Zobrazeno 1 - 10
of 308
pro vyhledávání: '"Bidirectional Long-Short Term Memory (biLSTM)"'
Publikováno v:
地质科技通报, Vol 43, Iss 4, Pp 224-234 (2024)
Objective With increasing difficulty in phosphate ore prospecting, there are an increasing number of geological exploration reports. The manual recognition of geological information related to phosphate rock mineralization in massive documents is tim
Externí odkaz:
https://doaj.org/article/becb4eb0096d4d35a708062b56e25fb4
Publikováno v:
Radioengineering, Vol 33, Iss 2, Pp 236-245 (2024)
With the continuous advancement of Internet of Things (IoT) intelligence, IoT security issues have become more and more prominent in recent years. The research on IoT security has become a hot spot. A lightweight IoT intrusion detection model fusing
Externí odkaz:
https://doaj.org/article/25f8255edabb4ff19d97e7ddf9f00fc4
Autor:
Abbas Akkasi
Publikováno v:
Natural Language Processing Journal, Vol 9, Iss , Pp 100102- (2024)
The rapid digitization of the economy is transforming the job market, creating new roles and reshaping existing ones. As skill requirements evolve, identifying essential competencies becomes increasingly critical. This paper introduces a novel ensemb
Externí odkaz:
https://doaj.org/article/80be188df2d34b718ae8555f0b0b34bd
Autor:
Tasnim Nishat Islam, Hafiz Imtiaz
Publikováno v:
Healthcare Analytics, Vol 5, Iss , Pp 100329- (2024)
In this study, we propose a computationally-light and robust neural network for estimating heart rate in remote healthcare systems. We develop a model that can be trained on consumer-grade graphics processing units (GPUs), and can be deployed on edge
Externí odkaz:
https://doaj.org/article/759d2d5f2f334048b10fd6238b540135
Publikováno v:
IEEE Access, Vol 12, Pp 125369-125383 (2024)
With the continuous development of IoT technology, the application of Long Rang wireless link transmission in urban environments has gradually increased, making reliable wireless link quality transmission particularly important. Traditional wireless
Externí odkaz:
https://doaj.org/article/f4524a6802514227adf5daff87896404
Publikováno v:
IEEE Access, Vol 12, Pp 124790-124800 (2024)
Wind power prediction is important in successfully integrating renewable energy sources into the grid. This study is focused on a sub-domain of wind power prediction and compares Bidirectional Long Short Term Memory (BiLSTM) and Bidirectional Gated R
Externí odkaz:
https://doaj.org/article/49278a1e1a1e458dba041a885b1ef1dc
Autor:
Yong Chang, Guangqing Bao
Publikováno v:
IEEE Access, Vol 12, Pp 78463-78479 (2024)
This study addresses the challenges posed by the strong noise and nonstationary characteristics of vibration signals to enhance the efficiency and accuracy of rolling-bearing fault diagnosis in electric motors. A fault diagnosis model is proposed bas
Externí odkaz:
https://doaj.org/article/81a06f670133402b985648b7d81837a7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9194-9213 (2024)
To effectively mitigate disaster damage, it is crucial to obtain landslide information quickly and accurately with the abundant remote sensing images. Although related landslide detection research has been carried out a lot in recent years, all of th
Externí odkaz:
https://doaj.org/article/ca0823b5848f44b48ab6e066e01f14e9
Publikováno v:
IEEE Access, Vol 12, Pp 50710-50722 (2024)
This research proposes a dam deformation prediction model based on clustering partitioning and Bidirectional Long Short-Term Memory (BiLSTM) networks to address the limitations of traditional monitoring models in characterizing the distribution chara
Externí odkaz:
https://doaj.org/article/f87fa50e25b24d918f61a0cca28d07e8
Publikováno v:
电力工程技术, Vol 43, Iss 1, Pp 117-126 (2024)
Aiming at the problems that multiplex influencing factors and strong uncertainty in distribution network load caused by the capacity accumulation of distributed generation and new loads, a load prediction method using memory neural network and curve
Externí odkaz:
https://doaj.org/article/60de67b16373418fbe408fdb8dd2c703