Results 81 to 90 of about 80,914 (224)

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

, 2017
In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data ...
Kim, Daesik, Kim, Jeesoo, Kwak, Nojun, Lee, Sangkuk, Yoo, Youngjoon   +4 more
core   +1 more source

Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit

Sustainability, 2023
In the present scenario, air quality prediction (AQP) is a complex task due to high variability, volatility, and dynamic nature in space and time of particulates and pollutants. Recently, several nations have had poor air quality due to the high emission
Sasikumar Gurumoorthy, Aruna Kumari Kokku, Przemysław Falkowski-Gilski, P. Divakarachari   +3 more
semanticscholar   +1 more source

Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predictions

Entropy
Spatio-temporal prediction is crucial in intelligent transportation systems (ITS) to enhance operational efficiency and safety. Although Transformer-based models have significantly advanced spatio-temporal prediction performance, recent research ...
Yuxuan Wang, Zhouyuan Zhang, Shu Pi, Haishan Zhang, Jiatian Pi   +4 more
doaj   +1 more source

A conditional random field based feature learning framework for battery capacity prediction

Scientific Reports, 2022
This paper proposes a network model framework based on long and short-term memory (LSTM) and conditional random field (CRF) to promote Li-ion battery capacity prediction results.
Hai-Kun Wang, Yang Zhang, Mohong Huang
doaj   +1 more source

Gated Recurrent Fuzzy Neural Network Sliding Mode Control of a Micro Gyroscope

Mathematics, 2023
This paper proposes a non-singular fast terminal sliding mode control (NFTSMC) method for micro gyroscopes with unknown uncertainty based on gated recurrent fuzzy neural networks (GRFNNs).
Jiapeng Xie, Juntao Fei, Cuicui An
doaj   +1 more source

Compressing Recurrent Neural Network with Tensor Train

, 2017
Recurrent Neural Network (RNN) are a popular choice for modeling temporal and sequential tasks and achieve many state-of-the-art performance on various complex problems.
Nakamura, Satoshi, Sakti, Sakriani, Tjandra, Andros   +2 more
core   +1 more source

Automated Image Captioning with Multi-layer Gated Recurrent Unit

2022 30th European Signal Processing Conference (EUSIPCO), 2022
Describing the semantic content of an image via natural language, known as image captioning, has recently attracted substantial interest in computer vision and language processing communities. Current image captioning approaches are mainly based on an encoder-decoder framework in which visual information is extracted by an image encoder and captions ...
Moral, Ozge Taylan, Kilic, Volkan, Onan, Aytug, Wang, Wenwu   +3 more
openaire   +2 more sources

The Reliability of Diagnosing Schizophrenia Using the GRU Layer in Conjunction with EEG Rhythms

Frontiers in Biomedical Technologies
Purpose: At resting state, the human brain releases cycles of Electroencephalography (EEG), which has been proven aberrant in persons with schizophrenia.
Pankaj Kumar Sahu, Karan Jain
doaj   +1 more source

Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

, 2018
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
Bai, Kun, Pan, Yu, Wang, Fei, Wang, Maolin, Xu, Jing, Xu, Zenglin, Ye, Jinmian   +6 more
core   +1 more source

On the Memory Properties of Recurrent Neural Models [PDF]

, 2017
In this paper, we investigate the memory properties of two popular gated units: long short term memory (LSTM) and gated recurrent units (GRU), which have been used in recurrent neural networks (RNN) to achieve state-of-the-art performance on several ...
Benetos, E., Garcez, A., Russell, A. J.
core