&lt;bold&gt;Computer Vision and Fuzzy-Neural Systems &lt;/bold&gt;

Kulkarni, Arun D.

doi:10.1117/1.1640620

Cited by 71 publications

(54 citation statements)

References 0 publications

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“…La lógica difusa-LF se presenta como una herramienta para lograr esta transformación; sus fundamentos son los conjuntos difusos según los cuales el grado de pertenencia de un elemento a un conjunto está determinado por una función de pertenencia que puede tomar todos los valores reales comprendidos en el intervalo [0, 1] [6][7][8][9]. LF permite definir grados variables de pertenencia que se asemejan, en razonamiento y patrones, al pensamiento humano, dejando conceptos determinísticos tradicionales.…”

Section: Sistemas De Lógica Difusaunclassified

Sistema de inferencia difuso para la valoración de empresas

López

Laserna

Corrales

2014

rev.ing.univ.Medellin

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ResumenEl objetivo de esta investigación es proponer una herramienta basada en lógica difusa que permita implementarse en la valoración de empresas, admitiendo la inclusión de variables y criterios no cuantitativos en dicho proceso. Inicialmente, se exploran algunas de las principales metodologías de valoración de empresas, se identifican los factores que las impactan, y se establecen como insumo para la herramienta que propone esta investigación. Posteriormente, se presenta el marco teórico de lógica difusa y sobre el cual se sustenta la herramienta. Luego, se establecen las características en términos difusos de los componentes de la misma y se comparten algunos resultados. La herramienta presentada, además de incluir variables cualitativas y apoyar la toma de decisiones de inversión, permite el seguimiento a la gestión, ya que facilita medir las variaciones que algunas decisiones organizacionales generan en el valor de la empresa.Palabras clave: valoración, lógica difusa, finanzas.

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Section: Sistemas De Lógica Difusaunclassified

Sistema de inferencia difuso para la valoración de empresas

López

Laserna

Corrales

2014

rev.ing.univ.Medellin

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“…Each node acts as a separate decision, and leads to a class at bottom of the tree, or the leaves. These trees act as multi-stage classifiers and are more efficient than single-stage classifiers since decisions are made at multiple levels and reduce the computational load [17]. By selecting a leaf node and traversing up the tree recording attributes and decision values until the root is reached, the rule can be created by listing those conditions.…”

Section: B Data Analysismentioning

confidence: 99%

“…For a given input more than one rule may fire, in a FLS multiple regions are combined in the output space to produce a composite region. A general schematic of a FLS is shown in Figure 2 [17].…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction Trees

Taylor¹,

Kulkarni²,

Mokhtari³

2016

ijacsa

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Abstract-The assessment of students' metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, we built a fuzzy inference system (FIS) with the extracted rules as a rule base and classified the test dataset with the FIS. The extracted rules are evaluated using measures such as the overall efficiency and Kappa coefficient.

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“…It is a concept well known in physics. Entropy measurement method is to measure the degree of the disorder of any sign [14]. In recent years, the entropy is widely used in the field of signal processing has become a concept.…”

Section: Digital Image Processing Stagesmentioning

confidence: 99%

“…This data must be making the computer can process. At this point, the decision must be given to the outer limits of the data in the image of an object belonging to whether you get the inside line [12][13][14].…”

Section: Digital Image Processing Stagesmentioning

confidence: 99%

Çoklu Entropi̇ Ve Yapay Si̇ni̇r Aği Yöntemi̇ (Çe-Ysa) Kullanarak Rna Vi̇rüsleri̇ni̇n Otomati̇k Bulunmasi

Doğantekin¹,

Avcı²,

Poyraz³

et al. 2014

NWSA

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Nowadays, there are many studies on microbiologic diagnosis literature. In this study, the Multi-entropy and Artificial Neural Network (ME-ANN) system is presented for automatic detection of RNA virus images. This system consists of four stages. They are respectively pre-processing, feature extraction, classification and test of correct detection ratio of this ME-ANN method. In pre-processing stage, it is used the center -edge changing method. In this method, Euclidian distances are calculated the from center pixels of an object on image to edges of this object. Therefore, the distance vector has been obtained.This calculating is repeated for each of RNA virus images used in this study. In feature extraction stage, the norm, the logarithmic energy and threshold entropy values are calculated as feature vector. The obtained these features are invariant from rotation and scale of these RNA virus images. In classification stage, these obtained feature vector is given to the ANN classifier. Finally the test stage is performed for evaluation the correct detection ratio of ME-ANN algorithm for RNA virus images. The correct detection ratio of the proposed system is 94.02%.

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<bold>Computer Vision and Fuzzy-Neural Systems </bold>

Cited by 71 publications

References 0 publications

Sistema de inferencia difuso para la valoración de empresas

Sistema de inferencia difuso para la valoración de empresas

Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction Trees

Çoklu Entropi̇ Ve Yapay Si̇ni̇r Aği Yöntemi̇ (Çe-Ysa) Kullanarak Rna Vi̇rüsleri̇ni̇n Otomati̇k Bulunmasi

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