Publications

Prof. Kasabov is a prolific publisher of books, journal papers, conference proceedings and has several patents to his name.

Full lists of his publications can be found on:

Professor Kasabov’s AUT Homepage

Google Scholar

ResearchGate

AUT Knowledge Engineering and Discovery Research Institute (KEDRI)

Patents

N.Kasabov, Data Analysis and Predictive Systems and Related Methodologies, US patent 9,002,682 B2, 7 April 2015.
N.Kasabov, V.Feigin, Z.Hou, Y.Chen, Improved method and system for predicting outcomes based on spatio/spectro-temporal data, PCT patent WO2015/030606 A2, US2016/0210552 A1. Granted/Publication date: 21 July 2016.

Books

Kasabov, N., Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer (2018) 750p

N. Kasabov (ed) Spring Handbook of Bio/and Neuroinformatics, Springer, 2014

Benuskova, L. and N.Kasabov, Computational neuro-genetic modelling: Integrating bioinformatics and brain science data, information and knowledge via computational intelligence, Springer, New York, 2007, 290 pages

Kasabov, N. Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines, Springer Verlag, London, (2003) 308p

Recent Publications

Below is a small list of most recent publications (which can also be found on Professor Kasabov’s AUT Homepage )

October 2025: A multispectral pansharpening method based on CNN-DI network with mixture of experts

2 Sep 2025: SAIN: Search-And-INfer, A mathematical and computational framework for personalised multimodal data modelling with applications in health care

2 Jul 2025: NEeuroMOorphic neuro-response decoding system for adaptive and personalised neuro-prosthetics control

11 Feb 2025: Decoding brain signals in a neuromorphic framework for a personalized adaptive control of human prosthetics

2025: A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG Data

24 June 2025: Calming the mind: Spiking neural networks reveal how havening touch to reduce persistent distress attenuates left temporal electroencephalographic connectivity

14 March 2025: Decoding brain signals in a neuromorphic framework for a personalized adaptive control of human prosthetics

Izhikevich neurons in NeuCube for longitudinal data classification

LarTap: A luminance-aware framework with text-correlation priors for multi-exposure image fusion

Machine learning-guided high-definition transcranial direct current stimulation prevents cybersickness

MLFuse: Multi-scenario feature joint learning for multi-modality image fusion

Modeling the effect of prior knowledge on memory efficiency for the study of transfer of learning: A spiking neural network approach

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

NeuDen: a framework for the integration of neuromorphic evolving spiking neural networks with dynamic evolving neuro-fuzzy systems for predictive and explainable modelling of streaming data