scholarly journals Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jinlei Zhang ◽  
Xue Qiu ◽  
Xiang Li ◽  
Zhijie Huang ◽  
Mingqiu Wu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Optimization Algorithm ◽  
Weather Prediction ◽  
Single Mode ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Recognition Model ◽  
Multimodal Emotion Recognition

Emotion recognition is a research hotspot in the field of artificial intelligence. If the human-computer interaction system can sense human emotion and express emotion, it will make the interaction between the robot and human more natural. In this paper, a multimodal emotion recognition model based on many-objective optimization algorithm is proposed for the first time. The model integrates voice information and facial information and can simultaneously optimize the accuracy and uniformity of recognition. This paper compares the emotion recognition algorithm based on many-objective algorithm optimization with the single-modal emotion recognition model proposed in this paper and the ISMS_ALA model proposed by recent related research. The experimental results show that compared with the single-mode emotion recognition, the proposed model has a great improvement in each evaluation index. At the same time, the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The experimental results show that the many-objective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

scholarly journals Multimodal Emotion Recognition Model Based on a Deep Neural Network with Multiobjective Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mingyong Li ◽  
Xue Qiu ◽  
Shuang Peng ◽  
Lirong Tang ◽  
Qiqi Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiobjective Optimization ◽  
Emotion Recognition ◽  
Optimization Algorithm ◽  
Rapid Development ◽  
Single Mode ◽  
Recognition Model ◽  
Model Based ◽  
Mode Recognition ◽  
Multimodal Emotion Recognition

With the rapid development of deep learning and wireless communication technology, emotion recognition has received more and more attention from researchers. Computers can only be truly intelligent when they have human emotions, and emotion recognition is its primary consideration. This paper proposes a multimodal emotion recognition model based on a multiobjective optimization algorithm. The model combines voice information and facial information and can optimize the accuracy and uniformity of recognition at the same time. The speech modal is based on an improved deep convolutional neural network (DCNN); the video image modal is based on an improved deep separation convolution network (DSCNN). After single mode recognition, a multiobjective optimization algorithm is used to fuse the two modalities at the decision level. The experimental results show that the proposed model has a large improvement in each evaluation index, and the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The results show that the multiobjective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

scholarly journals Personnel Emotion Recognition Model for Internet of Vehicles Security Monitoring

2021 ◽  
Author(s):  
Erkang Fu ◽  
Xi Li ◽  
Zhi Yao ◽  
Yuxin Ren ◽  
Yuanhao Wu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Hot Spot ◽  
Development Trend ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Low Carbon ◽  
Internet Of Vehicles ◽  
Recognition Model ◽  
Model Combining

Abstract In recent years, the Internet of vehicles (IOV) with intelligent networked automobiles as the terminal node has gradually become the development trend of the automotive industry and the research hot spot in related fields. This is due to its characteristics of intelligence, networking, low-carbon and energy saving. Real time emotion recognition for drivers and pedestrians in the community can be utilized to prevent fatigue driving and malicious collision, keep safety verification and pedestrian safety detection. This paper mainly studies the face emotion recognition model that can be utilized for IOV. Considering the fluctuation of image acquisition perspective and image quality in the application scene of IOV, the natural scene video similar to vehicle environment and its galvanic skin response (GSR) are utilized to make the testing set of emotion recognition. Then an expression recognition model combining codec and Support Vector Machine (SVM) classifier is proposed. Finally, emotion recognition testing is completed on the basis of Algorithm 1. The matching accuracy between the emotion recognition model and GSR is 82.01%. In the process of model testing, 189 effective videos are involved and 155 are correctly identified.

A novel signal to image transformation and feature level fusion for multimodal emotion recognition

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Bahar Hatipoglu Yilmaz ◽  
Cemal Kose
Keyword(s):  
Emotion Recognition ◽  
Classification Performance ◽  
Image Features ◽  
Multimodal Fusion ◽  
Support Vector ◽  
Scale Invariant ◽  
Transform Method ◽  
Vector Machines ◽  
Multimodal Emotion Recognition ◽  
Scale Invariant Feature

Abstract Emotion is one of the most complex and difficult expression to be predicted. Nowadays, many recognition systems that use classification methods have focused on different types of emotion recognition problems. In this paper, we aimed to propose a multimodal fusion method between electroencephalography (EEG) and electrooculography (EOG) signals for emotion recognition. Therefore, before the feature extraction stage, we applied different angle-amplitude transformations to EEG–EOG signals. These transformations take arbitrary time domain signals and convert them two-dimensional images named as Angle-Amplitude Graph (AAG). Then, we extracted image-based features using a scale invariant feature transform method, fused these features originates basically from EEG–EOG and lastly classified with support vector machines. To verify the validity of these proposed methods, we performed experiments on the multimodal DEAP dataset which is a benchmark dataset widely used for emotion analysis with physiological signals. In the experiments, we applied the proposed emotion recognition procedures on the arousal-valence dimensions. We achieved (91.53%) accuracy for the arousal space and (90.31%) for the valence space after fusion. Experimental results showed that the combination of AAG image features belonging to EEG–EOG signals in the baseline angle amplitude transformation approaches enhanced the classification performance on the DEAP dataset.

scholarly journals Personnel emotion recognition model for Internet of vehicles security monitoring in community public space

