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Least-Squares Analysis

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Neural network ensemble model for prediction of erythrocyte sedimentation rate (ESR) using partial least squares regression.

Scientific reports
The erythrocyte sedimentation rate (ESR) is a non-specific blood test for determining inflammatory conditions. However, the long measurement time (60 min) to obtain ESR is an obstacle for a prompt evaluation. In this study, to reduce the measurement ...

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

IEEE journal of biomedical and health informatics
A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of a...

Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions.

IEEE transactions on pattern analysis and machine intelligence
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separ...

Prediction of textile pilling resistance using optical coherence tomography.

Scientific reports
This paper describes a new method of textile pilling prediction, based on multivariate analysis of the spatial layer above the surface. The original idea of the method is the acquisition of 3D fabric image using optical coherence tomography (OCT) wit...

Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.

Environmental science and pollution research international
As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various typ...

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets.

Molecules (Basel, Switzerland)
Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established....

Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method.

PloS one
Studying the constitutive relation of soft clays is of critical importance for fundamentally understanding their complex consolidation behavior. This study proposes a fractional differential constitutive model in conjunction with an intelligent displ...

Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion.

Methods (San Diego, Calif.)
DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is im...

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms.

Scientific reports
Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedie...

Linear or non-linear multivariate calibration models? That is the question.

Analytica chimica acta
Concepts from data science, machine learning, deep learning and artificial neural networks are spreading in many disciplines. The general idea is to exploit the power of statistical tools to interpret complex and, in many cases, non-linear data. Spec...