Development of a predictive model to determine the geographic origin of corbicular pollen in Boyacá

ilustracionesLa producción de polen corbicular es una actividad pecuaria de gran relevancia en el Colombia, especialmente en el departamento de Boyacá. La obtención de un sello de Denominación de Origen para este producto puede agregar valor y mejorar las condiciones económicas de los productores. Sin embargo, es fundamental contar con mecanismos que corroboren si una muestra de producto proviene de una determinada región para verificar el origen del producto. Desafortunadamente, las estrategias de verificación geográfica actuales son costosas y de difícil acceso para un productor. En este trabajo se propuso un nuevo modelo basado en segmentación por color y aprendizaje automático supervisado para identificar automáticamente el origen geográfico de una muestra de polen a partir de imágenes digitales adquiridas en condiciones controladas. La estrategia propuesta logró un alto desempeño, en particular F1-score=0.85, lo que sugiere que el método puede determinar con un alto nivel de certeza el productor y, en consecuencia, el origen geográfico. (Texto tomado de la fuente).The production of corbicular pollen is an agriculture activity of great relevance in the country, especially in the department of Boyac´a. Obtaining a Denomination of Origin seal for this product can add value and improve the economic conditions of producers. However, it is essential to have mechanisms that corroborate whether a product sample comes from a certain region to verify the origin of the product. Unfortunately, current geo-verification strategies are expensive and difficult for a producer to access. In this work, a new model based on color segmentation and supervised machine learning was proposed to automatically identify the geographic origin of a pollen sample from digital images acquired under controlled conditions. The proposed strategy achieved a performance of 0.85 in the F1-score, which suggests that the method can determine with a high level of certainty the producer and, consequently, the geographical origin.Incluye anexosMaestríaMagíster en Ciencias - Matemática AplicadaAprendizaje de máquina y matemáticas aplicadas a las ciencias pecuaria

Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization

Evolving Spiking Neural Network (ESNN) is widely used in classification problem. However, ESNN like any other neural networks is incapable to find its own parameter optimum values, which are crucial for classification accuracy. Thus, in this study, ESNN is integrated with an improved Particle Swarm Optimization (PSO) known as Dynamic Population Particle Swarm Optimization (DPPSO) to optimize the ESNN parameters: the modulation factor (Mod), similarity factor (Sim) and threshold factor (C). To find the optimum ESNN parameter value, DPPSO uses a dynamic population that removes the lowest particle value in every pre-defined iteration. The integration of ESNN-DPPSO facilitates the ESNN parameter optimization searching during the training stage. The performance analysis is measured by classification accuracy and is compared with the existing method. Five datasets gained from University of California Irvine (UCI) Machine Learning Repository are used for this study. The experimental result presents better accuracy compared to the existing technique and thus improves the ESNN method in optimising its parameter values

Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization

Evolving Spiking Neural Network (ESNN) is widely used in classification problem. However, ESNN like any other neural networks is incapable to find its own parameter optimum values, which are crucial for classification accuracy. Thus, in this study, ESNN is integrated with an improved Particle Swarm Optimization (PSO) known as Dynamic Population Particle Swarm Optimization (DPPSO) to optimize the ESNN parameters: the modulation factor (Mod), similarity factor (Sim) and threshold factor (C). To find the optimum ESNN parameter value, DPPSO uses a dynamic population that removes the lowest particle value in every pre-defined iteration. The integration of ESNN-DPPSO facilitates the ESNN parameter optimization searching during the training stage. The performance analysis is measured by classification accuracy and is compared with the existing method. Five datasets gained from University of California Irvine (UCI) Machine Learning Repository are used for this study. The experimental result presents better accuracy compared to the existing technique and thus improves the ESNN method in optimising its parameter values