Results 11 to 20 of about 142 (100)

Analyzing darknet traffic through machine learning and neucube spiking neural networks

Intelligent and Converged Networks
The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels. Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.
Iman Akour, Mohammad Alauthman, Khalid M. O. Nahar, Ammar Almomani, Brij B. Gupta   +4 more
doaj   +2 more sources

Using EEG Data and NeuCube for the Study of Transfer of Learning [PDF]

2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020
Deeper and long-lasting learning occurs through a critical review of prior knowledge in the light of the new context, and a transfer of the acquired knowledge to new settings. Attention to the task is one of factors that enable transfer of learning (TL). This study adopts a cognitive neuroscience approach to the study of TL.
Mojgan Hafezi Fard, Krassie Petrova, Maryam Doborjeh, Nikola Kasabov   +3 more
openaire   +1 more source

Online Traffic Accident Spatial‐Temporal Post‐Impact Prediction Model on Highways Based on Spiking Neural Networks

Journal of Advanced Transportation, Volume 2021, Issue 1, 2021., 2021
Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post‐impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem.
Duowei Li, Jianping Wu, Depin Peng, Alain Lambert   +3 more
wiley   +1 more source

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware [PDF]

Journal of Neural Engineering, 2019
Abstract Objective . The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and the realization of the spiking ...
Jan Behrenbeck, Zied Tayeb, Cyrine Bhiri, Christoph Richter, Oliver Rhodes, Nikola Kasabov, Josafath I Espinosa-Ramos, Steve Furber, Gordon Cheng, Jörg Conradt   +9 more
openaire   +2 more sources

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model

IEEE Transactions on Neural Systems and Rehabilitation Engineering
This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students’ ability ...
Lingling Wang, Yixin Liu, Yiqing Li, Renxiang Chen, Xiaohong Liu, Li Fu, Yao Wang   +6 more
doaj   +1 more source

Modeling functional brain connections in methamphetamine and opioid abusers

Medicine in Novel Technology and Devices
Substance abuse has become a significant problem worldwide. The primary purpose of this study was to model the functional brain connectivity in methamphetamines (Meth) and opioids (Op) groups in comparison with the healthy control (HC) group.
Nasimeh Marvi, Javad Haddadnia, Mohammad Reza Fayyazi Bordbar   +2 more
doaj   +1 more source

NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals [PDF]

, 2012
The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Spatio- and spectro-temporal data (SSTD) are the most common data collected to measure brain signals and brain activities, along with the recently obtained gene and protein data. Yet, there are no computational models to integrate
openaire   +2 more sources

Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach

Big Data and Cognitive Computing
The transfer of learning (TL) is the process of applying knowledge and skills learned in one context to a new and different context. Efficient use of memory is essential in achieving successful TL and good learning outcomes.
Mojgan Hafezi Fard, Krassie Petrova, Nikola Kirilov Kasabov, Grace Y. Wang   +3 more
doaj   +1 more source

Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data [PDF]

, 2015
The paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the case study of functional Magnetic Resonance Image (fMRI). The method is based on NeuCube spiking neural network (SNN) architecture, where the spatio-temporal relationships between STBD streams are learned and simultaneously the clusters are created.
Maryam Gholami Doborjeh, Nikola K. Kasabov   +1 more
openaire   +1 more source

Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data

, 2023
EEG data collected from several subjects when perfoming complex spatio-temporal ...
Nikola Kasabov, Yongyao Tan Tan, Maryam Doborjeh, Enmei Tu, Jie Yang   +4 more
openaire   +1 more source