Results 31 to 40 of about 275,127 (313)
Growing Artificial Neural Networks [PDF]
, 2021 14 pages, Accepted for publication in Springer Nature - Book Series: Transactions on Computational Science and Computational Intelligence, Advances in Artificial Intelligence and Applied Cognitive Computing - Springer ID: 89066307 (Book ID: 495585_1_En)
John Mixter, Ali Akoglu
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Modeling toothpaste brand choice: An empirical comparison of artificial neural networks and multinomial probit model [PDF]
, 2010 Copyright @ 2010 Atlantis PressThe purpose of this study is to compare the performances of Artificial Neural Networks (ANN) and Multinomial Probit (MNP) approaches in modeling the choice decision within fast moving consumer goods sector.
Aktas, E +7 more
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Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
Biomedical Engineering and Computational Biology, 2016 Abraham Pouliakis +7 more
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]
Pesquisa Agropecuária Brasileira, 2017 : The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva +9 more
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Estimation of soil properties by an artificial neural network
Magazine of Civil Engineering, 2022 Empirical dependencies are often used in various fields of geotechnics and civil engineering. The existing empirical formulas are mainly developed with the use of regression and multiple regression.
Ofrikhter Ian +3 more
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Artificial nonmonotonic neural networks
Artificial Intelligence, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Basilis Boutsinas, Michael N. Vrahatis
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TRAINING OF ARTIFICIAL NEURAL NETWORK
Journal of Numerical and Applied Mathematics, 2021 The methodology of neural networks is even more often applied in tasks of management and decision-making, including in the sphere of trade and finance. The basis of neural networks is made by nonlinear adaptive systems which proved the efficiency at the solution of problems of forecasting.
I. Sh. Didmanidze +2 more
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Evolving artificial neural networks [PDF]
Proceedings of the IEEE, 1999 Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures ...
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The use of artificial neural networks and multiple linear regression in modelling work–health relationships: translating theory into analytical practice [PDF]
, 2010 Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes.
Cox, Tom +3 more
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SHS Web of Conferences, 2018 In the work performed adaptation of artificial neural networks in modern security systems potentially dangerous technical objects — high-rise buildings as tools for assessing and forecasting in management decision.
Peganov Nikolay +2 more
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