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.

Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

2021 ◽  
pp. 036119812199347
Author(s):  
Lei Lin ◽  
Siyuan Gong ◽  
Srinivas Peeta ◽  
Xia Wu
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Road Transportation ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

scholarly journals A Dense Long Short-Term Memory Model for Enhancing the Imagery-Based Brain-Computer Interface

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaofei Zhang ◽  
Tao Wang ◽  
Qi Xiong ◽  
Yina Guo
Keyword(s):  
Short Term Memory ◽  
Recognition Algorithm ◽  
Training Process ◽  
Short Term ◽  
Data Set ◽  
Static State ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Model Training ◽  
The One

Imagery-based brain-computer interfaces (BCIs) aim to decode different neural activities into control signals by identifying and classifying various natural commands from electroencephalogram (EEG) patterns and then control corresponding equipment. However, several traditional BCI recognition algorithms have the “one person, one model” issue, where the convergence of the recognition model’s training process is complicated. In this study, a new BCI model with a Dense long short-term memory (Dense-LSTM) algorithm is proposed, which combines the event-related desynchronization (ERD) and the event-related synchronization (ERS) of the imagery-based BCI; model training and testing were conducted with its own data set. Furthermore, a new experimental platform was built to decode the neural activity of different subjects in a static state. Experimental evaluation of the proposed recognition algorithm presents an accuracy of 91.56%, which resolves the “one person one model” issue along with the difficulty of convergence in the training process.

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhengqiang Ge ◽  
Xinyu Liu ◽  
Qiang Li ◽  
Yu Li ◽  
Dong Guo
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Short Term ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Term Memory ◽  
Protection Mechanism ◽  
Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

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 Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Abdullah Alharbi ◽  
Wael Alosaimi ◽  
Radhya Sahal ◽  
Hager Saleh
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Real Time ◽  
Short Term Memory ◽  
Stream Processing ◽  
Care Providers ◽  
Short Term ◽  
Term Memory ◽  
Risk Of Death ◽  
Long Short Term Memory

Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement (e.g., artificial intelligence and stream processing technologies) allows healthcare sectors to consolidate and analyze massive health-based data to discover risks by making more accurate predictions. Therefore, this work proposes a real-time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate risk in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time-series dataset is extracted from Medical Information Mart for Intensive Care (MIMIC-II). Recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BI-LSTM) are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance.

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