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080263

1d_cnn_for_mineral_classification

Data and codes for 1D_CNN_For_Mineral_Classification

bss-plsr-for-spectroscopy-analysis

Blind Source Separation - Partial Least Squares Regression framework for spectroscopy mixture data, such as Raman and FTIR. The BSS code identifies the sources in the mixtures, which are then correlated with reference spectra of the chemical species. If the user has defined target species to be quantified, the signals of the non-target species can be subtracted from the mixtures. Finally, PLSR is performed on the BSS-preprocessed mixtures to quantify the concentrations.

clustering-microraman-for-chemical-mapping

cnn-rnn-ram

deepcid

Deep-Learning-Based Components Identification for Raman Spectroscopy

deepresolution

Deep-Learning-Based Multivariate Curve Resolution

delphi

Code and examples for "Learning on Knowledge Graph Dynamics Provides Early Warning of Impactful Research".

diabetes_raman

do-it-dl

<Do it! 딥러닝 입문> 도서의 주피터 노트북

do-it-python

emsa

Code for paper Extended Multiplicative Signal Augmentation: An alternative to pre-processing of infrared spectra

gan-tutorial

Simple Implementation of many GAN models with PyTorch.

gans-for-1d-signal

implementation of several GANs with pytorch

jupyter_metabqc

metabcomparisonbinaryml

https://cimcb.github.io/MetabComparisonBinaryML/

metabprojectionviz

https://cimcb.github.io/MetabProjectionViz/

metabprojectionviz_mytest

msvr

MSVR (Multiple-output Support Vector Regression) python module

pymcr

MCR-ALS

pypls

Implementation of PLS-DA and OPLS-DA for high dimensional data, like MS in metabolomics.

raman

Scripts and data for "Transfer Learning Based Raman Spectra Identification"

raman-1

Deep CNN for Raman Applications

raman-diabetes

The dataset of our work where the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, is used to discern between diabetic patients (DM2) and healthy controls (Ctrl), with a high degree of accuracy. This script pre-processes the spectra to reproduce Fig. 1 of our paper: Use of Raman Spectroscopy to Screen Diabetes Mellitus with Machine Learning Tools Edgar Guevara, Juan Carlos Torres-Galván, Miguel G. Ramírez-Elías, Claudia Luevano-Contreras and Francisco Javier González Biomedical Optics Express (2018) _______________________________________________________________________________ Copyright (C) 2018 Edgar Guevara, PhD CONACYT-Universidad Autónoma de San Luis Potosí Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología _______________________________________________________________________________

sersnet

spectralmachine

Machine learning software for rapid analysis of scientific spectra.

srs2021

Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: De-Noising and Segmentation via One-Shot Deep Learning