Predicting human decision making in psychological tasks with recurrent neural networks. by Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

“…We propose here to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by human subjects engaged in gaming activity, the first application of such methods in this research domain. …”
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Recurrent neural network for end-to-end modeling of laminar-turbulent transition by Muhammad I. Zafar, Meelan M. Choudhari, Pedro Paredes, Heng Xiao

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Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear by Minh-Tuan Nguyen, Viet-Hung Nguyen, Suk-Jun Yun, Yong-Hwa Kim

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Detecting Distributed Denial of Service attacks using Recurrent Neural Network by Roheen Qamar, Baqir Ali Zardari, Aijaz Ahmed Arain, Fida Hussain Khoso, Fareed Ahmed Jokhio

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Predicting Alzheimer's disease progression using deep recurrent neural networks by Minh Nguyen, Tong He, Lijun An, Daniel C. Alexander, Jiashi Feng, B.T. Thomas Yeo

“…We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). …”
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On the Adaptability of Recurrent Neural Networks for Real-Time Jazz Improvisation Accompaniment by Kosmas Kritsis, Kosmas Kritsis, Theatina Kylafi, Maximos Kaliakatsos-Papakostas, Aggelos Pikrakis, Vassilis Katsouros

“…Simple implementations of Recurrent Neural Networks are employed both for modeling the predictions of the artificial agent and for modeling the expectations of human intention. …”
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Classifying and diacritizing Arabic poems using deep recurrent neural networks by Gheith A. Abandah, Mohammed Z. Khedher, Mohammad R. Abdel-Majeed, Hamdi M Mansour, Salma F Hulliel, Lara M Bisharat

Subjects: “…Bidirectional recurrent neural networks…”
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ODRNN: Optimized deep recurrent neural networks for automatic detection of Leukaemia by K. Dhana Shree, S. Logeswari

Subjects: “…Optimized deep recurrent neural network and red deer optimization…”
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Stability Analysis of Recurrent Neural Networks with Random Delay and Markovian Switching by Enwen Zhu, Yong Wang, Yueheng Wang, Hanjun Zhang, Jiezhong Zou

“…In this paper, the exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with random delay and Markovian switching. …”
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Remapping in a recurrent neural network model of navigation and context inference by Isabel IC Low, Lisa M Giocomo, Alex H Williams

Subjects: “…Recurrent neural network models…”
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QBMG: quasi-biogenic molecule generator with deep recurrent neural network by Shuangjia Zheng, Xin Yan, Qiong Gu, Yuedong Yang, Yunfei Du, Yutong Lu, Jun Xu

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Automatic detection of cell-cycle stages using recurrent neural networks. by Abin Jose, Rijo Roy, Daniel Moreno-Andrés, Johannes Stegmaier

“…We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. …”
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Attention-Based Radar PRI Modulation Recognition With Recurrent Neural Networks by Xueqiong Li, Zhangmeng Liu, Zhitao Huang

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Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting by Halit Apaydin, Hajar Feizi, Mohammad Taghi Sattari, Muslume Sevba Colak, Shahaboddin Shamshirband, Kwok-Wing Chau

“…In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). …”
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Emotion Recognition from EEG Signals Using Recurrent Neural Networks by M. Kalpana Chowdary, J. Anitha, D. Jude Hemanth

“…The main objective of this study is to classify the emotions from electroencephalogram signals using variant recurrent neural network architectures. Three architectures are used in this work for the recognition of emotions using EEG signals: RNN (recurrent neural network), LSTM (long short-term memory network), and GRU (gated recurrent unit). …”
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Memory augmented recurrent neural networks for de-novo drug design. by Naveen Suresh, Neelesh Chinnakonda Ashok Kumar, Srikumar Subramanian, Gowri Srinivasa

“…A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output. …”
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Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things by Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas, Jaime Lloret

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Predicting human decision making in psychological tasks with recurrent neural networks by Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

“…We propose here to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by human subjects engaged in gaming activity, the first application of such methods in this research domain. …”
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Supervised Brain Network Learning Based on Deep Recurrent Neural Networks by Shijie Zhao, Yan Cui, Linwei Huang, Li Xie, Yaowu Chen, Junwei Han, Lei Guo, Shu Zhang, Tianming Liu, Jinglei Lv

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Engineering recurrent neural networks from task-relevant manifolds and dynamics by Pollock, Eli, Jazayeri, Mehrdad

“…Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. …”
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