Results 91 to 100 of about 154,880 (170)

Bidirectional LSTM (Bi-LSTM) network.

, 2019
Bidirectional LSTM (Bi-LSTM) network.
Yinhai Wang (182679), Hao Yang (328526), Jianwei Wang (128689), Xin Fu (448766), Chenxi Liu (286325)   +4 more
core   +1 more source

Fzaero/LSTM-Trajectory-Prediction: LSTM Trajectory Prediction Tool

, 2018
<p>LSTM Trajectory Prediction Tool predicts trajectory of lever upped object from given lever up location. While action is being done, it also analyzes current trajectory up to that point and decides on if action was applied to correct lever up ...
Ahmet Tekden
core   +1 more source

LSTM-based distributed conditional generative adversarial network for data-driven 5G-enabled maritime UAV communications

, 2023
5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications.
Muhammad Asif (466310), Iftikhar Rasheed (11909380), Asim Ihsan (22138465), Wali Ullah Khan (23584119), Manzoor Ahmed (666129), Khaled M Rabie (24021969)   +5 more
core  

Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

, 2018
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia.
Mougiakakou, Stavroula Georgia, Marko V. Jankovic, Sun, Qingnan, Stavroula G. Mougiakakou, Bally, Lia, Qingnan Sun, Jankovic, Marko, Lia Bally   +7 more
core   +1 more source

Comparison of LSTM and EMD-LSTM model.

, 2019
Comparison of LSTM and EMD-LSTM model.
Quanchao Chen (7372619), Di Wen (313779), Peng Gao (35465), Jie Zhang (64655), Hongxia Lv (7372628), Dingjun Chen (7372625), Xuqiang Li (7372622)   +6 more
core   +1 more source

Automated phase recognition in the cardiac catheterization laboratory with deep learning. [PDF]

BMC Biomed Eng
Vijfvinkel TS, Frassini E, Weijenberg Y, Nikookar B, Butler RM, Constandse J, van der Elst M, Hendriks BHW, van den Dobbelsteen JJ.   +8 more
europepmc   +1 more source

Observation and forecasted charts for test data of (a) Stacked-LSTM, (b) CNN, (c) Bi-LSTM, (d) CNN-LSTM, (e) ConvLSTM, and (f) proposed CNN-Bi-LSTM.

Observation and forecasted charts for test data of (a) Stacked-LSTM, (b) CNN, (c) Bi-LSTM, (d) CNN-LSTM, (e) ConvLSTM, and (f) proposed CNN-Bi-LSTM.
Robab Kalantari (18121294), Amirhossein Amini (10163250)   +1 more
core   +1 more source

Interval setting of BO-LSTM hyperparameters. Comparison of MSE, MRE, MAE, R² and CI width between BP, RBF, LSTM and BO-LSTM neural network. Note: [BP, RBF, LSTM, BO-LSTM].

Interval setting of BO-LSTM hyperparameters. Comparison of MSE, MRE, MAE, R2 and CI width between BP, RBF, LSTM and BO-LSTM neural network. Note: [BP, RBF, LSTM, BO-LSTM].
Peiang Wang (24375501), Xueqian Wang (166794), Mingwei Liu (3369224), Sisi Chen (166395), Songtao Wang (427401)   +4 more
core   +1 more source

Applying Masking Techniques in LSTM Models for ASD Prediction

openAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by atypical patterns of brain connectivity. Functional Magnetic Resonance Imaging (fMRI) provides a non-invasive way to capture these patterns as time series ...
SKURATIVSKA, KATERYNA
core  

Enhancing multivariate quality monitoring: Integrating LSTM and MLS-SVR with Max-MCUSUM frameworks. [PDF]

MethodsX
Ahsan M, Hakim MRR, Hidayatullah SBS, Lee MH.   +3 more
europepmc   +1 more source