Results 191 to 200 of about 323,557 (229)

State-of-Charge Estimation and Health Prognosis for Lithium-Ion Batteries Based on Temperature-Compensated Bi-LSTM Network and Integrated Attention Mechanism

IEEE transactions on industrial electronics (1982. Print)
The state-of-charge and health prognosis are important factors for electric vehicles. The long short-term memory (LSTM) is used to estimate battery states, and it attracts a lot of attention.
Peihang Xu, Chengchao Wang, Jinlu Ye, T. Ouyang   +3 more
semanticscholar   +1 more source

Stock Price Analysis Using LSTM & Bi-LSTM

As subjective as it may seem, choosing the best strategy for international business development is one of the more difficult tasks in international finance. This research investigates the use of machine learning in this case for stock price forecasting and analysis. The chosen approach is the application of Long Short Term Memory (LSTM) neural networks.
Sachit Rawat, Sujal Gupta, Shahzeb Khan
openaire   +1 more source

CT-LSTM

Proceedings of the 2nd International Conference on Computer Science and Application Engineering, 2018
Visual1 tracking still is a very challenging problem due to complex appearance variations. Deep learning has become a new way to automatically dig features for object appearance modeling. Several recent tracking algorithms use deep models pre-trained on large-scale classification datasets, and are later transferred online for object tracking.
openaire   +1 more source

Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM

International Journal of Sustainable Agricultural Management and Informatics, 2022
R. Murugesan, Eva Mishra, Akash Hari Krishnan   +2 more
openaire   +1 more source

Bidirectional LSTM with attention mechanism and convolutional layer for text classification

Neurocomputing, 2019
Neural network models have been widely used in the field of natural language processing (NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of arbitrary length, are common methods for sequence modeling tasks.
Gang Liu, Jiabao Guo
semanticscholar   +1 more source

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

2023 6th International Conference on Information Systems and Computer Networks (ISCON), 2023
Satya Prakash, Anand Singh Jalal, Pooja Pathak   +2 more
openaire   +1 more source

LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes

2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), 2019
The development of cyberspace brings both opportunities and threats, among which Distributed Denial of Service (DDoS) is one of the most destructive attacks. A mass of DDoS attack detection methods have been proposed. But more or less there are some problems, either the construction process is complex, or low accuracy, or poor generalization ability ...
Yan Li, Yifei Lu
openaire   +1 more source

LSTM,LSTM-GAN

LSTM  for dynamic factors ,LSTM-GAN for collapses, PN for mapping and ...
openaire   +1 more source

SACS-LSTM

Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, 2021
Huxiao Wang, Danyang Zhou, Yanyu Zhang, Nianwen Ning, Yi Zhou   +4 more
openaire   +1 more source

Learning to Forget: Continual Prediction with LSTM

Neural Computation, 2000
Felix Alexander Gers, J. Schmidhuber, Fred Cummins   +2 more
semanticscholar   +1 more source