scholarly journals A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data

2021 ◽  
Vol 13 (7) ◽  
pp. 163
Author(s):  
Guizhe Song ◽  
Degen Huang
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Superior Performance ◽  
Short Term ◽  
Contextual Model ◽  
Twitter Data ◽  
Competitive Model ◽  
Long Short Term Memory ◽  
Local Feature Extraction ◽  
Disaster Prediction

The massive amount of data generated by social media present a unique opportunity for disaster analysis. As a leading social platform, Twitter generates over 500 million Tweets each day. Due to its real-time characteristic, more agencies employ Twitter to track disaster events to make a speedy rescue plan. However, it is challenging to build an accurate predictive model to identify disaster Tweets, which may lack sufficient context due to the length limit. In addition, disaster Tweets and regular ones can be hard to distinguish because of word ambiguity. In this paper, we propose a sentiment-aware contextual model named SentiBERT-BiLSTM-CNN for disaster detection using Tweets. The proposed learning pipeline consists of SentiBERT that can generate sentimental contextual embeddings from a Tweet, a Bidirectional long short-term memory (BiLSTM) layer with attention, and a 1D convolutional layer for local feature extraction. We conduct extensive experiments to validate certain design choices of the model and compare our model with its peers. Results show that the proposed SentiBERT-BiLSTM-CNN demonstrates superior performance in the F1 score, making it a competitive model in Tweets-based disaster prediction.

Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network

2019 ◽  
Vol 31 (6) ◽  
pp. 1085-1113 ◽  
Author(s):  
Po-He Tseng ◽  
Núria Armengol Urpi ◽  
Mikhail Lebedev ◽  
Miguel Nicolelis
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Unscented Kalman Filter ◽  
Bipedal Walking ◽  
Motor Tasks ◽  
Short Term ◽  
Term Memory ◽  
Cortical Areas ◽  
Long Short Term Memory ◽  
Ensemble Activity

Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large ( N = 134–402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.

scholarly journals Developing Deep Survival Model for Remaining Useful Life Estimation Based on Convolutional and Long Short-Term Memory Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chia-Hua Chu ◽  
Chia-Jung Lee ◽  
Hsiang-Yuan Yeh
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Survival Model ◽  
Remaining Useful Life ◽  
Superior Performance ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory ◽  
Prediction Approach

The application of mechanical equipment in manufacturing is becoming more and more complicated with technology development and adoption. In order to keep the high reliability and stability of the production line, reducing the downtime to repair and the frequency of routine maintenance is necessary. Since machine and components’ degradations are inevitable, accurately estimating the remaining useful life of them is crucial. We propose an integrated deep learning approach with convolutional neural networks and long short-term memory networks to learn the latent features and estimate remaining useful life value with deep survival model based on the discrete Weibull distribution. We conduct the turbofan engine degradation simulation dataset from Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA to validate our approach. The improved results have proven that our proposed model can capture the degradation trend of a fault and has superior performance under complex conditions compared with existing state-of-the-art methods. Our study provides an efficient feature extraction scheme and offers a promising prediction approach to make better maintenance strategies.

Deep Belief Neural Networks and Bidirectional Long-Short Term Memory Hybrid for Speech Recognition

2015 ◽  
Vol 40 (2) ◽  
pp. 191-195 ◽  
Author(s):  
Łukasz Brocki ◽  
Krzysztof Marasek
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Superior Performance ◽  
Acoustic Model ◽  
Short Term ◽  
Term Memory ◽  
Internal Memory ◽  
Long Short Term Memory

Abstract This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (LSTM) hybrid used as an acoustic model for Speech Recognition. It was demonstrated by many independent researchers that DBNNs exhibit superior performance to other known machine learning frameworks in terms of speech recognition accuracy. Their superiority comes from the fact that these are deep learning networks. However, a trained DBNN is simply a feed-forward network with no internal memory, unlike Recurrent Neural Networks (RNNs) which are Turing complete and do posses internal memory, thus allowing them to make use of longer context. In this paper, an experiment is performed to make a hybrid of a DBNN with an advanced bidirectional RNN used to process its output. Results show that the use of the new DBNN-BLSTM hybrid as the acoustic model for the Large Vocabulary Continuous Speech Recognition (LVCSR) increases word recognition accuracy. However, the new model has many parameters and in some cases it may suffer performance issues in real-time applications.

scholarly journals HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

2018 ◽  
Author(s):  
Dejiang Kong ◽  
Fei Wu
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Location Based Services ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Time ◽  
Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

scholarly journals Temporal Belief Memory: Imputing Missing Data during RNN Training

2018 ◽  
Author(s):  
Yeo Jin Kim ◽  
Min Chi
Keyword(s):  
Septic Shock ◽  
Missing Data ◽  
Real Time ◽  
Short Term Memory ◽  
Early Prediction ◽  
Short Term ◽  
Time Intervals ◽  
Domain Experts ◽  
Long Short Term Memory ◽  
Temporal Sequences

We propose a bio-inspired approach named Temporal Belief Memory (TBM) for handling missing data with recurrent neural networks (RNNs). When modeling irregularly observed temporal sequences, conventional RNNs generally ignore the real-time intervals between consecutive observations. TBM is a missing value imputation method that considers the time continuity and captures latent missing patterns based on irregular real time intervals of the inputs. We evaluate our TBM approach with real-world electronic health records (EHRs) consisting of 52,919 visits and 4,224,567 events on a task of early prediction of septic shock. We compare TBM against multiple baselines including both domain experts' rules and the state-of-the-art missing data handling approach using both RNN and long-short term memory. The experimental results show that TBM outperforms all the competitive baseline approaches for the septic shock early prediction task. 

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