Mansoura Journal for Computer and Information Sciences最新文献_第2页

A Multimodal Wireless System for Instant Quizzing and Feedback 用于即时测验和反馈的多模态无线系统

Mansoura Journal for Computer and Information Sciences Pub Date : 2018-06-01 DOI: 10.21608/mjcis.2018.311998

Khaled Mohammed, A. Tolba, Mohammed M Elmogy

引用次数: 1

Video Analysis For Human Action Recognition Using Deep Convolutional Neural Networks 基于深度卷积神经网络的人体动作识别视频分析

Mansoura Journal for Computer and Information Sciences Pub Date : 2018-06-01 DOI: 10.21608/mjcis.2018.311989

Nehal N. Mostafa, M. F. Alrahmawy, O. Nomair

引用次数: 0

Arabic characters descriptors for lexicon reduction in Arabic handwriting 阿拉伯语手写体中用于词典缩减的阿拉伯字符描述符

Mansoura Journal for Computer and Information Sciences Pub Date : 2018-06-01 DOI: 10.21608/mjcis.2018.311990

Nada Essa, Eman El- Daydamony, A. Atwan

引用次数: 0

Swarm Intelligence based Fault-Tolerant Real-Time Cloud Scheduler 基于群智能的容错实时云调度

Mansoura Journal for Computer and Information Sciences Pub Date : 2018-06-01 DOI: 10.21608/mjcis.2018.311991

A. Abohamama, M. F. Alrahmawy, Mohamed A. Elsoud, Taher T. Hamza

{"title":"Swarm Intelligence based Fault-Tolerant Real-Time Cloud Scheduler","authors":"A. Abohamama, M. F. Alrahmawy, Mohamed A. Elsoud, Taher T. Hamza","doi":"10.21608/mjcis.2018.311991","DOIUrl":"https://doi.org/10.21608/mjcis.2018.311991","url":null,"abstract":"Cloud computing is a distributed computing paradigm that is deployed in many real-life applications. Many of these applications are real-time such as scientific computing, financial transactions, etc. Therefore, improving the dependability of cloud environments is extremely important to fulfill the reliability and availability requirements of different applications, especially real-time applications. Fault tolerance is the most common approach for improving the system’s dependability. In addition to traditional fault tolerance techniques such as replication, job migration, software rejuvenation, etc, fault-tolerant scheduling algorithms can play a great role toward more dependable systems. In this paper, an ACO based fault-tolerant soft real-time cloud scheduler is developed to minimize deadlines missing rate, makespan, and the imbalance in distributing the workload among the different machines. The performance of proposed scheduler has been assessed under different scenarios. Also, it has been compared to other well-known scheduling algorithms and the experimental results have shown the superiority of the proposed algorithm.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116796861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Data Security Evaluation Based on Trend Line Rules Model 基于趋势线规则模型的数据安全评估

Mansoura Journal for Computer and Information Sciences Pub Date : 2017-12-01 DOI: 10.21608/mjcis.2017.311960

Nazar K. Khorsheed, Mohammad A. El-Dosuky, Taher T. Hamza, M. Z. Rashad

引用次数: 0

A secure Multimodal Biometric Authentication with Cryptographic key Management Using Double Random Phase Encoding 基于双随机相位编码的密钥管理安全多模态生物特征认证

Mansoura Journal for Computer and Information Sciences Pub Date : 2017-12-01 DOI: 10.21608/mjcis.2017.311956

Eman Tarek, O. Ouda, A. Atwan

{"title":"A secure Multimodal Biometric Authentication with Cryptographic key Management Using Double Random Phase Encoding","authors":"Eman Tarek, O. Ouda, A. Atwan","doi":"10.21608/mjcis.2017.311956","DOIUrl":"https://doi.org/10.21608/mjcis.2017.311956","url":null,"abstract":"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.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126789462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Cloud-Based IoT Mashup Algorithm 自动基于云的物联网混搭算法

Mansoura Journal for Computer and Information Sciences Pub Date : 2017-12-01 DOI: 10.21608/mjcis.2017.311953

Dalia Elwi, O. Nomair, S. Elmougy

引用次数: 0

A Wrapper Feature Selection Technique for Improving Diagnosis of Breast Cancer 一种提高乳腺癌诊断的包膜特征选择技术

Mansoura Journal for Computer and Information Sciences Pub Date : 2017-12-01 DOI: 10.21608/mjcis.2017.311961

Amal F. Goweda, Mohammed M Elmogy, S. Barakat

{"title":"A Wrapper Feature Selection Technique for Improving Diagnosis of Breast Cancer","authors":"Amal F. Goweda, Mohammed M Elmogy, S. Barakat","doi":"10.21608/mjcis.2017.311961","DOIUrl":"https://doi.org/10.21608/mjcis.2017.311961","url":null,"abstract":"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.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133252041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancement Of Text Recognition In Scene Images 场景图像中文本识别的增强

Mansoura Journal for Computer and Information Sciences Pub Date : 2017-12-01 DOI: 10.21608/mjcis.2017.311954

Moayed Hamad, O. Abu-Elnasr, S. Barakat

{"title":"Enhancement Of Text Recognition In Scene Images","authors":"Moayed Hamad, O. Abu-Elnasr, S. Barakat","doi":"10.21608/mjcis.2017.311954","DOIUrl":"https://doi.org/10.21608/mjcis.2017.311954","url":null,"abstract":"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.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"6 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116660746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Iris Recognition Using Multi-feature Fusion 基于多特征融合的高效虹膜识别

Mansoura Journal for Computer and Information Sciences Pub Date : 2017-12-01 DOI: 10.21608/mjcis.2017.311791

Shaimaa A.M. Hegazy, Mostafa G.M. Mostafa, Ahmed Abu Elfetouh

引用次数: 0