The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform

Yifung Huang - One of the best experts on this subject based on the ideXlab platform.

  • the development of grey Statistic Toolbox and apply in student s test score
    Systems Man and Cybernetics, 2005
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

  • SMC - The Development of Grey Statistic Toolbox And Apply in Student’s Test Score
    2005 IEEE International Conference on Systems Man and Cybernetics, 1
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

Kunli Wen - One of the best experts on this subject based on the ideXlab platform.

  • the development of grey Statistic Toolbox and apply in student s test score
    Systems Man and Cybernetics, 2005
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

  • SMC - The Development of Grey Statistic Toolbox And Apply in Student’s Test Score
    2005 IEEE International Conference on Systems Man and Cybernetics, 1
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

Meili Yiou - One of the best experts on this subject based on the ideXlab platform.

  • the development of grey Statistic Toolbox and apply in student s test score
    Systems Man and Cybernetics, 2005
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

  • SMC - The Development of Grey Statistic Toolbox And Apply in Student’s Test Score
    2005 IEEE International Conference on Systems Man and Cybernetics, 1
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

Chiachang Tong - One of the best experts on this subject based on the ideXlab platform.

  • the development of grey Statistic Toolbox and apply in student s test score
    Systems Man and Cybernetics, 2005
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

  • SMC - The Development of Grey Statistic Toolbox And Apply in Student’s Test Score
    2005 IEEE International Conference on Systems Man and Cybernetics, 1
    Co-Authors: Kunli Wen, Chiachang Tong, Meili Yiou, Yifung Huang
    Abstract:

    The theme of this paper is to design a 'grey Statistic model Toolbox' via C++ in the GM model, for easy studying and learning in the grey system theory. In the past research about the grey model Toolbox, focused on case-by-case needs. Although all studies have shown good results, only the individual case results can be presented. The full details are still lacking. Therefore, in this study, firstly, the C++ is used to develop a Toolbox for a grey Statistic model. Secondly the clustering of student's test score is presented as the sample, and the feasibility of the Toolbox is implemented, creating this study. Through the results, the grey Statistic Toolbox in the GM field not only presents a lot of figures to make the results more clear, but also upgrades the analytical level in the grey system theory during the whole implementation of this approach

Pascual Belda Aitana - One of the best experts on this subject based on the ideXlab platform.

  • Caracterización del trastorno del espectro autista basado en técnicas de aprendizaje automático a partir de características extraídas de la conectividad funcional del cerebro en estado de reposo
    'Universitat Politecnica de Valencia', 2018
    Co-Authors: Pascual Belda Aitana
    Abstract:

    [ES] El trastorno del espectro autista (TEA) es un desorden de tipo neurológico cuya prevalencia se ha visto enormemente incrementada en los últimos años, hecho probablemente condicionado a las mejoras en las metodologías diagnósticas, pero que no deja de resultar alarmante. A pesar de su prevalencia, y al igual que ocurre con muchos otros trastornos de tipo neurológico o psiquiátrico, las bases neurológicas del TEA aún no han sido completamente esclarecidas. El objetivo del presente Trabajo Final de Máster es evaluar el potencial de distintas técnicas de aprendizaje automático en el diagnóstico del TEA a partir del análisis de imagen de resonancia magnética funcional en estado de reposo. Dicho de otra manera, dado un conjunto de imágenes cerebrales, deseamos conocer si es posible discriminar aquellos sujetos con TEA de aquellos sujetos de referencia o controles. Para ello se analizarán un total de 157 sujetos (75 sujetos con TEA y 82 controles) de la base de datos ABIDE (Autism Brain Imaging Data Exchange). Para llevar a cabo este estudio, la herramienta de software escogida ha sido Matlab®. Para abordar dicho objetivo, en primer lugar, y para cada uno de los sujetos incluidos en el estudio, se calculará la conectividad funcional, cuantificada a partir del coeficiente de correlación de Pearson, entre cada par de regiones cerebrales a partir de la señal temporal BOLD. Después se llevará a cabo un análisis comparativo previo en un subconjunto de sujetos haciendo uso de la Network-Based Statistic Toolbox (NBS). Este análisis de la conectividad nos proporcionará una serie de enlaces entre regiones que muestran diferencias significativas entre grupos y que, por tanto, resultan clave para determinar el patrón de conectividad funcional asociado al TEA. Los valores de conectividad funcional de cada uno de los enlaces que muestran diferencias significativas entre los dos grupos se utilizarán como características para la clasificación del subconjunto restante de sujetos. Así, se aplicarán diversas técnicas de clasificación, como los k-vecinos más próximos, las máquinas de vectores de soporte o las redes neuronales artificiales, para producir un modelo predictivo que permita distinguir entre las imágenes pertenecientes a sujetos con TEA y sujetos control. También se aplicará la técnica de validación cruzada (cross-validation) para una correcta evaluación del modelo generado. Los resultados obtenidos muestran que las redes neuronales artificiales (y más concretamente, el perceptrón multicapa, que ha sido la arquitectura escogida en este trabajo) proporcionan el modelo con una mayor capacidad predictiva de entre los cuatro elaborados, con un porcentaje de acierto en la clasificación en torno al 80% en el conjunto de validación. Se espera que los resultados obtenidos puedan contribuir al desarrollo de herramientas de apoyo al diagnóstico de TEA, y que puedan ser también extrapolables a otras patologías de origen neurológico, como el alcoholismo o la enfermedad de Alzheimer.[EN] The Autism Spectrum Disorder (ASD) is a neurological disorder with an increasing prevalence, and even though this growth is probably related to the improvements in diagnostic methodologies, the increase in the number of ASD diagnosed patients is an alarming fact. Despite its prevalence, and as it happens in many other neurological and psychiatric disorders, the neurological basis of the ASD have not been completely established yet. The aim of this work is to evaluate the potential of machine learning techniques in ASD diagnosis based on the analysis of resting state functional magnetic resonance images. In other words, we want to determine if it is possible, having a set of brain images, to determine which subjects have ASD, and which ones are subjects with a neurotypical development, or control subjects. In order to address this problem, we have analyzed images pertaining to 157 subjects (75 with ASD and 82 controls) obtained from ABIDE (Autism Brain Imaging Data Exchange). The analysis of these images has been performed using Matlab. To achieve this target, the first step is to obtain the functional brain connectivity for each one of the subjects in the study, using Pearson¿s correlation coefficient of BOLD signal between every pair of regions of the brain. Then, we will perform a comparative analysis in a subset of patients using the Network-Based Statistic Toolbox (NBS). This connectivity analysis will provide us with a group of connections among different regions that usually appear significantly altered in one of the groups, and that are therefore key brain alterations in order to determine the ASD characteristic pattern. The obtained set of altered connections will be used as features to classify the remaining subset of patients. Therefore, we will apply several classification techniques, such as k-Nearest Neighbors (KNN), Support Vector Machines (SVMs) or Artificial Neural Networks, to produce a predictive model that distinguishes between images pertaining to ASD subjects, and images pertaining to control subjects. The obtained results show that artificial neural networks (and more concretely, the multilayer perceptron, which is the architecture that has been chosen in this work) provide us with the model with de highest predictive power among the four tested model, with an accuracy near 80% in the validation data. It is expected that the obtained results may contribute to the development of diagnosis support tools in ASD, and that can also be extrapolated to other neurological disorders, such as alcoholism or Alzheimer disease.Pascual Belda, A. (2018). Caracterización del trastorno del espectro autista basado en técnicas de aprendizaje automático a partir de características extraídas de la conectividad funcional del cerebro en estado de reposo. http://hdl.handle.net/10251/110763TFG