Zobrazeno 1 - 10
of 778
pro vyhledávání: '"Recurrent neural networks (RNNs)"'
Autor:
A.E. Abdelkareem
Publikováno v:
Diyala Journal of Engineering Sciences, Vol 17, Iss 3 (2024)
This research delves into the evaluation of Deep learning signal constellation identification (DL-SCI) algorithms in underwater acoustic communications using Orthogonal Frequency Division Multiplexing (OFDM). It distinctly examines at how effective t
Externí odkaz:
https://doaj.org/article/46118b42e0654f08a33fc85fef2ce801
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5071-5081 (2024)
Abstract To solve a distributed optimal resource allocation problem, a collective neurodynamic approach based on recurrent neural networks (RNNs) is proposed in this paper. Multiple RNNs cooperatively solve a global constrained optimization problem i
Externí odkaz:
https://doaj.org/article/9f1905bcc72246c1ae9a6d96aba5efa7
Autor:
Nevena Rankovic, Dragica Rankovic
Publikováno v:
Journal of Theoretical and Applied Electronic Commerce Research, Vol 19, Iss 1, Pp 381-395 (2024)
Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both com
Externí odkaz:
https://doaj.org/article/e1dc63456fb54e5c99c2ec079bd900a0
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 7608-7620 (2024)
Precipitation nowcasting is a critical aspect of meteorological services, which helps people make reasonable arrangements. Nowadays the methods based on recurrent neural networks are widely employed as the primary solution for precipitation nowcastin
Externí odkaz:
https://doaj.org/article/047384ce94b947bfaf93761713890445
Publikováno v:
Sensors, Vol 24, Iss 11, p 3495 (2024)
Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on t
Externí odkaz:
https://doaj.org/article/c55fe1acb64b49bf85fd09b552fb27e0
Publikováno v:
IEEE Access, Vol 11, Pp 33148-33159 (2023)
Recurrent Neural Networks (RNNs) and their variants have been demonstrated tremendous successes in modeling sequential data such as audio processing, video processing, time series analysis, and text mining. Inspired by these facts, we propose human a
Externí odkaz:
https://doaj.org/article/7d06b84ef9734e368abcc746ff48f9d2
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-10 (2022)
Abstract Resource constraints, e.g., limited product inventory or financial strength, may affect consumers’ choices or preferences in some recommendation tasks but are usually ignored in previous recommendation methods. In this paper, we aim to min
Externí odkaz:
https://doaj.org/article/9b9de3df81a648e282243845f002f3ad
Publikováno v:
Alexandria Engineering Journal, Vol 61, Iss 10, Pp 7585-7603 (2022)
Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work reports a comparative
Externí odkaz:
https://doaj.org/article/3043705ccb4e4d859070c29177b74eb9
Autor:
Daniel J. Cruz, Manuel R. Barbosa, Abel D. Santos, Rui L. Amaral, Jose Cesar de Sa, Jose V. Fernandes
Publikováno v:
Metals, Vol 14, Iss 1, p 84 (2024)
The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials
Externí odkaz:
https://doaj.org/article/12fca1643ef94eebbc7e88b2da3b9a9e
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