Results 181 to 190 of about 323,557 (229)

LSTM Network

2023
The world wide web (WWW) is an advanced system with an unmatched amount of digital data. Today's internet usage is accessible through common search engines like Google and Yahoo. Cybercriminals have become more assertive on social media. As a result, numerous commercial and trade websites are hacked, leading to forced trafficking of women and children ...
Anil Kumar, Abhay Bhatia, Arun Kashyap, Manish Kumar   +3 more
openaire   +1 more source

Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition

European Conference on Computer Vision, 2016
3D action recognition – analysis of human actions based on 3D skeleton data – becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to develop RNN-based learning methods
Jun Liu, Amir Shahroudy, Dong Xu, Gang Wang   +3 more
semanticscholar   +1 more source

A novel genetic LSTM model for wind power forecast

, 2021
Variations of produced power in windmills may influence the appropriate integration in power-driven grids which may disrupt the balance between electricity demand and its production.
Farah Shahid, Aneela Zameer, Muhammad Muneeb   +2 more
semanticscholar   +1 more source

App-LSTM

Proceedings of the 7th International Conference on Human-Agent Interaction, 2019
While many works involving human-agent interactions have focused on individuals or crowds, modelling interactions on the group scale has not been considered in depth. Simulation of interactions with groups of agents is vital in many applications, enabling more comprehensive and realistic behavior encompassing all possibilities between crowd and ...
Fangkai Yang, Christopher Peters
openaire   +1 more source

E-LSTM

Proceedings of the 56th Annual Design Automation Conference 2019, 2019
Various models with Long Short-Term Memory (LSTM) network have demonstrated prior art performances in sequential information processing. Previous LSTM-specific architectures set large on-chip memory for weight storage to alleviate the memory-bound issue and facilitate the LSTM inference in cloud computing. In this paper, E-LSTM is proposed for embedded
Runbin Shi, Junjie Liu, Hayden K.-H. So, Shuo Wang, Yun Liang   +4 more
openaire   +1 more source

Vision-LSTM: xLSTM as Generic Vision Backbone

International Conference on Learning Representations
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM ...
Benedikt Alkin, Maximilian Beck, Korbinian Poppel, Sepp Hochreiter, Johannes Brandstetter   +4 more
semanticscholar   +1 more source

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

IEEE Transactions on Cybernetics
Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and
Jing Yang, Yuehai Chen, Shaoyi Du, Badong Chen, Jose C. Principe   +4 more
openaire   +2 more sources

LSTM-Exploit: Intelligent Penetration Based on LSTM Tool

2021
LSTM-Exploit based on the cyclic neural network “LSTM” will analyze existing exploit rules, explore the internal relationships between payloads applicable to different systems to design and create intelligent penetration tools.
Ximin Wang, Luyi Huang, Junlan Zhu, Wenbo He, Zhaopeng Qin, Ming Yuan   +5 more
openaire   +1 more source

A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China.

Journal of Environmental Management
Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring
Hanxiao Shi, Anlei Wei, Xiaozhen Xu, Yaqi Zhu, Hao Hu, Songjun Tang   +5 more
semanticscholar   +1 more source

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

Biomedical Signal Processing and Control, 2019
We aimed at learning deep emotion features to recognize speech emotion. Two convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D CNN LSTM network and one 2D CNN LSTM network, were constructed to learn local and global ...
Jianfeng Zhao, Xia Mao, Lijiang Chen
semanticscholar   +1 more source