Sistemas inteligentes

Esta línea de investigación se centra en el estudio y desarrollo de Sistemas Inteligentes basados en estrategias de adaptación de Redes Neuronales y Sistemas Difusos. Interesa especialmente la transferencia de tecnología a las áreas de minería de datos, robótica y sistemas distribuidos peer-to-peer (P2P). Los temas centrales se encuentran relacionados con la investigación de nuevas estrategias de clustering basadas en redes neuronales competitivas que permitan conocer la topología de la información disponible. Los resultados obtenidos son aplicados tanto a la Minería de Datos como a la búsqueda eficiente de recursos en sistemas distribuidos P2P completamente descentralizada. En el área de la robótica, el énfasis está puesto en el estudio, investigación y desarrollo de aplicaciones de tiempo real basadas en redes neuronales evolutivas, especialmente aplicadas a situaciones cuya solución requiere del aprendizaje de estrategias. Se trabaja en el desarrollo de nuevos métodos para la resolución de problemas utilizando agentes capaces de percibir y actuar en entornos complejos cuyos resultados son aplicados directamente en esta área. Resulta también de interés el estudio de métodos para la generación automática de Sistemas Difusos adecuados para la resolución de diversos tipos de problemas. El objetivo central es la aplicación de estrategias evolutivas para la construcción de Sistemas con reglas compactas, adecuadas y de fácil interpretación.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Sistemas inteligentes

Esta línea de investigación se centra en el estudio y desarrollo de Sistemas Inteligentes basados en estrategias de adaptación de Redes Neuronales y Sistemas Difusos. Interesa especialmente la transferencia de tecnología a las áreas de minería de datos, robótica y sistemas distribuidos peer-to-peer (P2P). Los temas centrales se encuentran relacionados con la investigación de nuevas estrategias de clustering basadas en redes neuronales competitivas que permitan conocer la topología de la información disponible. Los resultados obtenidos son aplicados tanto a la Minería de Datos como a la búsqueda eficiente de recursos en sistemas distribuidos P2P completamente descentralizada. En el área de la robótica, el énfasis está puesto en el estudio, investigación y desarrollo de aplicaciones de tiempo real basadas en redes neuronales evolutivas, especialmente aplicadas a situaciones cuya solución requiere del aprendizaje de estrategias. Se trabaja en el desarrollo de nuevos métodos para la resolución de problemas utilizando agentes capaces de percibir y actuar en entornos complejos cuyos resultados son aplicados directamente en esta área. Resulta también de interés el estudio de métodos para la generación automática de Sistemas Difusos adecuados para la resolución de diversos tipos de problemas. El objetivo central es la aplicación de estrategias evolutivas para la construcción de Sistemas con reglas compactas, adecuadas y de fácil interpretación.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Sistemas inteligentes

Esta línea de investigación se centra en el estudio y desarrollo de Sistemas Inteligentes basados en estrategias de adaptación de Redes Neuronales y Sistemas Difusos. Interesa especialmente la transferencia de tecnología a las áreas de minería de datos, robótica y sistemas distribuidos peer-to-peer (P2P). Los temas centrales se encuentran relacionados con la investigación de nuevas estrategias de clustering basadas en redes neuronales competitivas que permitan conocer la topología de la información disponible. Los resultados obtenidos son aplicados tanto a la Minería de Datos como a la búsqueda eficiente de recursos en sistemas distribuidos P2P completamente descentralizada. En el área de la robótica, el énfasis está puesto en el estudio, investigación y desarrollo de aplicaciones de tiempo real basadas en redes neuronales evolutivas, especialmente aplicadas a situaciones cuya solución requiere del aprendizaje de estrategias. Se trabaja en el desarrollo de nuevos métodos para la resolución de problemas utilizando agentes capaces de percibir y actuar en entornos complejos cuyos resultados son aplicados directamente en esta área. Resulta también de interés el estudio de métodos para la generación automática de Sistemas Difusos adecuados para la resolución de diversos tipos de problemas. El objetivo central es la aplicación de estrategias evolutivas para la construcción de Sistemas con reglas compactas, adecuadas y de fácil interpretación.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A New Measure of Cluster Validity Using Line Symmetry

[[abstract]]Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of "line symmetry" is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. Several data sets are used to illustrate the performance of the proposed measure.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]TW

신경망 인공지능 의사결정 모델을 이용한 발치 진단의 새로운 방법 제안

학위논문 (박사)-- 서울대학교 대학원 : 치의학대학원 치의과학과 치과교정학전공, 2016. 2. 김태우.Introduction: The diagnosis of extractions in the orthodontic treatment is important and difficult, because that decision has tendency to be based on the practitioners experiences. The purpose of this study was to construct an artificial intelligent expert system for the diagnosis of extraction using neural network machine learning (NNML) and to evaluate performance of this model. Methods: The subjects consisted of 156 patients in total. Input data consisted of 12 cephalometric variables and additional six indices. Output data consisted of three bits to divide extraction patterns. Four NNML models for the diagnosis of extractions were constructed using backpropagation algorithm, and were evaluated. Results: The success rates of the models showed 93% for the diagnosis of extraction versus non-extraction, and showed 84% for the detailed diagnosis of the extraction patterns. Conclusions: This study suggests that artificial intelligent expert systems using neural network machine learning could be useful in orthodontics. Improving performance was achieved by the components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization.I. Introduction 1 II. Review of Literature 3 III. Material and Methods 9 IV. Results 13 V. Discussion 15 VI. Conclusion 19 VII. References 30Docto

