2016 12th International Computer Engineering Conference (ICENCO)最新文献

Grey wolf optimizer-based back-propagation neural network algorithm 基于灰狼优化器的反向传播神经网络算法

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856471

M. F. Hassanin, Abdullah M. Shoeb, A. Hassanien

引用次数: 16

Global distributed clustering technique for randomly deployed wireless sensor networks 随机部署无线传感器网络的全局分布式聚类技术

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856437

Walaa Abdellatief, Osama S. Youness, H. Abdelkader, Mohee Hadhoud

{"title":"Global distributed clustering technique for randomly deployed wireless sensor networks","authors":"Walaa Abdellatief, Osama S. Youness, H. Abdelkader, Mohee Hadhoud","doi":"10.1109/ICENCO.2016.7856437","DOIUrl":"https://doi.org/10.1109/ICENCO.2016.7856437","url":null,"abstract":"Wireless sensor network applications are composed of a vast number of inexpensive battery-powered sensors. One of its primary applications is environmental monitoring for physical phenomena in rigid areas such as forests and volcanoes. In such applications, a large number of sensors are randomly scattered by aircraft over the area of monitoring. These applications mainly depend on clustering to arrange nodes into groups to facilitate their communication. Previously proposed clustering techniques are classified into two types, which are distributed or centralized techniques. Each of these types has advantages as well as some flaws. In this paper, we propose a globally distributed clustering technique. This technique depends on some global information about the network to allow each node to decide its role in the produced clusters locally. This information is assumed to be known by default by the BS for any communication or topological control activities. Simulation results show that the proposed technique achieves less power consumption and therefore longer network lifetime when compared with other clustering techniques.","PeriodicalId":332360,"journal":{"name":"2016 12th International Computer Engineering Conference (ICENCO)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282842","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: 3

Combined Space-time-frequency codes for four time slots with beamforming 采用波束形成的四时隙空时频组合码

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856466

H. A. Elkader, G. Abdel-Hamid, A. S. T. El-dien, Asmaa A. Nassif

引用次数: 0

A robust local data and membership information based FCM algorithm for noisy image segmentation 一种鲁棒的基于局部数据和隶属度信息的FCM算法用于噪声图像分割

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856451

R. Gharieb, G. Gendy, A. Abdelfattah

{"title":"A robust local data and membership information based FCM algorithm for noisy image segmentation","authors":"R. Gharieb, G. Gendy, A. Abdelfattah","doi":"10.1109/ICENCO.2016.7856451","DOIUrl":"https://doi.org/10.1109/ICENCO.2016.7856451","url":null,"abstract":"This paper presents a technique for incorporating local data and membership information into the standard fuzzy C-means (FCM) algorithm. The objective function associated with the technique consists of a modified version of the standard FCM function plus a weighted regularized FCM-like one. In the first function, the Euclidian pixel-to-cluster distances are computed using the original data. However, in the second one, they are computed by replacing the original data by locally smoothed one to reduce additive noise. Both distances are also modified to account for the distances in the pixel neighborhood. In both functions, to incorporate the local membership information, the resultant pixel-to-cluster distance is weighted by the reciprocal of the average of the membership to this cluster in the pixel vicinity. Results clustering synthetic and medical images are presented. The performance of the proposed robust local data and membership information FCM (RFCM) is compared with the standard FCM, local spatial information based FCM (SFCM), and data and local data and membership weighted FCM (LDMWFCM).","PeriodicalId":332360,"journal":{"name":"2016 12th International Computer Engineering Conference (ICENCO)","volume":"39 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114086214","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: 5

Minimizing energy of cluster-based Cooperative Spectrum Sensing in CRN using Multi Objective Genetic Algorithm 基于多目标遗传算法的CRN集群协同频谱感知能量最小化

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856465

Ibrahim Salah, W. Saad, M. Shokair, M. Elkordy

引用次数: 7

Face recognition system using HMM-PSO for feature selection 人脸识别系统采用HMM-PSO进行特征选择

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856453

Mai Mohamed Mahmoud Farag, T. Elghazaly, H. Hefny

引用次数: 9

Mapping functional requirements of ERP SPL on an extended form of Feature Model 在特征模型的扩展形式上映射ERP SPL的功能需求

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856458

Mohamed Ali, Eman S. Nasr, Mervat H. Geith

引用次数: 4

Recognizing Fake identities in Online Social Networks based on a Finite Automaton approach 基于有限自动机方法的在线社交网络虚假身份识别

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856436

M. Torky, A. Meligy, H. Ibrahim

{"title":"Recognizing Fake identities in Online Social Networks based on a Finite Automaton approach","authors":"M. Torky, A. Meligy, H. Ibrahim","doi":"10.1109/ICENCO.2016.7856436","DOIUrl":"https://doi.org/10.1109/ICENCO.2016.7856436","url":null,"abstract":"Online Social Networks (OSNs) are a great venue for scammers to impersonate the identities of users via creating fake profiles. Fake profiles are a popular tool for the intruders which can be used to carry out malicious activities such as impersonation attacks and harming persons' reputation and privacy in (OSN). Hence, recognizing the identities of fake profiles is one of the critical security problems in OSNs. In this paper, we proposed a detection mechanism called Fake Profiles Recognizer (FPR) for recognizing and detecting Fake Profiles in OSNs. The detection methodology in FPR is based on the functionality of Regular Expression and Deterministic Finite Automaton (DFA) approaches for recognizing the identity of profiles. We evaluated our detection system on three popular types of Online Social Networks: Facebook, Google+, and Twitter. The results explored high accuracy, efficiency, and low False Positive Rate of FPR mechanism in detecting the identities of Fake Profiles. In addition, our proposed detection mechanism achieved strong competitive results compared with other detection mechanisms in the literature.","PeriodicalId":332360,"journal":{"name":"2016 12th International Computer Engineering Conference (ICENCO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132417828","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: 11

Real-time automatic multi-style license plate detection in videos 实时自动多风格车牌检测视频

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856460

Asmaa Elbamby, E. Hemayed, D. Helal, M. Rehan

引用次数: 11

Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite 利用正弦余弦算法训练前馈神经网络，改进对纳米亚硒酸盐养殖鱼肝酶的预测

2016 12th International Computer Engineering Conference (ICENCO) Pub Date : 2016-12-01 DOI: 10.1109/ICENCO.2016.7856442

A. Sahlol, A. Ewees, Ahmed Monem Hemdan, A. Hassanien

引用次数: 46