Zobrazeno 1 - 10
of 966
pro vyhledávání: '"Graph Neural Network (GNN)"'
Autor:
Fatima Noor, Muhammad Junaid, Atiah H. Almalki, Mohammed Almaghrabi, Shakira Ghazanfar, Muhammad Tahir ul Qamar
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we intr
Externí odkaz:
https://doaj.org/article/48aba1d18322418eb660ee8569610698
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Integrating the Knowledge Graphs (KGs) into recommendation systems enhances personalization and accuracy. However, the long-tail distribution of knowledge graphs often leads to data sparsity, which limits the effectiveness in practical appli
Externí odkaz:
https://doaj.org/article/3b6c1853e7074229aad97171a61e2294
Autor:
Ruirui Liu, Yiping Jiang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1802-1816 (2025)
The precise and timely forecasting of vertical total electron content (VTEC) in the ionosphere is crucial for navigation, communication systems, and space weather monitoring. Recent research has applied deep learning to predict VTEC maps, treating th
Externí odkaz:
https://doaj.org/article/5dc46aa9753047b196595008bf58fc91
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets for validation. To address this limitation, several
Externí odkaz:
https://doaj.org/article/5c2fe4029c4546e5ab65eb233231229e
Autor:
Zhiyuan Ding, Yulang Huang, Xiangzhu Zeng, Shiyin Jiang, Shuyang Feng, Zhenduo Wang, Ling Wang, Zeng Wang, Yingying Xu, Yan Liu
Publikováno v:
NeuroImage, Vol 297, Iss , Pp 120755- (2024)
Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale laye
Externí odkaz:
https://doaj.org/article/a9f9d86f0abb4bbb87575760aeb4ca7b
Autor:
Shigeru Maya
Publikováno v:
IEEE Access, Vol 12, Pp 185706-185727 (2024)
With the proliferation of cloud services and high-capacity hard drives, the volume of stored document data is rapidly increasing. Consequently, large-scale document retrieval tasks have been attracting significant attention. Recently, embedding-based
Externí odkaz:
https://doaj.org/article/2009564ddf1640519ab3768c1a7d4c6a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 20315-20330 (2024)
Remote sensing scene classification (RSSC) is essential in Earth observation, with applications in land use, environmental status, urban development, and disaster risk assessment. However, redundant background interference, varying feature scales, an
Externí odkaz:
https://doaj.org/article/9c8c6aa3dcee4ef2b672573bbc2baf23
Publikováno v:
IEEE Access, Vol 12, Pp 168000-168009 (2024)
The graph coloring problem functions as a fundamental and pivotal combinatorial optimization task and has played an essential role in various domains such as wireless spectrum management, register planning, and event scheduling. However, traditional
Externí odkaz:
https://doaj.org/article/ce7e17eb82f54e9c9064c862aaaf5591
Autor:
Zonghao Li, Anthony Chan Carusone
Publikováno v:
IEEE Access, Vol 12, Pp 150032-150045 (2024)
This paper presents a fully open-sourced AMS integrated circuit optimization framework based on reinforcement learning (RL). Specifically, given a certain circuit topology and target specifications, this framework optimizes the circuit in both schema
Externí odkaz:
https://doaj.org/article/9dd4790acacb4f449b20f852e1205f2a
Publikováno v:
IEEE Access, Vol 12, Pp 146328-146342 (2024)
Blockchain technology has ushered in a transformative paradigm of decentralized and transparent systems, offering innovative solutions across diverse sectors. While these systems strive for unparalleled transparency and trustlessness in a fully distr
Externí odkaz:
https://doaj.org/article/10d5a4e538724ac5a0a12a5aa73aed3c