Results 11 to 20 of about 11,734,216 (296)
Hydrological concept formation inside long short-term memory (LSTM) networks [PDF]
Hydrology and Earth System Sciences, 2022 Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned?
T. Lees +12 more
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SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS [PDF]
Barekeng, 2023 Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a
Muhammad Nazhif Abda Putera Khano +3 more
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Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks [PDF]
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.
F. Kratzert +5 more
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PM2.5 Forecast in Korea using the Long Short-Term Memory (LSTM) Model. [PDF]
Asia Pac J Atmos Sci, 2022 AbstractThe National Institute of Environmental Research, under the Ministry of Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of particulate matter with diameters of ≤ 2.5 μm (PM2.5) in terms of four grades (low, moderate, high, and very high) over 19 districts nationwide.
Ho CH, Park I, Kim J, Lee JB.
europepmc +3 more sources
Semantic similarity dissociates shortfrom long-term recency effects: testing a neurocomputational model of list memory [PDF]
, 2006 The finding that recency effects can occur not only in immediate free recall (i.e., short-term recency) but also in the continuous-distractor task (i.e., long-term recency) has led many theorists to reject the distinction between short- and long-term ...
Usher, Marius +3 more
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Forecasting Nonadiabatic Dynamics using Hybrid Convolutional Neural Network/Long Short-Term Memory Network [PDF]
, 2021 Modeling nonadiabatic dynamics in complex molecular or condensed-phase systems has been challenging especially for the long-time dynamics. In this work, we propose a time series machine learning scheme based on the hybrid convolutional neural network ...
Jiebo, Li +3 more
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Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model [PDF]
Wireless Personal Communications, 2021 Abstract Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may ...
K. Naresh Kumar Thapa, Duraipandian N
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, 2021 Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments.
Deo, Ravinesh C. +9 more
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Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole text from beginning to end or vice versa sometimes, which makes it inefficient to process long texts.
Ting Huang +2 more
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Prediction of molten pool temperature in laser solid forming based on PSO-LSTM
Frontiers in Materials, 2023 The temperature of the molten pool in Laser Solid Forming has a direct effect on the dimensional accuracy and mechanical properties of the parts. Accurate prediction of the melt pool temperature is important to ensure the stability of the melt pool ...
Junhua Wang +10 more
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