Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest

Abstract

In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human–computer interaction), etc. Such emotions could be detected from various means, such as information integration from facial expressions, gestures, speech, etc. Though such physical depictions contribute to emotion detection, EEG (electroencephalogram) signals have gained significant focus in emotion detection due to their sensitivity to alterations in emotional states. Hence, such signals could explore significant emotional state features. However, manual detection from EEG signals is a time-consuming process. With the evolution of artificial intelligence, researchers have attempted to use different data mining algorithms for emotion detection from EEG signals. Nevertheless, they have shown ineffective accuracy. To resolve this, the present study proposes a DNA-RCNN (Deep Normalized Attention-based Residual Convolutional Neural Network) to extract the appropriate features based on the discriminative representation of features. The proposed NN also explores alluring features with the proposed attention modules leading to consistent performance. Finally, classification is performed by the proposed M-RF (modified-random forest) with an empirical loss function. In this process, the learning weights on the data subset alleviate loss amongst the predicted value and ground truth, which assists in precise classification. Performance and comparative analysis are considered to explore the better performance of the proposed system in detecting emotions from EEG signals that confirms its effectiveness.

Facial and speech Emotional Recognition based on Improved Deep Model

“…The highlights extricated from the proposed BoDF demonstrate have much littler measurements. In [37], propose a Profound Normalized Attention-Based Remaining Convolutional Neural Arrange (DNA-RCNN) to extricate fitting highlights based on the discriminative representation of highlights. The proposed NN moreover investigates alluring highlights with proposed consideration modules that lead to reliable execution.…”

“…Among its advantages is having a proportional number of files in each emotion, which avoids the problems caused by training algorithms with unbalanced data. In addition, RAVDEESS is a reference dataset in the research community that has been used in several works [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface

“…Alsubai [22] presents a DNA-RCNN (Deep Normalized Attention-based Residual CNN) for extracting the suitable features dependent upon the discriminative representation of features. The presented NN also discovers alluring features with the presented attention elements that lead to consistent results.…”

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

“…This method carries out sentiment classification on vectorised reviews utilizing two methods of Word2Vec such as Skip Gram and Continuous Bag of Words (BoW) in three various vector sizes (100, 200, 300), with the use of six BiGRU and two convolutional layers (MBi‐GRUMCONV). Alsubai et al 22 presented a DNA‐RCNN (Deep Normalized Attention‐based Residual CNN) for extracting the suitable features dependent upon the discriminative representation of features. The presented NN also searches alluring features with presented attention components leading to a consistent solution.…”