Results 21 to 30 of about 208,635 (216)

Deep Bidirectional LSTM Network Learning-Aided OFDMA Downlink and SC-FDMA Uplink

, 2021
In this paper, deep learning (DL)-aided signal detection is proposed for the orthogonal frequency division multiple access (OFDMA) in the downlink and for the single carrier frequency division multiple access (SC-FDMA) in the uplink. A deep bidirectional
Saha, Ritu, Awal, Md Abdul, Mohammad Ismat Kadir, Kadir, Rafiul, Ritu Saha, Rafiul Kadir, Md. Abdul Awal, Kadir, Mohammad Ismat   +7 more
core   +1 more source

Bidirectional Model Transformations in QVT: Semantic Issues and Open Questions [PDF]

, 2007
We consider the OMG's Queries, Views and Transformations (QVT) standard as applied to the specification of bidirectional transformations between models. We discuss what is meant by bidirectional transformations, and the model-driven development scenarios
Stevens, Perdita
core   +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

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

LSTM-CNN structure schematic.

, 2022
LSTM-CNN structure schematic.
Yihang Zhao (6743393), Yifei Wang (95207), Silong Zhou (14203305), Danyang Li (1490554), Haibo Pu (13205789), Xianmeng Zhao (14203308), Xuan Wu (126953), Mingwei Chen (1668166)   +7 more
core   +1 more source

Ship Roll Prediction Algorithm Based on Bi-LSTM-TPA Combined Model

Journal of Marine Science and Engineering, 2021
When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness. Aiming at the problem of low accuracy of ship roll angle prediction by traditional prediction algorithms and single neural ...
Yuchao Wang, Hui Wang, Dexin Zou, Huixuan Fu   +3 more
doaj   +1 more source

Cybersecurity Named Entity Recognition Using Bidirectional Long Short-Term Memory with Conditional Random Fields

Tsinghua Science and Technology, 2021
Network texts have become important carriers of cybersecurity information on the Internet. These texts include the latest security events such as vulnerability exploitations, attack discoveries, advanced persistent threats, and so on.
Pingchuan Ma, Bo Jiang, Zhigang Lu, Ning Li, Zhengwei Jiang   +4 more
doaj   +1 more source

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

, 2021
This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks.
Rusul L. Abduljabbar, Pei-Wei Tsai, Hussein Dia   +2 more
core   +1 more source

A Study on Sensor System Latency in VR Motion Sickness

Journal of Sensor and Actuator Networks, 2021
One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to
Ripan Kumar Kundu, Akhlaqur Rahman, Shuva Paul   +2 more
doaj   +1 more source