Results 61 to 70 of about 323,557 (229)

LSTM-CNN Architecture for Human Activity Recognition

IEEE Access, 2020
In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance.
Kun Xia, Jianguang Huang, Hanyu Wang
semanticscholar   +1 more source

Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

, 2017
We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As
Chang Ming-Wei, Heilman Michael, Hu Baotian, Luu Anh Tuan, Mikolov Tomas, Nickel Maximilian, Plate Tony, Qiu Xipeng, Robertson Stephen E., Wang Di, Wang Mengqiu, Yao Xuchen, Zhou Guangyou   +12 more
core   +1 more source

SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction [PDF]

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty of studies, while it is hard to be fully expressed by hand-craft rules. Recent studies based on LSTM networks have shown great ability to learn social behaviors.
Zhang, Pu, Ouyang, Wanli, Zhang, Pengfei, Xue, Jianru, Zheng, Nanning   +4 more
openaire   +2 more sources

Heat recovery optimization of a shell and tube bundle heat exchanger with continuous helical baffles for air ventilation systems

International Journal of Air-Conditioning and Refrigeration
We report a numerical evaluation of the impact of continuous helical baffle on the heat recovery efficiency of counterflow tube bundle heat exchangers. The baffle inclination angle has been varied from $$11^{\circ }$$ 11 ∘ to $$22^{\circ }$$ 22 ∘ . Since
Md Ashfaqul Bari, Manuel Münsch, Bastian Schöneberger, Bernhard Schlagbauer, Andrea Alina Tiu, Andreas Wierschem   +5 more
doaj   +1 more source

Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

International Journal of Innovative Science and Research Technology
Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational ...
Jinxin Xu, Zhuoyue Wang, Xinjin Li, Zichao Li, Zhenglin Li   +4 more
semanticscholar   +1 more source

Bivariate Beta-LSTM

, 2019
Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function.
Jang, JoonHo, Moon, Il-Chul, Shin, Seung jae, Song, Kyungwoo   +3 more
core   +1 more source

EA-LSTM: Evolutionary attention-based LSTM for time series prediction [PDF]

Knowledge-Based Systems, 2019
Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within multiple time-steps is insufficient.
Li, Youru, Zhu, Zhenfeng, Kong, Deqiang, Han, Hua, Zhao, Yao   +4 more
openaire   +2 more sources

Menstrual cups and cash transfer to reduce sexual and reproductive harm and school dropout in adolescent schoolgirls in western Kenya: a cluster randomised controlled trialResearch in context

EClinicalMedicine, 2023
Summary: Background: High rates of sexual and reproductive health (SRH) harms and interrupted schooling are global challenges for adolescent girls, requiring effective interventions.
Garazi Zulaika, Elizabeth Nyothach, Anna Maria van Eijk, Duolao Wang, Valarie Opollo, David Obor, Linda Mason, Tao Chen, Emily Kerubo, Boaz Oyaro, Alex Mwaki, Alie Eleveld, Isaac Ngere, Eunice Fwaya, Feiko O. ter Kuile, Daniel Kwaro, Penelope A. Phillips-Howard   +16 more
doaj   +1 more source

Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach

Fractal and Fractional, 2023
Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in ...
Phumudzo Lloyd Seabe, C. Moutsinga, E. Pindza   +2 more
semanticscholar   +1 more source

Deep Learning with Long Short-Term Memory for Time Series Prediction

, 2018
Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms,
Chen, Xianfu, Hua, Yuxiu, Li, Rongpeng, Liu, Zhiming, Zhang, Honggang, Zhao, Zhifeng   +5 more
core   +1 more source