Inspection offers an opportunity to detect and remove defects at various points during software development. Early detection will reduce the effect of propagation and amplification of defects into the later phases of software development. Collaborative inspection on the web aims to eliminate the time factor needed to assemble the inspection or review team at a physical location. Through the collaborative mode, software teams can perform software inspection and review from geographically separated places asynchronously. These newly introduced practices have proven that collaborative inspection and review of artifacts on the web is feasible. This paper provides a model for collaborative inspection and review including possible features of model and tool that will support collaborative inspection and review on the web. arabic 10 English 64
Abdusalam Nwesri, Khairuddin Hashim(10-2008)

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%.
Hassan Ali Hassan Ebrahem, Abdelhamid Elwaer, Marwa Solla, Fatima Ben Lashihar, Hala Shaari, Rudwan A. Husain(7-2021)