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Journal of Trends in Computer Science and Smart Technology

A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3

A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
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Fake News Detection using Data Mining Techniques
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Future Challenges of the Internet of Things in the Health Care Domain - An Overview
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Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2

A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2

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Volume - 3 | Issue - 2 | june 2021

A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Pages: 95-113
Published
14 July, 2021
Abstract

The role of facial expression recognition in social science and human-computer interaction has received a lot of attention. Deep learning advancements have resulted in advances in this field, which go beyond human-level accuracy. This article discusses various common deep learning algorithms for emotion recognition, all while utilising the eXnet library for achieving improved accuracy. Memory and computation, on the other hand, have yet to be overcome. Overfitting is an issue with large models. One solution to this challenge is to reduce the generalization error. We employ a novel Convolutional Neural Network (CNN) named eXnet to construct a new CNN model utilising parallel feature extraction. The most recent eXnet (Expression Net) model improves on the previous model's inaccuracy while having many fewer parameters. Data augmentation techniques that have been in use for decades are being utilized with the generalized eXnet. It employs effective ways to reduce overfitting while maintaining overall size under control.

Keywords

Deep learning sentiment analysis

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