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Multivariate Analysis

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Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early can...

Anomaly detection in multivariate time series data using deep ensemble models.

PloS one
Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, c...

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifyi...

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

Frontiers in endocrinology
BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features u...

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Psychophysiology
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches...

Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets.

International journal of pharmaceutics
Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by...

Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

Fa yi xue za zhi
In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropri...

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

Journal of hazardous materials
Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in...

A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also reconstr...

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

IEEE journal of biomedical and health informatics
The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And ...