Results 51 to 60 of about 18,004 (194)

CNN-BiLSTM prediction (1,000).

, 2019
CNN-BiLSTM prediction (1,000).
Ruixiong Ma (7866257), Haiou Wang (7866260), Siquan Hu (7866254)   +2 more
core   +1 more source

BERT BiLSTM-Attention Similarity Model

, 2021
Semantic similarity models are a core part of many of the applications of natural language processing (NLP) that we may be encountering daily, which makes them an important research topic.
Dina Ismail, Ismail, Dina, Ahmed Fayed, Ahmed Rafea, Aboutaleb, Ahmed, Nada A. GabAllah, Nourhan Sakr, Ahmed Aboutaleb, Sakr, Nourhan, Gaballah, Nada A., Rafea, Ahmed, Fayed, Ahmed   +11 more
core   +1 more source

Face expression recognition based on NGO-BILSTM model [PDF]

, 2023
IntroductionFacial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions.
Jiarui Zhong, Liuhan Yi, Tangxian Chen
core   +1 more source

Time-series prediction of pier settlement and system development for the Beijing–Shanghai high-speed railway based on BiLSTM

Shenzhen Daxue xuebao. Ligong ban
To improve the accuracy of pier settlement prediction during both the construction and operational stages, we propose a settlement prediction method based on a bidirectional long short-term memory (BiLSTM) network by using the underpass project beneath ...
Hu Zhenghan, Wang Changdan, Wang Jinglong, Li Xiaolong, Xu Jingjing   +4 more
doaj   +1 more source

Environmental Control for Edible Fungi Cultivation Based on Temporal Information and Deep Learning

Food Bioengineering, EarlyView.
ABSTRACT Currently, there are still prevalent issues in greenhouse environmental regulation, such as response lag, low control accuracy, and difficulty in coping with sudden environmental disturbances. To achieve high‐precision and dynamic control of the edible fungi cultivation environment, this study proposes an edible fungi environmental control ...
Xiangyan Wang, Shuangcheng Huang, Qingbo Lu, Xinyi Guo   +3 more
wiley   +1 more source

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Structure of BiLSTM model.

, 2019
Structure of BiLSTM model.
Peng Su (300618), K. Vijay-Shanker (781828), Gang Li (34549), Cathy Wu (216122)   +3 more
core   +1 more source

Research Progress of Multimodal Named Entity Recognition [PDF]

Zhengzhou Daxue xuebao. Gongxue ban
In order to solve the problems in studies of multimodal named entity recognition, such as the lack of text feature semantics, the lack of visual feature semantics, and the difficulty of graphic feature fusion, a series of multimodal named entity ...
WANG Hairong, XU Xi, WANG Tong, JING Boxiang   +3 more
doaj   +1 more source

SparGlu: A sparse‐data evaluation framework for gated recurrent unit‐based blood glucose prediction across varying monitoring intervals

Journal of Intelligent Medicine, EarlyView.
A sparse‐aware GRU model accurately predicts blood glucose levels using downsampled and limited data, achieving clinically safe forecasts even under low‐monitoring conditions. By effectively capturing glucose dynamics through residual learning, the model supports personalized diabetes self‐management, offering a practical solution for resource‐limited ...
Genet Tadese Aboye, Jasper Gielen, Gizeaddis Lamesgin Simegn, Jean‐Marie Aerts   +3 more
wiley   +1 more source

Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia [PDF]

Wave energy is regarded as one of the powerful renewable energy sources and depends on the assessment of significant wave height (Hs) for feasibility. Hence, this study explores the potential of wave energy by assessing and predicting Hs for two study ...
Prakash, Reema, Raj, Nawin
core   +1 more source