Abstract-In all the regions of the world, heart failure is common and on raise caused by several aetiologies. Although the development of the treatment is fast, there are still lots of cases that lose their lives in emergence sections because of slow response to treat these cases. In this paper we propose an expert system that can help the practitioners in the emergency rooms to fast diagnose the disease and advise them with the appropriate operations that should be taken to save the patient's life. Based on the mostly binary information given to the system, Bayesian Network model was selected to support the process of reasoning under uncertain or missing information. The domain concepts and the relations between them were building by using ontology supported by the Semantic Web Rule Language to code the rules. The system was tested on 105 patients and several classification functions were tested and showed remarkable results in the accuracy and sensitivity of the system.

According to World Health Organization, coronary artery diseases are responsible for 17% of the death in the world. Diagnosing the disease in the right time could lower the danger that it may cause. This paper presents an expert system (CardioOWL) that has the ability to diagnose any kind of coronary artery diseases. CardioOWL supplies the expert with the diagnosing strategies that could be used and suggests the drugs and/or other required operations to be taken. CardioOWL depends on ontology knowledge about the patient's symptoms to build the knowledge base and then it utilizes Semantic Web Rule Language (SWRL) to deduce the suitable medicine and the required operation for the patient. The system was tested by some general practitioners using several test cases. The system proves to have very good precession and recall.

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XML documents are generated from heterogeneous resources. They may share the same data but in different Schema, which make it difficult to retrieve information from them. In this paper we propose a new technique that first; minimizes the size of the XML documents by reducing the redundancy of the structure part and generate the repository for these documents, and second; relaxes and decomposes the XPath query in two stages to determine the relevant documents and the relevant part within these documents. The results show significant precision and recall comparing with the exact XPath queries.

XML has become the standard way for representing and transforming data over the World Wide Web. Moreover, these documents are becoming the way to represent the object used in Mobile-learning technology. The problem with XML documents is that they have a very high ratio of redundancy, which makes these documents demanding large storage capacity and high network band-width for transmission. These documents need to be decompressed and being used without or with minimum decompression. This paper presents the complete testing process for the XML compressing and Querying System (XCVQ) that has the ability to compress the XML documents and retrieve the required information according to all kinds of queries.