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Fundamentals of neural networks
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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
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Journal ArticleDOI
A comparison of machine learning techniques with a qualitative response model for auditor’s going concern reporting
M. Anandarajan,A Anandarajan +1 more
TL;DR: The results of the study indicate that the artificial neural network model has a superior predictive ability in determining the type of going concern audit report that should be issued to the client.
Journal ArticleDOI
PVT correlations for Pakistani crude oils using artificial neural network
TL;DR: In this paper, neural network-based models were used to predict the bubble point pressure, oil formation volume factor and viscosity as a function of the solution gas-oil ratio, gas specific gravity, and temperature.
Journal ArticleDOI
A novel neural network approach to cDNA microarray image segmentation
TL;DR: A new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed and applied to a set of real-world cDNA images, shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.
Journal ArticleDOI
Estimation of reference evapotranspiration using data driven techniques under limited data conditions
TL;DR: The present study measures the effectiveness of selected machine learning techniques [evolutionary regression (ER), artificial neural network (ANN), multi nonlinear regression (MLNR), and support vector machines (SVMs) for the estimation of reference evapotranspiration under limited data conditions.
Book ChapterDOI
Modelling and Optimization
Kapil Gupta,Neelesh Kumar Jain +1 more
TL;DR: Regression analysis and artificial neural networks (ANN) were employed for WSEM process modelling and single-objective and multi-objectives optimization of WSEM parameters aimed to minimizing micro-geometry errors, surface roughness and enhancing the WSEM productivity for miniature gear manufacturing.