Pub Date : 2018-06-01DOI: 10.21608/mjcis.2018.311998
Khaled Mohammed, A. Tolba, Mohammed M Elmogy
This paper presents a wireless system for instant quizzing in the classroom and collecting students’ feedback on teachers performance. This system is integrated with a student attendance management system to facilitate management of quizzing and quiz marking in addition to questionnaires about Quizzes. Such a system is very essential for following attendance and student learning progress in addition to formative assessment. The system uses two communication technologies: Wi-Fi, and Radio Frequency Identification (RFID). Such a low-cost system assures attendance follow up to assure abiding by the university bylaws, avoid spoofing and cheating, and enhance both teaching and learning. A student recommendation system is also implemented to increase student retention and enhance students success rate.
{"title":"A Multimodal Wireless System for Instant Quizzing and Feedback","authors":"Khaled Mohammed, A. Tolba, Mohammed M Elmogy","doi":"10.21608/mjcis.2018.311998","DOIUrl":"https://doi.org/10.21608/mjcis.2018.311998","url":null,"abstract":"This paper presents a wireless system for instant quizzing in the classroom and collecting students’ feedback on teachers performance. This system is integrated with a student attendance management system to facilitate management of quizzing and quiz marking in addition to questionnaires about Quizzes. Such a system is very essential for following attendance and student learning progress in addition to formative assessment. The system uses two communication technologies: Wi-Fi, and Radio Frequency Identification (RFID). Such a low-cost system assures attendance follow up to assure abiding by the university bylaws, avoid spoofing and cheating, and enhance both teaching and learning. A student recommendation system is also implemented to increase student retention and enhance students success rate.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"6 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":"126932316","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}
Pub Date : 2018-06-01DOI: 10.21608/mjcis.2018.311989
Nehal N. Mostafa, M. F. Alrahmawy, O. Nomair
In the last few years, human action recognition potential applications have been studied in many fields such as robotics, human computer interaction, and video surveillance systems and it has been evaluated as an active research area. This paper presents a recognition system using deep learning to recognize and identify human actions from video input. The proposed system has been fine-tuned by partial training and dropout of the classification layer of Alexnet and replacing it by another one that use SVM. The performance of the network is boosted by using key frames that were extracted via applying Kalman filter during dataset augmentation. The proposed system resulted in oromising performance compared to the state of the art approaches. The classification accuracy reached 92.35%.
{"title":"Video Analysis For Human Action Recognition Using Deep Convolutional Neural Networks","authors":"Nehal N. Mostafa, M. F. Alrahmawy, O. Nomair","doi":"10.21608/mjcis.2018.311989","DOIUrl":"https://doi.org/10.21608/mjcis.2018.311989","url":null,"abstract":"In the last few years, human action recognition potential applications have been studied in many fields such as robotics, human computer interaction, and video surveillance systems and it has been evaluated as an active research area. This paper presents a recognition system using deep learning to recognize and identify human actions from video input. The proposed system has been fine-tuned by partial training and dropout of the classification layer of Alexnet and replacing it by another one that use SVM. The performance of the network is boosted by using key frames that were extracted via applying Kalman filter during dataset augmentation. The proposed system resulted in oromising performance compared to the state of the art approaches. The classification accuracy reached 92.35%.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"1 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":"125519379","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}
Pub Date : 2018-06-01DOI: 10.21608/mjcis.2018.311990
Nada Essa, Eman El- Daydamony, A. Atwan
This paper introduces an advanced Arabic handwriting recognition technique using lexicon reduction. The lexicon reduction technique stands on extracting the Arabic character shape descriptors. The technique implementation consists of two major stages. The first stage presents a method for extracting the shape descriptor of each character. The second stage suggests Aho-Corasik string searching algorithm for Arabic character recognition. Various stages have been evaluated on the IFN/ENIT database. The results demonstrate the efficiency of the suggested technique.
