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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Bo Liu Heng Li Yutao Zhou Yuqing Peng Ahmed Elazab Changmiao Wang Northwest China Research Institute of Electronics Equipment Xi’an China Shenzhen Institutes of Adavanced Technology Chinese Academy of Sciences Shenzhen China Computer Science Department Misr Higher Institute for Commerce and Computers Mansoura City Egypt School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen P.R.China University of Science and Technology of China Hefei P.R.China

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188249

High-resolution (HR) remote sensing imagery is quite beneficial for subsequent interpretation. Obtaining HR images can be achieved by upgrading the imaging device. Yet, the cost to perform this task is very huge. Thus, it is necessary to obtain HR images from low-resolution (LR) ones. In the literature, the super-resolution image reconstruction methods based on deep learning have unparalleled advantages in comparison to traditional reconstruction methods. This work is inspired by these current mainstream methods and proposes a novel cascaded conditional Wasserstein generative adversarial network (CCWGAN) architecture with the residual dense block to generate high quality remote sensing images. We validate the proposed method on the NWPU VHR-10 dataset. Experimental results show our CCWGAN method has superior performance compared with the state-of-the-art GAN methods.

关键词： Gallium nitride Image edge detection Remote sensing Generative adversarial networks Training Generators Deep learning

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Preface

Studies in Big Data

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Studies in Big Data 2020年 78卷 v页

作者： Hassanien, Aboul-Ella Dey, Nilanjan Elghamrawy, Sally Department of Information Technology Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Department of Computer Engineering MISR Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering JIS University Kolkata India

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Image watermarking algorithm based on quaternion and chaotic Lorenz system

Image watermarking algorithm based on quaternion and chaotic...

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2019第十一届数字图像处理国际会议

作者： Jialiang Peng Bassem Abd El-Atty Hany S.Khalifa Ahmed A.Abd El-Latif School of Data Science and Technology Heilongjiang University Mathematics and Computer Science Department Faculty of Science Menoufia University Computer science department Misr Higher Institute of Commerce and Computers Department of Cybersecurity College of Computer Science and Engineering University of Jeddah

Quaternions are hyper-complex numbers applied in three-dimensional space,which are widely used in physics and image *** this paper,we utilize the features of quaternions for constructing a new substitution box(S-box) based on Lorenz *** performance of the proposed S-box is investigated by S-box evaluation *** results demonstrate that the constructed S-box has significant qualities for viable applications in security *** well as,a new image watermarking mechanism is introduced which its embedding procedures based on the suggested *** results demonstrate the efficiency of the presented image watermarking technique.

关键词： image watermarking Lorenz system quaternion substitution boxes quality analysis

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Degrees of Diaphragma Sellae Descent during Transsphenoidal Pituitary Adenoma Resection:Predictive Factors and Effect on Outcome

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Current Medical Science 2018年第5期38卷 888-893页

作者： Ahmed Abdelmaksoud Peng FU Osamah Alwalid Ahmed Elazab Ahmed Zalloom Wei XIANG Xiao-bing JIANG Hong-yang ZHAO Department of Neurosurgery Union HospitalTongji Medical CollegeHuazhong University of Science and Technology Wuhan 430022China Department of Radiology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China School of Biomedical Engineering Health Science CenterShenzhen UniversityShenzhen 518060China Computer Science Department Misr Higher Institute for Commerce and ComputersMansoura 11001Egypt

