AIMC Topic: Least-Squares Analysis

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Machine learning combined with non-targeted LC-HRMS analysis for a risk warning system of chemical hazards in drinking water: A proof of concept.

Talanta
Guaranteeing clean drinking water to the global population is becoming more challenging, because of the cases of water scarcity across the globe, growing population, and increased chemical footprint of this population. Existing targeted strategies fo...

Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters.

Computational intelligence and neuroscience
Detection of defects including cracks and spalls on wall surface in high-rise buildings is a crucial task of buildings' maintenance. If left undetected and untreated, these defects can significantly affect the structural integrity and the aesthetic a...

Thermodynamic integration network approach to ion transport through protein channels: Perspectives and limits.

Journal of computational chemistry
We present a molecular dynamics simulation study of alkali metal cation transport through the double-helical and the head-to-head conformers of the gramicidin ion channel. Our approach is based on a thermodynamic integration network, which consists o...

Training sparse least squares support vector machines by the QR decomposition.

Neural networks : the official journal of the International Neural Network Society
The solution of an LS-SVM has suffered from the problem of non-sparseness. The paper proposed to apply the KMP algorithm, with the number of support vectors as the regularization parameter, to tackle the non-sparseness problem of LS-SVMs. The idea of...

Sorptive equilibrium profile of fluoride onto aluminum olivine [(FeMg)SiO] composite (AOC): Physicochemical insights and isotherm modeling by non-linear least squares regression and a novel neural-network-based method.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
A novel aluminum/olivine composite (AOC) was prepared by wet impregnation followed by calcination and was introduced as an efficient adsorbent for defluoridation. The adsorption of fluoride was modeled with one-, two- and three-parameter isotherm equ...

Artificial neural networks (ANNs) and partial least squares (PLS) regression in the quantitative analysis of cocrystal formulations by Raman and ATR-FTIR spectroscopy.

Journal of pharmaceutical and biomedical analysis
The present work describes the development of an efficient, fast and accurate method for the quantification of polymer-based cocrystal formulations. Specifically, the content of carbamazepine-nicotinamide (CBZ/NIC) and ibuprofen-nicotinamide (IBU/NIC...

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method.

Scientific reports
Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture conte...

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly...

T-wave end detection using neural networks and Support Vector Machines.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines.

Inter-class sparsity based discriminative least square regression.

Neural networks : the official journal of the International Neural Network Society
Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring ...