scholarly journals Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition

2021 ◽  
Author(s):  
Woo‐Hyun Hwang ◽  
Dong‐Hyun Kang ◽  
Deok‐Hwan Kim
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Network Model ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Bidirectional Lstm ◽  
Lstm Network

Ant Colony Optimisation Applied to Non-Slicing Floorplanning

2015 ◽  
Vol 9 ◽  
pp. 193-203
Author(s):  
Mirzakhmet SYZDYKOV ◽  
◽  
Madi UZBEKOV ◽  
Keyword(s):  
Ant Colony ◽  
Ant Colony Optimisation

A Comparative Study of Subject-Dependent and Subject-Independent Strategies for EEG-Based Emotion Recognition using LSTM Network

2020 ◽  
Author(s):  
Debarshi Nath ◽  
Anubhav ◽  
Mrigank Singh ◽  
Divyashikha Sethia ◽  
Diksha Kalra ◽  
...  
Keyword(s):  
Comparative Study ◽  
Emotion Recognition ◽  
Lstm Network

Pedestrian Stride-Length Estimation Based on Bidirectional LSTM Network

2020 ◽  
Author(s):  
Zhang Ping ◽  
Meng Zhidong ◽  
Wang Pengyu ◽  
Deng Zhihong
Keyword(s):  
Stride Length ◽  
Length Estimation ◽  
Bidirectional Lstm ◽  
Lstm Network

scholarly journals Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 219
Author(s):  
Dhananjay Thiruvady ◽  
Kerri Morgan ◽  
Susan Bedingfield ◽  
Asef Nazari
Keyword(s):  
Integer Programming ◽  
Gender Equity ◽  
Programming Model ◽  
Original Data ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Integer Programming Model ◽  
Problem Instances ◽  
Work Integrated Learning ◽  
Time Required

The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames.

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.

EEG based emotion recognition using fusion feature extraction method

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27057-27074 ◽  
Author(s):  
Qiang Gao ◽  
Chu-han Wang ◽  
Zhe Wang ◽  
Xiao-lin Song ◽  
En-zeng Dong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Fusion Feature
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