Results 51 to 60 of about 2,212,722 (355)

A long short‐term memory‐based model for greenhouse climate prediction

International Journal of Intelligent Systems, 2021
Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops.
Yuwen Liu, Dejuan Li, Shaohua Wan, Fan Wang, Wanchun Dou, Xiaolong Xu, Shancang Li, Rui Ma, Lianyong Qi   +8 more
semanticscholar   +1 more source

Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

Hydrology and Earth System Sciences, 2018
. Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models.
Frederik Kratzert, D. Klotz, Claire Brenner, K. Schulz, M. Herrnegger   +4 more
semanticscholar   +1 more source

Long Short-Term Memory Spiking Networks and Their Applications [PDF]

International Conference on Neuromorphic Systems 2020, 2020
Recent advances in event-based neuromorphic systems have resulted in significant interest in the use and development of spiking neural networks (SNNs). However, the non-differentiable nature of spiking neurons makes SNNs incompatible with conventional backpropagation techniques.
Sriram Vishwanath, Ali Lotfi Rezaabad
openaire   +3 more sources

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

PLoS ONE, 2017
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long ...
Wei Bao, Jun Yue, Yulei Rao
semanticscholar   +1 more source

Long-term associative learning predicts verbal short-term memory performance [PDF]

, 2017
Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information ...
A Murray, A Woodward, AD Baddeley, AL Theakston, B Macken, B Macken, B MacWhinney, Bill Macken, C Bannard, C Catmur, C Hulme, C Hulme, C Rowland, D Albert, D Swingley, D Wechsler, DH Rakison, DM Jones, E Colunga, E Hoff, E Jefferies, EH Wiig, FN Dempster, G Jones, G Jones, G Jones, G Jones, G Jones, GA Miller, Gary Jones, I Walker, J Bybee, J Fiser, J Huttenlocher, J Masterson, J Rispens, JF Hay, JR Saffran, K Graf Estes, KA Ericsson, L Smith, LM Archibald, M Botvinick, M Botvinick, M Melby-Lervåg, M Vihman, MC MacDonald, MH Messer, MM Botvinick, MPA Page, NZ Kirkham, R Allen, RC Atkinson, RN Aslin, S Doebel, S Majerus, SE Gathercole, SE Gathercole, SE Gathercole, SE Gathercole, SL Frank, TA Farmer, WJ Macken   +62 more
core   +1 more source

Generating image descriptions with multidirectional 2D long short‐term memory

IET Computer Vision, 2017
Connecting visual imagery with descriptive language is a challenge for computer vision and machine translation. To approach this problem, the authors propose a novel end‐to‐end model to generate descriptions for images.
Shuohao Li, Jun Zhang, Qiang Guo, Jun Lei, Dan Tu   +4 more
doaj   +1 more source

Dependency-based long short term memory network for drug-drug interaction extraction

BMC Bioinformatics, 2017
Background Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have ...
Wei Wang, Xi Yang, Canqun Yang, Xiaowei Guo, Xiang Zhang, Chengkun Wu   +5 more
doaj   +1 more source

Long short-term memory networks in memristor crossbar arrays [PDF]

Nature Machine Intelligence, 2019
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of parameters, however, have a bottleneck in computing power resulting from limited memory capacity and data ...
Ning Ge, Qing Wu, Yunning Li, Mingyi Rao, Hao Jiang, Can Li, Can Li, John Paul Strachan, Wenhao Song, Peng Lin, Daniel Belkin, Miao Hu, Mark Barnell, Jianhua Yang, Peng Yan, Zhongrui Wang, Qiangfei Xia, R. Stanley Williams   +17 more
openaire   +4 more sources

Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Water, 2019
Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data ...
Xuan-Hien Le, H. V. Ho, Giha Lee, Sungho Jung   +3 more
semanticscholar   +1 more source

Klasifikasi Ekspresi Teks Berbahasa Jawa Menggunakan Algoritma Long Short Term Memory

Komputika, 2021
Bahasa Jawa bisa dikatakan Bahasa yang unik karena bahasa Jawa mempunyai banyak arti meskipun satu kata yang sama tetapi beda daerah. Suku Jawa adalah suku terbesar yaitu 41% atau sekitar 95.217.022 jiwa.
Oddy Virgantara Putra, Aziz Musthafa, Kukuh Prasetyo Wibowo   +2 more
doaj   +1 more source