scholarly journals Sarcasm Detection Using Multi-Head Attention Based Bidirectional LSTM

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 6388-6397 ◽  
Author(s):  
Avinash Kumar ◽  
Vishnu Teja Narapareddy ◽  
Veerubhotla Aditya Srikanth ◽  
Aruna Malapati ◽  
Lalita Bhanu Murthy Neti
Keyword(s):  
Bidirectional Lstm

BuTTER: BidirecTional LSTM for Food Named-Entity Recognition

2020 ◽  
Author(s):  
Gjorgjina Cenikj ◽  
Gorjan Popovski ◽  
Riste Stojanov ◽  
Barbara Korousic Seljak ◽  
Tome Eftimov
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Bidirectional Lstm

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.

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