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Harish, B. S.
- Interpretation of ECG using Modified Intuitionistic Fuzzy C-Means Clustering for Arrhythmia Data
Abstract Views :199 |
PDF Views:1
Authors
C. K. Roopa
1,
B. S. Harish
2
Affiliations
1 Department of Computer Science, JSS Technical Institution Campus, Mysuru, IN
2 Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, IN
1 Department of Computer Science, JSS Technical Institution Campus, Mysuru, IN
2 Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 1 (2018), Pagination: 1788-1793Abstract
An electrocardiogram (ECG) is defined as a measure of variation in the electrical activity of the heart and is broadly used in detection and classification of heart-related diseases. The abnormalities present in the heart can be easily analyzed through the variation in electrical signal captured from the heart through impulse waveforms which are generated by certain specialized cardiac tissues. Different authors have developed various clustering models and classification techniques for detecting heart-related diseases. However there still exists a limitation in terms of accuracy. In this article, we proposed a new modified unsupervised clustering algorithm for effective detection of heart diseases. To select the best discriminate feature for effective learning, this article make use of feature selection methods such as principal component analysis, linear discriminative analysis, and regularized locality preserving indexing. The reduced features set are clustered using modified intuitionistic Fuzzy C-means clustering (mifcm) method. The experiment results proved that the proposed method effectively identifies the discriminative features. Further the obtained accuracy is also better when compared to other existing method.Keywords
Electrocardiogram, Heart Diseases, Feature Selection, Intuitionistic Fuzzy C-Means.
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- Symbolic Representation of Internet Traffic Data using Multiple Kernel Fuzzy C-Means
Abstract Views :748 |
PDF Views:1
Authors
N. Manju
1,
B. S. Harish
2
Affiliations
1 Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, IN
2 Department of Information Science and Engineering, JSS Science and Technology University, IN
1 Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, IN
2 Department of Information Science and Engineering, JSS Science and Technology University, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 4 (2019), Pagination: 1951-1955Abstract
Network traffic classification is a core part of the network traffic management. Network management is a critical task since the various new applications are emerging every moment and increase in the number of users of an internet. Due to this problem, there is a need of internet traffic classification for smooth management of an internet by the internet service providers (ISP). Network traffic can be classified based on port, payload and statistical approach. In the proposed work, a novel method to represent internet traffic data based on clustering of feature vector using Multiple Kernel Fuzzy C-Means (MKFCM) is proposed. Further, feature vector of each cluster is used to build an interval valued representation (symbolic) using mean and standard deviation. In addition, this interval valued features are stored in knowledge base as a representative of the cluster. Further, to classify the symbolic interval data, we used symbolic classifier. To validate the effectiveness of the proposed model, experimentation is conducted on standard Cambridge University internet traffic dataset. Further, the proposed symbolic classifier compared with other existing classifiers such as Naïve Bayes, KNN and SVM classifier. The experiment outcome infers that; the proposed symbolic representation classifier performs better than other classifiers.Keywords
Internet Traffic, Representation, Symbolic Feature, Classification.References
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