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The International Conference on Informatics and Systems (INFOS)

作者： Hala M. Ebied Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

The face recognition system consists of a feature extraction step and a classification step. In this paper, the researcher studies the use of linear and nonlinear methods for feature extraction in the face recognition system. The linear Principal component analysis (PCA) which is widely used in the face recognition is used to construct the feature space and extract features. The Kernel-PCA is extended from PCA to represent nonlinear mappings in a higher-dimensional feature space. Several parameters of Kernel functions are investigated and expected to affect the recognition performance. The k-nearest neighbor classifier with Euclidean distance is used in the classification step. Our experiments are carried out on the ORL face database which contains variability in expression, pose, and facial details. Experimental results show that Kernel-PCA with Gaussian function can give a correct recognition rate similar to PCA and higher than Kernel-PCA with polynomial function.

关键词： Principal component analysis Feature extraction Face Kernel Face recognition Polynomials Databases

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Moving object detection based on temporal and spatial resolution mosaic

Moving object detection based on temporal and spatial resolu...

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International Conference on computer Engineering and Systems

作者： Mohammed A.-Megeed Salem Department of Scientific Computing Faculty of Computer and Information Sciences Ain Shams University in Cairo Egypt

Algorithms that offer a simultaneous accurate detection of moving objects and low computational complexity are yet a challenge for video surveillance applications. Multiresolution analysis and the wavelet transform can form bases for the design of such algorithms. They can be functioned to enable the analysis and the processing in dimensions to match the nature of the input data and to allow the analysis of the images in a degree of visual details corresponding to the relevance of the information to be extracted. The algorithm proposed in this paper uses the multiresolution analysis to represent the images in a mosaic of different resolutions based on the view conditions of the scene. The 3D wavelet packet is then used to analyse the image sequence spatially and temporally. The algorithm is tested and evaluated using many scenes for vehicles traffic and pedestrian zones. In a comparison with previous work, the results visually and statistically were much improved.

关键词： Spatial resolution Wavelet analysis Three dimensional displays Algorithm design and analysis

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Cloud statistical significance estimation for optimal local alignment of huge DNA sequences

Cloud statistical significance estimation for optimal local ...

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The International Conference on Informatics and Systems (INFOS)

作者： Ahmad M. Hosny Howida A. Shedeed Ashraf S. Hussein Mohamed F. Tolba Department of Scientific Computing Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Confidence in a pairwise local sequence alignment is a fundamental problem in bioinformatics. For huge DNA sequences, this problem is highly compute-intensive because it involves evaluating thousands of local alignments to construct an empirical score distribution. Recent parallel solutions support only small sequence sizes and/or are based on sophisticated infrastructures that are not available for most research labs. This paper presents an efficient parallel solution for evaluating the statistical significance for a pair of huge DNA sequences using cloud infrastructures. This solution can receive requests from various researchers via web-portal and allocate resources according to the demand. As it is cloud-based solution, it improves robustness, scalability and performance. The fundamental innovation in this research work is proposing an efficient solution that utilizes both shared and distributed memory architectures using the cloud technology to enhance the performance of evaluating the statistical significance for pair of DNA sequences. In this manner, the condition of the sequence size is released to be in megabyte-scale, which was not supported before. The present solution was verified against other recent parallel solutions, and the performance evaluation was carried out on Microsoft's Cloud. The results show that the performance scales with relatively linear speedup, as the number of instances increases.

关键词： Cloud computing computer architecture DNA Estimation Bioinformatics computers Parallel processing

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Security And Fire Detection System Using Deep Learning Techniques

Security And Fire Detection System Using Deep Learning Techn...

