Listar Grupo de Investigación y Desarrollo de Ingeniería de Software – GIDISOFT por autor "50ad5b371a57d0bc8149a95c0f62781c"

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Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Fuentes, Jairo; Aguilar, Jose; Montoya, Edwin; Pinto, Ángel (Information (Switzerland), 2024-02-05)

In this paper, we propose autonomous cycles of data analysis tasks for the automation of the production chains aimed to improve the productivity of Micro, Small and Medium Enterprises (MSMEs) in the context of agroindustry. ...
Learning algorithm for the recursive pattern recognition model

Puerto Cuadros, Eduard Gilberto; Aguilar, Jose (Applied Artificial IntelligenceInglaterra, 2016-10-28)

In this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the ...

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Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector
...
Fuentes, Jairo | 2024-02-05

In this paper, we propose autonomous cycles of data analysis tasks for the automation of the production chains aimed to improve the productivity of Micro, Small and Medium Enterprises (MSMEs) in the context of agroindustry. In the autonomous cycles of data analysis tasks, each task interacts with the others and has different functions, in order to reach the goal of the cycle. In this article, we identify three industrial-automation processes within the production chain, in which autonomous cycles can be applied. The first cycle is responsible to identify the type of input to be transformed—such as quantity, quality, time, and cost—based on information from the organization and its context. The second cycle selects the technological level used in the raw-material transformation, characterizing the platform of plant processing. The last cycle identifies the level of specialization of the generated product, such as the quality and value of the product. Finally, we apply the first autonomous cycle to define the type of input to be transformed in a coffee factory.

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Learning algorithm for the recursive pattern recognition model
...
Puerto Cuadros, Eduard Gilberto | 2016-10-28

In this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the first, called Aprendizaje_nuevo, is used to learn new patterns and creates a new pattern recognition module in the model. The other, called Aprendizaje_por_refuerzo, is used to reinforce a pattern and adapts the module that represents the pattern to the changes in it. The algorithm is tested in various contexts (text and images) to analyze its capacities of learning and of recognition of the model.

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Listar Grupo de Investigación y Desarrollo de Ingeniería de Software – GIDISOFT por autor "50ad5b371a57d0bc8149a95c0f62781c"

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

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Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

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Listar Grupo de Investigación y Desarrollo de Ingeniería de Software – GIDISOFT por autor "50ad5b371a57d0bc8149a95c0f62781c"

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector ﻿

Learning algorithm for the recursive pattern recognition model ﻿

Envíos recientes

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model

Portales Institucionales

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

Learning algorithm for the recursive pattern recognition model