{"title":"Arabic characters descriptors for lexicon reduction in Arabic handwriting","authors":"Nada Essa, Eman El- Daydamony, A. Atwan","doi":"10.21608/mjcis.2018.311990","DOIUrl":"https://doi.org/10.21608/mjcis.2018.311990","url":null,"abstract":"This paper introduces an advanced Arabic handwriting recognition technique using lexicon reduction. The lexicon reduction technique stands on extracting the Arabic character shape descriptors. The technique implementation consists of two major stages. The first stage presents a method for extracting the shape descriptor of each character. The second stage suggests Aho-Corasik string searching algorithm for Arabic character recognition. Various stages have been evaluated on the IFN/ENIT database. The results demonstrate the efficiency of the suggested technique.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"20 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":"132491325","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}
Pub Date : 2018-06-01DOI: 10.21608/mjcis.2018.311991
A. Abohamama, M. F. Alrahmawy, Mohamed A. Elsoud, Taher T. Hamza
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.
{"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}
Pub Date : 2017-12-01DOI: 10.21608/mjcis.2017.311960
Nazar K. Khorsheed, Mohammad A. El-Dosuky, Taher T. Hamza, M. Z. Rashad
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.
{"title":"Data Security Evaluation Based on Trend Line Rules Model","authors":"Nazar K. Khorsheed, Mohammad A. El-Dosuky, Taher T. Hamza, M. Z. Rashad","doi":"10.21608/mjcis.2017.311960","DOIUrl":"https://doi.org/10.21608/mjcis.2017.311960","url":null,"abstract":"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.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"25 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":"128737088","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}
Pub Date : 2017-12-01DOI: 10.21608/mjcis.2017.311956
Eman Tarek, O. Ouda, A. Atwan
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.
{"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}
Pub Date : 2017-12-01DOI: 10.21608/mjcis.2017.311953
Dalia Elwi, O. Nomair, S. Elmougy
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.
物联网(Internet of Things, IoT)和云计算是近年来信息通信技术发展的两个最重要的趋势，引起了许多研究者的关注。将这两种趋势结合起来的新趋势被称为物联网云(CoT)。在本文中，我们专注于将物联网与云计算集成，因为物联网可以从云计算提供的无限存储和无限处理能力中获得好处。首先，我们提出了一个支持物联网即服务(TaaS)和物联网混搭即服务(MaaS)的CoT架构。其次，我们开发了一种自动化的物联网混搭算法(IoTMA)，通过组合现有的物联网服务和web服务，在更短的响应时间内开发应用程序，而不需要高的编程经验。实验结果表明，与近期的一些相关研究成果相比，我们的算法缩短了响应时间。
{"title":"Automatic Cloud-Based IoT Mashup Algorithm","authors":"Dalia Elwi, O. Nomair, S. Elmougy","doi":"10.21608/mjcis.2017.311953","DOIUrl":"https://doi.org/10.21608/mjcis.2017.311953","url":null,"abstract":"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.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"59 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":"117037128","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}
Pub Date : 2017-12-01DOI: 10.21608/mjcis.2017.311961
Amal F. Goweda, Mohammed M Elmogy, S. Barakat
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.
{"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}
Pub Date : 2017-12-01DOI: 10.21608/mjcis.2017.311954
Moayed Hamad, O. Abu-Elnasr, S. Barakat
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.
{"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}
Pub Date : 2017-12-01DOI: 10.21608/mjcis.2017.311791
Shaimaa A.M. Hegazy, Mostafa G.M. Mostafa, Ahmed Abu Elfetouh
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{"title":"Efficient Iris Recognition Using Multi-feature Fusion","authors":"Shaimaa A.M. Hegazy, Mostafa G.M. Mostafa, Ahmed Abu Elfetouh","doi":"10.21608/mjcis.2017.311791","DOIUrl":"https://doi.org/10.21608/mjcis.2017.311791","url":null,"abstract":".","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"174 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":"116125281","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}
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