Results 51 to 60 of about 30,189 (295)

Hybrid Deep Learning Model for Heart Disease Prediction Using Recurrent Neural Network (RNN)

, 2023
In this paper, we use a recurrent neural network (RNN) that combines multiple gated recurrent units (GRUs), long short-term memory (LSTM), and the Adam optimizer to develop a new hybrid deep learning model for heart disease prediction.
Omankwu, Obinnaya Chinecherem & Ubah, Valetine Ifeanyi
core   +1 more source

Recurrent neural network wind power prediction based on variational modal decomposition improvement

AIP Advances, 2023
In order to avoid the problem that the traditional recurrent neural network (RNN) wind power prediction model cannot take into account both the law of wind power variation and the impact of sudden change factors, this paper proposes an improved cyclic ...
Chongqing Shi, Xiaoli Zhang
doaj   +1 more source

Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks

, 2004
The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neu-ral network (RNN), to that of a linear benchmark VAR model.
Jonathan A. Tepper, Tepper, JA, Nilsson, B, Nilsson, Birger, Thomas Elger, Birger Nilsson, Binner, JM, Binner, Jane M.; id_orcid, Elger, T, Tepper, Jonathan A., Elger, Thomas, Jane M. Binner   +11 more
core   +1 more source

Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability

Energies, 2023
This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN).
Gopu Venugopal, Arun Kumar Udayakumar, Adhavan Balashanmugham, Mohamad Abou Houran, Faisal Alsaif, Rajvikram Madurai Elavarasan, Kannadasan Raju, Mohammed H. Alsharif   +7 more
doaj   +1 more source

Ensembles of Gradient Boosting Recurrent Neural Network for Time Series Data Prediction

IEEE Access
Ensemble deep learning can combine strengths of neural network and ensemble learning, which gradually becomes a new emerging research direction. However, the existing methods either lack theoretical support or demand large integrated models.
Shiqing Sang, Fangfang Qu, Pengcheng Nie
doaj   +1 more source

Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network Framework

, 2018
Some unethical companies may hire workers (fake review spammers) to write reviews to influence consumers' purchasing decisions. However, it is not easy for consumers to distinguish real reviews posted by ordinary users or fake reviews post by fake review
Jia-Wei Liou, Chien-Chang Chen, Wang, Chih-Chien;Day, Min-Yuh;Chen, Chien-Chang;Liou, Jia-Wei, Chih-Chien Wang, Min-Yuh Day   +4 more
core   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

Advanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng, Zihan Li, Yifeng Shao, Jinhao Liu, Li Yu, Shuguang Li   +5 more
wiley   +1 more source

Solution of Text Title Selection Based on Neural Network [PDF]

Jisuanji gongcheng, 2018
To choose the most appropriate answer from the waiting list as the title of the article,this paper proposes a model which is based on Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN).The model contains the characteristics of extracting
Lü Guoying,GUAN Yong,LI Ru,GUO Shaoru,TAN Hongye
doaj   +1 more source

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs [PDF]

2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019
In The 25th International Symposium on High-Performance Computer Architecture (HPCA 2019)
Zhe Li 0001, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Chang Liu, Qinru Qiu, Wenyao Xu, Xue Lin 0001, Xuehai Qian, Yanzhi Wang 0001   +10 more
openaire   +2 more sources

Weibull recurrent neural networks for failure prognosis using histogram data

, 2023
Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network.
Dhada, Maharshi,, Parlikad, Ajith Kumar,, Steinert, Olof,, Lindgren, Tony,   +3 more
core   +1 more source