度引用学科排名




书目名称Advances in Computing and Network Communications读者反馈




书目名称Advances in Computing and Network Communications读者反馈学科排名

去掉

绝缘

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.

昏迷状态

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

Zukunftsfähige 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

不适

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