The Experts below are selected from a list of 135954 Experts worldwide ranked by ideXlab platform
Hung T. Nguyen - One of the best experts on this subject based on the ideXlab platform.
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non invasive nocturnal hypoglycemia detection for insulin dependent diabetes mellitus using genetic fuzzy logic method
International Journal of Computational Intelligence and Applications, 2012Co-Authors: Steve S H Ling, Hung T. Nguyen, Phyo Phyo San, F H F LeungAbstract:Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy Reasoning Model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy Model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.
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natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy Reasoning Model
Artificial Intelligence in Medicine, 2012Co-Authors: Sai Ho Ling, Hung T. NguyenAbstract:Introduction: Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the Modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-Reasoning system. Methods: Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-Reasoning Model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated. Results: From our clinical study of 16 children with T1DM, natural occurrence of nocturnal-hypoglycemic episodes was associated with increased heart rates and increased corrected QT intervals. All the data sets were collected from the Government of Western Australia's Department of Health. All data were organized randomly into a training set (8 patients with 320 data points) and a testing set (another 8 patients with 269 data points). To prevent the phenomenon of overtraining, we separated the training set into 2 sets (4 patients in each set) and a fitness function was introduced for this training process. The testing performances of the proposed algorithm for detection of advanced hypoglycemic episodes (sensitivity=85.71% and specificity=79.84%) and hypoglycemic episodes (sensitivity=80.00% and specificity=55.14%) were given. Conclusion: We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-Reasoning Model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.
Rafael Bello - One of the best experts on this subject based on the ideXlab platform.
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A connectionist fuzzy case-based Reasoning Model
Lecture Notes in Computer Science, 2006Co-Authors: Yanet Rodriguez, María M. García, Bernard De Baets, Carlos Morell, Rafael BelloAbstract:This paper presents a new version of an existing hybrid Model for the development of knowledge-based systems, where case-based Reasoning is used as a problem solver. Numeric predictive attributes are Modeled in terms of fuzzy sets to define neurons in an associative Artificial Neural Network (ANN). After the Fuzzy-ANN is trained, its weights and the membership degrees in the training examples are used to automatically generate a local distance function and an attribute weighting scheme. Using this distance function and following the Nearest Neighbor rule, a new hybrid Connectionist Fuzzy Case-Based Reasoning Model is defined. Experimental results show that the Model proposed allows to develop knowledge-based systems with a higher accuracy than when using the original Model. The Model takes the advantages of the approaches used, providing a more natural framework to include expert knowledge by using linguistic terms.
Germano Lambert-torres - One of the best experts on this subject based on the ideXlab platform.
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Particle swarm optimization for fuzzy membership functions optimization
IEEE International Conference on Systems Man and Cybernetics, 2002Co-Authors: Ahmed A. A. Esmin, Alexandre Rasi Aoki, Germano Lambert-torresAbstract:The use of fuzzy logic to solve control problems have been increasing considerably in the past years. The successfulness of fuzzy application depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. One way to improve the performance of the fuzzy Reasoning Model is the use of genetic algorithm. In this paper it is shown that a particle swarm optimization (PSO) algorithm learning mechanism, supplements the performance of fuzzy Reasoning Model. The PSO is able to generate an optimal set of parameters for fuzzy Reasoning Model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a strategy for the membership functions automatic adjustment, using PSO algorithms, and presents an application designed to park a vehicle into a garage, beginning from any start position.
Kyung In Kang - One of the best experts on this subject based on the ideXlab platform.
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a case based Reasoning cost estimating Model using experience by analytic hierarchy process
Building and Environment, 2007Co-Authors: Sung Hoon An, Kyung In KangAbstract:A case-based Reasoning Model is proposed, where experience is included in all processes of construction cost estimating by the analytic hierarchy process. The Model overcomes the difficulty of measuring experience for determining the weights of attributes. The accuracy of three different Models was compared. The Model using the analytic hierarchy process was more accurate, reliable, and explanatory than the other Models, and closer to the original aim of the case-based Reasoning Model, for solving new problems using experience from previous cases.
Feniosky Penamora - One of the best experts on this subject based on the ideXlab platform.
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distributed multi Reasoning mechanism to support conceptual structural design
Journal of Structural Engineering-asce, 2000Co-Authors: Lucio Soibelman, Feniosky PenamoraAbstract:The conceptual phase of structural design involves selecting preliminary materials, selecting the overall structural form of the building, producing a rough dimensional layout, and considering technological possibilities. Decisions are made on the basis of such information as height of the building, building use, typical live load, wind velocity, earthquake loading, design fundamental period, design acceleration, maximum lateral deflection, spans, story height, and other client requirements. More detailed information about the task itself, constraints, possible solution principles, and known solutions for similar problems is extremely useful in the process of defining and finding a solution to the design problem. This paper presents the M-RAM, which is intended to assist engineers in the conceptual phase of the structural design of tall buildings by providing designers with adapted past design solutions generated by a distributed multi-Reasoning mechanism. The objective of the M-RAM (Multi-Reasoning Model...
