Zobrazeno 1 - 10
of 1 002
pro vyhledávání: '"sequence prediction"'
Publikováno v:
Intelligent and Converged Networks, Vol 5, Iss 3, Pp 237-247 (2024)
Sensing signals of many real-world network systems, such as traffic network or microgrid, could be sparse and irregular in both spatial and temporal domains due to reasons such as cost reduction, noise corruption, or device malfunction. It is a funda
Externí odkaz:
https://doaj.org/article/2c03bdd5636f44828a450823187801a3
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-19 (2024)
Abstract Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are lim
Externí odkaz:
https://doaj.org/article/ef97044f66df4e07981df4b436366c5e
A Real-Time SAR Ship Detection Method Based on Improved CenterNet for Navigational Intent Prediction
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 19467-19477 (2024)
Utilizing massive spatio-temporal sequence data and real-time synthetic aperture radar (SAR) ship target monitoring technology, it is possible to effectively predict the future trajectories and intents of ships. While real-time monitoring technology
Externí odkaz:
https://doaj.org/article/d328219dd9c244a8b2dc86413cf92ee1
Publikováno v:
IEEE Access, Vol 12, Pp 33832-33842 (2024)
Recently, significant developments have been made in Long Short-Term Memory (LSTM) networks within the realm of synthesis music. Notwithstanding these advancements, several challenges persist warranting further research. Primarily, there exists an ab
Externí odkaz:
https://doaj.org/article/2f7e28d61031405dad0f657765291829
Publikováno v:
Mathematics, Vol 12, Iss 22, p 3456 (2024)
Driving risk prediction is crucial for advanced driving technologies, with deep learning approaches leading the way in driving safety analysis. Current driving risk prediction methods typically establish a mapping between driving features and risk st
Externí odkaz:
https://doaj.org/article/cf10c5b5607046258c7231015e8bf05d
Publikováno v:
Buildings, Vol 14, Iss 10, p 3279 (2024)
The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in
Externí odkaz:
https://doaj.org/article/c3ba15d4b4004e31b623ca864581fc5b
Publikováno v:
Sensors, Vol 24, Iss 18, p 6049 (2024)
Precipitation nowcasting, which involves the short-term, high-resolution prediction of rainfall, plays a crucial role in various real-world applications. In recent years, researchers have increasingly utilized deep learning-based methods in precipita
Externí odkaz:
https://doaj.org/article/084ab0923ca54129bb052c458a2c9a57
Akademický článek
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Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-20 (2023)
Abstract Apart from nodes and links, for many networked systems, we have access to data on paths, i.e., collections of temporally ordered variable-length node sequences that are constrained by the system’s topology. Understanding the patterns in su
Externí odkaz:
https://doaj.org/article/3953d41fb2e247478feaf133a2d2811f
Autor:
Minhad Khairun Nisa’, Farayez Araf, Reaz Mamun Bin Ibne, Bhuiyan Mohammad Arif Sobhan, Samdin Siti Balqis, Miraz Mahdi H.
Publikováno v:
Measurement Science Review, Vol 23, Iss 2, Pp 86-91 (2023)
Dementia is not a specific disease, but a general term for age-related decline or loss of memory, cognitive abilities including problem solving and decision-making, and one’s own language, which significantly interfere with daily life. Researchers
Externí odkaz:
https://doaj.org/article/7d3c9cb602674e6fa08f010c84f84441