Cephalometric Variables Prediction from Lateral Photographs Between Different Skeletal Patterns Using Regression Artificial Neural Networks

Objective: This study aimed to design an artificial neural network for the prediction of cephalometric variables via a lateral photograph in skeletal Class I, II, and III patterns.Methods: A total of 94 patients were recruited for this prospective study, with an age range of 15-20 years (41 boys and 53 girls) seeking orthodontic treatment. According to cephalometric analysis, using AutoCAD 21.0, they were allocated into three groups. Thirty with skeletal Class I (14 boys and 16 girls), 34 with skeletal Class II (14 boys and 20 girls), and 30 with skeletal Class III malocclusion (13 boys and 17 girls) according to SNA, SNB, and ANB angles measured from cephalometric radiographs. The study includes (1) finding the correlation of the skeletal measurements between lateral profile photographs and cephalometric radiographs for the recruited patients and (2) designing a specific artificial neural networks for the assessment of skeletal factors via lateral photographs, these artificial neural networks are trained and tested with the total of 94 standard lateral cephalograms.Results: This novel Network provided models of regression that can forecast the cephalometric variables through analogous photographic measurements with excellent predictive power R = 0.99 and limited estimation error for each malocclusion (Class I, II, and III).Conclusion: This study suggests that artificial intelligence would be useful as an accurate method in orthodontics for the prediction of cephalometric variables and its performance was achieved by several factors such as proper selection of the input data, preferable generalization, and organization

Adaptive Double Self-Organizing Map for Clustering Gene Expression Data

This thesis presents a novel clustering technique known as adaptive double self- organizing map (ADSOM) that addresses the issue of identifying the correct number of clusters. ADSOM has a flexible topology and performs clustering and cluster visualization simultaneously, thereby requiring no a priori knowledge about the number of clusters. ADSOM combines features of the popular self-organizing map with two- dimensional position vectors, which serve as a visualization tool to decide the number of clusters. It updates its free parameters during training and it allows convergence of its position vectors to a fairly consistent number of clusters provided that its initial number of nodes is greater than the expected number of clusters. A novel index is introduced based on hierarchical clustering of the final locations of position vectors. The index allows automated detection of the number of clusters, thereby reducing human error that could be incurred from counting clusters visually. The reliance of ADSOM in identifying the number of clusters is proven by applying it to publicly available gene expression data from multiple biological systems such as yeast, human, mouse, and bacteria

Use of Self Organized Maps for Feature Extraction of Hyperspectral Data

In this paper, the problem of analyzing hyperspectral data is presented. The complexity of multi-dimensional data leads to the need for computer assisted data compression and labeling of important features. A brief overview of Self-Organizing Maps and their variants is given and then two possible methods of data analysis are examined. These methods are incorporated into a program derived from som_toolbox2. In this program, ASD data (data collected by an Analytical Spectral Device sensor) is read into a variable, relevant bands for discrimination between classes are extracted, and several different methods of analyzing the results are employed. A GUI was developed for easy implementation of these three stages

Enhanced data clustering and classification using auto-associative neural networks and self organizing maps

This thesis presents a number of investigations leading to introduction of novel applications of intelligent algorithms in the fields of informatics and analytics. This research aims to develop novel methodologies to reduce dimensions and clustering of highly non-linear multidimensional data. Improving the performance of existing methodologies has been based on two fundamental approaches. The first is to look into making novel structural re-arrangements by hybridisation of conventional intelligent algorithms which are Auto-Associative Neural Networks (AANN) and Self Organizing Maps (SOM) for data clustering improvement. The second is to enhance data clustering and classification performance by introducing novel fundamental algorithmic changes known as M3-SOM in the data processing and training procedure of conventional SOM. Both approaches are tested, benchmarked and analysed using three datasets which are Iris Flowers, Italian Olive Oils and Wine through case studies for dimension reduction, clustering and classification of complex and non-linear data. The study on AANN alone shows that this non-linear algorithm is able to efficiently reduce dimensions of the three datasets. This paves the way towards structurally hybridising AANN as dimension reduction method with SOM as clustering method (AANNSOM) for data clustering enhancement. This hybrid AANNSOM is then introduced and applied to cluster Iris Flowers, Italian Olive Oils and Wine datasets. The hybrid methodology proves to be able to improve data clustering accuracy, reduce quantisation errors and decrease computational time when compared to SOM in all case studies. However, the topographic errors showed inconsistency throughout the studies and it is still difficult for both AANNSOM and SOM to provide additional inherent information of the datasets such as the exact position of a data in a cluster. Therefore, M3-SOM, a novel methodology based on SOM training algorithm is proposed, developed and studied on the same datasets. M3-SOM was able to improve data clustering and classification accuracy for all three case studies when compared to conventional SOM. It is also able to obtain inherent information about the position of one data or "sub-cluster" towards other data or sub-cluster within the same class in Iris Flowers and Wine datasets. Nevertheless, it faces difficulties in achieving the same level of performance when clustering Italian Olive Oils data due to high number of data classes. However, it can be concluded that both methodologies have been able to improve data clustering and classification performance as well as to discover inherent information inside multidimensional data