A hybrid deep learning model for efficient intrusion detection in big data environment
The volume of network and Internet traffic is expanding daily, with data being created at the
zettabyte to petabyte scale at an exceptionally high rate. These can be characterized as big …
zettabyte to petabyte scale at an exceptionally high rate. These can be characterized as big …
Biometric identification system using EEG signals
AB Tatar - Neural Computing and Applications, 2023 - Springer
This study focuses on using EEG signal-based behavioral biometric data to classify and
identify persons. A person identification system based on a nonlinear model with excellent …
identify persons. A person identification system based on a nonlinear model with excellent …
A novel tensorial scheme for EEG-based person identification
W Li, Y Yi, M Wang, B Peng, J Zhu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Biometrics have attracted growing research interests as information security and safety gain
increasing attention to date. As a kind of important biomedical signal, electroencephalogram …
increasing attention to date. As a kind of important biomedical signal, electroencephalogram …
Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
This paper proposes a novel method and algorithms for the design of MRI structured
personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling …
personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling …
A hybrid classifier combination for home automation using EEG signals
Over the years, the usage of artificial intelligence (AI) algorithms is increased to develop
various smart applications using Internet-of-Things. Home automation is a fast emerging …
various smart applications using Internet-of-Things. Home automation is a fast emerging …
Eternal-Thing 2.0: Analog-Trojan-Resilient Ripple-Less Solar Harvesting System for Sustainable IoT
Recently, harvesting natural energy is gaining more attention than other conventional
approaches for sustainable IoT. System on chip power requirement for the internet of things …
approaches for sustainable IoT. System on chip power requirement for the internet of things …
On the minimal amount of EEG data required for learning distinctive human features for task-dependent biometric applications
Biometrics is the process of measuring and analyzing human characteristics to verify a given
person's identity. Most real-world applications rely on unique human traits such as …
person's identity. Most real-world applications rely on unique human traits such as …
A deep spatio-temporal model for EEG-based imagined speech recognition
Automatic speech recognition interfaces are becoming increasingly pervasive in daily life as
a means of interacting with and controlling electronic devices. Current speech interfaces …
a means of interacting with and controlling electronic devices. Current speech interfaces …
Brainprint based on functional connectivity and asymmetry indices of brain regions: A case study of biometric person identification with non‐expensive …
Brain‐computer interface applications for biometric person identification have increased
their interest in recent years since they are potentially more secure and more difficult to …
their interest in recent years since they are potentially more secure and more difficult to …
EEG‐based biometric identification using frequency‐weighted power feature
JC Monsy, AP Vinod - IET Biometrics, 2020 - Wiley Online Library
The brain activity of a person sensed using electroencephalograph (EEG) has a unique
neuronal signature consisting of relevant subject‐specific information, which can be used for …
neuronal signature consisting of relevant subject‐specific information, which can be used for …