Utilização de redes neurais artificiais para a determinação do número de refeições diárias de um restaurante universitário

Rocha, José Celso; Matos, Felipe Delestro; Frei, Fernando

doi:10.1590/s1415-52732011000500007

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Artificial neural networks can learn and generalize information from successive presentation of examples (Rocha, Matos and Frei 2011). This process is called training and allows the network to extract information regarding the existing relationships between the variables that affect a given event (Sheikhtaheri et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Rosa¹,

Dornelles²,

Silva³

et al. 2023

RGSA

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Purpose: The objective of the study is to analyze the management of the fungicide that seeks a longer interval between the last application and the harvest of oat grains, with an indication of the cultivars with the highest satisfactory productivity without application in the grain filling. Validate artificial neural network models in the expectation of satisfactory productivity with food security, through the interaction between management, pathogen, genotype, and environment. Method/design/approach: The study was conducted in 2015, 2016, 2017, in a randomized block design, in a 22 x 4 factorial scheme, for 22 white oat cultivars (recommended and no longer present in the current Brazilian recommendation) and 4 fungicide application conditions (no application; one application at 60 days after emergence (DAE); two applications, 60 and 75 DAE; and three applications, at 60, 75, 90 DAE), with three repetitions. Results and conclusion: The oat cultivars URS Altiva, URS, Guará, URS Charrua, FAEM 4 Carlasul, IPR Aphrodite, and UPFPS Farroupilha showed satisfactory yields in absence of fungicide applications. URS Altiva, FAEM 4 Carlasul, and IPR Aphrodite showed significant yield increases with fungicide application before grain filling. Artificial neural networks are efficient in predicting productivity and are a reliable alternative for simulating scenarios to validate more sustainable management practices. Originality/value: An unprecedented study seeking more sustainable managements to reduce the use of fungicides in the control of oat diseases and development of simulation models by artificial intelligence, providing opportunities for analysis of different scenarios of the complex interaction plant, pathogen, and environment.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Rosa¹,

Dornelles²,

Silva³

et al. 2023

RGSA

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“…A utilização de algoritmo genético para obtenção de arquiteturas de RNA ótimas apresenta resultados rápidos e eficientes para diversos problemas [8,9], no entanto, durante o processo do AG estabelecido por Matos, os indivíduos (as arquiteturas de RNA) convergiram para um padrão que apresentaram resultados muito bons (79,2% de acerto total para as três classificações), porém, este padrão pode não representar a melhor solução, uma vez que para a classificação do grau 2, o acerto foi de apenas 16,7%, representando um mínimo local e não um mínimo global.…”

Section: Introductionunclassified

Utilização de Algoritmo Genético e Redes Neurais Artificiais para Classificação de Blastocistos Bovinos

Passalia¹,

Matos²,

Nogueira³

et al. 2016

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. A classificação de blastocistos bovinos apresenta grande importância comercial, já que está diretamente ligada às taxas de gestação. A aplicação da técnica de RNA, juntamente com AG, pode ser uma solução para evitar a classificação subjetiva. Foi construído um software baseado em AG para obter arquiteturas ótimas de RNA.Palavras-chave. Rede Neural Artificial, Algoritmo Genético, Blastocisto Bovino.

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“…The software (Blasto4Q) is based on artificial neural networks (ANNs), which is an artificial intelligence technique that solves non-linear problems with interconnected variables [ 11 – 13 ]. ANNs have been applied to various areas, including administrative aids [ 14 ] and stock market index predictions [ 15 ]. An ANN is a system that solves problems by simulating biological neurons.…”

Section: Introductionmentioning

confidence: 99%

“…The neurons in an ANN (also, “perceptrons”) must receive training data to learn and generalize output based on an input dataset. Once it is properly trained, an ANN can generate predictions without a pre-established classification [ 11 , 12 , 14 , 16 ]. Therefore, an ANN is an intelligent system that can solve a complex problem based on assisted learning.…”

Section: Introductionmentioning

confidence: 99%

A method using artificial neural networks to morphologically assess mouse blastocyst quality

Matos

Rocha

Nogueira³

2014

J Anim Sci Technol

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BackgroundMorphologically classifying embryos is important for numerous laboratory techniques, which range from basic methods to methods for assisted reproduction. However, the standard method currently used for classification is subjective and depends on an embryologist’s prior training. Thus, our work was aimed at developing software to classify morphological quality for blastocysts based on digital images.MethodsThe developed methodology is suitable for the assistance of the embryologist on the task of analyzing blastocysts.The software uses artificial neural network techniques as a machine learning technique. These networks analyze both visual variables extracted from an image and biological features for an embryo.ResultsAfter the training process the final accuracy of the system using this method was 95%. To aid the end-users in operating this system, we developed a graphical user interface that can be used to produce a quality assessment based on a previously trained artificial neural network.ConclusionsThis process has a high potential for applicability because it can be adapted to additional species with greater economic appeal (human beings and cattle). Based on an objective assessment (without personal bias from the embryologist) and with high reproducibility between samples or different clinics and laboratories, this method will facilitate such classification in the future as an alternative practice for assessing embryo morphologies.

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Utilização de redes neurais artificiais para a determinação do número de refeições diárias de um restaurante universitário

Cited by 4 publications

References 17 publications

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Utilização de Algoritmo Genético e Redes Neurais Artificiais para Classificação de Blastocistos Bovinos

A method using artificial neural networks to morphologically assess mouse blastocyst quality

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