Results 221 to 230 of about 92,424 (280)

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

open access: yesSustainable Development, EarlyView.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella   +4 more
wiley   +1 more source

Multilayer Perceptron Neural Network Analysis of Fluoroscopic Working Angle on Transcatheter Aortic Valve Implantation Complications. [PDF]

open access: yesCureus
Asif N   +9 more
europepmc   +1 more source

Multilayer perceptron for nonlinear programming

Computers and Operations Research, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jaques Reifman, Earl E. Feldman
exaly   +3 more sources

Evolving Multilayer Perceptrons

Neural Processing Letters, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pedro Ángel Castillo Valdivieso   +5 more
openaire   +2 more sources

A hyperbolic multilayer perceptron

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
We present a novel MLP-type neural network based on hyperbolic numbers $the hyperbolic multilayer perceptron (HMLP). The neurons of the HMLP compute 2D-hyperbolic orthogonal transformations as weight propagation functions. The HMLP can therefore be seen as the hyperbolic counterpart of the known complex MLP.
Sven Buchholz 0001, Gerald Sommer
openaire   +1 more source

Adaptive multilayer perceptrons

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
Multilayer perceptrons (MLP) with long- and short-term memories (LASTM) are proposed for adaptive processing. The activation functions of the output neurons of such a network are linear and thus the weights in the last layer affect the outputs of the network linearly and are called linear weights.
James T. Lo, Devasis Bassu
openaire   +1 more source

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