Results 41 to 50 of about 561 (123)

Spatial-temporal data modelling and processing for personalised decision support [PDF]

, 2015
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible.
Othman, Muhaini
core   +1 more source

An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks [PDF]

, 2018
This research study proposes a novel method of inter-related problems in face recognition using the NeuCube neuromorphic computational platform. We investigated age classification and gender recognition.
G Guo, JG Wang, N Kasabov, N Kasabov, N Kasabov, NK Kasabov, S Schliebs, S Song, TF Cootes, X Geng, Y Fu, YH Kwon, Z Li   +12 more
core   +2 more sources

Spiking Neural Networks for Computational Intelligence:An Overview [PDF]

, 2021
Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data ...
Dora, Shirin, Kasabov, Nikola
core   +2 more sources

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data [PDF]

, 2017
Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially
A Tanay, D Deng, D Zhou, E Capecci, E Tu, GA Carpenter, J Talairach, JA Hartigan, JC Bezdek, L Koessler, Lancaster JL et al, Lancaster JL et al, LF Abbott, M Deng, M Gholami Doborjeh, N Kasabov, N Kasabov, N Kasabov, N Kasabov, N Kasabov, N Kasabov, S Ogawa, S Song, SB Katwal, SC Johnson, T Kohonen, W Liao, W Maass, W Wang   +28 more
core   +2 more sources

Personalized Spiking Neural Network Models of Clinical and Environmental Factors to Predict Stroke [PDF]

, 2022
The high incidence of stroke occurrence necessitates the understanding of its causes and possible ways for early prediction and prevention. In this respect, statistical methods offer the “big picture,” but they have a weak predictive ability at an ...
Doborjeh, Maryam, Doborjeh, Zohreh, Enayatollahi, Reza, Feigin, Valery, Kasabov, Nikola, Krishnamurthi, Rita, Merkin, Alexander   +6 more
core   +3 more sources

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

, 2017
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices.
Adiraju, Prathyusha, Catthoor, Francky, Das, Anup, Dutt, Nikil, Groenendaal, Willemijn, Krichmar, Jeffrey L., Pradhapan, Paruthi, Rajan, Raj Thilak, Schaafsma, Siebren, Van Hoof, Chris   +9 more
core   +1 more source

EEG-based emotion classification using spiking neural networks [PDF]

, 2020
A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper.
Cao, Yi, Ding, Xuemei, Fu, Qiang, jiang, frank, Liu, Junxiu, Luo, Yuling, Qin, Yunbai, Wu, Guopei, Xie, Juntao   +8 more
core   +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

Identification of functional connections in biological neural networks using dynamical Bayesian networks [PDF]

, 2016
Investigation of the underlying structural characteristics and network properties of biological networks is crucial to understanding the system-level regulatory mechanism of network behaviors.
Dong, Chaoyi, Yue, Hong
core   +1 more source

Immersive visualisation of 3-dimensional neural network structures [PDF]

, 2015
No ...
Estevez, Javier, Marks, S, Scott, Nathan
core