Results 151 to 160 of about 208,635 (216)

Scene Text Recognition Based on Bidirectional LSTM and Deep Neural Network. [PDF]

Comput Intell Neurosci, 2021
Kantipudi MP, Kumar S, Kumar Jha A.
europepmc   +1 more source

Reliable Intelligent Diagnostics With Uncertainty Quantification for Mechanical System Condition Assessment

Quality and Reliability Engineering International, Volume 42, Issue 6, Page 2896-2916, October 2026.
ABSTRACT Health condition assessment of mechanical systems is essential to ensure their safe and reliable operation. Although AI‐driven diagnostic techniques have advanced significantly with the development of deep learning, the reliability of such techniques is often compromised due to the lack of uncertainty quantification (UQ)—particularly the ...
Guangjun Jiang, Haotian Ouyang, Zifei Xu
wiley   +1 more source

Deep Learning Enables Identification of Antimicrobial Peptides Through Mechanochromic Fingerprints

Angewandte Chemie, Volume 138, Issue 37, 7 September 2026.
We demonstrate a new platform for antimicrobial peptide identification by combining polydiacetylene, hyperspectral imaging, and deep learning. The trained model classifies distinct spectral fingerprints into 14 peptide classes with 96.79% accuracy, revealing previously hidden molecular information beyond conventional colorimetric sensing.
Jiali Chen, Che‐Lun Chin, Qingzhen Zhu, Ling Chao, Kaori Sugihara   +4 more
wiley   +2 more sources

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data. [PDF]

Sci Rep, 2021
Abduljabbar RL, Dia H, Tsai PW.
europepmc   +1 more source

Digital Twins for Offshore Wind Operation and Maintenance: A Review

Wind Energy, Volume 29, Issue 10, October 2026.
ABSTRACT Offshore wind has become a key focus for achieving carbon neutrality. However, offshore wind turbines face considerable operational and maintenance challenges due to harsh conditions and difficult access. Integrating digital twin technology helps support operation and maintenance activities.
Xiaomei Hu, Ravi Pandit, Yifan Zhao, Bernadin Namoano, Jiarong Hong   +4 more
wiley   +1 more source

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic. [PDF]

Neurocomputing (Amst), 2022
Quilodrán-Casas C, Silva VLS, Arcucci R, Heaney CE, Guo Y, Pain CC.   +5 more
europepmc   +1 more source

Mass‐Conserving LSTM With Dual States for Streamflow Prediction: Separating Quickflow and Slow Storage

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract We introduce a Mass‐Conserving Long Short‐Term Memory with Dual States (MC‐LSTM‐DS) with the intention of separating short‐ and long‐term memory for predicting streamflow. It enforces water balance through a new partition gate on precipitation.
Saurabh Toraskar, M. Niranjannaik, Abhilash Singh, Kumar Gaurav   +3 more
wiley   +1 more source

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series. [PDF]

Environ Sci Pollut Res Int, 2021
Said AB, Erradi A, Aly HA, Mohamed A.
europepmc   +1 more source

A Lagrangian Time‐Series Machine Learning Framework for Predicting Concentrations and Exploring Drivers of Atmospheric Aerosols: Model Development and Application to Cloud Condensation Nuclei in Marine Boundary Layer

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 10, October 2026.
Abstract Atmospheric aerosols play critical roles in climate and air quality. Accurately assessing their environmental impacts requires understanding aerosol abundance, distribution, and the key factors and processes that control them. Although machine learning is increasingly used in predicting aerosol concentrations, its application in exploring ...
Shengqian Zhou, Dong Qi, Hanyang Liu, Yevgeniy Vorobeychik, Jian Wang   +4 more
wiley   +1 more source

A passive islanding detection method using deep neural bidirectional LSTM-CNN. [PDF]

Sci Rep
Ozturk H, Nese SV.
europepmc   +1 more source