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Ajit Narayanan
Researcher at Auckland University of Technology
Publications - 163
Citations - 2489
Ajit Narayanan is an academic researcher from Auckland University of Technology. The author has contributed to research in topics: Yield (wine) & Artificial neural network. The author has an hindex of 21, co-authored 145 publications receiving 2226 citations. Previous affiliations of Ajit Narayanan include Universities UK & University of Exeter.
Papers
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Proceedings ArticleDOI
Quantum-inspired genetic algorithms
Ajit Narayanan,M. Moore +1 more
TL;DR: It is informally shown that the quantum inspired genetic algorithm performs better than the classical counterpart for a small domain.
Journal ArticleDOI
Quantum artificial neural network architectures and components
Ajit Narayanan,Tammy Menneer +1 more
TL;DR: Overall, this work provides a first insight into the expected behaviour of individual components of QUANNs, if and when quantum hardware is ever built, and raises questions about the interface between quantum and classical components of futureQUANNs.
Journal ArticleDOI
Assessing the professional performance of UK doctors: an evaluation of the utility of the General Medical Council patient and colleague questionnaires
TL;DR: The GMC patient and colleague questionnaires offer a reliable basis for the assessment of professionalism among UK doctors and would be capable of discriminating a range of professional performance among doctors, and potentially identifying a minority whose practice should be subjected to further scrutiny.
Journal ArticleDOI
Mining viral protease data to extract cleavage knowledge
TL;DR: The aim of the work reported here is to investigate whether it is possible to generalise from known cleavage Sites to unknown cleavage sites for two specific viruses-HIV and HCV, and to contribute to the understanding of viral protease function in general.
Journal ArticleDOI
Discovering Gene Networks with a Neural-Genetic Hybrid
Edward Keedwell,Ajit Narayanan +1 more
TL;DR: A novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component and shows that it is capable of finding gene networks that fit the data.