Stress emotion classification using optimized convolutional neural network for online transfer learning dataset

Autor:	G, Linda Rose, M, Punithavalli
Rok vydání:	2022
Předmět:	Machine Learning Human-Computer Interaction Emotions Biomedical Engineering Bioengineering Neural Networks Computer General Medicine Computer Science Applications
Zdroj:	Computer Methods in Biomechanics and Biomedical Engineering. 25:1576-1587
ISSN:	1476-8259 1025-5842
Popis:	Nowadays, deep learning methods with transfer learning (TL) makes ease of stress emotion classification tasks. Amongst, an optimized convolutional neural network with TL (OCNNTL) executes OCNN-based classification on emotion and stress data domains to learn high-level features at the top layers. However, it fails to handle the abrupt concept drift in real-time; besides, it end up with huge time complication while on gathering the required data and its transformation. To tackle the aforementioned concerns, a novel online OCNNTL (O2CNNTL) model is proposed; whereas, OCNNTL process initiates in the stress-emotion domain via the prior knowledge acquired by learning the training data both from the stress as well as the emotion domains. Moreover in O2CNNTL model, the concept-drifting data streams are taken into account for solving the online classification by the OCNN classifier; whereas, to enhance the learning efficiency a regularization learning technique is instigated on varied feature spaces. Thus, the proposed O2CNNTL achieves higher efficiency than the state-of-the-art models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fce1171bafb9e59ffc0b26065e6210bb https://doi.org/10.1080/10255842.2021.2024169 Zobrazit plný text záznamu