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Least-Squares Analysis

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Measuring haemolysis in cattle serum by direct UV-VIS and RGB digital image-based methods.

Scientific reports
A simple, rapid procedure is required for the routine detection and quantification of haemolysis, one of the main sources of unreliable results in serum analysis. In this study, we compared two different approaches for the rapid determination of haem...

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network.

Sensors (Basel, Switzerland)
pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectr...

Semisupervised Feature Selection With Sparse Discriminative Least Squares Regression.

IEEE transactions on cybernetics
In big data time, selecting informative features has become an urgent need. However, due to the huge cost of obtaining enough labeled data for supervised tasks, researchers have turned their attention to semisupervised learning, which exploits both l...

Design and Implementation of Trace Inspection System Based upon Hyperspectral Imaging Technology.

Computational intelligence and neuroscience
Trace inspection is a key technology for collecting crime scenes in the criminal investigation department. A lot of information can be obtained by restoring and analyzing the remaining traces on the scene. However, with the development of digital tec...

A powerful tool for near-infrared spectroscopy: Synergy adaptive moving window algorithm based on the immune support vector machine.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Traditional trial-and-error methods are time-consuming and inefficient, especially very unfriendly to inexperienced analysts, and are sometimes still used to select preprocessing methods or wavelength variables in near-infrared spectroscopy (NIR). To...

Research on soft sensing method of photosynthetic bacteria fermentation process based on ant colony algorithm and least squares support vector machine.

Preparative biochemistry & biotechnology
Photosynthetic bacteria wastewater treatment is an efficient water pollution treatment method, but photosynthetic bacteria fermentation is a multivariable, non-linear, and time-varying process. So it is difficult to establish an accurate model. Aimin...

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach.

IEEE transactions on neural networks and learning systems
This article addresses the problem of estimating brain effective connectivity from electroencephalogram (EEG) signals using a Granger causality (GC) characterized on state-space models, extended from the conventional vector autoregressive (VAR) proce...

A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging.

Sensors (Basel, Switzerland)
The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the w...

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the proce...

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.

Scientific reports
Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods per...