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Information on the Web is growing dramatically. Without a recommended system, the users may spend lots of time on the Web in finding the information they are interested in [5]. With the Web becoming the most popular media for collecting, sharing, and distributing information, it is very common for educational institutions, and organizations to develop Web-Based Training (WBT) systems [6]. Data mining...
Infrastructure investment decisions consider future infrastructure demand projections from freight models, the quality of which depends on fidelity of input freight data. The Freight Analysis Framework Version 2.2 (FAF2.2) being a primary source of freight data for infrastructure planning provides commodity origin-destination flows for 114 zones within the USA. Freight disaggregation approaches using...
Gene selection aims at detecting biologically relevant genes to assist biologists' research. The cDNA microarray data used in gene selection is usually "wide". With more than ten thousand genes, but only less than a hundred of samples, many biologically irrelevant genes can gain their statistical relevance by sheer randomness. Moreover, even for genes that are biologically relevant, biologists...
This paper proposes a simple technique for automatic detection and delineation of cardiac beat (QRS-complex) in Electrocardiogram (ECG) using Fuzzy C-Means (FCM) clustering algorithm. The power line interference and baseline wander present in the ECG signal is removed using digital filtering techniques. Absolute slope of the filtered ECG signal is calculated to enhance the QRS-complexes in the ECG...
In this paper, we present and compare three clustering approaches which group fingerprints according to its minutiae points locations. The best technique for grouping fingerprints is based on the ART1 clustering algorithm. We compare the quality of clustering of ART1 based clustering with k-mean clustering technique and self organizing neural network (SOM) [1] clustering algorithm in terms of intra-cluster...
In this paper, we present a novel approach to group fingerprints according to its minutiae point's locations. Our technique for grouping fingerprints is based on the ART1 neural network. We compare the quality of clustering of our ART1 based clustering technique with that of the self organizing neural network (SOM) clustering algorithm in terms of intra-cluster distances. Our results show that the...
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