Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001A PROPOSED METHOD FOR INCREASE ACCURACY OF CLASSIFICATION P300 SPELLER1181982110.21608/ijicis.2016.19821ENA.KhalafFaculty of Computers and Information, Mansoura University, EgyptM.El-DesoukyFaculty of Computers and Information, Mansoura University, EgyptM.RashadFaculty of Computers and Information, Mansoura University, EgyptJournal Article20181125A P300 speller is one of applications the brain computer interface (BCI), introduced by Farwell and Donchin in 1988 N. In this paper proposed a new method for increase the accuracy of classification P300 speller. Uses a dataset for (16) healthy subjects. The new method including is feature extraction using Principle Component Analysis (PCA), and the classification using Support Vector Machine Linear (SVML). As it was calculated performance and activity of each electrode whether correlated or uncorrelated of speller task. Show that the proposed method is accurate and<br />efficient.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001CLASSIFICATION OF LOW QUALITY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND DEEP BELIEF NETWORK19281982210.21608/ijicis.2016.19822ENE.El-AshmonyDepartment of Computer Science, Faculty of Computers and Information,Mansoura University, Mansoura 35516, EgyptM.El-DosukyDepartment of Computer Science, Faculty of Computers and Information,Mansoura University, Mansoura 35516, EgyptSamirElmougyDepartment of Computer Science, Faculty of Computers and Information,Mansoura University, Mansoura 35516, EgyptJournal Article20181125Low quality images become more challenge and core problem in recent decade because of the ambiguity of contents of them. Convolutional deep neural networks are used for solving this problem. In this work, we used a combination of convolutional neural network and deep belief network to construct an efficient model able to classify low quality images. This model has the capability in extracting effective features from low quality images. Data augmentation is used through this model to increase the accuracy of the system. Scikit-Learn python library is used in implementation the system on STL-10 dataset. The results showed that the proposed model increase the accuracy of the system by 0.20%.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001REDUCING ATTRIBUTES of FACEBOOK USERS USING ROUGH SET THEORY29401982410.21608/ijicis.2016.19824ENW.AbdallahDept. of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.S.SarhanDept. of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.SamirElmougyDept. of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptJournal Article20181125Using social networks have become one of the daily activities that billions of peoples around the world do. So, great research efforts had been done to analyze and understand these virtual communities. Among other things, link prediction is a paramount task to analyze and understand these social networks. In this paper, we investigate link prediction problem using rough set theory to discard the irrelevant attributes that could be found in the profiles of Facebook users and the proposed work<br />induces accuracy 97.79%.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001REDUCING ERROR RATE OF DEEP LEARNING USING AUTO ENCODER AND GENETIC ALGORITHMS41531982310.21608/ijicis.2016.19823ENF.HabeebFaculty of Computers and Information, Mansoura University, EgyptSherihanAbueleninFaculty of Computers and Information, Mansoura University, EgyptSamirElmougyFaculty of Computers and Information, Mansoura University, EgyptJournal Article20181125Deep Learning (DL) techniques are considered as one of machine learning classes that model hierarchical abstractions in data input with the assistance of multiple layers. DL techniques have accomplished high performance in computer vision, natural language processing and automatic speech recognition. DL combines lower modules for classifier output and raw features input to produce new features at hierarchy higher layer. Deep Auto Encoder (DAE) is a DL aims to represent data to be utilized for rebuilding and classification. It is considered as one of the powerful algorithms in DL that gives higher accuracy and best performance. The proposed method in this work is based on using DAE and Genetic Algorithm (GA) through applying split-training and merging algorithms for DL. First, the network is divided into two initialized networks using DAE. Second, both of these networks were merged using GA. This proposed approach was evaluated based on the Mixed National Institute of Standards and Technology (MNIST) dataset and the obtained results showed that it achieve a higher performance and lower error rate in the classification.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001FACIAL EXPRESSION RECOGNITION BASED ON PRINCIPAL COMPONENTS ANALYSIS55631982510.21608/ijicis.2016.19825ENA.HewaDepartment of Computer Science, Faculty of Computer and Information, Mansoura University ,EgyptO.NomirDepartment of Computer Science, Faculty of Computer and Information, Mansoura University ,EgyptA.SalehDepartment of Computer Science, Faculty of Computer and Information, Mansoura University ,EgyptJournal Article20181125Recognizing facial expression is one of the most effective applications of image processing and has obtained great attention in latest years. A recognition system for facial expression is a computer based application which detects an individual facial expression for the purposes of authentication, criminal identification, passport verification, estimating age, and various other purposes. In this study, we propose a human recognition system based on facial expression. The system depends on extracting features using Principal Component Analysis (PCA) which later used in the training and recognition steps. The system is able to recognize diverse facial expressions such as Neutral, Anger, Disgust ,Fear, Happy, Sad and Surprise. The primary objective of this study is to improve the efficiency and to achieve better recognition rate using Support Vector Machine (SVM). The system is evaluated using the registered JAFFE Dataset of face images. The results show that or proposed system is robust and maintain high recognition rate.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001FAULT NODE RECOVERY ALGORITHM FOR ENHANCINGTHE LIFETIME OF AWIRELESS SENSOR NETWORK65781982610.21608/ijicis.2016.19826ENS.DawoodFaculty of computers and Information, Mansoura University, EgyptS.AbueleninFaculty of computers and Information, Mansoura University, EgyptA.AtwanFaculty of computers and Information, Mansoura University, EgyptJournal Article20181125Wireless Sensor Network (WSN) is type of network which consists of collection of tiny device called sensors nodes. In real wireless sensor networks, the sensor nodes use battery power supplies and thus have limited energy resources. This paper decrease the number of fault node and loss data and number of routing in the network based on enhanced Grade Diffusion with using Shortest Best Path .In the simulation, number of hop, power consumption, fault detection accuracy and time number of neighbour nodes measure the proposed algorithm. The proposed algorithm is also compared with distributed fault detection (DFD) and fault node recovery (FNR).Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001A SYSTEM FOR ACUTE LEUKEMIA CELLS SEGMENTATION AND CLASSIFICATION79871982910.21608/ijicis.2016.19829ENR.MohammedComputer Science Department, faculty of computer and informatics Mansoura University , EgyptO.NomirComputer Science Department, faculty of computer and informatics Mansoura University , EgyptI.I. KhalifaComputer Science Department, faculty of computer and informaticsT.HamzaComputer Science Department, faculty of computer and informaticsJournal Article20181125This research paper presents a system for the acute leukemia blast cells segmentation and classification. The research objective is to generate the features characterizing normal and infected cells. The proposed system consists of one segmentation method and one classification method of acute leukemia. The features extracted from the cell and adopted features are used as the input signals to the Multi Layer Perception (MLP) neural network classifier. The experimental results show that our proposed system is robust and effective in identifying acute leukemia blast cells.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001XML ABSTRACTIVE SUMMARY APPROACH89971983010.21608/ijicis.2016.19830ENH.ElmadanyComputer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, EgypM.AlfonseComputer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, EgypM.ArefComputer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, EgypJournal Article20181125Text summarization saves both time and effort required to manage a vast amount of information. The need to summarize text is increased. This paper introduces a XML Abstractive Summary (XAS) approach to summarize text in the format of XML document that is called XML summarization. XAS approach is considered a new attempt to produce abstractive summary for the xml document regarding to performance, size and accuracy. The output document is a concise and readable<br />version for the original one.Ain Shams University, Faculty of Computer and Information ScienceInternational Journal of Intelligent Computing and Information Sciences1687-109X16420161001A PROPOSED FRAMEWORK FOR THE INTEGRATION OF E-GOVERNMENT DATA AND SERVICES991083005410.21608/ijicis.2016.30054ENAmiraRezkDepartment of Information System, Faculty of Computer and Information Sciences, Mansoura University, EgyptSherifBarakatDepartment of Information System, Faculty of Computer and Information Sciences, Mansoura University, EgyptSirwanAbdullahDepartment of Information System, Faculty of Computer and Information Sciences, Mansoura University, EgyptJournal Article20190414