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Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network
2020 Asia Energy and Electrical Engineering Symposium (AEEES), 2020To address the serious challenges to traditional power systems caused by high variability of solar production, especially the ramp events, this paper is aimed at predicting solar radiation accurately at a very-short-term scale, i.e. several minutes ahead.
Yanbin Lin +5 more
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Simplified Gating in Long Short-Term Memory (LSTM) Recurrent Neural Networks [PDF]
2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 2017 5 pages, 4 Figures, 3 Tables.
Yuzhen Lu
exaly +3 more sources
Detecting Android malware using Long Short-term Memory (LSTM)
Journal of Intelligent & Fuzzy Systems, 2018Long Short-term Memory (LSTM) is a sub set of recurrent neural network (RNN) which is specifically used to train to learn long-term temporal dynamics with sequences of arbitrary length. In this paper, long short-term memory (LSTM) architecture is followed for Android malware detection.
R. Vinayakumar +3 more
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A Reversible-Logic Based Architecture for Long Short-Term Memory (LSTM) Network
2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021Any sequential learning task relies on the idea of connecting previous time-stamp information to the immediate present time-stamp task to predict the future. The underlying challenge is to understand the hidden patterns in the sequence by means of analyzing short- and long-term dependencies and temporal differences. Recurrent Neural Networks (RNNs) and
Kasem Khalil +3 more
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A Hybrid Siamese-LSTM (Long Short-Term Memory) for Classification of Alzheimer's Disease
International Journal of Software Innovation, 2022Alzheimer's disease (AD) is the most general type of dementia, which concludes in memory-related problems. For researchers, the AD diagnosis at an early stage is a complex task. Alzheimer's disease is a chronic neurodegenerative disease. The risk of further degeneration will be decreased by early diagnosis of the disease.
Aparna M., Srinivasa B. Rao
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Towards Memory Friendly Long-Short Term Memory Networks (LSTMs) on Mobile GPUs
2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2018Intelligent Personal Assistants (IPAs) with the capability of natural language processing (NLP) are increasingly popular in today's mobile devices. Recurrent neural networks (RNNs), especially one of their forms - Long-Short Term Memory networks (LSTMs), are becoming the core machine learning technique applied in the NLP-based IPAs.
Xingyao Zhang 0002 +4 more
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ERA-LSTM: An Efficient ReRAM-Based Architecture for Long Short-Term Memory
IEEE Transactions on Parallel and Distributed Systems, 2020Processing-in-memory (PIM) architecture based on resistive random access memory (ReRAM) crossbars is a promising solution to the memory bottleneck that long short-term memory (LSTM) faces. Based on the dataflow analysis of the LSTM computing paradigm, this article proposes to adopt the ReRAM-based analog approximate computing to conduct the LSTM ...
Jianhui Han, He Liu, Youhui Zhang
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Interpreting Pilot Behavior Using Long Short-Term Memory (LSTM) Models
2021Computational models of fighter pilot decision-making provide insight into a pilot’s behavior, facilitating pilot performance assessment. This paper describes our application of Long Short-Term Memory (LSTM) neural networks to a dataset of pilot actions during simulated missile avoidance to generate metrics of pilot behavior under pressure.
Ben Barone +5 more
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RVFL-LSTM: A Lightweight Model With Long-Short Term Memory for Time Series
Knowledge-Based SystemsXizhao Wang
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