AIMC Topic: Regression Analysis

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Data-driven regression analysis of amylose using Sombor molecular descriptors.

Scientific reports
Amylose, a vital polysaccharide component of starch, plays a significant role in plant energy storage and has important implications in nutrition and health. In this study, the structural characteristics of amylose are analyzed using Sombor indices, ...

Comparison of machine learning classification and regression models for prediction of academic performance among postgraduate public health students.

Scientific reports
Machine learning (ML) is an artificial intelligence tool that focuses on learning by generating models using established algorithms that represent a given dataset. It can be used as a predictive tool for students' academic performance (AP) at both un...

Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.

Breast cancer research : BCR
BACKGROUND: Accurate quantification of the Ki-67 proliferation index is essential for breast cancer prognosis and treatment planning. Current automated methods, including classical and deep learning approaches based on cell detection or segmentation,...

Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study.

JMIR medical informatics
BACKGROUND: Pelvic organ prolapse (POP) and stress urinary incontinence (SUI) often concurrently exist. The incontinence in some patients with POP resolves after POP surgery, but it persists in others. Some patients without SUI before surgery may dev...

Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach.

BMC bioinformatics
BACKGROUND: Longitudinal studies often require flexible methodologies for predicting response trajectories based on time-dependent and time-independent covariates. To address the complexities of longitudinal data, this study proposes a novel extensio...

Regression model and artificial neural network model to predict halonitromethane formation from amino acids during UV/monochloramine disinfection in bromide-containing real water.

Environmental pollution (Barking, Essex : 1987)
Halonitromethanes (HNMs) were high-toxicity nitrogenous disinfection byproducts generated by amino acids (AAs) during UV/monochloramine (UV/NHCl) disinfection in bromide-containing water. HNM concentrations fell over time, highlighting disinfection t...

Deep learning-based regression of food quality attributes using near-infrared spectroscopy and hyperspectral imaging: A review.

Food chemistry
Near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) are two popular non-destructive tools for food quality and safety inspection. For food quality attributes quantification, the key is to develop regression models to link the features (s...

A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression.

Journal of hazardous materials
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by th...

A methodology for coagulant virtual testing to improve dissolved organic matter removal in surface water treatment.

The Science of the total environment
Coagulation is one of the most crucial steps in a Drinking Water Treatment Plant (DWTP). The coagulant dose required for the removal of particles and natural organic matter (NOM) is typically determined through jar tests. However, this method is time...