Results 171 to 180 of about 323,557 (229)
Some of the next articles are maybe not open access.
International Journal of Grid and High Performance Computing, 2020
The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks.
Nancy Victor, Daphne Lopez
openaire +1 more source
The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks.
Nancy Victor, Daphne Lopez
openaire +1 more source
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
Applied Soft Computing, 2021In recent years, graph convolutional neural networks have become research focus and inspired new ideas for emotion recognition based on EEG. Deep learning has been widely used in emotion recognition, but it is still challenging to construct models and ...
Yongqiang Yin +4 more
semanticscholar +1 more source
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Misinformation in social media - such as fake news, rumors, or other forms of deceptive content - poses a significant threat to society and, hence, scalable strategies for an early detection of online cascades with misinformation are in dire need. The prominent approach in detecting online cascades with misinformation builds upon neural networks based ...
Francesco Ducci +2 more
openaire +1 more source
Misinformation in social media - such as fake news, rumors, or other forms of deceptive content - poses a significant threat to society and, hence, scalable strategies for an early detection of online cascades with misinformation are in dire need. The prominent approach in detecting online cascades with misinformation builds upon neural networks based ...
Francesco Ducci +2 more
openaire +1 more source
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
IEEE Transactions on Industrial Informatics, 2021Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent.
L. Ren +5 more
semanticscholar +1 more source
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018
Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing.
Cong Du, Peng Shu, Yong Li
openaire +1 more source
Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing.
Cong Du, Peng Shu, Yong Li
openaire +1 more source
The Performance of LSTM and BiLSTM in Forecasting Time Series
2019 IEEE International Conference on Big Data (Big Data), 2019Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling.
Sima Siami‐Namini +2 more
semanticscholar +1 more source
ACM Transactions on Multimedia Computing, Communications, and Applications, 2019
Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has
Zhandong Liu, Wengang Zhou, Houqiang Li
openaire +1 more source
Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has
Zhandong Liu, Wengang Zhou, Houqiang Li
openaire +1 more source
MethodsX
Highlights • A Monte Carlo method to assess machine learning time series algorithms is outlined.• Nine 2-hidden-layer algorithms with RNN, LSTM, and GRU structures are evaluated.
Ariana Yunita +6 more
semanticscholar +1 more source
Highlights • A Monte Carlo method to assess machine learning time series algorithms is outlined.• Nine 2-hidden-layer algorithms with RNN, LSTM, and GRU structures are evaluated.
Ariana Yunita +6 more
semanticscholar +1 more source
Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction
IEEE Transactions on Industrial Informatics, 2021Accurate prediction of remaining useful life (RUL) has been a critical and challenging problem in the field of prognostics and health management (PHM), which aims to make decisions on which component needs to be replaced when.
Meng Ma, Z. Mao
semanticscholar +1 more source
Predicting residential energy consumption using CNN-LSTM neural networks
Energy, 2019The rapid increase in human population and development in technology have sharply raised power consumption in today's world. Since electricity is consumed simultaneously as it is generated at the power plant, it is important to accurately predict the ...
T. Kim, Sung-Bae Cho
semanticscholar +1 more source