AIMC Topic: Regression Analysis

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Deep ensemble learning of sparse regression models for brain disease diagnosis.

Medical image analysis
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effecti...

A novel multi-target regression framework for time-series prediction of drug efficacy.

Scientific reports
Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods ...

Analysis of the Importance of Oxides and Clays in Cd, Cr, Cu, Ni, Pb and Zn Adsorption and Retention with Regression Trees.

PloS one
This study determines the influence of the different soil components and of the cation-exchange capacity on the adsorption and retention of different heavy metals: cadmium, chromium, copper, nickel, lead and zinc. In order to do so, regression models...

Highly predictive and interpretable models for PAMPA permeability.

Bioorganic & medicinal chemistry
Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound ent...

Dynamic predictive model for growth of Salmonella spp. in scrambled egg mix.

Food microbiology
Liquid egg products can be contaminated with Salmonella spp. during processing. A dynamic model for the growth of Salmonella spp. in scrambled egg mix - high solids (SEM) was developed and validated. SEM was prepared and inoculated with ca. 2 log CFU...

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

Bioresource technology
Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato s...

Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double-integration approaches to extract these parameters from inertial sensor data are, however, limited in their...

A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system.

Journal of hazardous materials
Ammonia (NH) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could pro...

A New Method for Self-Estimation of the Severity of Obstructive Sleep Apnea Using Easily Available Measurements and Neural Fuzzy Evaluation System.

IEEE journal of biomedical and health informatics
This paper proposes a neural fuzzy evaluation system (NFES) with significant variables selected from stepwise regression to predict apnea-hypopnea index (AHI) for evaluating obstructive sleep apnea (OSA). The variables considered are the change statu...

Multi-atlas and unsupervised learning approach to perirectal space segmentation in CT images.

Australasian physical & engineering sciences in medicine
Perirectal space segmentation in computed tomography images aids in quantifying radiation dose received by healthy tissues and toxicity during the course of radiation therapy treatment of the prostate. Radiation dose normalised by tissue volume facil...