Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/20830 |
Resumo: | This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization. |
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Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradictionCaracterización y clasificación de patrones de datos numéricos utilizando Lógica Paraconsistente Anotada y el concepto de contradicciónCaracterização e classificação de padrões de dados numéricos utilizando a Lógica Paraconsistente Anotada e o conceito da contradiçãoLógica Paraconsistente AnotadaAprendizaje automáticoClasificación de patronesModelo matemáticoEfecto de contradicción.Lógica Paraconsistente AnotadaAprendizado de máquinaClassificação de padrõesModelo matemáticoEfeito da contradição.Annotated Paraconsistent LogicMachine learningPattern classificationMathematical modelContradiction effect.This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization.Este artículo describe el desarrollo de un modelo matemático computacional que utiliza lógica paraconsistente anotada - LPA y un concepto derivado de él, el efecto de contradicción, para identificar patrones en datos numéricos con fines de clasificación de patrones. LPA soporta principios lógicos paraconsistentes y paracompletos, que permiten la manipulación de datos inconsistentes y contradictorios, y su uso permitió la identificación y cuantificación del atributo relacionado con la contradicción. Para validar el modelo se utilizaron serie de espectroscopia Raman obtenidas tras la exposición de proteínas, lípidos y ácidos nucleicos, recolectadas de muestras de células de tejido cutáneo previamente examinadas para la detección de lesiones cancerosas, identificadas como basal, melanoma y carcinoma normal. Se identificaron y cuantificaron los atributos relacionados con la contradicción, la derivada y la mediana obtenidos en la espectroscopia. Un proceso de aprendizaje automático con el 31,6% de las muestras detecta una secuencia espectroscópica capaz de caracterizar y clasificar el tipo de lesión a través de los atributos elegidos. El 68,4% de las muestras se utilizan para la clasificación. El modelo identificó un segmento de espectroscopia donde la clasificación de las muestras de prueba tenía una tasa de aciertos del 76,92%. Como diferencial e innovación de este trabajo, se destaca el uso de principios LPA en un proceso completo de entrenamiento, aprendizaje y clasificación de patrones para conjuntos de datos numéricos, con flexibilidad en la elección de atributos utilizados para la caracterización de patrones, y una serie de muestras de un tercio del total requerido para la caracterización.Este trabalho descreve o desenvolvimento de um modelo matemático computacional que utiliza a Lógica Paraconsistente Anotada - LPA e um conceito derivado dela, o efeito da contradição, para identificar padrões em dados numéricos para fins de classificação de padrões. A LPA admite princípios lógicos paraconsistentes e paracompletos, que permitem a manipulação de dados inconsistentes e contraditórios, e sua utilização permitiu a identificação e quantização do atributo relacionado à contradição. Para validação do modelo, foram utilizadas séries de espectroscopias Raman obtidas após exposição de proteínas, lipídios e ácidos nucléicos, coletados de amostras de células de tecido cutâneo previamente examinadas para detecção de lesões cancerígenas, identificadas como: carcinoma basal, melanoma e normal. Inicialmente foram identificados e quantizados os atributos relacionados à contradição, derivada e mediana obtidos das espectroscopias. Um processo de aprendizado de máquina com aproximadamente 31,6% de cada tipo de amostras detecta uma sequência de espectroscopias capaz de caracterizar e classificar o tipo de lesão por meio dos atributos escolhidos. Aproximadamente 68,4% das amostras são utilizadas para os testes de classificação. O modelo proposto identificou um segmento de espectroscopias onde a classificação das amostras de teste teve uma taxa de acerto de 76,92%. Como diferencial e inovação deste trabalho destaca-se a utilização dos princípios da LPA em um processo completo de treinamento, aprendizado e classificação de padrões para conjuntos de dados numéricos, com flexibilidade para escolher os atributos utilizados para a caracterização de padrões, e uma quantidade de amostras de cerca de um terço do total necessário para a caracterização.Research, Society and Development2021-10-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2083010.33448/rsd-v10i13.20830Research, Society and Development; Vol. 10 No. 13; e283101320830Research, Society and Development; Vol. 10 Núm. 13; e283101320830Research, Society and Development; v. 10 n. 13; e2831013208302525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/20830/19271Copyright (c) 2021 Mauricio Conceição Mario; Dorotéa Vilanova Garcia; João Inácio da Silva Filho; Landulfo Silveira Júnior; Heraldo Silveira Barbuyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMario, Mauricio ConceiçãoGarcia, Dorotéa Vilanova Silva Filho, João Inácio da Silveira Júnior, LandulfoBarbuy, Heraldo Silveira2021-11-21T18:26:28Zoai:ojs.pkp.sfu.ca:article/20830Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:40:21.920162Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction Caracterización y clasificación de patrones de datos numéricos utilizando Lógica Paraconsistente Anotada y el concepto de contradicción Caracterização e classificação de padrões de dados numéricos utilizando a Lógica Paraconsistente Anotada e o conceito da contradição |
title |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction |
spellingShingle |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction Mario, Mauricio Conceição Lógica Paraconsistente Anotada Aprendizaje automático Clasificación de patrones Modelo matemático Efecto de contradicción. Lógica Paraconsistente Anotada Aprendizado de máquina Classificação de padrões Modelo matemático Efeito da contradição. Annotated Paraconsistent Logic Machine learning Pattern classification Mathematical model Contradiction effect. |
title_short |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction |
title_full |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction |
title_fullStr |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction |
title_full_unstemmed |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction |
title_sort |
Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction |
author |
Mario, Mauricio Conceição |
author_facet |
Mario, Mauricio Conceição Garcia, Dorotéa Vilanova Silva Filho, João Inácio da Silveira Júnior, Landulfo Barbuy, Heraldo Silveira |
author_role |
author |
author2 |
Garcia, Dorotéa Vilanova Silva Filho, João Inácio da Silveira Júnior, Landulfo Barbuy, Heraldo Silveira |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Mario, Mauricio Conceição Garcia, Dorotéa Vilanova Silva Filho, João Inácio da Silveira Júnior, Landulfo Barbuy, Heraldo Silveira |
dc.subject.por.fl_str_mv |
Lógica Paraconsistente Anotada Aprendizaje automático Clasificación de patrones Modelo matemático Efecto de contradicción. Lógica Paraconsistente Anotada Aprendizado de máquina Classificação de padrões Modelo matemático Efeito da contradição. Annotated Paraconsistent Logic Machine learning Pattern classification Mathematical model Contradiction effect. |
topic |
Lógica Paraconsistente Anotada Aprendizaje automático Clasificación de patrones Modelo matemático Efecto de contradicción. Lógica Paraconsistente Anotada Aprendizado de máquina Classificação de padrões Modelo matemático Efeito da contradição. Annotated Paraconsistent Logic Machine learning Pattern classification Mathematical model Contradiction effect. |
description |
This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-22 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/20830 10.33448/rsd-v10i13.20830 |
url |
https://rsdjournal.org/index.php/rsd/article/view/20830 |
identifier_str_mv |
10.33448/rsd-v10i13.20830 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/20830/19271 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 13; e283101320830 Research, Society and Development; Vol. 10 Núm. 13; e283101320830 Research, Society and Development; v. 10 n. 13; e283101320830 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052756134985728 |