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EEG-based emotion recognition using random Convolutional Neural Networks
(
Elsevier Ltd
, 2022 , Article)
Emotion recognition based on electroencephalogram (EEG) signals is helpful in various fields, including medical healthcare. One possible medical application is to diagnose emotional disorders in patients. Humans tend to ...
Experimental evaluation of stochastic configuration networks: Is SC algorithm inferior to hyper-parameter optimization method?
(
Elsevier Ltd
, 2022 , Article)
To overcome the pitfalls of Random Vector Functional Link (RVFL), a network called Stochastic Configuration Networks (SCN) has been proposed. By constraining and adaptively selecting the range of randomized parameters using ...
Minimum Variance Embedded Random Vector Functional Link Network with Privileged Information
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Conference Paper)
A teacher in a school plays significant role in classroom while teaching the students. Similarly, learning via privileged information (LUPI) gives extra information generated by a teacher to 'teach' the learning algorithm ...
Representation learning using deep random vector functional link networks for clustering: Representation learning using deep RVFL for clustering
(
Elsevier Ltd
, 2022 , Article)
Random Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised ...
Oblique and rotation double random forest
(
Elsevier Ltd
, 2022 , Article)
Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. ...
Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Article)
Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve ...
Interpretability and accessibility of machine learning in selected food processing, agriculture and health applications
(
National Science Foundation
, 2022 , Other)
Artificial Intelligence (Al) and its data-centric branch of machine learning (ML) have greatly evolved over the last few decades. However, as Al is used increasingly in real world use cases, the importance of the ...