Raindrop removal from single image by Song, Rongzihan

“…For faster processing of images for surveillance, an Extreme Learning Machining(ELM) method is also used. It can classify these surveillance images into two parts: degraded images and non-degraded images. …”
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Effect of contextual information in human action recognition in videos by Koh, Hong Wei

“…The first phase of the project is to extract features from the videos using log covariance matrix with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) as the classifiers to discriminate the actions. …”
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Ensemble neural networks with input optimization for flood forecasting by Mohd Khairudin, Nazli, Mustapha, Norwati, Mohd Aris, Teh Noranis, Zolkepli, Maslina

“…This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. …”
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Discrete wavelet transform coefficients for emotion recognition from EEG signals by Ser, Wee, Huang, Guang-Bin, Yohanes, Rendi E. J.

“…Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.…”
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A novel low-velocity impact region identification method for cantilever beams using a support vector machine by Wang, Fengde, Kang, Yongtian, Xiao, Wensheng, Li, Changjiang, Liu, Qi

“…Through the comparative study, it is found that the recognition rate of SVM is higher than that of the probabilistic neural network (PNN) and extreme learning machine (ELM) for low-velocity impact area recognition of cantilever beams. …”
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Universal machine learning classifier using extreme learning machines by Chen, Jinnan

“…By using different scales of datasets, we could see the better performance of the proposed classifier and compared with the results of other popular algorithms, so the advantage of the ELM-based universal classifier could be demonstrated. …”
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Rupture and variable coupling behavior of the Mentawai segment of the Sunda megathrust during the supercycle culmination of 1797 to 1833 by Philibosian, Belle, Sieh, Kerry, Avouac, Jean-Philippe, Natawidjaja, Danny H., Chiang, Hong-Wei, Wu, Chung-Che, Perfettini, Hugo, Shen, Chuan-Chou, Daryono, Mudrik R., Suwargadi, Bambang W.

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Histone lysine and DNA methylation: dynamic marks in the chromatin by Belle, R

Investigating spatio-temporal variability of initial ²³⁰Th/²³²Th in intertidal corals by Chiang, Hong-Wei, Philibosian, Belle, Meltzner, Aron J., Wu, Chung-Che, Shen, Chuan-Chou, Edwards, R. Lawrence, Chuang, Chih-Kai, Suwargadi, Bambang W., Natawidjaja, Danny H.

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GPU-based commonsense reasoning for real-time query answering and multimodal analysis by Tran, Ha Nguyen

“…In addition, in order to extract important textual features from multimodal sources, we generate domain-specific graphs based on commonsense knowledge and apply GPU-based graph traversal for fast feature detection. Then, powerful ELM classifiers are applied to build the sentiment analysis model based on the extracted features. …”
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