Results 21 to 30 of about 13,592 (226)

DG-based SPO tuple recognition using self-attention M-Bi-LSTM

ETRI Journal, 2022
This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject?predicate?object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the
Joon-young Jung
doaj   +1 more source

Framewise phoneme classification with bidirectional LSTM networks [PDF]

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2006
In this paper, we apply bidirectional training to a long short term memory (LSTM) network for the first time. We also present a modified, full gradient version of the LSTM learning algorithm. We discuss the significance of framewise phoneme classification to continuous speech recognition, and the validity of using bidirectional networks for online ...
Alex Graves, Jürgen Schmidhuber
openaire   +1 more source

Sentiment analysis from textual data using multiple channels deep learning models

Journal of Electrical Systems and Information Technology, 2023
Text sentiment analysis has been of great importance over the last few years. It is being widely used to determine a person’s feelings, opinions and emotions on any topic or for someone.
Adepu Rajesh, Tryambak Hiwarkar
doaj   +1 more source

Early Action Prediction using 3DCNN with LSTM and Bidirectional LSTM

Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021
Predicting and identifying suspicious activities before hand is highly beneficial because it results in increased protection in video surveillance cameras’. Detecting and predicting human's action before it is carried out has a variety of uses like autonomous robots, surveillance, and health care. The main focus of the paper is on automated recognition
Manju D, Dr. Seetha M., Dr. Sammulal P.
openaire   +3 more sources

Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models [PDF]

ITM Web of Conferences
Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression
Wang Yuxin
doaj   +1 more source

Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

Applied Sciences, 2020
Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and ...
Hassan Afzaal, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, Travis Esau   +4 more
doaj   +1 more source

Combined Bidirectional Long Short-Term Memory with Mel-Frequency Cepstral Coefficients Using Autoencoder for Speaker Recognition

Applied Sciences, 2023
Recently, neural network technology has shown remarkable progress in speech recognition, including word classification, emotion recognition, and identity recognition. This paper introduces three novel speaker recognition methods to improve accuracy.
Young-Long Chen, Neng-Chung Wang, Jing-Fong Ciou, Rui-Qi Lin   +3 more
doaj   +1 more source

Heart Sound Segmentation Using Bidirectional LSTMs With Attention [PDF]

IEEE Journal of Biomedical and Health Informatics, 2020
This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state.
Tharindu Fernando, Houman Ghaemmaghami, Simon Denman, Sridha Sridharan, Nayyar Hussain, Clinton Fookes   +5 more
openaire   +4 more sources

Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model

Journal of Flood Risk Management, 2022
Rainfall–runoff modeling is a complex hydrological issue that still has room for improvement. This study developed a coupled bidirectional long short‐term memory (LSTM) with sequence‐to‐sequence (Seq2Seq) learning (BiLSTM‐Seq2seq) model to simulate multi‐
Qing Cao, Hanchen Zhang, Feilin Zhu, Zhenchun Hao, Feifei Yuan   +4 more
doaj   +1 more source

Named Entity Recognition with Bidirectional LSTM-CNNs [PDF]

Transactions of the Association for Computational Linguistics, 2016
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN ...
Jason P.C. Chiu, Eric Nichols
openaire   +3 more sources