A novel multi-segment feature fusion based fault classification approach for rotating machinery
Accurate and efficient rotating machinery fault diagnosis is crucial for industries to guarantee
the productivity and reduce the maintenance cost. This paper systematically proposes a new …
the productivity and reduce the maintenance cost. This paper systematically proposes a new …
A review and empirical analysis of neural networks based exchange rate prediction
TN Pandey, AK Jagadev, S Dehuri… - Intelligent Decision …, 2018 - content.iospress.com
Financial time series data is very chaotic, noisy, fluctuating and nonlinear as different events
have occurred in various time periods. Therefore, it is very challenging for researchers to …
have occurred in various time periods. Therefore, it is very challenging for researchers to …
Alternating direction method of multipliers for sparse convolutional neural networks
The storage and computation requirements of Convolutional Neural Networks (CNNs) can
be prohibitive for exploiting these models over low-power or embedded devices. This paper …
be prohibitive for exploiting these models over low-power or embedded devices. This paper …
Incremental relevance sample-feature machine: A fast marginal likelihood maximization approach for joint feature selection and classification
Y Mohsenzadeh, H Sheikhzadeh, S Nazari - Pattern Recognition, 2016 - Elsevier
Abstract The recently proposed Relevance Sample-Feature Machine (RSFM) performs joint
feature selection and classification with state-of-the-art performance in terms of accuracy …
feature selection and classification with state-of-the-art performance in terms of accuracy …
A fast and efficient conformal regressor with regularized extreme learning machine
D Wang, P Wang, J Shi - Neurocomputing, 2018 - Elsevier
A conformal regressor combines conformal prediction and a traditional regressor for point
predictions. It produces a valid prediction interval for a new testing input such that the …
predictions. It produces a valid prediction interval for a new testing input such that the …
[PDF][PDF] Colour difference classification for dyed fabrics based on differential evolution with dynamic parameter selection to optimise the output regularisation extreme …
Z Zhou, D Liu, J Zhang, Z Zhu, D Yang… - Fibres & Textiles in …, 2021 - bibliotekanauki.pl
A novel optimisation technique based on the differential evolution (DE) algorithm with
dynamic parameter selection (DPS-DE) is proposed to develop a colour difference …
dynamic parameter selection (DPS-DE) is proposed to develop a colour difference …
Fault diagnosis of self-aligning troughing rollers in a belt conveyor system using an artificial neural network and naive bayes algorithm
S Ravikumar, S Kanagasabapathy… - Emerging Trends in …, 2018 - taylorfrancis.com
The self-aligning troughing roller (SATR) is an essential element of the belt conveyor
system. It may fail due to multidimensional forces, derisory lubrication, culpable sealing …
system. It may fail due to multidimensional forces, derisory lubrication, culpable sealing …
A double-layer ELM with added feature selection ability using a sparse Bayesian approach
F Kiaee, C Gagné, H Sheikhzadeh - Neurocomputing, 2016 - Elsevier
Abstract The Sparse Bayesian Extreme Learning Machine (SBELM) has been recently
proposed to reduce the number of units activated on the hidden layer. To deal with high …
proposed to reduce the number of units activated on the hidden layer. To deal with high …
Wybór parametrów w celu optymalizacji regularyzacji wyjściowej maszyny uczącej się
Z Zhiyu, L Dexin, Z Jianxin, Z Zefei… - FIBRES & TEXTILES in …, 2021 - fibtex.lodz.pl
Wybór parametrów w celu optymalizacji regularyzacji wyjściowej maszyny uczącej się —
Fibres & Textiles in Eastern Europe Strona główna O nas Rozwój czasopisma Redakcja …
Fibres & Textiles in Eastern Europe Strona główna O nas Rozwój czasopisma Redakcja …
[PDF][PDF] A FAST LEARNING NETWORK WITH IMPROVED PARTICLE SWARM OPTIMIZATION FOR INTRUSION DETECTION SYSTEM
MH ALI - 2019 - core.ac.uk
In current days the intrusion detection systems (IDS) have several shortcomings such as
high rates of false positive alerts, low detection rates of rare but dangerous attacks, and the …
high rates of false positive alerts, low detection rates of rare but dangerous attacks, and the …