2017 12th International Conference on Computer Engineering and Systems (ICCES)最新文献_第3页

Density-based spatial clustering technique for wireless sensor networks 基于密度的无线传感器网络空间聚类技术

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275288

Walaa Abdellatief, Osama S. Youness

{"title":"Density-based spatial clustering technique for wireless sensor networks","authors":"Walaa Abdellatief, Osama S. Youness","doi":"10.1109/ICCES.2017.8275288","DOIUrl":"https://doi.org/10.1109/ICCES.2017.8275288","url":null,"abstract":"Clustering in wireless sensor networks (WSNs) is an important stage for the communication between sensor nodes. Many clustering techniques were introduced in the literature with different characteristics. The main goal of them is to facilitate a power-aware communication between a large number of deployed nodes. Clustering techniques can be classified into two main groups; centralized and distributed techniques. Centralized techniques consume too much overhead, especially with a large number of nodes. Distributed techniques depend on probabilistic mechanisms. The main drawback of these techniques is that it does not guarantee a uniform distribution of clusters because it does not consider the topological characteristic of the deployed network. As a result, the total energy consumption increases and the network lifetime decreases. This work proposes a distributed density-based clustering technique called Spatial Density-based Clustering (SDC). It overcomes the drawbacks of other related distributed techniques. It aims to achieve balanced energy consumption all over the constructed clusters. Compared to other distributed clustering techniques, simulation results show that the proposed technique achieves less energy consumption and longer network lifetime.","PeriodicalId":170532,"journal":{"name":"2017 12th International Conference on Computer Engineering and Systems (ICCES)","volume":"11 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":"125338968","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

Accuracy evaluation of Arabic text classification 阿拉伯语文本分类的准确性评价

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275333

M. Sayed, Rashed K. Salem, Ayman E. Khedr

引用次数: 9

Fault injection for FPGA applications in the space 故障注入在FPGA领域的应用

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275338

Radwa M. Tawfeek, Mohamed G. Egila, Y. Alkabani, I. Hafez

引用次数: 0

An efficient computation of minimal correction subformulas for SAT-based ATPG of digital circuits 基于sat的数字电路ATPG最小修正子公式的高效计算

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275337

L. G. Ali, A. Hussein, Hanafy M. Ali

引用次数: 7

A hybrid model to predict proteins tertiary structure 预测蛋白质三级结构的混合模型

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275282

M. Yousef, T. Abdelkader, Khaled A. ElBahnasy

引用次数: 4

Introducing IPS: An integration processing system for scalable architecture IPS:一种可扩展架构的集成处理系统

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275336

N. Mekhiel

引用次数: 0

Throughput maximization based spectrum allocation algorithm under different channel approaches for underlay cognitive radio networks 基于吞吐量最大化的底层认知无线网络信道分配算法

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275350

Kyrillos Youssef, N. Messiha, M. Abd-Elnaby

引用次数: 3

Elephant herd optimization with neural networks: A case study on acute Lymphoblastic Leukemia diagnosis 基于神经网络的象群优化:急性淋巴细胞白血病诊断案例研究

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275387

A. Sahlol, F. H. Ismail, A. Abdeldaim, A. Hassanien

{"title":"Elephant herd optimization with neural networks: A case study on acute Lymphoblastic Leukemia diagnosis","authors":"A. Sahlol, F. H. Ismail, A. Abdeldaim, A. Hassanien","doi":"10.1109/ICCES.2017.8275387","DOIUrl":"https://doi.org/10.1109/ICCES.2017.8275387","url":null,"abstract":"There are several types of cancer; each is classified by the type of cells that are affected. Leukemia is a kind of cancer that caused by excessive production of leukocytes that replaces normal blood cells. According to the growth speed overproduction of leukemic cells, they can be classified into four major types. This work focuses only on Acute Lymphoblastic Leukemia (ALL), which is also called childhood leukemia. The main goal of this work is to classify the Acute lymphoblastic leukemia cells normal or affected. The proposed approach starts by identifying and segmenting each blood cell then extracting features and finally, classifying them by a hybrid neural network. In this paper, the feed-forward neural network is trained by the Elephant Herd Optimization (EHO) algorithm which updates the weights and the biases of the network. The objective function is the reduction of the misclassification rate. ALL-IDB2 dataset is used in this work. It contains 260 microscopic images. EHO achieves acceptable results as it outperforms other classification methods as well as it overcomes neural networks that are optimized by the other optimization algorithms regarding diagnosing ALL.","PeriodicalId":170532,"journal":{"name":"2017 12th International Conference on Computer Engineering and Systems (ICCES)","volume":"49 2 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":"116318752","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: 12

A fragmentation algorithm for storage management in cloud database environment 一种云数据库环境下存储管理的碎片化算法

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275293

I. Eisa, Rashed K. Salem, H. Abdelkader

{"title":"A fragmentation algorithm for storage management in cloud database environment","authors":"I. Eisa, Rashed K. Salem, H. Abdelkader","doi":"10.1109/ICCES.2017.8275293","DOIUrl":"https://doi.org/10.1109/ICCES.2017.8275293","url":null,"abstract":"The past decade has witnessed using cloud DBMSs for enterprises' applications and managing their data. However, only a few number of cloud DBMSs have provided relational database as a service and get benefits from being in cloud data centers. Cloud DBMSs are grouped into two categories. The first category is scalable datastores that cannot preserve ACID for transactions over the entire database. The second one is scalable traditional DBMSs that scale difficulty as they migrate data or instances. This paper proposes a shared storage architecture for cloud DBMS that reduces migration of data that happens for preserving load balance between database instances after increasing scalability by adding new database instances or new storage devices. Moreover, it provides storage management module that locates database objects well on storage devices for parallel access in ‘write and read’ operations with reducing storage skewness and bottleneck, based on a new horizontally fragmentation algorithm. Also, it provides storage monitoring module for detecting skewness and reconfiguring the storage. The proposed Cloud DBMS overcomes limitations in scalable data stores and traditional DBMSs. We finally confirm the effectiveness of the proposed architecture on real data sets.","PeriodicalId":170532,"journal":{"name":"2017 12th International Conference on Computer Engineering and Systems (ICCES)","volume":"321 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132417970","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: 6

Hybrid controller for a software-defined architecture of industrial internet lab-scale process 工业互联网实验室规模过程软件定义体系结构的混合控制器

2017 12th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2017-12-01 DOI: 10.1109/ICCES.2017.8275316

M. Moness, A. M. Moustafa, Abdur-Rahman H. Muhammad, A. A. Younis

引用次数: 9