Biometric system, SDUMLA Database, Iris Recognition, Daugman’s Rubber sheet, Haar Wavelet Transformation (HWT), Principal Component Analysis (PCA), Euclidean Distance (ED).
Biometric technologies are very important these days for improving the accuracy of protecting private data from unauthorized access. It helps overcome deficiencies of current security traditional systems. For the last decade, researchers are developing new methodologies that employ biometrics to boost security field. This article proposes effective methods for Iris recognition based on multi-feature fusion. A feature fusion approach is implemented to improve the iris recognition rate. In particular, Haar Wavelet Transformation (HWT) features and principal Component Analysis (PCA) are used to model the iris texture. Both approaches are fused to improve performance. Fusion results are compared to those from each feature alone and with other reported work. The results obtained with the proposed method are better than the currently reported results.
[9002989.] رقم البحث : 9002989 -
Automatic Cloud-Based IoT Mashup Algorithm /
تخصص البحث :
Mansoura journal for computer and information sciences / / Vol.13 - No.1
تاريخ تقديم البحث 02/07/2019
تاريخ قبول البحث 02/07/2019
عدد صفحات البحث 9
Dalia Elwi( dalia_elwi@yahoo.com - ) - مؤلف رئيسي
Omaima Nomair( omnomir@yahoo.com - )
Samir Elmougy( samirelmougy@yahoo.com - )
Internet of Things, Cloud Computing, Cloud of Things, Mashup, CoT Architecture, IoTMA algorithm, Planning Graph,
Internet of Things (IoT) and cloud computing are two of the most important trends in information and communication technology that attract the attention of many researchers in recent years. A new trend is raised from integrating both trends called Cloud of Things (CoT). In this paper, we focus on integrating IoT with cloud computing because of the benefits that IoT can gained from unlimited storage and unlimited processing capabilities provided by cloud computing. Firstly, we propose a CoT architecture that supports Things as a Service (TaaS) and IoT Mashup as a Service (MaaS). Secondly, we develop an automatic IoT Mashup Algorithm (IoTMA) for application development in less response time by composing existing things services and web services without needing of high experience in programming. Experimental results proved that our algorithm reduced the response time compared to some other recent related works.
[9002990.] رقم البحث : 9002990 -
ENHANCEMENT OF TEXT RECOGNITION IN SCENE IMAGES /
تخصص البحث :
Mansoura journal for computer and information sciences / / Vol.13 - No.1
تاريخ تقديم البحث 02/07/2019
تاريخ قبول البحث 02/07/2019
عدد صفحات البحث 9
Moayed Hamad( Moayed_hamad@ yahoo.com - ) - مؤلف رئيسي
Osama Abu-Elnasr( mr_abuelnasr@ yahoo.com - )
Sherif Barakat( sherifiib@yahoo.com - )
Scene Text Detection, Maximally Stable Extremal Regions, Bounding Box, OCR, OCR Spell Checker.
Text detection and recognition in natural scene images has received significant attention in last years. However, it is still an unsolved problem, due to some difficulties such as some images may have complex background, low contrast, noise, and /or various orientation styles. Also, the texts in those images can be of different font types and sizes. These difficulties make the automatic text extraction and recognizing it very difficult. This paper proposes the implementation of an intelligent system for automatic detection of text from images and explains the system which extracts and recognizes text in natural scene images by using some text detection algorithms to enhance text recognition. The proposed system implements various algorithms, such as Maximally Stable Extremal Regions (MSER) algorithm to detect the regions in the image, Canny edges algorithm to enhance edge detection and Bounding Box algorithm to detect and segment area of interest. Once the text is extracted from the image, the recognition process is done using Optical character Recognition (OCR). The proposed system has been evaluated using public datasets (ICDAR2003 and the experimental results have proved the robust performance of the proposed system.
