Revista y Edición

Volumen 12 (2022): Edición 3 (July 2022)

Volumen 12 (2022): Edición 2 (April 2022)

Volumen 12 (2022): Edición 1 (January 2022)

Volumen 11 (2021): Edición 4 (October 2021)

Volumen 11 (2021): Edición 3 (July 2021)

Volumen 11 (2021): Edición 2 (April 2021)

Volumen 11 (2021): Edición 1 (January 2021)

Volumen 10 (2020): Edición 4 (October 2020)

Volumen 10 (2020): Edición 3 (July 2020)

Volumen 10 (2020): Edición 2 (April 2020)

Volumen 10 (2020): Edición 1 (January 2020)

Volumen 9 (2019): Edición 4 (October 2019)

Volumen 9 (2019): Edición 3 (July 2019)

Volumen 9 (2019): Edición 2 (April 2019)

Volumen 9 (2019): Edición 1 (January 2019)

Volumen 8 (2018): Edición 4 (October 2018)

Volumen 8 (2018): Edición 3 (July 2018)

Volumen 8 (2018): Edición 2 (April 2018)

Volumen 8 (2018): Edición 1 (January 2018)

Volumen 7 (2017): Edición 4 (October 2017)

Volumen 7 (2017): Edición 3 (July 2017)

Volumen 7 (2017): Edición 2 (April 2017)

Volumen 7 (2017): Edición 1 (January 2017)

Volumen 6 (2016): Edición 4 (October 2016)

Volumen 6 (2016): Edición 3 (July 2016)

Volumen 6 (2016): Edición 2 (April 2016)

Volumen 6 (2016): Edición 1 (January 2016)

Volumen 5 (2015): Edición 4 (October 2015)

Volumen 5 (2015): Edición 3 (July 2015)

Volumen 5 (2015): Edición 2 (April 2015)

Volumen 5 (2015): Edición 1 (January 2015)

Volumen 4 (2014): Edición 4 (October 2014)

Volumen 4 (2014): Edición 3 (July 2014)

Volumen 4 (2014): Edición 2 (April 2014)

Volumen 4 (2014): Edición 1 (January 2014)

Volumen 3 (2013): Edición 4 (October 2013)

Volumen 3 (2013): Edición 3 (July 2013)

Volumen 3 (2013): Edición 2 (April 2013)

Volumen 3 (2013): Edición 1 (January 2013)

Detalles de la revista
Formato
Revista
eISSN
2449-6499
Publicado por primera vez
30 Dec 2014
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

Volumen 4 (2014): Edición 1 (January 2014)

Detalles de la revista
Formato
Revista
eISSN
2449-6499
Publicado por primera vez
30 Dec 2014
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

5 Artículos
Acceso abierto

The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks

Publicado en línea: 30 Dec 2014
Páginas: 5 - 19

Resumen

Abstract

Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images

Acceso abierto

Quality Parameter Assessment on iris Images

Publicado en línea: 30 Dec 2014
Páginas: 21 - 30

Resumen

Abstract

Iris biometric for personal identification is based on capturing an eye image and obtaining features that will help in identifying a human being. However, captured images may not be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is important to assess image quality before applying feature extraction algorithm in order to avoid insufficient results. Poor quality images may affect the recognition as they have few sufficient feature information. Moreover, existing quality measures focuses on parameters or factors than feature information. In this paper, iris quality assessment research is extended by analysing the effect of entropy, contrast, area ratio, occlusion, blur, dilation and sharpness of an iris image which determines the iris size, amount of information and clearness of the features. A weighting method based on principal component analysis (PCA) is proposed to determine the influence each parameter has on the quality score. To test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA), Internal Collection (IC) and University of Beira Interior (UBIRIS) databases are used. A conclusion is drawn that the combination of blur, dilation and sharpness parameters have the most influence in the quality of the image as they weighed more than other parameters

