scholarly journals EMOTION DETECTION USING DEEP LEARNING ALGORITHM

2021 ◽  
Vol 06 (03) ◽  
Author(s):  
Shital S.Yadav ◽  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Input Image ◽  
Emotion Detection ◽  
Image Size ◽  
Human Machine Interaction ◽  
Deep Learning Algorithm ◽  
Japanese Female ◽  
Benchmark Datasets ◽  
Machine Interaction

Automatic emotion detection is a key task in human machine interaction,where emotion detection makes system more natural. In this paper, we propose an emotion detection using deep learning algorithm. The proposed algorithm uses end to end CNN. To increase computational efficiency of the deep network, we make use of trained weight parameters of the MobileNet to initialize the weight parameters of our system. To make our system independent of the input image size, we place global average pooling layer On top of the last convolution layer of it. Proposed system is validated for emotion detection using two benchmark datasets viz. Cohn–Kanade+ (CK+) and Japanese female facial expression (JAFFE). The experimental results show that the proposed method outperforms the other existing methods for emotion detection.

scholarly journals Emotion Detection Using Deep Learning Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 30-38
Author(s):  
Shital Sanjay Yadav ◽  
Anup S. Vibhute
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Input Image ◽  
The Other ◽  
Emotion Detection ◽  
Image Size ◽  
Deep Learning Algorithm ◽  
Prime Task ◽  
Japanese Female ◽  
Benchmark Datasets

Automatic emotion detection is a prime task in computerized human behaviour analysis. The proposed system is an automatic emotion detection using convolution neural network. The proposed end-to-end CNN is therefore named as ENet. Keeping in mind the computational efficiency, the deep network makes use of trained weight parameters of the MobileNet to initialize the weight parameters of ENet. On top of the last convolution layer of ENet, the authors place global average pooling layer to make it independent of the input image size. The ENet is validated for emotion detection using two benchmark datasets: Cohn-Kanade+ (CK+) and Japanese female facial expression (JAFFE). The experimental results show that the proposed ENet outperforms the other existing methods for emotion detection.

EEG Bases Emotion Detection Using Deep Learning Algorithm

2021 ◽  
pp. 79-89
Author(s):  
S. Thejaswini ◽  
N. Ramesh Babu ◽  
K. M. Ravikumar
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Emotion Detection ◽  
Deep Learning Algorithm

scholarly journals Aspect Based Emotion Detection and Topic Modeling on Social Media Reviews

2021 ◽  
Author(s):  
Ganesh N. Jorvekar ◽  
Mohit Gangwar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Emotion Detection ◽  
Natural Language Text ◽  
Deep Learning Algorithm ◽  
User Comments ◽  
Deep Learning Model

In recent years, the number of user comments and text materials has increased dramatically. Analysis of the emotions has drawn interest from researchers. Earlier research in the field of artificial-intelligence concentrate on identification of emotion and exploring the explanation the emotions can’t recognized or misrecognized. The association between the emotions leads to the understanding of emotion loss. In this Work we are trying to fill the gap between emotional recognition and emotional co-relation mining through social media reviews of natural language text. The association between emotions, represented as the emotional uncertainty and evolution, is mainly triggered by cognitive bias in the human emotion. Numerous types of features and Recurrent neural-network (RNN) as deep learning model provided to mine the emotion co-relation from emotion detection using text. The rule on conflict of emotions is derived on a symmetric basis. TF-IDF, NLP Features and Co-relation features has used for feature extraction as well as section and Recurrent Neural Network (RNN) and Hybrid deep learning algorithm for classification has used to demonstrates the entire research experiments. Finally evaluate the system performance with various existing system and show the effectiveness of proposed system.

Apple Fruit Recognition Based on a Deep Learning Algorithm Using an Improved Lightweight Network

2021 ◽  
Vol 37 (1) ◽  
pp. 123-134
Author(s):  
Jiangtao Ji ◽  
Xu Zhu ◽  
Hao Ma ◽  
Hui Wang ◽  
Xin Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Apple Fruit ◽  
List Type ◽  
Image Size ◽  
Yield Estimation ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Whale Optimization ◽  
Scaling Coefficient

HIGHLIGHTSA deep learning algorithm with an improved lightweight network was used to identify apple fruit.Multiscale pooling was used to reduce the image size and enrich the features.Compound scaling was used to scale the basic network.The optimal compound scaling coefficient for apple identification was obtained with the WOA algorithm.The proposed method achieved an average recognition precision rate of 94.43% and a speed of 0.051s.ABSTRACT. Accurate fruit identification is the basis for automating the operation of orchard production. To better apply the identification model in mobile devices so that venue becomes a less restrictive factor for application, this study proposes an apple fruit identification method based on an improved lightweight network named “MobileNetV3-Small.” The whale optimization algorithm was introduced to improve the model by obtaining an optimal compound-scaling coefficient for the MobileNetV3-Small network. A multiscale pooling approach was used for fruit recognition, comprising operations such as lossless scaling and feature extraction on sample images. The obtained images were then inputted into the model for recognition and classification. The experimental process was conducted on an apple data set. The test results show that the multiclass average precision of apple recognition using this model was 94.43% and the running time of recognition was 0.051 s per image. Both indicators outperformed the control network models of “MobileNetV3-Small,” ResNet-50, and VGG-19. This model is 14.63% more accurate and 1.95 times quicker on average in identification than the next best model. These findings indicate that the method can realize high-efficiency and high-precision recognition of apples with high stability and portability, which lays a good foundation for the mechanization of repetitive operations such as orchard yield estimation, fruit labeling, and fruit picking. Keywords: Apple recognition, Compound scaling, Deep learning algorithm, Lightweight network, Yield estimation.

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