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IEEE International Conference on Intelligent computing and Information Systems (ICICIS)

作者： Ahmed Nouh Khaled Ahmed Ahmed Tarek Nada Magdy Hala M. Ebied Ahmed Khaled Radwa Reda Scientific Computing Department Faculty Of Computer & Information Sciences Ain Shams University Cairo Egypt

Recently, the challenges of security systems in protecting citizens from crime and disasters have become very difficult. While some organizations have developed fire detection security systems, incidents of intrusions, thefts, and hostage-taking in government and private agencies indicate the inadequacy of current security systems. In this paper, the security system combines the detection of people, weapons, and fires through the analysis of surveillance cameras and videos is presented. The system is built by using deep learning techniques. The system deals with the video frames and processes them using a set of digital filters. The system analyzes the tires and goes through the processes of detecting faces, detecting weapons, and detecting fires respectively. After that, the classification stage begins to identify people’s faces if they are outlaws and to identify the type of weapon. The deep learning models were applied in the classification process: Convolutional Neural Network (CNN), VGG-19 network, and GoogleNet with Inception module. The Inception network archives the highest accuracy for face recognition. The VGG network archived 95.5%, and 99.7% for weapon and fire detection respectively.

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Using MLP and RBF neural networks for face recognition: An insightful comparative case study

Using MLP and RBF neural networks for face recognition: An i...

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International Conference on computer Engineering and Systems

作者： Hala M. Ebeid Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

In this paper, two architectures neural network (NN) classifier models have been compared, multilayer perceptron (MLP) neural network with back-propagation algorithm and radial basis function (RBF) neural network. Capabilities of the presented architectures have been compared. The feature projection vectors, obtained through the Principal Component Analysis or called Eigenfaces method, are used as the input vectors for the training and testing of both NN architectures. Several factors affect the recognition performance; experimental results are applied to the ORL database which contains variability in expression, pose, and facial details. The experimental result showed that the Eigenfaces/RBF system has recognition error rates that are lower than those of the Eigenfaces/MLP system by 3%. Thus the Eigenfaces/RBF system performs better than the Eigenfaces/MLP system in terms of correct recognition rates and training convergence speed of the network.

关键词： Artificial neural networks computers computer architecture Eigenvalues and eigenfunctions Estimation Computational modeling Image coding

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Improved self-organizing maps and speech compression

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International Journal of computer Science Issues 2012年第2期9卷 197-205页

作者： Ettaouil, Mohamed Lazaar, Mohamed UFR: Scientific Computing and Computer sciences Engineering sciences Modeling and Scientific Computing Laboratory Faculty of Science and Technology of Fez University Sidi Mohammed ben Abdellah Box 2202 Fez Morocco

Recent developments of Self-Organizing Maps or Kohonen networks become more and more interesting in many fields such as: pattern recognition, clustering, speech recognition, data compression, medical diagnosis... Kohonen networks is unsupervised learning models. The results obtained by the Kohonen networks are dependent on their parameters such as the architecture of the Kohonen map, the later has a great impact on the convergence of learning methods. The selection of the architecture of Kohonen networks, associated with a given problem, is one of the most important research problems in the neural network research. In this paper, we model this problem of neural architecture in terms of a mixed-integer non linear problem with linear constraints. To solve this model of optimization for the network architectures, we propose the genetic algorithm. Also, we implemented and evaluated the proposed method and speech compression algorithms. Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. The numerical results demonstrated the effectiveness of the new model. © 2012 International Journal of computer Science Issues.

关键词： Genetic algorithms

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On the selection of the proper wavelet for moving object detection

On the selection of the proper wavelet for moving object det...

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International Conference on computer Engineering and Systems

作者： Mohammed A.-Megeed Salem Department of Scientific Computing Faculty of Computer and Information Sciences Ain Shams University in Cairo Egypt

A precise event detection is a fundamental step in many applications. For instances, moving object detection is a fundamental task for a variety of traffic monitoring applications. Wavelets have been widely used for this purpose. However, The selection of the proper type of mother wavelet strongly affects the results of the analysis and processing. In this paper, the wavelets of the Daubechies family are exploited for extracting the relevant movement information in moving image sequences using 3D wavelet. The proposed algorithm is applied for traffic monitoring systems. The objective and subjective experimental results obtained by applying the wavelet types are compared and interpreted in terms of the wavelet properties and the characteristics of the image sequences. The comparisons show the superior performance of the low order Daubechies wavelets in the presence of noisy images and changing lighting conditions when compared to the application of high order Daubechies wavelets.

