Michael J. Watts

26 papers B 10C 1Journal 9Unranked 6
YearRankTypeTitle / Venue / Authors
2018 — conf
Bandwidth Reservation Approach to Improve Quality of Service in Software-Defined Networking: A Performance Analysis.
JCSSE
Amir Hossein Moravejosharieh, Michael J. Watts, Yu Song
2016 B conf
Sleep learning and Max-Min aggregation of Evolving Connectionist Systems.
IJCNN
Michael J. Watts
2014 B conf
Applying computational intelligence methods to modeling and predicting common bean germination rates.
IJCNN
André Bianconi, Michael J. Watts, Yanbo Huang, Adriane Beatriz de Souza Serapião, José Silvio Govone, X. Mi, Gustavo Habermann, Alessandro Ferrarini
2014 B conf
Evolving Connectionist Systems can predict outbreaks of the aphid Rhopalosiphum padi.
IJCNN
Michael J. Watts
2012 J jnl
IEEE CIS Social Media: Have You Joined Our Online Community? [Society Briefs].
IEEE Comput. Intell. Mag.
Albert Y. S. Lam, Michael J. Watts, Dongrui Wu, Pablo A. Estévez
2009 J jnl
A Decade of Kasabov's Evolving Connectionist Systems: A Review.
IEEE Trans. Syst. Man Cybern. Part C
Michael J. Watts
2008 J jnl
Comparing ensemble and cascaded neural networks that combine biotic and abiotic variables to predict insect species distribution.
Ecol. Informatics
Michael J. Watts, Susan P. Worner
2008 — conf
Predicting the Distribution of Fungal Crop Diseases from Abiotic and Biotic Factors Using Multi-Layer Perceptrons.
ICONIP (1)
Michael J. Watts, Susan P. Worner
2008 B conf
Using Multi-Layer Perceptrons to predict the presence of jellyfish of the genus Physalia at New Zealand beaches.
IJCNN
David R. Pontin, Michael J. Watts, Susan P. Worner
2008 — conf
Using Time Lagged Input Data to Improve Prediction of Stinging Jellyfish Occurrence at New Zealand Beaches by Multi-Layer Perceptrons.
ICONIP (1)
David R. Pontin, Susan P. Worner, Michael J. Watts
2008 J jnl
Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species.
Ecol. Informatics
Michael J. Watts, Susan P. Worner
2006 B conf
Nominal-scale Evolving Connectionist Systems.
IJCNN
Michael J. Watts
2006 C conf
Null-Model Validation of MLP Input Contribution Analysis in Ecology.
HIS
Michael J. Watts, Susan P. Worner
2006 B conf
Using MLP to Determine Abiotic Factors In uencing the Establishment of Insect Pest Species.
IJCNN
Michael J. Watts, Susan P. Worner
2004 J jnl
Fuzzy Rule Extraction From Simple Evolving Connectionist Systems.
Int. J. Comput. Intell. Appl.
Michael J. Watts
2003 J jnl
Adaptive speech recognition with evolving connectionist systems.
Inf. Sci.
Akbar Ghobakhlou, Michael J. Watts, Nikola K. Kasabov
2003 J jnl
Neural Nets Raise the Roof.
IEEE Intell. Syst.
Samuel Moyle, Michael J. Watts
2002 B conf
Evolutionary optimisation of MLP for modelling protein synthesis termination signal efficiency.
IEEE Congress on Evolutionary Computation
Michael J. Watts, Louise Major, Warren Tate
2002 B conf
Evolutionary optimisation of evolving connectionist systems.
IEEE Congress on Evolutionary Computation
Michael J. Watts, Nikola K. Kasabov
2002 — conf
Fuzzy Neural Networks in a Palm Environment.
AAAI/IAAI
Samuel Moyle, Michael J. Watts
2001 B conf
Dynamic optimisation of evolving connectionist system training parameters by pseudo-evolution strategy.
CEC
Michael J. Watts, Nikola K. Kasabov
1998 B conf
Genetic Algorithms for the Design of Fuzzy Neural Networks.
ICONIP
Michael J. Watts, Nikola K. Kasabov
1998 J jnl
Phoneme-Based Speech Recognition via Fuzzy Neural Networks Modeling and Learning.
Inf. Sci.
Nikola K. Kasabov, Robert Kozma, Michael J. Watts
1997 — conf
A Methodology for Speech Data Analysis and a Framework for Adaptive Speech Recognition Using Fuzzy Neural Networks.
ICONIP (2)
Nikola K. Kasabov, Robert Kozma, Richard Kilgour, Mark R. Laws, J. Taylor, Michael J. Watts, Andrew R. Gray
1997 J jnl
FuNN/2 - A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition.
Inf. Sci.
Nikola K. Kasabov, Jaesoo Kim, Michael J. Watts, Andrew R. Gray
1997 — conf
Genetic algorithms for structural optimisation, dynamic adaptation and automated design of fuzzy neural networks.
ICNN
Nikola K. Kasabov, Michael J. Watts