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Physical Sciences and Mathematics

University of Texas Rio Grande Valley

2022

Deep learning

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Deep Learning Model With Adaptive Regularization For Eeg-Based Emotion Recognition Using Temporal And Frequency Features, Alireza Samavat, Ebrahim Khalili, Bentolhoda Ayati, Marzieh Ayati Feb 2022

Deep Learning Model With Adaptive Regularization For Eeg-Based Emotion Recognition Using Temporal And Frequency Features, Alireza Samavat, Ebrahim Khalili, Bentolhoda Ayati, Marzieh Ayati

Computer Science Faculty Publications and Presentations

Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing the inner state of persons. There are extensive studies about emotion recognition, most of which heavily rely on staged complex handcrafted EEG feature extraction and classifier design. In this paper, we propose a hybrid multi-input deep model with convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM). CNNs extract time-invariant features from raw EEG data, and Bi-LSTM allows long-range lateral interactions between features. First, we propose a …


Differential Privacy In Privacy-Preserving Big Data And Learning: Challenge And Opportunity, Honglu Jiang, Yifeng Gao, S. M. Sarwar, Luis Garza Perez, Mahmudul Robin Feb 2022

Differential Privacy In Privacy-Preserving Big Data And Learning: Challenge And Opportunity, Honglu Jiang, Yifeng Gao, S. M. Sarwar, Luis Garza Perez, Mahmudul Robin

Computer Science Faculty Publications and Presentations

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer …