The diversified ensemble neural network
… in-depth study on effective ensemble design for neural networks. In this paper, … neural
networks, and the reasons may include: i) neural networks (and their ensemble) are less efficient; ii…
networks, and the reasons may include: i) neural networks (and their ensemble) are less efficient; ii…
Ensemble of convolutional neural networks for bioimage classification
… a powerful general-purpose image classification system able to … ensembles based on the
fusion of classifiers. Our main objective is to design a method that is both robust and effective …
fusion of classifiers. Our main objective is to design a method that is both robust and effective …
On learning effective ensembles of deep neural networks for intrusion detection
… detection accuracy than traditional supervised classification methods, across a … design of a
classification ensemble in general. As discussed previously, in our specific setting, this design …
classification ensemble in general. As discussed previously, in our specific setting, this design …
A dynamic ensemble learning algorithm for neural networks
… The purpose of this research is to design an NN ensemble … Hitherto, efforts have been
made to design ensemble by … NNs can produce a good ensemble that distribute errors over …
made to design ensemble by … NNs can produce a good ensemble that distribute errors over …
An ensemble of deep neural networks for kidney ultrasound image classification
S Sudharson, P Kokil - Computer Methods and Programs in Biomedicine, 2020 - Elsevier
… The presented method uses ensemble DNN models which … effectiveness of the proposed
approach, the ensemble based … ] has deep layered architecture design which helps in learning …
approach, the ensemble based … ] has deep layered architecture design which helps in learning …
An ensemble of convolutional neural networks for audio classification
… In [2], for example, ensembles of classifiers designed to fuse … even more robust in image
recognition and classification than have … This study uses two pre-trained GoogleNets: the first is …
recognition and classification than have … This study uses two pre-trained GoogleNets: the first is …
Ensemble learning of diffractive optical networks
… neural networks, where we exploit the parallel processing of … Ensemble learning is beneficial
for several reasons 37 ; if the … blind inference and image classification accuracies of 61.14 …
for several reasons 37 ; if the … blind inference and image classification accuracies of 61.14 …
An effective of ensemble boosting learning method for breast cancer virtual screening using neural network model
AH Osman, HMA Aljahdali - IEEE Access, 2020 - ieeexplore.ieee.org
… The RBF have benefits of flexible design and solid tolerance … The main objectives of using
the ensemble boosting learning … neural network features for endoscopic image classification," …
the ensemble boosting learning … neural network features for endoscopic image classification," …
Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting
MO Moreira, PP Balestrassi, AP Paiva… - … and Sustainable Energy …, 2021 - Elsevier
… The model is based on Artificial Neural Networks and uses the linear … neural networks and
models based on a support vector machines (SVM) promote good execution, and have good …
models based on a support vector machines (SVM) promote good execution, and have good …
Comparing solo versus ensemble convolutional neural networks for wetland classification using multi-spectral satellite imagery
A Jamali, M Mahdianpari, B Brisco, J Granger… - Remote Sensing, 2021 - mdpi.com
… rather than an empirical feature design. The representations of … considered highly efficient
approaches for image classification [… efficient MobileNet-224 was proposed by [38], which uses …
approaches for image classification [… efficient MobileNet-224 was proposed by [38], which uses …