Abdel-Badeeh M. Salem

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DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data which is containing noise and outliers. But the clusters detected by it contain large amount of density variation within them. It can not handle the local density variation that exists within the cluster. For good(More)
Pandemic influenza has great potential to cause large and rapid increases in deaths and serious illness. The objective of this paper is to develop an agent-based model to simulate the spread of pandemic influenza (novel H1N1) in Egypt. The proposed multi-agent model is based on the modeling of individuals' interactions in a space-time context. The proposed(More)
This paper aims to assess the effectiveness of three different clustering algorithms, used to detect breast cancer recurrent events. The performance of a classical k-means algorithm is compared with a much more sophisticated Self-Organizing Map (SOM-Kohonen network) and a cluster network, closely related to both k-means and SOM. The three clustering(More)
In this paper, we have used the Case Based Reasoning methodology to develop a case-based expert system prototype for supporting diagnosis of heart diseases. 110 cases were collected for 4 heart diseases namely; mitral stenosis, left-sided heart failure, stable angina pectoris and essential hypertension. Each case contains 207 attributes concerning both(More)
The efficient Semantic Web, needs knowledge to be presented in a well defined form which enables people and software agents to understand it. There are many knowledge representation techniques such as rules, frames, scripts, semantic network and ontology. The purpose of this paper is to illustrate the difference between semantic network and ontology(More)
Cancer is a class of diseases characterized by out-of-control cell growth. There are over 200 different types of cancer, and each is classified by the type of cell that is initially affected. This paper discusses the technical aspects of some of the ontology-based medical systems for cancer diseases. It also proposes an ontology based system for cancer(More)
Finding clusters in data is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Herein a new scalable clustering technique which addresses all these issues is proposed. In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. Within the last several(More)