关键词： Wavelet analysis Three dimensional displays Monitoring Image segmentation Phase locked loops Visualization

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An efficient solution for aligning huge DNA sequences

An efficient solution for aligning huge DNA sequences

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International Conference on computer Engineering and Systems

作者： Ahmad M. Hosny Howida A. Shedeed Ashraf S. Hussein Mohamed F. Tolba Department of Scientific Computing Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Recently, many parallel solutions were proposed in order to accelerate the exact methods for optimal alignment of DNA sequences. However, most of these solutions calculate only the alignment similarity score without finding the actual alignment. This paper presents an efficient solution to find the optimal alignment of the huge DNA sequences. This solution releases the condition of the sequence size to be in megabyte-scale instead of few kilobytes. The fundamental innovation in this work is developing an efficient, linear space complexity, parallel solution to achieve the optimum alignment with relatively good performance. The shared memory parallel architecture is the focus of this work therefore; we have considered off-the-shelf systems like multi-core CPUs as well as advanced shared memory platforms. Experimental results show that the proposed solution achieved high records compared to other solutions that targeted the same goal with less hardware requirements.

关键词： Complexity theory Microprocessors computer architecture Measurement Heuristic algorithms Hardware Program processors

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LimonDroid: a system coupling three signature-based schemes for profiling Android malware

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Iran Journal of computer Science 2021年第2期4卷 95-114页

作者： Tchakounté, Franklin Ngassi, Roger Corneille Ndjeumou Kamla, Vivient Corneille Udagepola, Kalum Priyanath Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Department of Mathematics and Computer Science National School of Agro-Industrial Science University of Ngaoundéré Ngaoundéré Cameroon Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia

Android remains an interesting target to attackers due to its openness. A contribution in the literature consists of providing similarity measurement such as fuzzy hashing to fight against code obfuscation techniques. Research works in this approach suffer from limited signature database. This work combines fuzzy hashing with YARA rules and VirusTotal signature-based schemes, to improve and consistency of the signature database. It is proposed LimonDroid, an Android system, which mimics Limon, a Desktop security tool that includes such schemes. LimonDroid has been tested with 341 malicious and 300 benign applications on a database of 12925 fuzzy-hashed malware signatures, 62 YARA malware families’ patterns and VirusTotal engine. Our approach gives a true-positive rate of 97.36%, a true negative rate of 98.33% and an accuracy of 97.82%. A comparison with similarity-based solutions reveals that LimonDroid is more efficient for users. The objective is not to propose a detection approach better than those in the literature. Instead, we aim at establishing a robust signature database able to identify malicious trends in Android apps. © Springer Nature Switzerland AG 2020.

关键词： Android malware Detection Fuzzy hashing Limon sandbox Similarity Static analysis VirusTotal YARA

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Analysis of Dorsal Palm Vein Pattern Recognition System 9

Analysis of Dorsal Palm Vein Pattern Recognition System

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9th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2019

作者： Rabie, Sherok M. Ebied, Hala M. Bayoumi, Sahar Institute of Graduate Studies Research Alexandria University Information Technology Department Alexandria Egypt Faculty of Computer and Information Sciences Ain Shams University Scientific Computing Department Cairo Egypt King Saud University Saudi Arabia

ISBN: (纸本)9781728139951

Individuals classification and recognition processes are a substantial growing field in many industry fields. In this paper, we presented a dorsal palm vein pattern recognition approach. Two approaches are presented. The first approach used the Principal Component Analysis (PCA) to extract features from images then the Multi-layer perceptron neural network for recognition step. The second approach Bag of features (BOF) used the Speeded-Up Robust Features (SURF) to extract local features from the training set for interest point selection then clustered in a representation set. The Support Vector Machine (SVM) technique is used in the classification phase. A comparison between the two approaches is proposed to observe the best approach that archived the higher classification accuracy. Here the dorsal palm vein images of the PUT database are used. The experiments show that the use of BOF is much better than PCA and MLP. Our experimental is able to recognize humans with accuracy 98% based on the BOF method. © 2019 IEEE.

关键词： Principal component analysis

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