Auto associative Extreme Learning Machine based non-linear principal component regression for big data applications

Assessment of inflow and outflow stenoses using big spectral data and radial‐based colour relation analysis on in vitro arteriovenous graft biophysical experimental model

“…These available data sets are indeed large and correlate to the characteristics of specific ecosystems, such as disease prevention, healthcare, and business trends [13,14]. Multidimensional big data require decision-making, machine learning, graph analytics, and visualisation to characterise the primary components of big data [15][16][17]. Therefore, intelligent machine learning with multiple linear regressions is carried out to map the relationship between additional explanatory variables and response variables, such as parallel computing models and multilayer neural networks.…”

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

“…Because the matrix multiplication operator of the Moore-Penrose generalized inverse matrix is the most expensive part, it is very difficult to calculate on a single machine, and the matrix operator is decomposed. Therefore, most research focused on the decomposition of matrix multiplication operation [42], [55]- [57], and proposed the double classifier algorithm that combines the most basic matrix decomposition method with other methods [58], [59]. Subsequent studies focused on some…”

“…Nonlinear principal component analysis (NLPCA) is used as a dimension reduction method, which takes into account the nonlinear relationship between features. Tejasviram et al [58] proposed that Auto Associative ELM (AAELM) perform NLPCA, extract the output of AAELM hidden This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Inflow and outflow stenoses screening on biophysical experimental arteriovenous graft using big spectral data and bidirectional associative memory machine learning model

“…After collecting a significant amount of data collection, the specific frequency-based features were extracted to determine the correlations with pre-DOS and post-DOS. Then, a machine learning model was used to map the relationship between the explanatory variables and response variables, such as parallel computing methods and multilayer neural networks [9][10][11].…”