AIMC Topic: Multivariate Analysis

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Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning.

Food research international (Ottawa, Ont.)
In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-...

DGMSCL: A dynamic graph mixed supervised contrastive learning approach for class imbalanced multivariate time series classification.

Neural networks : the official journal of the International Neural Network Society
In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-class instances typically correspond to critical events, such as system faults in power grids or abnormal health occurrences in medical monitoring. Despite being rare a...

Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.

Journal of medical systems
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into ...

Machine learning assessment of dredging impacts on the phytoplankton community on the Brazilian equatorial margin: A multivariate analysis.

Environmental pollution (Barking, Essex : 1987)
Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) serving as indicators of disturbance intensity. This study assessed the effects of dredging in the São ...

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.

Sensors (Basel, Switzerland)
Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data st...

ST-Tree with interpretability for multivariate time series classification.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack int...

Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.

Neural networks : the official journal of the International Neural Network Society
In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multiva...

Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

Journal of Alzheimer's disease : JAD
BACKGROUND: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local morphometric changes and disease status for early diagnosis could be aided by machine learning algori...

TV-Net: Temporal-Variable feature harmonizing Network for multivariate time series classification and interpretation.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series classification (MTSC), which identifies categories of multiple sensor signals recorded in continuous time, is widely used in various fields such as transportation, finance, and medical treatment. The focused challenge remains...

RFNet: Multivariate long sequence time-series forecasting based on recurrent representation and feature enhancement.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series exhibit complex patterns and structures involving interactions among multiple variables and long-term temporal dependencies, making multivariate long sequence time series forecasting (MLSTF) exceptionally challenging. Despite...