Reliable measurements of effluent quality are important for different operational tasks such as process monitoring, online simulation, and advanced control in the wastewater treatment process (WWTP). A kernel principal component analysis (KPCA) and extreme learning machine (ELM) based ensemble soft sensing model for effluent quality prediction was proposed. KPCA was used to extract nonlinear fe...

BACKGROUND The first aim of this study was to clarify the effective ratio of extractive from leaves of Morus Alba (ELM) to sucrose so as to apply this knowledge to the preparation of confections that could effectively suppress the elevation of postprandial blood glucose and insulin. The second aim was to identify the efficacy of confections prepared with the optimally effective ratio determined...

With the development of technology and the widespread use of machine learning, more and more models need to be trained to mine useful knowledge from large scale data. It has become a challenging problem to train multiple models accurately and efficiently so as to make full use of limited computing resources. As one of ELM variants, online sequential extreme learning machine (OS-ELM) provides a ...

Extreme Learning Machine (ELM) algorithm not only has gained much attention of many scholars and researchers, but also has been widely applied in recent years especially when dealing with big data because of its better generalization performance and learning speed. The proposal of SS-ELM (semi-supervised Extreme Learning Machine) extends ELM algorithm to the area of semi-supervised learning whi...

In this paper, a robust online sequential extreme learning machine (ROS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm opt imization selective ensemble (PSOSEN) is proposed. Noting that PSOSEN is a general selective ensembl...

In this paper, we discuss the connection of the kernel versions of the ELM classifier with infinite Single-hidden Layer Feedforward Neural networks and show that the original ELM kernel definition can be adopted for the calculation of the ELM kernel matrix for two of the most common activation functions, i.e., the RBF and the sigmoid functions. In addition, we show that a low-rank decomposition...

PURPOSE To investigate which spectral domain optical coherence tomography (SD-OCT) findings predict visual outcome after anti-vascular endothelial growth factor (VEGF) treatment in neovascular age-related macular degeneration (NV-AMD). METHODS We reviewed the medical records of patients with treatment-naïve NV-AMD who underwent three or more consecutive anti-VEGF injections. The patients were...

Extreme learning machines (ELM), as a learning tool, have gained popularity due to its unique characteristics and performance. However, the generalisation capability of ELM often depends on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in ELM prediction and improve its generalisation ability, this...

This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen ...

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as ad...