This study is aimed to classify degrees of diaphragma sellae (DS) descent into sella turcica according to the surgical field block caused by the descent and to construct predictive imaging criteria for the degree of descent,and in addition,to determine whether there is any correlation between the degree of DS descent and the operative outcome (in the form of cerebrospinal fluid leak and/or presence of residual tumor).Totally,72 patients were enrolled in our *** clinical and radiological data as well as the high definition videos of operations were retrospectively *** degree of DS descent during the operation was classified into five degrees according to surgical field block caused by the *** investigated the correlation between these five degrees and the clinical findings,radiological findings as well as the surgical *** found that the most important determining factors of DS descent degree were the volume and the height of the tumor portion above diaphragma *** the other hand,the total tumor volume,the maximum tumor height and the morphological pattern according to Wilson's system (modified from Hardy) had no statistically significant correlation with DS degree of *** of residual tumor on postoperative magnetic resonance images was significantly correlated with Wilson's classification and with supradiaphragmatic tumor *** the other hand,cerebrospinal fluid leak showed no statistically significant difference between variable degrees of DS *** data of the tumor portion above the diaphragma opening are more important than morphological data for prediction of surgical field block caused by descended *** DS prolapse significantly increases the difficulty of the operative procedure,residual tumor presence is mainly dependent on morphological classification,especially cavernous sinus invasion.

关键词： diaphragma sellae pituitary macroadenoma transsphenoidal surgery cerebrospinal fluid leak

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A Knowledge Management Framework for imbalanced data using Frequent Pattern Mining based on Bloom Filter 11

A Knowledge Management Framework for imbalanced data using F...

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11th International Conference on Computer engineering and Systems, ICCES 2016

作者： El-Ghamrawy, Sally M. Computers and Systems Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Egypt

ISBN: (纸本)9781509032679

Managing medical environments and organizations performance depend directly on the knowledge management (KM) systems. Knowledge Discovery (KD) is responsible for digging information from datasets and finding internal knowledge within organizations or external sources. Data mining (DM) is the core of KD process. Although recent mining techniques have proven their accuracy in discovering the knowledge from balanced data, where the class distribution is balanced, the problem of discovering knowledge from unbalanced data is still a challenge that needs to be addressed. A Clustered Knowledge Management Framework (CKMD) is presented in this paper, for enhancing the performance of KD from unbalanced data. A Simple Hybrid Sampling Approach (SHSA) is proposed to reduce the adverse impacts of imbalanced data. Mining frequent pattern process plays an important role in KD process. Moreover, a Frequent Pattern Mining algorithm based on Bloom Filter (FPMBF) is proposed to discover items that frequently co-occur in the data using the bloom filter, that requires a single scan of the data, which leads to less time consuming in discovering knowledge for imbalanced data. Finally, the performance of the proposed methods is evaluated using real datasets and comparative experiments. © 2016 IEEE.

关键词： Data mining

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A Knowledge Management Framework for imbalanced data using Frequent Pattern Mining based on Bloom Filter

A Knowledge Management Framework for imbalanced data using F...

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International Conference on Computer engineering and Systems

作者： Sally M. El-Ghamrawy Computers and Systems Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Egypt

ISBN: (纸本)9781509032686

关键词： Decision support systems Data mining Knowledge management Knowledge discovery Knowledge acquisition Filtering algorithms

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Optimum analysis of pavement maintenance using multi-objective genetic algorithms

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HBRC Journal 2015年第1期11卷 107-113页

作者： Elhadidy, Amr A. Elbeltagi, Emad E. Ammar, Mohammad A. Higher Misr Institute for Engineering and Technology Mansoura Egypt Faculty of Engineering Mansoura University 35516 Egypt Faculty of Engineering Tanta University Egypt

Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS) is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution. © 2014 Housing and Building National Research Center. Production and hosting by Elsevier B.V.

关键词： Genetic algorithms Markov-chain Multi-objective optimization Pavement maintenance

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Automatic Classification of Breast Tumors Using Features Extracted from Magnetic Resonance Images

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Procedia Computer Science 2016年 95卷 392-398页

作者： Ahmed M. Sayed Eman Zaghloul Tamer M. Nassef Biomedical Engineering Department Faculty of Engineering Helwan University Helwan 11792 Egypt Radiology and Imaging Department Faculty of Applied Medcial Sciences 6 Of October University Giza 12566 Egypt Computer and Software Department Faculty of Engineering Misr University for Science and Technology Giza 77 Egypt Communication and Electronics Department El-Gazeera Higher Institute for Engineering and Technology Cairo 11471 Egypt

