A Cluster Analysis Methodology for the Categorization of Soil Samples for Forensic Sciences Based on Elemental Fingerprint-Reference-Cited by-同舟云学术

A Cluster Analysis Methodology for the Categorization of Soil Samples for Forensic Sciences Based on Elemental Fingerprint

Published:2021-12-20 Issue: Volume: Page:1-20
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Maione Camila¹,Luíza da Costa Nattane¹²,Barbosa Fernando³^ORCID,Barbosa Rommel Melgaço¹

Affiliation:

1. Instituto de Informática, Universidade Federal de Goiás, Goiânia, Brazil

2. Núcleo de Informática, Instituto Federal de Educação, Urutaí, Brazil

3. Departamento de Análises Clínicas, Toxicológicas E Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil

Publisher

Informa UK Limited

Subject

Artificial Intelligence

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2010941

Reference46 articles.

1. Evaluating C-SVM, CRF and LDA classification for daily activity recognition

2. A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry

3. Chemometric Study on the Forensic Discrimination of Soil Types Using Their Infrared Spectral Characteristics

4. The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: signs of neutrino mass in current cosmological data sets

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1. Carbon and nitrogen stocks in Lithic soils: Environmental drivers and land-use implication in Brazilian dry ecosystems;Journal of Soils and Sediments;2024-02-09

2. A Critical Review of Soil Sampling and Data Analysis Strategies for Source Tracing of Soil in Forensic Investigations;Critical Reviews in Analytical Chemistry;2023-09-06

3. Soil texture identification using LIBS data combined with machine learning algorithm;Optik;2023-05

4. Exploring Spatial Variation of Soils Using Self-organizing Maps (SOMs) and UHPLC Data for Forensic Investigation;Algorithms for Intelligent Systems;2023