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Personalised Modelling



Knowledge Engineering has a particular interest and expertise in "Personalised Modelling (PM)"



















Acomputational model for PM; patent N.Kasabov, Data Analysis and Predictive Systems and Related Methodologies, US patent 9,002,682 B2, 7 April 2015). 


PM relates to the development of individual model Mx  for every person X (or a new vector X) for the accurate diagnosis or prognosis of the outcome for this person using a repository of historical data of other individuals (vectors). The following are selected projects of PM with the leadership or the involvement of Prof. Kasabov:

  • PM for predicting knee pain 6 and 12 months after operation using only-pre-operative personal data

  • PM for predicting onset of Alzheimer's Disease (AD) 2 and 4 years before the onset

  • PM for individual stroke prediction hours and days before the onset

  • PM for predicting response to Methadone treatment of drug addicts

  • PM for predicting response to Clozapine of schizophrenic patients

  • PM for cancer diagnosis

  • PM for brain data analysis, EEG, fMRI

  • PM in Bioinformatics

  • PM for predicting COVID-19 and other infectious diseases using multi-modal data

  • PM for predicting heart failure

Brain-inspired spiking neural networks (SNN) for spatio-temporal data and brain-inspired AI 

NeuCube is a brain-inspired computational architecture based on spiking neural networks (SNN). Designed and developed under the leadership of Prof. Kasbov NeuCube has been applied to projects using EEG and fMRI data for cognitive data modelling since 2014.








(The NeuCube SNN architecture  from:   Kasabov, N. NeuCube: A Spiking Neural Network Architecture for Mapping, Learning and Understanding of Spatio-Temporal Brain Data, Neural Networks vol.52 (2014), pp. 62-76,     


The following projects are using NeuCube:

  • Brain data modelling

  • EEG: peri-perceptual modelling; mindfulness; Depression; AD; response to treatment; BCI

  • fMRI: cognitive data modelling

  • fMRI+ DTI: response to treatment

  • EEG + MRI data: epilepsy

  • neurogenetic, integrated data

  • Gene expression over time

  • Audio/Visual data processing

  • Speech, sound and music recognition

  • Moving object recognition

  • Language processing

  • Multisensory streaming data

  • Health risk event prediction from temporal climate data (stroke)

  • Hazardous environmental event prediction (e.g. risk of earthquakes in NZ; flooding in Malaysia; pollution in London area; extreme weather from satellite images)

  • Brain-Computer Interfaces and knowledge transfer between humans and machines

  • Robot control

  • Neuro-rehabilitation robots (with China Academy of Sciences)

  • NeuCube is currently used in the labs of 25 countries

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