Acceso abierto

Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

Publicado en línea: 30 Dec 2014
Páginas: 31 - 42

Resumen

Abstract

The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches

Acceso abierto

Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters

Publicado en línea: 30 Dec 2014
Páginas: 43 - 56

Resumen

Abstract

Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used

Acceso abierto

Group Decision Making using Interval-Valued Intuitionistic Fuzzy Soft Matrix and Confident Weight of Experts

Publicado en línea: 30 Dec 2014
Páginas: 57 - 77

Resumen

Abstract

This article proposes an algorithmic approach for multiple attribute group decision making (MAGDM) problems using interval-valued intuitionistic fuzzy soft matrix (IVIFSM) and confident weight of experts. We propose a novel concept for assigning confident weights to the experts based on cardinals of interval-valued intuitionistic fuzzy soft sets (IVIFSSs). The confident weight is assigned to each of the experts based on their preferred attributes and opinions, which reduces the chances of biasness. Instead of using medical knowledgebase, the proposed algorithm mainly relies on the set of attributes preferred by the group of experts. To make the set of preferred attributes more important, we use combined choice matrix, which is combined with the individual IVIFSM to produce the corresponding product IVIFSM. This article uses IVIFSMs for representing the experts’ opinions. IVIFSM is the matrix representation of IVIFSS and IVIFSS is a natural combination of interval-valued intuitionistic fuzzy set (IVIFS) and soft set. Finally, the performance of the proposed algorithm is validated using a case study from real life

5 Artículos
Acceso abierto

The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks

Publicado en línea: 30 Dec 2014
Páginas: 5 - 19

Resumen

Abstract

Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images

Acceso abierto

Quality Parameter Assessment on iris Images

Publicado en línea: 30 Dec 2014
Páginas: 21 - 30

Resumen

Abstract

Iris biometric for personal identification is based on capturing an eye image and obtaining features that will help in identifying a human being. However, captured images may not be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is important to assess image quality before applying feature extraction algorithm in order to avoid insufficient results. Poor quality images may affect the recognition as they have few sufficient feature information. Moreover, existing quality measures focuses on parameters or factors than feature information. In this paper, iris quality assessment research is extended by analysing the effect of entropy, contrast, area ratio, occlusion, blur, dilation and sharpness of an iris image which determines the iris size, amount of information and clearness of the features. A weighting method based on principal component analysis (PCA) is proposed to determine the influence each parameter has on the quality score. To test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA), Internal Collection (IC) and University of Beira Interior (UBIRIS) databases are used. A conclusion is drawn that the combination of blur, dilation and sharpness parameters have the most influence in the quality of the image as they weighed more than other parameters

Acceso abierto

Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

Publicado en línea: 30 Dec 2014
Páginas: 31 - 42

Resumen

Abstract

The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches

Acceso abierto

Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters

Publicado en línea: 30 Dec 2014
Páginas: 43 - 56

Resumen

Abstract

Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used

Acceso abierto

Group Decision Making using Interval-Valued Intuitionistic Fuzzy Soft Matrix and Confident Weight of Experts

Publicado en línea: 30 Dec 2014
Páginas: 57 - 77

Resumen

Abstract

This article proposes an algorithmic approach for multiple attribute group decision making (MAGDM) problems using interval-valued intuitionistic fuzzy soft matrix (IVIFSM) and confident weight of experts. We propose a novel concept for assigning confident weights to the experts based on cardinals of interval-valued intuitionistic fuzzy soft sets (IVIFSSs). The confident weight is assigned to each of the experts based on their preferred attributes and opinions, which reduces the chances of biasness. Instead of using medical knowledgebase, the proposed algorithm mainly relies on the set of attributes preferred by the group of experts. To make the set of preferred attributes more important, we use combined choice matrix, which is combined with the individual IVIFSM to produce the corresponding product IVIFSM. This article uses IVIFSMs for representing the experts’ opinions. IVIFSM is the matrix representation of IVIFSS and IVIFSS is a natural combination of interval-valued intuitionistic fuzzy set (IVIFS) and soft set. Finally, the performance of the proposed algorithm is validated using a case study from real life

Planifique su conferencia remota con Sciendo