Search results for: A. Ismaeel

Authors: A. Ismaeel, C. S. Wang, D. S. Xu

Abstract:

The γ-TiAl based Ti-Al-Mn-Nb alloys were fabricated by selective laser melting (SLM) on the TC4 substrate. The microstructures of the alloys were investigated in detail. The results reveal that the alloy without electromagnetic stirring (EMS) consists of γ-TiAl phase with tetragonal structure and α2-Ti3Al phase with hcp structure, while the alloy with applied EMS consists of γ-TiAl, α2-Ti3Al and α-Ti with hcp structure, and the morphological structure of the alloy without EMS which exhibits near lamellar structure and the alloy with EMS shows duplex structure, the alloy without EMS shows some microcracks and pores while they are not observed in the alloy without EMS. The microhardness and wear resistance values decrease with applied EMS.

Keywords: Selective laser melting, γ-TiAl based alloys, microstructure, properties, electromagnetic stirring.

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Authors: Farhan Ismaeel Dakheel, Raoof Smko, K. Negrat, Abdelsalam Almarimi

Abstract:

In this paper we used data mining techniques to identify outlier patients who are using large amount of drugs over a long period of time. Any healthcare or health insurance system should deal with the quantities of drugs utilized by chronic diseases patients. In Kingdom of Bahrain, about 20% of health budget is spent on medications. For the managers of healthcare systems, there is no enough information about the ways of drug utilization by chronic diseases patients, is there any misuse or is there outliers patients. In this work, which has been done in cooperation with information department in the Bahrain Defence Force hospital; we select the data for Cardiac patients in the period starting from 1/1/2008 to December 31/12/2008 to be the data for the model in this paper. We used three techniques for finding the drug utilization for cardiac patients. First we applied a clustering technique, followed by measuring of clustering validity, and finally we applied a decision tree as classification algorithm. The clustering results is divided into three clusters according to the drug utilization, for 1603 patients, who received 15,806 prescriptions during this period can be partitioned into three groups, where 23 patients (2.59%) who received 1316 prescriptions (8.32%) are classified to be outliers. The classification algorithm shows that the use of average drug utilization and the age, and the gender of the patient can be considered to be the main predictive factors in the induced model.

Keywords: Data Mining, Clustering, Classification, Drug Utilization..

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