Computational Cognitive Analysis of ADHD Patients using Matlab Applications

Kanna, R. Kishore; Mutheeswaran, U.; Jouda, Ahmed Jaber; Asaad Hussein, Mustafa; Hussain, Ahmed; Al-Tahee, Mustafa

doi:10.1109/icacite57410.2023.10182435

Cited by 19 publications

(1 citation statement)

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“…It is possible to determine if a customer is satisfied with a product or service based on the emotional reaction they have to a photograph or video that was taken by an artificially intelligent system [18]. This can be done by observing the customer's reaction.…”

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

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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.

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Section: Literature Surveymentioning

confidence: 99%