Breast cancer is considered as the second leading cause of cancer deaths among women in the United States. Early detection of cancer is crucial in order to reduce its negative effects. Recently, magnetic resonance imaging (MRI) has become an important modality in the detection of breast cancer in daily practice. However, routine breast MRI has a moderate specificity that may increase its false positive rates. Therefore, automated detection techniques of malignancy can provide an important tool for clinicians. In this study, different data classification methods were examined to classify breast tumors screened using contrast enhanced MRI. The used data set included 20 subjects categorized clinically into two groups; benign and malignant tumors. MRI scans were first preprocessed to extract imaging features. Then two classification methods were exploited to differentiate between the two tumor's categories using the extracted features. The used classification methods were K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA). The results show a relatively significant classification accuracy compared with pathological analysis, and also the calculated resubstitution error. In summary, the proposed automatic classification techniques can be used as noninvasive diagnostic tools for breast cancer, with the capability of decreasing false positive errors associated with regular MRI diagnosis.

关键词： Data classification Feature Extraction K-Nearest Neighbour Linear Discriminant Analysis Magnetic Resonance Imaging Breast Cancer.

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Enhanced slow sand filter for iron and manganese removal: Using fabrics

Enhanced slow sand filter for iron and manganese removal: Us...

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American Water Works Association Annual Conference and Exposition 2012, ACE 2012

作者： Fadel, Ahmed El-Hadidy, Adel Fadel, Hatem A. Public Works Department Faculty of Engineering Mansoura University Egypt Textile Engineering Faculty of Engineering Mansoura University Mansoura Egypt Civil Engineering Department Misr Higher Institute for Engineering and Technology Mansoura 35516 Egypt

ISBN: (纸本)9781622763030

Last years, slow sand filtration has been proved to be a simple low cost technology for iron and manganese removal without the addition of chemicals. Application of nonwoven fabrics in the slow filter has been studied before and found to be advantageous in treating turbid surface water. In this research, pilot-scale slow sand filters were tested using iron and manganese rich groundwater (Fe > 1.0 mg/L and Mn > 4.0 mg/L). The pilot plant comprises a tray aerator followed by two identical slow sand filters were working in parallel. A comparative study has been conducted where;one filter was tested using nonwoven polyester fabric while the other filter was tested without fabrics. A mechanically bonded nonwoven fabric with low punching density, high surface hardness and high mass per unit of area was found to be suitable for this application. Results obtained from the comparative study showed that the thickness of the sand removed during the filter cleaning has been reduced by 1.5 cm, the run length of the filter with fabrics has been increased by 50 %, iron and manganese removal efficiencies were generally 96 % and 99 % respectively on average for both filters and the removal of turbidity was almost the same. Thus constituting a high good potential of using nonwoven fabrics in the slow sand filtration systems. 2012 © American Water Works Association.

关键词： Groundwater

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Web image retrieval search engine based on semantically shared annotation

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International Journal of Computer Science Issues 2012年第2 2-3期9卷 223-228页

作者： Riad, Alaa Elminir, Hamdy Abd-Elghany, Sameh Faculty of Computers and Information Sciences Mansoura University Mansoura Egypt Head of Electronic and Communication Dept Misr Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Computers and Information Sciences Mansoura University Mansoura Egypt

This paper presents a new majority voting technique that combines the two basic modalities of Web images textual and visual features of image in a re-annotation and search based framework. The proposed framework considers each web page as a voter to vote the relatedness of keyword to the web image, the proposed approach is not only pure combination between image low level feature and textual feature but it take into consideration the semantic meaning of each keyword that expected to enhance the retrieval accuracy. The proposed approach is not used only to enhance the retrieval accuracy of web images;but also able to annotated the unlabeled images. © 2012 International Journal of Computer Science Issues.

关键词： Search engines

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