An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

10.36227/techrxiv.14790786.v1 ◽

2021 ◽

Author(s):

Debasish Jyotishi ◽

Samarendra Dandapat

Keyword(s):

Short Term Memory ◽

Healthcare Services ◽

Signal Acquisition ◽

Biometric System ◽

Proposed Model ◽

Subject Identification ◽

Lstm Network ◽

Person Verification ◽

Biometric Information ◽

Electrocardiogram Ecg

The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, we have designed a novel attention based hierarchical long short-term memory (LSTM) model to learn the biometric representation correspond- ing to a person. The hierarchical LSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions. This addresses the long term dependency issue of the LSTM network in our application. The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person. These ECG complexes are given more weight to learn better biometric representation. The proposed system is less complex and more efficient as it does not require the detection of any fiducial points. We have evaluated the proposed model for both the person verification and identification problems using two on-the-person ECG databases and two off-the-person ECG databases. The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods.

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A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

Sustainability ◽

10.3390/su12104107 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4107

Author(s):

Wafa Shafqat ◽

Yung-Cheol Byun

Keyword(s):

Recommendation System ◽

Short Term Memory ◽

Contextual Information ◽

Optimal Size ◽

Effective Parameters ◽

Travel History ◽

Proposed Model ◽

Processing Information ◽

Lstm Network ◽

Gated Recurrent Unit

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

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Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090366 ◽

2019 ◽

Vol 8 (9) ◽

pp. 366 ◽

Cited By ~ 5

Author(s):

Yong Han ◽

Cheng Wang ◽

Yibin Ren ◽

Shukang Wang ◽

Huangcheng Zheng ◽

...

Keyword(s):

High Efficiency ◽

Short Term Memory ◽

Spatial Scales ◽

Optimization Procedure ◽

Stochastic Gradient Descent ◽

Short Term ◽

Passenger Flow ◽

Proposed Model ◽

Gradient Descent Algorithm ◽

Lstm Network

The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

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On the Use of Generative Deep Learning Approaches for Generating Hidden Test Scripts

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194021500480 ◽

2021 ◽

Vol 31 (10) ◽

pp. 1447-1468

Author(s):

Mert Oz ◽

Caner Kaya ◽

Erdi Olmezogullari ◽

Mehmet S. Aktas

Keyword(s):

Web Application ◽

Web Applications ◽

Short Term Memory ◽

Learning Approaches ◽

Generation Task ◽

Clickstream Data ◽

Test Script ◽

Proposed Model ◽

Action Sequences ◽

Lstm Network

With the advent of web 2.0, web application architectures have been evolved, and their complexity has grown enormously. Due to the complexity, testing of web applications is getting time-consuming and intensive process. In today’s web applications, users can achieve the same goal by performing different actions. To ensure that the entire system is safe and robust, developers try to test all possible user action sequences in the testing phase. Since the space of all the possibilities is enormous, covering all user action sequences can be impossible. To automate the test script generation task and reduce the space of the possible user action sequences, we propose a novel method based on long short-term memory (LSTM) network for generating test scripts from user clickstream data. The experiment results clearly show that generated hidden test sequences are user-like sequences, and the process of generating test scripts with the proposed model is less time-consuming than writing them manually.

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Forecasting the spread of COVID-19 using LSTM network

BMC Bioinformatics ◽

10.1186/s12859-021-04224-2 ◽

2021 ◽

Vol 22 (S6) ◽

Author(s):

Shiu Kumar ◽

Ronesh Sharma ◽

Tatsuhiko Tsunoda ◽

Thirumananseri Kumarevel ◽

Alok Sharma

Keyword(s):

Short Term Memory ◽

Forecasting Model ◽

Policy Makers ◽

Business Partners ◽

Global Pandemic ◽

Proposed Model ◽

Travel Restrictions ◽

The Government ◽

Lstm Network ◽

Novel Coronavirus

Abstract Background The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. Results The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand’s data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. Conclusion The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.

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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

Environmental Science and Pollution Research ◽

10.1007/s11356-021-14286-7 ◽

2021 ◽

Author(s):

Ahmed Ben Said ◽

Abdelkarim Erradi ◽

Hussein Ahmed Aly ◽

Abdelmonem Mohamed

Keyword(s):

Time Series ◽

Clustering Algorithm ◽

Short Term Memory ◽

Multivariate Time Series ◽

Health Sector ◽

Cumulative Number ◽

Disease Propagation ◽

Proposed Model ◽

Bidirectional Lstm ◽

Lstm Network

AbstractTo assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

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LSTM Networks for Online Cross-Network Recommendations

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/532 ◽

2018 ◽

Cited By ~ 6

Author(s):

Dilruk Perera ◽

Roger Zimmermann

Keyword(s):

Short Term Memory ◽

Auxiliary Information ◽

User Preference ◽

Time Intervals ◽

User Interactions ◽

Linear Relationships ◽

Proposed Model ◽

Target Network ◽

Lstm Network

Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues. The proposed model contains three main extensions to the standard LSTM: First, an attention gated mechanism to capture long-term user preference changes. Second, a higher order interaction layer to alleviate data sparsity. Third, time aware LSTM cell gates to capture irregular time intervals between user interactions. We illustrate our solution using auxiliary information from Twitter and Google Plus to improve recommendations on YouTube. Extensive experiments show that the proposed model consistently outperforms state-of-the-art in terms of accuracy, diversity and novelty.

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An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

Sensors ◽

10.3390/s21175858 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5858

Author(s):

Jakub Drzazga ◽

Bogusław Cyganek

Keyword(s):

Sleep Disordered Breathing ◽

Short Term Memory ◽

Classification Error ◽

Normal Breathing ◽

Proposed Model ◽

Respiratory Event ◽

Shallow Breathing ◽

Long Short Term Memory ◽

Lstm Network ◽

Thoracic And Abdominal

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.

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An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Sensors ◽

10.3390/s21082811 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2811

Author(s):

Waseem Ullah ◽

Amin Ullah ◽

Tanveer Hussain ◽

Zulfiqar Ahmad Khan ◽

Sung Wook Baik

Keyword(s):

Time Complexity ◽

Short Term Memory ◽

Smart Cities ◽

Vital Role ◽

Training Data ◽

Surveillance Videos ◽

Video Frames ◽

Proposed Model ◽

Lstm Network ◽

Increase In Accuracy

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

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An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Journal of Healthcare Engineering ◽

10.1155/2019/6320651 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 12

Author(s):

Junli Gao ◽

Hongpo Zhang ◽

Peng Lu ◽

Zongmin Wang

Keyword(s):

Short Term Memory ◽

Recurrent Network ◽

Short Term ◽

Ecg Signals ◽

Beat Classification ◽

Long Short Term Memory ◽

Lstm Network ◽

Reliable Solution ◽

Electrocardiogram Ecg

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

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