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Erkang Fu ◽  
Xi Li ◽  
Zhi Yao ◽  
Yuxin Ren ◽  
Yuanhao Wu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Public Space ◽  
Hot Spot ◽  
Development Trend ◽  
Support Vector ◽  
Expression Recognition ◽  
Low Carbon ◽  
Internet Of Vehicles ◽  
Recognition Model ◽  
Model Combining

AbstractIn recent years, the Internet of vehicles (IOV) with intelligent networked automobiles as terminal node has gradually become the development trend of automotive industry and research hot spot in related fields. This is due to its characteristics of intelligence, networking, low-carbon and energy saving. Real time emotion recognition for drivers and pedestrians in the community can be utilized to prevent fatigue driving and malicious collision, keep safety verification and pedestrian safety detection. This paper mainly studies the face emotion recognition model that can be utilized for IOV. Considering the fluctuation of image acquisition perspective and image quality in the application scene of IOV, the natural scene video similar to vehicle environment and its galvanic skin response (GSR) are utilized to make the testing set of emotion recognition. Then an expression recognition model combining codec and Support Vector Machine classifier is proposed. Finally, emotion recognition testing is completed on the basis of Algorithm 1. The matching accuracy between the emotion recognition model and GSR is 82.01%. In the process of model testing, 189 effective videos are involved and 155 are correctly identified.

scholarly journals Multimodal Emotion Recognition from Art Using Sequential Co-Attention

2021 ◽  
Vol 7 (8) ◽  
pp. 157
Author(s):  
Tsegaye Misikir Tashu ◽  
Sakina Hajiyeva ◽  
Tomas Horvath
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Experimental Results ◽  
Multimodal Emotion Recognition

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the model to focus on learning informative and refined representations for both feature extraction and modality fusion. The resulting system can be used to categorize artworks according to the emotions they evoke; recommend paintings that accentuate or balance a particular mood; search for paintings of a particular style or genre that represents custom content in a custom state of impact. Experimental results on the WikiArt emotion dataset showed the efficiency of the approach proposed and the usefulness of three modalities in emotion recognition.

scholarly journals Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yongrui Huang ◽  
Jianhao Yang ◽  
Pengkai Liao ◽  
Jiahui Pan
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Multimodal Fusion ◽  
Support Vector ◽  
Basic Emotion ◽  
Input Signals ◽  
Vector Machines ◽  
Fusion Methods ◽  
Multimodal Emotion Recognition

This paper proposes two multimodal fusion methods between brain and peripheral signals for emotion recognition. The input signals are electroencephalogram and facial expression. The stimuli are based on a subset of movie clips that correspond to four specific areas of valance-arousal emotional space (happiness, neutral, sadness, and fear). For facial expression detection, four basic emotion states (happiness, neutral, sadness, and fear) are detected by a neural network classifier. For EEG detection, four basic emotion states and three emotion intensity levels (strong, ordinary, and weak) are detected by two support vector machines (SVM) classifiers, respectively. Emotion recognition is based on two decision-level fusion methods of both EEG and facial expression detections by using a sum rule or a production rule. Twenty healthy subjects attended two experiments. The results show that the accuracies of two multimodal fusion detections are 81.25% and 82.75%, respectively, which are both higher than that of facial expression (74.38%) or EEG detection (66.88%). The combination of facial expressions and EEG information for emotion recognition compensates for their defects as single information sources.

scholarly journals Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis

2020 ◽  
Vol 10 (11) ◽  
pp. 3667 ◽  
Author(s):  
Xianfeng Yuan ◽  
Zhaoming Miao ◽  
Ziao Liu ◽  
Zichen Yan ◽  
Fengyu Zhou
Keyword(s):  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Analog Circuits ◽  
Experimental Results ◽  
Whale Optimization Algorithm ◽  
Support Vector ◽  
Initial Population ◽  
Whale Optimization ◽  
The One ◽  
Searching Ability

The whale optimization algorithm (WOA) is a new swarm intelligence (SI) optimization algorithm, which has the superiorities of fewer parameters and stronger searching ability. However, previous studies have indicated that there are shortages in maintaining diversity and avoiding local optimal solutions. This paper proposes a multi-strategy ensemble whale optimization algorithm (MSWOA) to alleviate these deficiencies. First, the chaotic initialization strategy is performed to enhance the quality of the initial population. Then, an improved random searching mechanism is designed to reduce blindness in the exploration phase and speed up the convergence. In addition, the original spiral updating position is modified by the Levy flight strategy, which leads to a better tradeoff between local and global search. Finally, an enhanced position revising mechanism is utilized to improve the exploration further. To testify the superiorities of the proposed MSWOA algorithm, a series of comparative experiments are carried out. On the one hand, the numerical optimization experimental results, which are conducted under nineteen widely used benchmark functions, indicate that the performance of MSWOA stands out compared with the standard WOA and six other well-designed SI algorithms. On the other hand, MSWOA is utilized to tune the parameters of the support vector machine (SVM), which is applied to the fault diagnosis of analog circuits. Experimental results confirm that the proposed method has higher diagnosis accuracy than other competitors. Therefore, the MSWOA is successfully applied as a novel and efficient optimization algorithm.

Sign in / Sign up

Export Citation Format

Share Document