Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Conv1D"'
Autor:
Ayokunle Olalekan Ige, Malusi Sibiya
Publikováno v:
IEEE Access, Vol 12, Pp 144082-144105 (2024)
Deep learning architectures have brought about new heights in computer vision, with the most common approach being the Convolutional Neural Network (CNN). Through CNN, tasks previously deemed unattainable, including facial recognition, autonomous dri
Externí odkaz:
https://doaj.org/article/90d321062bef46b2b69b0f2371610694
Publikováno v:
Asian Journal of Atmospheric Environment, Vol 17, Iss 1, Pp 1-22 (2023)
Abstract The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality
Externí odkaz:
https://doaj.org/article/469cea4ecfed4fc5a33f3b8268bd3985
Publikováno v:
Heliyon, Vol 9, Iss 6, Pp e16456- (2023)
Accurate reservoir inflow forecasting is crucial for efficient water management. In this study, different deep learning models, including Dense, Long short-term memory (LSTM), and one-dimensional convolutional neural networks (Conv1D), were used to b
Externí odkaz:
https://doaj.org/article/af765027df944787a037a21bfafb99bd
Publikováno v:
Mathematics, Vol 11, Iss 22, p 4630 (2023)
Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper propos
Externí odkaz:
https://doaj.org/article/6f5b92bae0be40f5860de50baa657490
Publikováno v:
Anti-Corrosion Methods and Materials, 2021, Vol. 68, Issue 5, pp. 396-403.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ACMM-06-2020-2334
Autor:
Rajib Maity, Mohd Imran Khan, Subharthi Sarkar, Riya Dutta, Subhra Sekhar Maity, Manali Pal, Kironmala Chanda
Publikováno v:
Journal of Water and Climate Change, Vol 12, Iss 6, Pp 2774-2796 (2021)
This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, r
Externí odkaz:
https://doaj.org/article/752bb8734eb9453f885d498ec3e5dc41
Autor:
Changjiang Xiao, Xiaohua Tong, Dandan Li, Xiaojian Chen, Qiquan Yang, Xiong Xv, Hui Lin, Min Huang
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 112, Iss , Pp 102971- (2022)
Ocean temperature is a vital physical variable of the oceans. Accurately predicting the long lead dynamics of the three-dimensional ocean temperature (3D-OT) can help us identify in advance potential extreme events (e.g., droughts and floods) that ma
Externí odkaz:
https://doaj.org/article/1f6c786d0e684d36baf40b0467fb687d
Autor:
Mohd Imran Khan, Rajib Maity
Publikováno v:
IEEE Access, Vol 8, Pp 52774-52784 (2020)
Deep Learning (DL) is an effective technique for dealing with complex systems. This study proposes a hybrid DL approach, a combination of one-dimensional Convolutional Neural Network (Conv1D) and Multi-Layer Perceptron (MLP) (hereinafter referred to
Externí odkaz:
https://doaj.org/article/35c7ff2a64164325a2f54676c5eb5e67
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