Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses.

Introduction: The development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security.

Methods: In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.

Adaptive Margin Aware Complement-Cross Entropy Loss for Improving Class Imbalance in Multi-View Sleep Staging Based on EEG Signals.

Sleep data are typically characterized by class imbalance, which can cause the model to be overly biased toward frequent classes, resulting in low accuracy of minority class classification. However, the minority class of sleep staging has important value in diagnosing certain disorders, such as an N1 Stage that is too short indicating possible hypersomnia or narcolepsy. To address this problem, we propose a multi-view CNN model based on adaptive margin-aware loss.