Classification of spectroscopic data - challenges, benchmarking and limitations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU137605" target="_blank" >RIV/00216305:26620/20:PU137605 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Classification of spectroscopic data - challenges, benchmarking and limitations

Popis výsledku v původním jazyce

In modern spectroscopy, we are often dealing with large and highly complex datasets. As an example, in Laser-Induced Breakdown Spectroscopy (LIBS), measurements with a 1 kHz repetition rate were reported. Such a measurement often results in huge, high-dimensional data that are impossible to explore and analyze by a hand. Even more, many common methods (Principal Component Analysis + classifier, Support Vector Machines, etc.) may become insufficient and new strategies are required. Classification of large spectroscopic data is a challenging task due to the nature of spectra. Modern machine learning (ML) techniques based on artificial neural networks (ANN) are opening new possibilities, but often there is a lack of understanding in the decision processes (for classification). In this work, we extensively study modern approaches to classification with a focus on the explainability of decision factors. Innovative models with the incorporation of physics (or spectra modeling) are discussed. Besides mention

Název v anglickém jazyce

Classification of spectroscopic data - challenges, benchmarking and limitations

Popis výsledku anglicky

In modern spectroscopy, we are often dealing with large and highly complex datasets. As an example, in Laser-Induced Breakdown Spectroscopy (LIBS), measurements with a 1 kHz repetition rate were reported. Such a measurement often results in huge, high-dimensional data that are impossible to explore and analyze by a hand. Even more, many common methods (Principal Component Analysis + classifier, Support Vector Machines, etc.) may become insufficient and new strategies are required. Classification of large spectroscopic data is a challenging task due to the nature of spectra. Modern machine learning (ML) techniques based on artificial neural networks (ANN) are opening new possibilities, but often there is a lack of understanding in the decision processes (for classification). In this work, we extensively study modern approaches to classification with a focus on the explainability of decision factors. Innovative models with the incorporation of physics (or spectra modeling) are discussed. Besides mention

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů