Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

2020 ◽  
pp. 106680 ◽  
Author(s):  
Ndiye M. Kebonye ◽  
Peter N. Eze ◽  
Kingsley John ◽  
Asa Gholizadeh ◽  
Julie Dajčl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Toxic Element ◽  
Simulation Technique ◽  
Sequential Gaussian Simulation ◽  
Self Organizing Map ◽  
Potentially Toxic Element ◽  
Artificial Neural ◽  
Gaussian Simulation ◽  
Mining Soils

scholarly journals What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations

2009 ◽  
Vol 91 (2) ◽  
pp. 121-132 ◽  
Author(s):  
NATACHA NIKOLIC ◽  
YOUNG-SEUK PARK ◽  
MAGALI SANCRISTOBAL ◽  
SOVAN LEK ◽  
CLAUDE CHEVALET
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Model ◽  
Unsupervised Classification ◽  
Genetic Distances ◽  
Population Diversity ◽  
Factorial Correspondence Analysis ◽  
Self Organizing Map ◽  
Additional Information ◽  
Artificial Neural

SummaryGeneral and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

2008 ◽  
Vol 34 (6) ◽  
pp. 782-790 ◽  
Author(s):  
Manuel Alvarez-Guerra ◽  
Cristina González-Piñuela ◽  
Ana Andrés ◽  
Berta Galán ◽  
Javier R. Viguri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sediment Quality ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
Self Organizing

scholarly journals Factor Knowledge Mining Using the Techniques of AI Neural Networks and Self-Organizing Map

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Pao-Kuan Wu ◽  
Tsung-Chih Hsiao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Hybrid Technique ◽  
Self Organizing Map ◽  
Knowledge Mining ◽  
Classification Techniques ◽  
Artificial Neural ◽  
Self Organizing

This paper offers a hybrid technique combined by artificial neural networks (ANN) and self-organizing map (SOM) as a way to explore factor knowledge. ANN and SOM are two kinds of pattern classification techniques based on supervised and unsupervised mechanisms, respectively. This paper proposes a new aspect to combine ANN and SOM as NNSOM process in order to delve into factor knowledge other than pattern classification. The experimental material is conducted by the investigation of street night market in Taiwan. NNSOM process can yield two results about factor knowledge: first, which factor is the most important factor for the development of street night market; second, what value of this factor is most positive to the development of street night market. NNSOM process can combine the advantages of supervised and unsupervised mechanisms and be applied to different disciplines.

scholarly journals Use of computational intelligence in the genetic divergence of colored cotton plants

2021 ◽  
Vol 37 ◽  
pp. e37007
Author(s):  
Daniel Bonifácio Oliveira Cardoso ◽  
Luiza Amaral Medeiros ◽  
Gabriela de Oliveira Carvalho ◽  
Izabela Motta Pimentel ◽  
Gabriella Xavier Rojas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Computational Intelligence ◽  
Self Organizing Map ◽  
Improvement Program ◽  
Technological Characteristics ◽  
Cotton Genotypes ◽  
Artificial Neural ◽  
Self Organizing

The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP - 05, UFUJP - 08, UFUJP - 09, UFUJP - 10, UFUJP - 11, UFUJP - 13, UFUJP - 16, UFUJP - 17 and two commercial cultivars: BRS Rubi (RC) and BRS Topázio (TC). The experimental design used was complete randomized block (CRB) with three replications. The following evaluations were carried out at full maturation: yield of cottonseed (kg ha-1) and the technological characteristics, which include, fiber length, micronaire, maturation, length uniformity, short fiber index, elongation and strength, using the HVI (High volume instrument) device. Genetic dissimilarity was measured using the generalized Mahalanobis distance and after obtaining the dissimilarity matrix, the genotypes were grouped using a hierarchical clustering method (UPGMA). A discriminant analysis and the Kohonen Self-Organizing Map (SOM) by Artificial Neural Networks (ANN’s) were performed through computational intelligence. SOM was able to detect differences and organize the similarities between accesses in a more coherent way, forming a larger number of groups, when compared to the method that uses the Mahalanobis matrix. It was also more accurate than the discriminant analysis, since it made it possible to differentiate groups more coherently when comparing their phenotypic behavior. The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's Self-Organizing Map being the most adequate to classify and group cotton genotypes.

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