What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His main research concerns Artificial intelligence, Machine learning, Artificial neural network, Spiking neural network and Pattern recognition.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Data mining.
The various areas that Nikola Kasabov examines in his Machine learning study include Fuzzy set, Fuzzy rule, Inference and Knowledge extraction.
His work focuses on many connections between Artificial neural network and other disciplines, such as Incremental learning, that overlap with his field of interest in Classifier and Pruning.
His research in Spiking neural network intersects with topics in Feature, Electroencephalography, Spike, Probabilistic logic and Temporal database.
His Pattern recognition study incorporates themes from Facial recognition system and Neuron.
His most cited work include:
- DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction (1041 citations)
- Foundations of neural networks, fuzzy systems, and knowledge engineering (801 citations)
- Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning (407 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Artificial neural network, Spiking neural network and Pattern recognition.
His Artificial intelligence study frequently draws connections to adjacent fields such as Computer vision.
Nikola Kasabov interconnects Classifier and Data mining, Knowledge extraction in the investigation of issues within Machine learning.
His Artificial neural network study integrates concerns from other disciplines, such as Speech recognition, Adaptive system, Adaptive learning and Gene regulatory network.
Nikola Kasabov has included themes like Encoding, Electroencephalography, Cognition, Spike and Probabilistic logic in his Spiking neural network study.
His Pattern recognition study combines topics from a wide range of disciplines, such as Facial recognition system and Cluster analysis.
He most often published in these fields:
- Artificial intelligence (73.91%)
- Machine learning (31.56%)
- Artificial neural network (30.00%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (73.91%)
- Spiking neural network (27.34%)
- Pattern recognition (22.97%)
In recent papers he was focusing on the following fields of study:
Nikola Kasabov spends much of his time researching Artificial intelligence, Spiking neural network, Pattern recognition, Computer vision and Machine learning.
Artificial intelligence and Temporal database are frequently intertwined in his study.
His Spiking neural network research is included under the broader classification of Artificial neural network.
His studies in Pattern recognition integrate themes in fields like Visualization, Encoding and Data set.
He has researched Machine learning in several fields, including Feature extraction and Functional magnetic resonance imaging.
His Image research incorporates elements of Fuzzy logic and Contrast.
Between 2015 and 2021, his most popular works were:
- Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications (84 citations)
- Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data (46 citations)
- Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Nikola Kasabov focuses on Artificial intelligence, Spiking neural network, Pattern recognition, Machine learning and Electroencephalography.
His Artificial intelligence study frequently draws parallels with other fields, such as Computer vision.
His Spiking neural network study contributes to a more complete understanding of Artificial neural network.
The Pattern recognition study combines topics in areas such as Image noise, Remote sensing and Data set.
His study in the field of Concept drift, Liquid state machine and Cross-validation is also linked to topics like Focus.
His Electroencephalography research is multidisciplinary, incorporating elements of Degeneration, Deep learning, Perception and Cognitive impairment.
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