Multi-Source Domain Transfer Discriminative Dictionary Learning Modeling for Electroencephalogram-Based Emotion Recognition

Gu, Xiaoqing; Cai, Weiwei; Gao, Ming; Jiang, Yizhang; Ning, Xin; Qian, Pengjiang

doi:10.1109/tcss.2022.3153660

Cited by 67 publications

(40 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning [ 16 – 18 ] is one of the key technologies for tapping the value of data at the level of big data; at the level of deep learning, the collection and use of a large amount of data has the potential to improve the accuracy of machine models. The meanings of TP and TN when classifying a class in a machine model both represent the case where the classification result is correct: TP is a positive class, and TN is a negative class.…”

Section: Research and Construction Of A Decision Support System For U...mentioning

confidence: 99%

College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Few-Shot Learning has had a significant influence on how people live, work, and learn. Physical education is a requirement for a college diploma. Sports management systems, which focus on data collection, organization, and analysis, as well as timeliness and guidance, are one of the current challenges in the field of physical education at the country’s top colleges and universities. The amount of sex in the room is minimal. Time is money when it comes to making college sports decisions, and this paper uses data from physical fitness tests to illustrate this point. Use Few-Shot Learning technology to extract relevant data from the data, allowing teachers to provide more scientific and effective guidance and suggestions to students. The design and implementation of this paper collect data from physical fitness tests in real-time using mobile edge computing, analyze the data, and display the results using machine learning technology, which mines deep features and displays analysis results, can be used to evaluate students’ physical fitness. The data and information in the physical fitness analysis system are more readable and time-saving, allowing students to better understand their true level of physical fitness. Because of the results of data mining, teachers can provide more specific guidance and recommendations for each student’s physical characteristics.

show abstract

Section: Research and Construction Of A Decision Support System For U...mentioning

confidence: 99%

College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In the part of music emotion classification [15], it first extracts a number of spectral features in music to form a sequence that can capture feature information from the essence of music. Afterward, a three-layer GRU combined with attention mechanism (AM) model architecture is used to classify the emotion of music.…”

Section: Multi-feature Extraction Of Music Emotionmentioning

confidence: 99%

Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

Niu

2022

Mobile Information Systems

View full text Add to dashboard Cite

A large number of music platforms have appeared on the Internet recently. The deep learning framework for music recommendation is still very limited when it comes to accurately identifying the emotional type of music and recommending it to users. Languages, musical styles, thematic scenes, and the ages to which they belong are all common classifications. And this is far from sufficient, posing difficulties in music classification and identification. As a result, this paper uses the methods of music emotion multi-feature extraction, BiGRU model design, and music theme scene classification model design to improve the accuracy of music emotion recognition. It develops the BiGRU emotion recognition model and compares it to other models. BiGRU can correctly identify happy and sad emotion music up to 79 percent and 81.01 percent of the time, respectively. It goes far beyond Rnet-LSTM, and the greater the difference in emotion categories, the easier it is to analyze the feature sequence containing emotional features and the higher the recognition accuracy. This is especially evident in the accuracy with which happiness and sadness are recognized. It can meet users' needs for music recognition in a variety of settings.

show abstract

“…The application of health monitoring data mainly refers to the use of Internet technology and big data technology [ 4 , 5 ] for data mining and analysis, the analysis and integration of health information and data at all levels, and the improvement of health services, to make the operation of various medical industries at all levels more efficient and make the services of various medical industries at all levels better under the background of China's informatization. Machine learning [ 6 – 8 ] can not only be applied to the preprocessing stage of monitoring data but also, more importantly, it is necessary to establish a learning model and actively learn the information contained in the data to evaluate the safety of the structural state and reflect the health state in time. In the face of a large amount of information generated all the time, the traditional manual method is simply unable to make efficient and rapid judgment [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Human Exercise Health Monitoring Data of Smart Bracelet Based on Machine Learning

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The smart bracelet has become a hot-selling commodity, according to a daily consumption survey. Based on people’s interest and concern for their health, the smart bracelet, as a design and application for achieving healthy weight loss monitoring, is quickly becoming a popular new favorite. This bracelet detects fat using the near-infrared diffuse reflection principle, with the goal of assisting people in controlling and maintaining a healthy weight. A large amount of data has been accumulated in all walks of life due to the development of the Internet network and data storage technology. As a result, the emergence of machine learning plays a critical role in the data analysis of human sports health monitoring of smart bracelets. Based on machine learning, this paper investigates the data analysis of human sports health monitoring smart bracelets. When the population index reaches 50 in the analysis of health monitoring data, the average accuracy of data mining is 86.8 percent, the average accuracy of the association rule algorithm is 85.9 percent, the average accuracy of the collaborative filtering algorithm is 84.3 percent, and the average accuracy of the machine learning algorithm is 90.1 percent in this paper. Among the four algorithms, the method presented in this paper is clearly the most effective, stable, and accurate. The system’s stability and accuracy have been greatly improved by the addition of GPS-assisted and hand-up misjudgment algorithms. Because the smart bracelet is inexpensive, easy to wear, and consistent with consumer psychology, it is becoming increasingly popular to use it to monitor the human body’s sports health.

show abstract

Multi-Source Domain Transfer Discriminative Dictionary Learning Modeling for Electroencephalogram-Based Emotion Recognition

Cited by 67 publications

References 26 publications

College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology

College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology

Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

Analysis of Human Exercise Health Monitoring Data of Smart Bracelet Based on Machine Learning

Contact Info

Product

Resources

About