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Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
(Universiti Malaysia Perlis (UniMAP), 2012-06-18)
This study deals with Online Sequential Extreme Learning Machine (OS-ELM) modeling
of a gasoline engine to predict the injected fuel flow of the engine. The single hidden layer
feedforward networks (SLFN) trained by ...
Detection of tuberculosis bacilli in tissue slide images using HMLP network trained by Extreme Learning Machine
(Kauno Technologijos Universitetas, 2012-04)
This paper proposes an automated detection of tuberculosis bacilli in Ziehl-Neelsen-stained tissue slides using image processing and neural network. Image segmentation using CY-based colour filter and k-mean clustering ...
3D object recognition system using multiple views and cascaded multilayered perceptron network
(IEEE Conference Publications, 2004-12)
This paper proposes an effective method for recognition and classification of 3D objects using multiple views technique and neural networks system. In the processing stage, we propose to use 2D moment invariants as the ...
3D object recognition using 2D moments and HMLP network
(IEEE Conference Publications, 2004-07)
This paper proposes a method for recognition and classification of 3D objects using 2D moments and HMLP network. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged ...
Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
(Universiti Malaysia Perlis (UniMAP), 2010-10-16)
Automatic detection of Mycobacterium tuberculosis
improves accuracy, sensitivity and efficiency of
diagnosis compared to manual method. However, the
process is difficult, especially in Zeihl-Neelsen stained
tissue ...
Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
(Institute of Electrical and Electronics Engineers (IEEE), 2012-02-27)
The application of image processing and artificial
intelligence for computer-aided tuberculosis (TB) diagnosis has
received considerable attention over the past several years and
still is an active research area. Several ...