Results 251 to 260 of about 15,297,754 (301)
Applied Energy, 2019 This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Network for pattern recognition with the Long Short-Term Memory Network for half-hourly global solar radiation (GSR) forecasting.
Ravinesh Deo, Sujan Ghimire, Nawin Raj
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Long Short Term Memory Networks for Lexical Normalization of Tweets
2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbreviations, skipping of words, spelling errors, etc.
Pratham Nayak +3 more
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Classification of HRV using Long Short-Term Memory Networks
2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), 2020This work focus on detection of diseases from Heart Rate Variability (HRV) series using Long Short-Term Memory (LSTM) networks. First, non-linear models are used to extract sequences of features that characterize the HRV series. These time sequences are then used as input for the LSTM.
Leite, Argentina +2 more
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Short-term load forecasting using a long short-term memory network
2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017Load forecasting is an essential part of a power system. It enhances the energy-efficiency and reliable operation of the power system. As depicted in the proposal of the smart grid, an increasing number of smart meters have been being installed in many utilities on a global scale.
Chang Liu +3 more
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Long short term memory networks for short-term electric load forecasting
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time.
Apurva Narayan, Keith W. Hipel
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Long short-term memory neural network for glucose prediction
Neural Computing and Applications, 2020Diabetes is a chronic disease that affects a high percentage of the world population and produces different and serious complications to patients. Most diabetes complications may be avoided by controlling the blood glucose levels exhaustively. Moreover, a prediction of future glucose levels has shown to be fundamental in helping patients to plan and ...
Jaime Carrillo-Moreno +5 more
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Long short-term memory neural network for network traffic prediction
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017This paper proposes a model of neural network which can be used to combine Long Short Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation coefficient is added to model to improve the accuracy of prediction model. It can provide better than the other traditional precision of the model.
Qinzheng Zhuo +3 more
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Irrigation and Drainage, 2022
AbstractEvapotranspiration (ET) is a vital component of the hydrological cycle, and accurate estimation of reference evapotranspiration (ET0) is of great importance in agriculture water resources planning and management. In this study, long short‐term memory network (LSTM), artificial neural network (ANN), extreme learning machine (ELM), and their ...
Xiaoxu Long +4 more
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AbstractEvapotranspiration (ET) is a vital component of the hydrological cycle, and accurate estimation of reference evapotranspiration (ET0) is of great importance in agriculture water resources planning and management. In this study, long short‐term memory network (LSTM), artificial neural network (ANN), extreme learning machine (ELM), and their ...
Xiaoxu Long +4 more
openaire +1 more source