2014 9th International Conference on Computer Engineering & Systems (ICCES)最新文献_第3页

Building OWL ontology for obesity related cancer 构建肥胖相关癌症OWL本体

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030953

M. A. Elhefny, Mohammed M Elmogy, A. A. Elfetouh

引用次数: 10

A tool suite for estimation and prediction of software dynamic defect models 一个用于估计和预测软件动态缺陷模型的工具套件

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030975

A. Yousef

{"title":"A tool suite for estimation and prediction of software dynamic defect models","authors":"A. Yousef","doi":"10.1109/ICCES.2014.7030975","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030975","url":null,"abstract":"As a common software engineering practice, software dynamic defect models are used to estimate and predict the software testing process progress, effectiveness, and the number of future defects over the next weeks. Practitioners use these dynamic defect models to ensure that the delivery of software to customers is possible from the quality point of view and to predict the release date. Old literature suggested several classic defect models including Putnam, Exponential, Rayleigh and Weibull. Recent literature claimed that modern projects follow linear combinations of Rayleigh due to projects complexity. This claim verification has not been generalized because the project samples size was very small. This paper proposes a tool suite for dynamic defect models. The tool suite consists of an open repository of dynamic defects empirical data and many supporting tools. Data concerning defects are collected from several software projects and products and added to the open repository. This includes open source software and commercial software projects. The proposed tools are designed and implemented and made publicly available on the web. They are used to view the dynamic defects, find the best dynamic defect model that fits the data according to several performance criteria and predict future number of defects. The application of these tools on the empirical data showed that linear combinations of Rayleigh and Weibull has better performance than classic models in both curve fitting and predictability of commercial software.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131380881","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: 2

GPU implementation for Arabic Sign Language real time recognition using Multi-level Multiplicative Neural Networks 使用多层乘法神经网络的阿拉伯手语实时识别的GPU实现

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030986

A. S. Elons

引用次数: 3

Hybrid Named Entity Recognition - Application to Arabic Language 混合命名实体识别-应用于阿拉伯语

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030933

Mohamed A. Meselhi, Hitham M. Abo Bakr, I. Ziedan, K. Shaalan

引用次数: 8

Simulations and performance evaluation of Real-Time Multi-core Systems 实时多核系统的仿真与性能评估

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030960

Mridula Sharma, H. Elmiligi, F. Gebali

引用次数: 0

Machine Learning based Approach for Water pollution detection via fish liver microscopic images analysis 基于机器学习的鱼肝显微图像污染检测方法

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030968

Asmaa Hashem Sweidan, Nashwa El-Bendary, A. Hassanien, O. Hegazy, A. Mohamed

{"title":"Machine Learning based Approach for Water pollution detection via fish liver microscopic images analysis","authors":"Asmaa Hashem Sweidan, Nashwa El-Bendary, A. Hassanien, O. Hegazy, A. Mohamed","doi":"10.1109/ICCES.2014.7030968","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030968","url":null,"abstract":"This article presents an automatic classification approach for assessing water quality based on fish liver histopathology. As fish liver is a good bioindicator for detecting water chemical pollution, the proposed approach utilizes fish liver microscopic images in order to detect water pollution. The proposed approach consists of three phases; namely pre-processing, feature extraction, and classification phases. Since color and texture are the most important characteristics of microscopic fish liver images, the proposed system uses colored histogram and Gabor wavelet transform for classifying water quality degree. Also, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification, respectively. Collected datasets contain colored JPEG images of 125 images as training dataset and 45 images as testing dataset, respectively. Training dataset is divided into 4 classes representing the different histopathlogical changes and their corresponding water quality degrees. Experimental results showed that the proposed classification approach has obtained water quality classification accuracy of 93.3%, using SVMs linear kernel function with 37 images per class for training.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125881478","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: 7

Aquatic weeds prediction: A comparative study 水生杂草预测的比较研究

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030969

E. Emary, Rania E. Elesawy, Salwa M. Abou El Ella, A. Hassanien

引用次数: 0

Real time image mosaicing system based on feature extraction techniques 基于特征提取技术的实时图像拼接系统

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030983

Ebtsam Adel, Mohammed M Elmogy, Hazem Elbakry

{"title":"Real time image mosaicing system based on feature extraction techniques","authors":"Ebtsam Adel, Mohammed M Elmogy, Hazem Elbakry","doi":"10.1109/ICCES.2014.7030983","DOIUrl":"https://doi.org/10.1109/ICCES.2014.7030983","url":null,"abstract":"Image mosaicing/stitching is considered as an active research area in computer vision and computer graphics. Image mosaicing is concerned with combining two or more images of the same scene into one panoramic image with high resolution. There are two main types of techniques used for creating image stitching: direct methods and feature-based methods. The greatest advantages of feature-based methods over the other methods are their speed, robustness, and the availability of creating panoramic image of a non-planar scene with unrestricted camera motion. In this paper, we propose a real time image stitching system based on ORB feature-based technique. We compared the performance of our proposed system with SIFT and SURF feature-based techniques. The experiment results show that the ORB algorithm is the fastest, the highest performance, and it needs very low memory requirements. In addition, we make a comparison between different feature-based detectors. The experimental result shows that SIFT is a robust algorithm but it takes more time for computations. MSER and FAST techniques have better performance with respect to speed and accuracy.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124236393","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: 15

An overview on non-parametric spectrum sensing in cognitive radio 认知无线电中的非参数频谱感知技术综述

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030919

Ahmed O. Abdul Salam, R. Sheriff, S. Al-Araji, K. Mezher, Q. Nasir

引用次数: 1

On-demand distributed on-card bytecode verification 按需分布式卡上字节码验证

2014 9th International Conference on Computer Engineering & Systems (ICCES) Pub Date : 2014-12-01 DOI: 10.1109/ICCES.2014.7030964

A. Mamdouh, Ayman M. Bahaa-Eldin, M. Sobh

引用次数: 1