scholarly journals Real-Time Document Image Classification Using Deep CNN and Extreme Learning Machines

2017 ◽  
Author(s):  
Andreas Kolsch ◽  
Muhammad Zeshan Afzal ◽  
Markus Ebbecke ◽  
Marcus Liwicki
Keyword(s):  
Image Classification ◽  
Real Time ◽  
Document Image ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Deep Cnn

scholarly journals Real‑Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm

2021 ◽  
pp. 102764
Author(s):  
Tianqing Hu ◽  
Mohammad Khishe ◽  
Mokhtar Mohammadi ◽  
Gholam-Reza Parvizi ◽  
Sarkhel H. Taher Karim ◽  
...  
Keyword(s):  
Real Time ◽  
Optimization Algorithm ◽  
Extreme Learning Machines ◽  
X Ray ◽  
Learning Machines ◽  
Deep Cnn

scholarly journals Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Chris Wilson Antuvan ◽  
Federica Bisio ◽  
Francesca Marini ◽  
Shih-Cheng Yen ◽  
Erik Cambria ◽  
...  
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Muscle Synergies ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Real Time Classification

scholarly journals Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

2019 ◽  
Vol 9 (8) ◽  
pp. 1707 ◽  
Author(s):  
Junjie Lu ◽  
Jinquan Huang ◽  
Feng Lu
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Evidence Theory ◽  
Aircraft Engine ◽  
Training Sample ◽  
Performance Estimation ◽  
Support Vector ◽  
Extreme Learning Machines ◽  
The Real ◽  
Learning Machines

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

Deep convolutional representations and kernel extreme learning machines for image classification

2018 ◽  
Vol 78 (20) ◽  
pp. 29271-29290 ◽  
Author(s):  
Xiaobin Zhu ◽  
Zhuangzi Li ◽  
Xiao-Yu Zhang ◽  
Peng Li ◽  
Ziyu Xue ◽  
...  
Keyword(s):  
Image Classification ◽  
Extreme Learning Machines ◽  
Learning Machines
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