Department of Information Systems

More ...

About Department of Information Systems

Facts about Department of Information Systems

We are proud of what we offer to the world and the community

5

Publications

3

Academic Staff

120

Students

0

Graduates

Programs

Major

...

Details

Who works at the Department of Information Systems

Department of Information Systems has more than 3 academic staff members

staff photo

Dr. Mohamed Abdeldaiem Abdelhadi Mahboub

الدكتور محمد عبدالدائم عبدالهادى احد اعضاء هيئة التدريس بقسم نظم المعلومات, كلية تقنية المعلومات ,تخصصه الدقيق, نظم استرجاع البيانات ودرجته العلمية استاذ مشارك.

Publications

Some of publications in Department of Information Systems

A Developed Secured Model for Searching Into Secured Encrypted Data

Abstract:- Our research paper has presented a new developed model for secured search into a big data of document collections. The developed model has investigated the importance of secured search and also the need for its practices in the real world. We have actually, studied both side of encryption in practical techniques issues and theoretical issues to improve the integration of information retrieval and cryptographic methods used for secured searching. We have chosen 3DES encryption technique to encrypt document data collections. Our new developed secured model has provided an efficient secured searching and/or security and authenticity over all. arabic 9 English 58
Mohamed Abdeldaiem Abdelhadi Mahboub, , (9-2019)
Publisher's website

Comparative Study on Inverted File versus Signature File performance in Information Retrieval System used by Arabic Language

Abstract--- In this research paper we have presented a comparison among two Information Retrieval models namely, Inverted file and Signature file for investigating their performance in Arabic Information Retrieval Systems. We have studied both models as to judge the models performance and their effectiveness. arabic 16 English 108
Mohamed Abdeldaiem Abdelhadi Mahboub(2-2015)
Publisher's website

OPTIMIZATION SEGMENTATION AND CLASSIFICATION FROM MRI OF BRAIN TUMOR AND ITS LOCATION CALCULATION USING MACHINE LEARNING AND DEEP LEARNING APPROACH

The manual detection and classification finding correct location and identifying type of tumor becomes a rigorous and hectic task for the radiologists. Medical diagnosis via image processing and machine learning is considered one of the most important issues of artificial intelligence systems. Deep learning has been used successfully in supervised classification tasks in order to learn complex patterns. The main contributions of this paper are as create a more generalized method for brain tumor classification using deep learning a variety of neural networks were constructed based on the preprocessing of image data., analyze the application of tumorless brain images on brain tumor classification and empirically evaluate neural networks on the given datasets with per image accuracy and per patient accuracy. And also presents an efficient image segmentation using machine learning algorithm with some optimization techniques to detect brain tumors. arabic 19 English 128
Mohamed Abdeldaiem Abdelhadi Mahboub(3-2019)
Publisher's website

Department of Information Systems in photos

Department of Information Systems Albums