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Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
In the realm of machine learning (ML) systems featuring client-host connections, the enhancement of privacy security can be effectively achieved through federated learning (FL) as a secure distributed ML methodology.
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings.
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism
Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals.
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder
Developing a subject-independent MI-BCI system to reduce the calibration phase is still challenging due to the subject-dependent characteristics of the MI signals.
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm
In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown.
