Titlebook: Computational Data and Social Networks; 12th International C Minh Hoàng Hà,Xingquan Zhu,My T. Thai Conference proceedings 2024 The Editor(s

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书目名称Computational Data and Social Networks影响因子(影响力)




书目名称Computational Data and Social Networks影响因子(影响力)学科排名




书目名称Computational Data and Social Networks网络公开度




书目名称Computational Data and Social Networks网络公开度学科排名




书目名称Computational Data and Social Networks被引频次




书目名称Computational Data and Social Networks被引频次学科排名




书目名称Computational Data and Social Networks年度引用




书目名称Computational Data and Social Networks年度引用学科排名




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书目名称Computational Data and Social Networks读者反馈学科排名

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继承人

A Gaussian Distribution Labeling Method for Speech Quality Assessmentuality is represented as mean of scores provided by many listeners’ opinions and is a floating-point value, so the straightforward approach is treating SQA as a regression task. However, speech quality score is labeled by crowdsourcing, resulting in a lack of uniformity among listeners. This discrep

Communal

Tartar

Fake Face Recognition on Images Generated by Various Deepfakes Toolse sophisticated synthetic media created using deep learning techniques that alter or replace visual and audio elements in images, videos, and audio recordings. While Deepfakes offer potential benefits in entertainment and training, they also raise ethical, social, and security concerns. Many people

cataract

Multi-scale Aggregation Network for Speech Emotion Recognitionnal speech feature extraction methods tend to be highly sensitive to factors that are emotionally irrelevant, such as the speaker, speaking styles, and background noise, rather than capturing the underlying emotional nuances. The most efficiently used feature extraction method for SER is deep convol

cataract

FLINT

多骨

Mining the Potential Temporal Features Based on Wearable EEG Signals for Driving State Analysisn road safety. Currently, EEG-based detection is one of the most intuitive and effective means for fatigue driving. We introduce a model known as EFDD (EEG-based Fatigue Driving Detection Model), in our study, by analyzing EEG signals, we extract time-domain and frequency-domain features respectivel

COUCH