[9002991.] رقم البحث : 9002991 -
A secure multimodal biometric authentication with cryptographic key management using double random phase encoding /
تخصص البحث :
Mansoura journal for computer and information sciences / / Vol.13 - No.1
تاريخ تقديم البحث 02/07/2019
تاريخ قبول البحث 02/07/2019
عدد صفحات البحث 10
Eman Tarek( eman_tarek@mans.edu.eg - ) - مؤلف رئيسي
Multibiometric systems are more efficient and reliable than unibiometric systems as they can provide lower error rates as well as robustness against frauds and subsystem failures. However, the deployment of multibiometric systems in large-scale biometric applications increases the risk of users’ privacy violation because once a multibiometric system is compromised; multiple biometric traits are disclosed to adversaries. As a result, protecting biometric templates stored in centralized databases of multibiometric systems has become a necessary prerequisite to allow wide-spread deployment of these systems. In this paper, we propose a biometric template protection method for securing image templates in multibiometric systems using the double random phase encoding (DRPE) scheme. DRPE is a well-known image encryption scheme and therefore it is more suited to secure image-based biometric templates. First, the proposed method encodes a randomly generated key as a binary image. Second, the phase components of two images captured from two different biometric modalities; namely, palmprint and fingerprint are convolved to produce a multi-biometric image of the same size as the binary image-encoded key. Finally, image-encoded key is encrypted using DRPE employing the multi-biometric image as a cipher key. During authentication, the encoded key is correctly recovered only if genuine biometric images are presented to the system; otherwise, the authentication process fails. Therefore, the proposed method can not only protect image-based biometric templates but also can provide a reliable means for securing cryptographic keys. Experimental results illustrate that the proposed method can secure both biometric templates and cryptographic keys without sacrificing the recognition accuracy of the underlying unprotected biometric recognition system.
[9002992.] رقم البحث : 9002992 -
Data Security Evaluation Based on Trend Line Rules Model /
تخصص البحث :
Mansoura journal for computer and information sciences / / Vol.13 - No.1
تاريخ تقديم البحث 02/07/2019
تاريخ قبول البحث 02/07/2019
عدد صفحات البحث 5
Nazar.K. Khorsheed( nizarkhorshd@yahoo. com - ) - مؤلف رئيسي
Cloud Computing; trend line Models, Symmetric and Asymmetric Algorithms
With the rise in demand for cloud services, most companies attempt to provide a lot of cloud services and benefit from them, one of the most important services is accounting the cost of data ciphering in the clouds market. This proposed work proved that the cryptographic keys are variable as evident mathematically, which in turn makes it difficult to guess the decoding of the data, and extends the cloud security model by generating both private and public keys based on local cost and trend line rules respectively. Due to the increased decoding time as evident from the proof results, the suitable security level is implemented and tested using Symmetric and Asymmetric encryption algorithms.
[9002994.] رقم البحث : 9002994 -
A Wrapper Feature selection Technique for Improving Diagnosis of Breast Cancer /
تخصص البحث :
Mansoura journal for computer and information sciences / / Vol.13 - No.1
تاريخ تقديم البحث 02/07/2019
تاريخ قبول البحث 02/07/2019
عدد صفحات البحث 8
Amal F. Goweda( amal_goweda@yahoo.com - ) - مؤلف رئيسي
Mohammed Elmogy( melmogy@mans.edu.eg - )
Sherif Barakat( sherifiib@yahoo.com - )
Cancer Classification; Feature selection; Naïve Bayes (NB); Forward Greedy Search.
Nowadays, cancer is considered as a fairly common disease. Regarding the number of newly detected cases, breast cancer is ranked as one of the most leading cancer types to death in women. It can be cured, if it is identified and treated in its early stages. Therefore, this study explores a proposed integrated wrapper feature selection method called wrapper naïve-greedy search (WNGS) to improve the accuracy of the breast cancer diagnosis. WNGS is based on a wrapper method, which is blended with a greedy forward search to select optimal feature subset. WNGS method integrates a wrapper method based on Naïve Bayes (NB) classifier as a learning scheme with a forward greedy search method. Then, the selected feature subset is fed to a classifier to determine breast cancer. In addition, K-nearest neighbor-greedy search (KNN-GS) is used for comparison. In KNN-GS method, k-nearest neighbor (KNN) classifier is used as a learning scheme while a forward greedy search method is used to search through features. NB is used as the classifier for classification process for both methods. By applying these two methods, data features are reduced, and the classification rate is improved. Both methods are tested on two different benchmark breast cancer datasets. Accuracy results showed that WNGS method outperformed KNN-GS method. Also, WNGS method overcame KNN-GS regarding precision, recall, F-measure, and sensitivity.
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