This paper is presented an algorithm for testing primality of a large number in faster way and low storage space. Today the almost techniques which are used for testing the primality of the large number are struggling to reduce the cost of both processing time and storage space. Therefore, an intensive research by mathematicians and computers scientists is going in this field. The suggested algorithm is based on using of solutions of the quadratic equation (QE). The technique is aiming to find any two distinct numbers which are representing the obtained two roots of the QE. If these two roots are exist, in this case the given integer is not prime and the algorithm can identify the two roots in efficient cost of time and storage. The algorithm has appeared that it is working well by producing fantastic results. From these results, new ideas and thoughts have been listed for further analysis and discussion for testing of the primality of numbers
Omran Emhemmd Ahmed Farhat, Nahed Fathi M Farah, Ali K. Sabera(5-2019)

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)

Antipersonnel landmines represent a very serious hazard endangering the lives of many people living in armed conflict counties. The huge number of human lives lost due to this phenomenon has been a strong motivation for this research. Deep Learning (DL) is considered a very useful tool in object detection, image classification, face recognition and other computer vision activities. This paper focuses on DL for the problem of landmines recognition in order to identify its type based on shape features. This research work consists of several stages: gathering a new dataset of Anti-Personnel Mines (APMs) images for training and testing purposes, employing several augmentation strategies to boost the diversity of training data, applying four different Convolutional Neural Network (CNN) models namely VGG, ResNet, MiniGoogleNet and MobileNet, and evaluating their performances on APMs recognition. In conclusion, results indicate that MiniGoogleNet exceed all of other three models in recognizing APMs with the highest accuracy rate of 97%. arabic 9 English 69
Hassan Ali Hassan Ebrahem, Abdelhamid Elwaer, Marwa Solla, Fatima Ben Lashihar, Hala Shaari, Rudwan A. Husain(7-2021)