The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Janis Antonovics - One of the best experts on this subject based on the ideXlab platform.
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Breeding system evolution in Leavenworthia : breeding system variation and reproductive success in natural populations of Leavenworthia crassa (Cruciferae)
American Journal of Botany, 1991Co-Authors: Elizabeth E. Lyons, Janis AntonovicsAbstract:Populations of Leavenworthia crassa studied for 3 years exhibited among- and within-population genetic variation for a suite of floral and reproductive traits (flower Width, Petal length, anther position, ability to set seeds in the absence of pollinators, time to first flowering) associated with breeding system. We used electrophoretic markers to show that a population with small, monomorphically colored flowers with introrse anthers has a significantly lower outcrossing rate (t=0.03) than did a population with larger, polymorphically colored flowers with extrorse anthers (t=0.33) (...)
Elizabeth E. Lyons - One of the best experts on this subject based on the ideXlab platform.
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Breeding system evolution in Leavenworthia : breeding system variation and reproductive success in natural populations of Leavenworthia crassa (Cruciferae)
American Journal of Botany, 1991Co-Authors: Elizabeth E. Lyons, Janis AntonovicsAbstract:Populations of Leavenworthia crassa studied for 3 years exhibited among- and within-population genetic variation for a suite of floral and reproductive traits (flower Width, Petal length, anther position, ability to set seeds in the absence of pollinators, time to first flowering) associated with breeding system. We used electrophoretic markers to show that a population with small, monomorphically colored flowers with introrse anthers has a significantly lower outcrossing rate (t=0.03) than did a population with larger, polymorphically colored flowers with extrorse anthers (t=0.33) (...)
Dipti Upadhyay - One of the best experts on this subject based on the ideXlab platform.
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Identification of Iris Plant Using FeedForward Neural Network On The Basis OfFloral Dimensions
International Journal of Innovative Research in Science Engineering and Technology, 2014Co-Authors: Shrikant Vyas, Dipti UpadhyayAbstract:The categorization and recognition of type on the basis of individual characteristics and behaviors form a preliminary measure and is an important target in the behavioural sciences. Current statistical methods do not always give satisfactory results. A Feed Forward Artificial Neural Network is the computer model inspired by the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal Width, Petal length, and Petal Width. Using this data set a Neural Network (NN) is used for the classification of iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of the neural system in iris class identification
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Classification Of Iris Plant Using Feedforward Neural Network
2014Co-Authors: Shrikant Vyas, Dipti UpadhyayAbstract:The classification and recognition of type on the basis of individual features and behaviors constitute a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal Width, Petal length, and Petal Width. Using this data set a Neural Network (NN) is used for the classification of iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of the neural system in iris class identification.
Victoria Sosa - One of the best experts on this subject based on the ideXlab platform.
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Morphological variation in populations of Bletia purpurea (Orchidaceae) and description of the new species B. riparia
Brittonia, 2002Co-Authors: Rene A. Palestina, Victoria SosaAbstract:Bletia purpurea is the most widespread species in its genus. Morphological variation has been recognized throughout the range of its distribution. In this paper, the morphological variation from 63 populations (583 individuals) of Bletia purpurea is assessed to determine whether more than one species were present. Forty-four quantitative and qualitative characters were examined by univariate analyses and exploratory multivariate analyses. Univariate analyses indicate that quantitative characters such as lateral sepal Width, Petal. Width, lip length, and lip Width are significantly different for populations from Acazónica, Mexico. Floral parts in the populations from Acazónica are the smallest among all populations. Qualitative characters such as Petals covering the lip midlobe and horizontal lip position are found exclusively in the same populations. We concluded that these populations should be described as a new species, B. riparia . Multivariate analyses indicated that morphological variation among the other populations cannot be ascribed to geographic distribution or ecological factors. Bletia pupurea es la especie más ampliamente distribuída del género. Se ha reconocido una gran variación morfológica en todo su rango de distribución. En este artículo, se caracteriza la variabilidad morfológica de 63 poblaciones (583 individuos) de Bletia purpurea para determinar si es posible reconocer más de una especie. Se consideraron 44 caracteres cuantitativos y cualitativos y se analizaron con métodos univariados y métodos exploratorios multivariados. Los ánalisis univariados indican que caracteres cuantitativos tales como ancho del sépalo lateral, ancho del pétalo y longitud del labelo fueron significativos para las poblaciones de Acazónica, México. Los elementos florales en las poblaciones de Acazónica fueron los más pequeños de entre todas las poblaciones estudiadas. Caracteres cualitativos tales como pétalos cubriendo el lóbulo medio del labelo y una posición horizontal del labelo fueron también encontrados únicamente en estas mismas poblaciones. Se concluye que deben ser descritas como una nueva especie, B. riparia . Los análisis multivariados indican que la variación morfológica del resto de las poblaciones estudiadas no puede atribuírse a su distribución geográfica o a factores ecológicos.
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Morphological variation in populations of Bletia purpurea (Orchidaceae) and description of the new species B. riparia
Brittonia, 2002Co-Authors: Rene A. Palestina, Victoria SosaAbstract:Bletia purpurea is the most widespread species in its genus. Morphological variation has been recognized throughout the range of its distribution. In this paper, the morphological variation from 63 populations (583 individuals) ofBletia purpurea is assessed to determine whether more than one species were present. Forty-four quantitative and qualitative characters were examined by univariate analyses and exploratory multivariate analyses. Univariate analyses indicate that quantitative characters such as lateral sepal Width, Petal. Width, lip length, and lip Width are significantly different for populations from Acazonica, Mexico. Floral parts in the populations from Acazonica are the smallest among all populations. Qualitative characters such as Petals covering the lip midlobe and horizontal lip position are found exclusively in the same populations. We concluded that these populations should be described as a new species,B. riparia. Multivariate analyses indicated that morphological variation among the other populations cannot be ascribed to geographic distribution or ecological factors.
Shrikant Vyas - One of the best experts on this subject based on the ideXlab platform.
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Identification of Iris Plant Using FeedForward Neural Network On The Basis OfFloral Dimensions
International Journal of Innovative Research in Science Engineering and Technology, 2014Co-Authors: Shrikant Vyas, Dipti UpadhyayAbstract:The categorization and recognition of type on the basis of individual characteristics and behaviors form a preliminary measure and is an important target in the behavioural sciences. Current statistical methods do not always give satisfactory results. A Feed Forward Artificial Neural Network is the computer model inspired by the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal Width, Petal length, and Petal Width. Using this data set a Neural Network (NN) is used for the classification of iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of the neural system in iris class identification
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Classification Of Iris Plant Using Feedforward Neural Network
2014Co-Authors: Shrikant Vyas, Dipti UpadhyayAbstract:The classification and recognition of type on the basis of individual features and behaviors constitute a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal Width, Petal length, and Petal Width. Using this data set a Neural Network (NN) is used for the classification of iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of the neural system in iris class identification.