Zobrazeno 1 - 10
of 3 336
pro vyhledávání: '"self attention mechanism"'
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background With the increasing impact of tuberculosis on public health, accurately predicting future tuberculosis cases is crucial for optimizing of health resources and medical service allocation. This study applies a self-attention mechani
Externí odkaz:
https://doaj.org/article/d0a4c1e1551f4aada5a3343b885f3969
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Time series classification finds widespread applications in civil, industrial, and military fields, while the classification performance of time series models has been improving with the recent development of deep learning. However, the issu
Externí odkaz:
https://doaj.org/article/a6c52420a31f4c208905d0a6e98b871e
Autor:
Cai Sulong
Publikováno v:
Journal of Intelligent Systems, Vol 33, Iss 1, Pp 87-90 (2024)
As society develops and educational needs continue to change, the traditional way of teaching ideology and politics is facing challenges in terms of efficiency and effectiveness evaluation. In response to the low efficiency of ideological and politic
Externí odkaz:
https://doaj.org/article/620758904eba4ed9a9a15a0af0862fa4
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The preparation of sintered NdFeB magnets is complex, time-consuming, and costly. Data-driven machine learning methods can enhance the efficiency of material synthesis and performance optimization. Traditional machine learning models based o
Externí odkaz:
https://doaj.org/article/4183d760fe8f4f7696bb412e6469fafc
Autor:
Mengyuan Xiong, Shuangjin Zheng, Wei Liu, Rongsheng Cheng, Lihui Wang, Haijun Zhang, Guona Wang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract In the field of oil drilling, accurately predicting the Rate of Penetration (ROP) is crucial for improving drilling efficiency and reducing costs. Traditional prediction methods and existing machine learning approaches often lack accuracy an
Externí odkaz:
https://doaj.org/article/0691f202351744a18075732ae1e5d532
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 8383-8401 (2024)
Abstract The influence maximization problem that has drawn a great deal of attention from researchers aims to identify a subset of influential spreaders that can maximize the expected influence spread in social networks. Existing works on the problem
Externí odkaz:
https://doaj.org/article/82985cff3d26404898aa8974049ee337
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 8163-8177 (2024)
Abstract With the continuous accumulation of massive amounts of mobile data, point-of-interest (POI) recommendation has become a vital task for location-based social networks. Deep neural networks or matrix factorization (MF) alone are challenging to
Externí odkaz:
https://doaj.org/article/6abf081c45bb478598ca663d785070a7
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Mine flooding accidents have occurred frequently in recent years, and the predicting of mine water inflow is one of the most crucial flood warning indicators. Further, the mine water inflow is characterized by non-linearity and instability,
Externí odkaz:
https://doaj.org/article/66ae1477c80b4054980db05b97e2f2af
Publikováno v:
Systems Science & Control Engineering, Vol 12, Iss 1 (2024)
In modern manufacturing industry, in order to adapt to changes in the general environment, the manufacturing industry must improve production efficiency. To this end, this article introduces an improved genetic algorithm based on rule selection to ta
Externí odkaz:
https://doaj.org/article/8387321fab9041e490a7bb03e7311d39
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
IntroductionWith the rapid development of the tourism industry, the demand for accurate and personalized travel route recommendations has significantly increased. However, traditional methods often fail to effectively integrate visual and sequential
Externí odkaz:
https://doaj.org/article/58a345691c954482b3a7d4fa0e1c92f3