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A Hybrid CNN-LSTM-Based Emotional Status Determination using Physiological Signals
BGC Trust University Bangladesh Bidyanagar, Chandanaish, Bangladesh.ORCID iD: 0000-0002-4279-0878
BGC Trust University Bangladesh Bidyanagar, Chandanaish, Bangladesh.ORCID iD: 0000-0001-5028-4986
BGC Trust University Bangladesh Bidyanagar, Chandanaish, Bangladesh.
University of Chittagong, Chittagong, Bangladesh.ORCID iD: 0000-0002-3090-7645
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2022 (English)In: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering: TCCE 2021 / [ed] M. Shamim Kaiser; Kanad Ray; Anirban Bandyopadhyay; Kavikumar Jacob; Kek Sie Long, Springer Nature, 2022, p. 149-161Conference paper, Published paper (Refereed)
Abstract [en]

Automatic real-time emotion recognition based on GSR and ECG signals becomes an effective computer-aided tool for emotional recognition as a challenge to pattern recognition. Traditional machine learning methods require the development and extraction of various features dependent on extensive domain knowledge. As a result, non-domain experts can find these methods challenging. On the other hand, deep learning methods have been widely used in several current studies to learn features and identify various types of data. In this paper, to characterize human emotion states, we proposed a hybrid neural network that combines ‘Convolutional Neural Network (CNN)’ and ‘Long-Term Short-Term Memory (LSTM)’. Our dataset consists of four types of emotions which are happy, sad, fear, angry. We have trained our model with CNN-LSTM. Our proposed CNN-LSTM model gives 100% training accuracy and 99.05% validation accuracy with RMSProp optimizer. We also compare our result with machine learning algorithms: Random forest, Logistic Regression, Support Vector Machine, and Naïve Bayes. The comparison result clearly shows that our proposed CNN-LSTM gives the best result among the other classifiers.

Place, publisher, year, edition, pages
Springer Nature, 2022. p. 149-161
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 348
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-89918DOI: 10.1007/978-981-16-7597-3_12Scopus ID: 2-s2.0-85126196202OAI: oai:DiVA.org:ltu-89918DiVA, id: diva2:1648054
Conference
3rd International Conference on Trends in Cognitive Computation Engineering (TCCE 2021), Johor, Malaysia, October 21-22, 2021
Note

ISBN för värdpublikation: 978-981-16-7596-6, 978-981-16-7597-3

Available from: 2022-03-29 Created: 2022-03-29 Last updated: 2022-07-04Bibliographically approved

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Andersson, Karl

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