Research on Classroom Emotion Recognition Algorithm Based on Visual Emotion Classification

Yuan, Qinying

doi:10.1155/2022/6453499

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…High accuracy in detection and classification tasks; able to detect multiple faces in a single image [75] Require a large amount of training data to achieve high accuracy [75] Hybrid algorithm Support vector machine +convolutional neural network (SVM +CNN)…”

Section: Preprocessing Methodsmentioning

confidence: 99%

Academic Emotion Classification Using FER: A Systematic Review

Lek

Teo

2023

Human Behavior and Emerging Technologies

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Facial emotion expressions are among the most potent, natural, and powerful means of human communication. Due to the COVID-19 pandemic, educational institutions worldwide are forced to switch rapidly to remote and online learning. Students are currently in an emergency state and must adapt to various and readily accessible learning methods, such as mobile learning applications or an e-learning system. A systematic literature review (SLR) is conducted to extract and synthesize information such as the emotion classifier used in the facial expression recognition (FER) system, the dataset used, the preprocessing technique applied, the feature extraction approach used, and the strength and limitation of the previous studies. Based on the search criteria, 701 publications were initially retrieved from five different digital databases, of which 48 studies have been chosen as primary studies for further analysis. Based on the findings of this study, the deep learning approach is the most frequently adopted approach in classifying student emotions during online learning. FER-2013 is the most commonly used FER dataset in FER studies, while DAiSEE is the most used academic emotion dataset. Moreover, support vector machine (SVM) is the conventional learning emotion classifier that is widely used in the FER systems, while convolutional neural network (CNN) is the most frequently used deep learning classifier. Next, it was found that the number of real-time FER systems is less than that of non-real-time FER systems. Finally, the top-1 accuracy of 94.6% was achieved by the long-term recurrent convolutional network on the academic emotion dataset, and the limitation is that it has low illumination and a lack of frontal pose.

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Section: Preprocessing Methodsmentioning

confidence: 99%

Academic Emotion Classification Using FER: A Systematic Review

Lek

Teo

2023

Human Behavior and Emerging Technologies

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Nanoscale Confinement of Dip‐Pen Nanolithography Written Phospholipid Structures on CuZr Nanoglasses

Vasantham,

Boltynjuk,

Nandam

et al. 2023

Adv Materials Inter

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Nanoglasses have attracted considerable interest among material scientists due to their novel and surprising properties. However, there is still a significant gap in understanding how nanoglasses interact with biomaterials and their effects on living cells. Previous cell studies have reported indications of possible proliferation effects, but a comprehensive understanding of differentiating nanoglass influences from distinct material or topography effects is yet to be established. In this study, the interaction between nanoglass surfaces and phospholipids, which are fundamental components of cell membranes, is investigated. The findings reveal a unique stabilizing effect exhibited by nanoglasses on structures created using lipid dip‐pen nanolithography, preventing their spreading over the surface (“confinement”). This discovery suggests that nanoglasses can potentially influence the structure of cell membranes, providing a conceivable mechanism for how nanoglasses may impact cell behavior.

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