الفهرس 
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Abstract
A Literature review that was carried out revealed lack in quality assurance performance evaluation techniques. The researcher’s scope proved a novelty in handling towards optimality in different educational institutes. The hyperdization between Data Envelopment Analysis (DEA) and the two proposed intelligent technique proved to be experience in Quality Assurance (QA) evaluation. A conclusion based on the survey performed on different Artificial Neural Network (ANN) metals and techniques was a perfect guide in selecting the most combined and proper artificial neural network method to deal with the proposed DEA algorithm. The indicated new fuzzy sets and fuzzy logic artificial intelligent techniques proved enhancement in the correction of questionnaire feedback analysis. Consequently, the sensitive inputs of the DEA models were correctly tackled giving a better measure output indication. The analytical methods should a complete satisfaction when it was presented against hand measure quality assurance indexes using paper based evaluation techniques. This research argues that to fully assess the relative efficiency of higher educational institutes is necessary to model the educational operation of institutes and control the inputs necessary to provide a certain level of quality assurance. In this research, data envelopment analysis (DEA) is utilized to analyze the relative efficiency of 19 different faculties of Menofia University. In research methodological terms, this research has employed a two stages, firstly, output efficiency score were estimated by solving data envelopment analysis (DEA) program with an ideal faculty that have exact standard values measured by quality assurance team and approved to be the best for highest quality of teaching and Faculties of Menofia University as DMUs. Secondly, training an artificial neural network (ANN) by set the output of the network equivalent to the output efficiency of the faculty that result from the previous stage. The result from second stage was the 105 necessary modification in the input entities data in order to increase this output efficiency and be globally ranked. Fuzzy quantitative evaluation technique is used to evaluate the student’s evaluation of the teaching quality by using the student’s feedback for each course.