… model based on hybrid mode decomposition (HMD) method and online sequential outlier
robust extreme learning machine (OSORELM) for short-term wind speed prediction. In data pre…

… ELM on MapReduce was inefficient for updated big data learning and proposed elastic
ELM for improvement. The most time-consuming part for big data learning of ELM is the matrix …

… The classification and regression analysis can benefit tremendously from the use of the extreme
learning machine (… , which make it an appealing framework for the analysis of large data. …

… Sentiment Analysis (SA) of a large quantity of user reviews on e-commerce platforms. It is
still challenging to envisage the accurate sentiment … a new optimized Machine Learning (ML) …

… This paper proposed a framework for cloud-based breast cancer diagnosis using Extreme
Learning Machine (ELM) as a classifier. Cloud computing can provide unceasing services …

… load, we present robust regularized extreme learning machine frameworks to reduce the effect
… usability of the proposed extreme learning machine with asymmetric Huber loss functions. …

… In the present study, a new modified version of Convolutional Neural Network (CNN) was …
based on combining it with the extreme learning machine and optimizing the network based on …

… easily be carried out on big data [33]. Deep Auto Encoders (DAEs) provide excellent results
in obtaining or achieving accuracy and understanding data. DAE has improved performance …

… In this work, a hybrid model (SDA-GA-ELM) based on extreme learning machine (ELM),
genetic algorithm (GA) and customized similar day analysis (SDA) has been developed to …

… extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey
wolf optimizer … system (ANFIS), an extreme learning machine, a support vector regression …