AIMC Topic: Least-Squares Analysis

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Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.

Neuroscience research
Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy bec...

A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.

PloS one
Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collisi...

Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices.

Sensors (Basel, Switzerland)
It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-se...

Statistical guarantees for regularized neural networks.

Neural networks : the official journal of the International Neural Network Society
Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories. For example, there are very few statistical guarantees for learning neural networks from data, especially for classes of estimators...

Deep ANC: A deep learning approach to active noise control.

Neural networks : the official journal of the International Neural Network Society
Traditional active noise control (ANC) methods are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. In this...

Effective Automatic Method Selection for Nonlinear Regression Modeling.

International journal of neural systems
Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regressi...

Development of a portable oil type classifier using laser-induced fluorescence spectrometer coupled with chemometrics.

Journal of hazardous materials
Due to the recurrent small spills, oil pollution along coastal regions is still a major environmental issue. Standardized oil fingerprinting techniques are useful for oil spill identifications, but time- and resource-consuming. There have been ongoin...

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis.

PloS one
In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, partic...

Fast discrimination and quantification analysis of Curcumae Radix from four botanical origins using NIR spectroscopy coupled with chemometrics tools.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Curcumae Radix (Yujin) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Yujin from four botanical origins (Guiyujin, Huangyujin, Lvyujin and Wenyujin), near infrared (NIR) spec...

On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks.

Food chemistry
In this paper, we present an analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions. Indeed, we analys...