GSM Enabled Patient Monitoring System Using Arduino Application for Cardiac Support

Jebakumar, Samson; Hemalatha, R. J.; Kanna, R. Kishore

doi:10.1007/978-3-031-35078-8_24

Cited by 5 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be done by observing the customer's reaction. In the past, a range of machine learning techniques, such as Random Forest and Support Vector Machine (SVM), were used in order to predict sentiment from photographs that had been modified [19].…”

Section: Literature Surveymentioning

confidence: 99%

CNN Based Face Emotion Recognition System for Healthcare Application

Kishore Kanna,

Panigrahi,

Sahoo

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately. OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases. RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios. CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.

show abstract

Section: Literature Surveymentioning

confidence: 99%