Deep Bidirectional LSTM Network Learning-Aided OFDMA Downlink and SC-FDMA Uplink

2021 ◽  
Author(s):  
Rafiul Kadir ◽  
Ritu Saha ◽  
Md. Abdul Awal ◽  
Mohammad Ismat Kadir
Keyword(s):  
Network Learning ◽  
Bidirectional Lstm ◽  
Lstm Network

scholarly journals Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kilin Shi ◽  
Tobias Steigleder ◽  
Sven Schellenberger ◽  
Fabian Michler ◽  
Anke Malessa ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Dynamic Change ◽  
Cold Pressor Test ◽  
Cold Pressor ◽  
Contactless Measurement ◽  
Pressor Test ◽  
Skin Contact ◽  
Bidirectional Lstm ◽  
Lstm Network

AbstractContactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.

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

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.

Bidirectional LSTM Network with Ordered Neurons for Speech Enhancement

2020 ◽  
Author(s):  
Xiaoqi Li ◽  
Yaxing Li ◽  
Yuanjie Dong ◽  
Shan Xu ◽  
Zhihui Zhang ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Bidirectional Lstm ◽  
Lstm Network

Story-Plot Recommender Using Bidirectional LSTM Network

2021 ◽  
pp. 31-44
Author(s):  
Manmohan Dogra ◽  
Jayashree Domala ◽  
Jenny Dcruz ◽  
Safa Hamdare
Keyword(s):  
Bidirectional Lstm ◽  
Lstm Network

scholarly journals A Deep Learning Approach for a Source Code Detection Model Using Self-Attention

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yao Meng ◽  
Long Liu
Keyword(s):  
Deep Learning ◽  
Source Code ◽  
Classification Performance ◽  
Detection Model ◽  
Learning Framework ◽  
Code Clone ◽  
Representation Model ◽  
Discriminative Model ◽  
Bidirectional Lstm ◽  
Lstm Network

With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. At-biLSTM is composed of a representation model and a discriminative model. The representation model firstly transforms the source code into an abstract syntactic tree and splits it into a sequence of statement trees; then, it encodes each of the statement trees with a deep-first traversal algorithm. Finally, the representation model encodes the sequence of statement vectors via a bidirectional LSTM network, which is a classical deep learning framework, with a self-attention layer and outputs a vector representing the given source code. The discriminative model identifies the code clone depending on the vectors generated by the presentation model. Our proposed model retains both the syntactics and semantics of the source code in the process of encoding, and the self-attention algorithm makes the classifier concentrate on the effect of key statements and improves the classification performance. The contrast experiments on the benchmarks OJClone and BigCloneBench indicate that At-LSTM is effective and outperforms the state-of-art approaches in source code clone detection.

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