Entity

Time filter

Source Type

Madinat Sittah Uktubar, Egypt

Modern science and Arts University is located in Cairo, Egypt .It was founded in 1996. Wikipedia.


Azar A.T.,Modern Sciences and Arts University
International Journal of Industrial and Systems Engineering | Year: 2012

System dynamics (SD) is a powerful methodology and computer simulation modelling technique for framing, understanding and discussing complex issues and problems. It is widely used to analyse a range of systems in, e.g. business, ecology, medical and social systems as well as engineering. The methodology focuses on the way one quantity can affect others through the flow of physical entities and information. Often such flows come back to the original quantity causing a feedback loop. The behaviour of the system is governed by these feedback loops. There are two important advantages of taking systems dynamics approach. The interrelationship of the different elements of the systems can be easily seen in terms of cause and effects. Thus the true cause of the behaviour can be identified. The other advantage is that it possible to investigate which parameters or structures need to be changed in order to improve behaviour. This paper deals with the design of a framework for SD models and gives an overview of the current SD simulation packages. Copyright © 2012 Inderscience Enterprises Ltd. Source


Abdallah O.M.,Modern Sciences and Arts University
E-Journal of Chemistry | Year: 2011

Sensitive, simple and accurate high performance liquid chromatographic (HPLC) methods for the determination of atorvastatin (AT), fluvastatin (FL) and pravastatin (PV) have been developed. The proposed methods involve the use of a 150 mmx4.6 mm Zorbax Extend-C18 column (5 μm particle size) and different chromatographic conditions for the separation of the three statins. Linearity range was 5-40, 5-30 and 10-60 μg mL-1 for AT, FL and PV respectively. The developed methods proved to be successful in the determination of all studied drugs in spiked human plasma samples. Source


Azar A.T.,Modern Sciences and Arts University
International Journal of Biomedical Engineering and Technology | Year: 2011

Biomedical Engineering applies the principles of engineering, biology, and medicine to create devices and methods that solve problems in the health care industry. Due to the diversity of the field, it has become essential to identify tracks or specialisations that students can engage in during their final years of study. However, due to the limited resources, especially in the Arab world, most universities stick to limited specialisations. In this paper, we shall review the state of the art tracks in Biomedical Engineering globally with concentration on the Arab World. We shall also focus on the prospect of the field and the expected future requirements. © 2011 Inderscience Enterprises Ltd. Source


Azar A.T.,Modern Sciences and Arts University
International Journal of Intelligent Systems Technologies and Applications | Year: 2011

Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in haemodialysed patients. The continuous growth of the blood urea concentration over the 30-60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of haemodialysis (HD). The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate HD prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated post-dialysis blood urea (C eq) is therefore crucial in order to estimate the equilibrated (true) Kt/V. Measuring post-dialysis urea rebound (PDUR) requires a 30- or 60-min post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and PDUR in the form of a Takagi-Sugeno-Kang fuzzy inference system. The advantage of this neuro-fuzzy hybrid approach is that it does not require 30-60-min post-dialysis urea sample. Adaptive neuro-fuzzy inference system (ANFIS) was constructed to predict equilibrated urea (C eq) taken at 60 min after the end of the HD session in order to predict PDUR. The accuracy of the ANFIS was prospectively compared with other traditional methods for predicting equilibrated urea (C eq), PDUR and equilibrated dialysis dose ( eqKt/V). The results are highly promising, and a comparative analysis suggests that the proposed modelling approach outperforms other traditional urea kinetic models. © 2011 Inderscience Enterprises Ltd. Source


Azar A.T.,Modern Sciences and Arts University
International Journal of Modelling, Identification and Control | Year: 2011

The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. This paper presents an intelligent diagnosis system using hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Wavelet-transform is used for effective feature extraction and ANFIS is considered for the classifier model. It can parameterise the incoming ECG signals and then classify them into eight major types for health reference: left bundle branch block (LBBB), normal sinus rhythm (NSR), pre-ventricular contraction (PVC), atrial fibrillation (AF), ventricular fibrillation (VF), complete heart block (CHB), ischemic dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). The inclusion of adaptive neuro-fuzzy interface system (ANFIS) in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical problem domains. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95% is achieved which is a significant improvement. Copyright © 2011 Inderscience Enterprises Ltd. Source

Discover hidden collaborations