Zobrazeno 1 - 10
of 284
pro vyhledávání: '"graph networks"'
Autor:
Yongqing ZHOU, Dawei HAO, Yuchen FAN, Xintong WEN, Chang WEI, Xin LIU, Wenzhen ZHANG, Heyang WANG
Publikováno v:
Meitan xuebao, Vol 49, Iss 10, Pp 4127-4137 (2024)
Due to the introduction of large-scale renewable energy to the electric grid, the coal-fired units are running more under load cycling conditions and this has dramatically increased the difficulty of boiler in the control of NOx emissions. The real-t
Externí odkaz:
https://doaj.org/article/de4de299066a402fbf7a1591b26aa293
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background Compared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limi
Externí odkaz:
https://doaj.org/article/f628e944211d4862b1343e635b2e39a5
Autor:
Qionghao Huang, Jili Chen
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-26 (2024)
Abstract Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the cru
Externí odkaz:
https://doaj.org/article/fb289fffb2a34d9d8c1c80c1f9506b18
Autor:
Qionghao Huang, Yan Zeng
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 3, Pp 3557-3575 (2024)
Abstract Academic performance is a crucial issue in the field of Online learning analytics. While deep learning-based models have made significant progress in the era of big data, many of these methods need help to capture the complex relationships p
Externí odkaz:
https://doaj.org/article/7c9217e3605e43fe805a0d71be9d984a
Publikováno v:
IEEE Access, Vol 12, Pp 157630-157656 (2024)
In the fast-changing video surveillance area, there exists a critical need for new ways in which the huge and complex data availed from CCTV systems can be correctly analyzed. Most already-existing methods of person detection and video analysis are n
Externí odkaz:
https://doaj.org/article/d0dd9adc9e9b472692a83c5ae5579302
Publikováno v:
IEEE Access, Vol 12, Pp 140611-140627 (2024)
The prediction of traffic flow has emerged as a pivotal element within the domain of intelligent transport systems, garnering considerable interest and attention from various quarters. SpatioTemporal Graph Neural Networks (STGNNS) have been extensive
Externí odkaz:
https://doaj.org/article/82d4ad5d8e024f12bb1505cdc1178baa
Publikováno v:
Energies, Vol 17, Iss 14, p 3557 (2024)
Distributed photovoltaic (PV) power stations generally lack historical meteorological data, which is one of the main reasons for their insufficient power prediction accuracy. To address this issue, this paper proposes a power prediction method for re
Externí odkaz:
https://doaj.org/article/d383c92c47a2479f8fae120f9c8badce
Publikováno v:
Knowledge, Vol 3, Iss 3, Pp 293-306 (2023)
Deep learning is widely used in many real-life applications. Despite their remarkable performance accuracies, deep learning networks are often poorly calibrated, which could be harmful in risk-sensitive scenarios. Uncertainty quantification offers a
Externí odkaz:
https://doaj.org/article/ea6e3b3cac01457ea0e27c24f46222ef
Publikováno v:
Applied Sciences, Vol 14, Iss 5, p 1696 (2024)
Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in
Externí odkaz:
https://doaj.org/article/1faaa6c6ab8e4dbd8271f887c2b5a48c
Autor:
Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, Georgia Karapostoli, Markus Seidel, Rosamaria Venditti, Luka Lambrecht, Emanuele Usai, Muhammad Ahmad, Javier Fernandez Menendez, Kaori Maeshima, the CMS-HCAL Collaboration
Publikováno v:
Sensors, Vol 23, Iss 24, p 9679 (2023)
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acqui
Externí odkaz:
https://doaj.org/article/e76afd8526164a548b0af49997b91372