AIMC Topic: Normal Distribution

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A Robust Gaussian Process Paradigm for Predictive Modeling on Small Data sets in Environmental Science: A Case Study in Ballasted Flocculation.

Environmental science & technology
Environmental processes including ballasted flocculation (BF) present significant optimization challenges due to complex multicomponent interactions and small, heterogeneous experimental data sets that frequently lead to overfitted machine learning (...

Homogeneous multi-antibiotics residual identification in various actual water via SERS spectra multilayer perceptron algorithm combined with Gaussian kernel density estimation data augmentation.

Analytica chimica acta
BACKGROUND: Antibiotic residues pose varying degrees of potential hazards to the water environment and human health due to their diverse types. Surface-enhanced Raman spectroscopy (SERS) technology can achieve rapid detection of various antibiotic re...

Gaussian process modelling of infectious diseases using the Greta software package and GPUs.

Journal of theoretical biology
Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta software for Bay...

Predictive modeling of hemoglobin refractive index using Gaussian process regression with interpretability through partial dependence plots.

PloS one
Accurately predicting the refractive index of hemoglobin across various wavelengths and concentrations is critical for advancing optical diagnostic techniques in biological and clinical applications. This study introduces a predictive model based on ...

Gaussian random fields as an abstract representation of patient metadata for multimodal medical image segmentation.

Scientific reports
Growing rates of chronic wound occurrence, especially in patients with diabetes, has become a recent concerning trend. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Innovative dee...

Optimizing stroke lesion segmentation: A dual-approach using Gaussian mixture models and nnU-Net.

Computers in biology and medicine
Machine learning-based stroke lesion segmentation models are widely used in biomedical imaging, but their ability to detect treatment effects remains largely unexplored. Gaussian Mixture Models (GMM) and nnU-Net are among the most prominent and well-...

Addressing model discrepancy in a clinical model of the oxygen dissociation curve.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Many mathematical models suffer from model discrepancy, posing a significant challenge to their use in clinical decision-making. In this article, we consider methods for addressing this issue. In the first approach, a mathematical model is treated as...

Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.

NeuroImage
Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in the detection of brain abnormalities. While deep learning is accurate in estimating brain age, the a...

Enhancing interdisciplinary image segmentation through a Gaussian-based modified local consensus spatial fuzzy approach.

Computers in biology and medicine
This study aims to introduce a generic fuzzy-based approach tailored explicitly for classifying images originating from an array of diverse sources, having varying degrees of spectral and spatial resolutions, inhomogeneity, artifacts, and entirely di...

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO Emissions.

Environmental science & technology
Satellite data provides essential insights into the spatiotemporal distribution of CO concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and...