This study is focus on the supervised algorithm in order to classify the emotions from speech. The fuzzy-knn classifier algorithm comparing with the classical knn has the advantage to quantify the "strength"...
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ISBN:
(纸本)9781467311762
This study is focus on the supervised algorithm in order to classify the emotions from speech. The fuzzy-knn classifier algorithm comparing with the classical knn has the advantage to quantify the "strength" of the membership to a class. In the classical knnalgorithm, the decision regarding the assigning of an instance to a class was taken only based on the majority number of neighbors in a particular class;each neighbor has the same importance in the classification process. Therefore the results obtained with fuzzy knn algorithm are improved compared to those obtained in our previous studies. This paper aims to analyze the percentages of the emotion classification using statistical parameters extracted from the SROL emotional database. The features vectors contain 17 parameters;in the future we intend to extend the number of parameters used for classification of the emotions.
This paper describes an approach using computational intelligence methods to form a hybrid model as a classification method for 2-lead cardiac arrhythmias. The hybridization of methods can increase the performance in ...
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This paper describes an approach using computational intelligence methods to form a hybrid model as a classification method for 2-lead cardiac arrhythmias. The hybridization of methods can increase the performance in a system and take advantage of the benefits offered by such techniques in solving complex problems. The interpretation of electrocardiograms is a useful task for physicians, but when it comes to reviewing more than 24 h of information, it becomes a laborious task for them. For this reason, the design a computational model that helps in such a task is very useful for the timely medical diagnosis. The hybrid model is build using artificial neural networks and fuzzy logic. Training and testing of the hybrid model was with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database. The heartbeats are preprocessed to improve results of classification. Ten different classes of normal and arrhythmia signals for building the hybrid model are considered. We used two electrode signals or leads included in the MIT-BIH arrhythmia database, MLII and V1, V2, or V3 as second electrode signal. The hybrid model is composed by two basic module units, as described below. A basic module unit to perform the classification for each signal lead is used. Each basic module unit is composed of three different classifiers based on the following models: fuzzy knn algorithm, multilayer perceptron with gradient descent and momentum (MLP-GDM), and multilayer perceptron with scaled conjugate gradient backpropagation (MLP-SCG). The outputs from the classifiers are combined using a fuzzy system for integration of results. We designed two fuzzy systems, Mamdani type-1 fuzzy system (type-1 FIS) and an interval type-2 fuzzy system (IT2FIS). The reason is to perform a comparison between type-1 FIS and IT2FIS in the hybrid model. We have obtained best results in the classification rate using IT2FIS instead of type-1 FIS in the basic units. Finally, a typ
作者：
Shen, Yi-ZhenDing, Yong-ShengDonghua Univ
Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ
Coll Informat Sci & Technol Engn Res Ctr Digitized Text & Fash Technol Minist Educ Shanghai 201620 Peoples R China
Membrane protein and its interaction network has become a novel research direction in bioinformatics. Multiple researches on these interactions can improve our understanding of diseases and provide the basis to revolu...
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ISBN:
(纸本)9781424455690
Membrane protein and its interaction network has become a novel research direction in bioinformatics. Multiple researches on these interactions can improve our understanding of diseases and provide the basis to revolutionize therapeutic treatments. In this paper, a novel membrane protein interaction network simulator is proposed for system biology studies by ensemble intelligent method including spectrum analysis and fuzzy knn algorithm. We consider biological system as a set of active computational components interacting with each other and an external environment. Then we can use the network simulator to construct membrane protein interaction networks. Based on the proposed approach, we find out that the membrane protein interaction network almost has the some dynamic and collective characters, such as small-world network, topological character, and hierarchical module structure. These characters of the membrane protein interaction network will be valuable for its relatively biological and medical studies.
Data Mining has great scope in the field of medicine. In this article we introduced two new fuzzy approaches for prediction of diabetes disease and liver disorder. Many researchers have proposed the use of K-nearest n...
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ISBN:
(纸本)9781479917341
Data Mining has great scope in the field of medicine. In this article we introduced two new fuzzy approaches for prediction of diabetes disease and liver disorder. Many researchers have proposed the use of K-nearest neighbor (knn) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for preprocessing and then using knn for classification. In our first approach fuzzy c-means clustering algorithm is used to cluster the data. Finally, the classification is done using knn. Similarly, in our second method fuzzy c-means clustering is followed by classification using fuzzyknn. PIMA diabetes and liver disorder data sets are used to test our methods. We are able to obtain models more precise than any others available in the literature. The second approach produced better result than the first one. Classification is done using ten folds cross-validation technique. The introduction of fuzzyalgorithms certainly has a positive effect on the outcome of diabetes disease and liver disorder prediction models. These fuzzy models led to remarkable increase in classification accuracy.
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