2018 13th International Conference on Computer Engineering and Systems (ICCES)最新文献_第2页

Enhancing Recommendations in Mobile Social Network 在移动社交网络中加强推荐

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639289

H. Ibrahim, T. Abdelkader, R. E. Gohary

引用次数: 1

Augmenting Multi-Objective Genetic Algorithm and Dynamic Programming for Online Coverage Path Planning 基于增强多目标遗传算法和动态规划的在线覆盖路径规划

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639412

Mina G. Sadek, Amr E. Mohamed, A. El-Garhy

引用次数: 5

Learning Transformations for Automated Classification of Manifestation of Tuberculosis using Convolutional Neural Network 基于卷积神经网络的结核表现自动分类的学习变换

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639200

Asmaa Abbas, M. Abdelsamea

{"title":"Learning Transformations for Automated Classification of Manifestation of Tuberculosis using Convolutional Neural Network","authors":"Asmaa Abbas, M. Abdelsamea","doi":"10.1109/ICCES.2018.8639200","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639200","url":null,"abstract":"automated classification of tuberculosis in x-ray images is of an increasing interest to all researchers and physicians. Due to the high level of intensity inhomogeneity and variations, statistical machine-learning approaches usually fail to offer a generic solution to image classification. Convolution neural networks (CNNs) have demonstrated superior effectiveness in computer-aided diagnosis systems. Transfer learning can provide a powerful deep learning solutions to the limited availability of labelled images. In this paper we study the effect of knowledge transferred from a pre-trained ImageNet, in different ways via a pre-trained CNN model, to classify chest x-ray images as having manifestations of tuberculosis or as healthy. We evaluated and compared various models using the learning curve between training and validation set, and receiver operating characteristic (ROC) curve. Our experiments revealed that using fine-tuning technique outperformed both shallow-tuning and deep-tuning techniques and achieved 0.998 for the AUC, 0.999 for specificity, and 0.997 for sensitivity rate.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115788739","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: 22

ICCES 2018 Author Index ICCES 2018作者索引

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639282

引用次数: 0

WPA-WPA2 PSK Cracking Implementation on Parallel Platforms 并行平台上的WPA-WPA2 PSK破解实现

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639328

A. Abdelrahman, H. Khaled, E. Shaaban, W. Elkilani

引用次数: 8

Detecting Spam Tweets using Character N-gram Features 使用字符N-gram特征检测垃圾推文

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639297

M.M. Ashour, Cherif R. Salama, M. El-Kharashi

引用次数: 12

ICCES 2018 Session PC2: Parallel and Cloud Computing II ICCES 2018会议PC2:并行和云计算II

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639244

引用次数: 0

Comparative Biomechanical Analysis of Lumbar Disc Arthroplasty using Finite Element Modeling 利用有限元模型对腰椎间盘置换术进行生物力学分析

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639276

H. Afify, M. Mabrouk, S. Marzouk

{"title":"Comparative Biomechanical Analysis of Lumbar Disc Arthroplasty using Finite Element Modeling","authors":"H. Afify, M. Mabrouk, S. Marzouk","doi":"10.1109/ICCES.2018.8639276","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639276","url":null,"abstract":"Lumbar total disc replacement (LTDR) is a surgical procedure for the treatment of degenerative disc disease (DDD) and lumbar spinal distortion to preserve range of motion (ROM). The SB Charité™ is the first device for LTDR but it produces more complications and long-term issues, causing a shortage of SB Charité™ disc. Currently, the evolution of lumbar disc arthroplasty based on some criteria like prosthetics structure, biomechanical model, tissue engineering and biomaterials approach. The optimal biomechanical model is based on kinematic and kinetic parameters together to ensure a long-term implantation with a lower rate of damage from disc implant in the spinal column. The finite element method (FEE) of human lumbar spinal is advanced to support biomechanical modeling techniques which are related to biomaterials guideline for choosing better material for implantation. Therefore, this paper presented a 3D biomechanical FEM model of L1 to L3 lumbar spines by different types of discs and materials. We applied a compressive force, and compressive force plus extension moment to this model to calculate Tresca stress of annulus fibers, strain and von Mises stress on the vertebral endplate at each intervertebral level under COMSOL Multiphysics® software.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125826516","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

Sub-Grid Partitioning Algorithm for Distributed Outlier Detection on Big Data 大数据分布式离群点检测的子网格划分算法

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639409

Mohamed Sakr, Walid Atwa, A. Keshk

{"title":"Sub-Grid Partitioning Algorithm for Distributed Outlier Detection on Big Data","authors":"Mohamed Sakr, Walid Atwa, A. Keshk","doi":"10.1109/ICCES.2018.8639409","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639409","url":null,"abstract":"Anomaly detection or outlier detection has become a major research problem in the era of big data. It is used in many applications, remove noise from signals and in credit card fraud detection. One type of outlier detection is Density-based outlier detection. Its major uniqueness is in detecting outlier points in different densities. One of the algorithms that are based on density based outlier detection is Local Outlier Factor (LOF). LOF gives every point a score that identifies its outlierness compared to other points. In this paper, we propose a new algorithm called sub-Grid partition (SGP) algorithm. SGP algorithm helps in calculating the LOF for Big Data in a distributed environment. SGP algorithm splits the tuples into small grids each grid is splitted into sub-grids. Sub-grids in the border are duplicated in every processing node for calculating the LOF for every tuple in these grids. Duplication of sub-grids lead to increase in the number of tuples that will be processed but in the other hand reduces the network overhead required for communication between processing nodes and reducing processing node idle time waiting for the requested tuple. In the end, we evaluate the performance of the SGP algorithm through a series of simulation experiments over real data sets.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647282","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: 4

Group Sparsity based Signal Detection for Massive Multi User Spatial Modulation Cyclic Prefix Single Carrier Systems 基于群稀疏性的海量多用户空间调制循环前缀单载波系统信号检测

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639419

S. Said, S. El-Araby, W. Saad, M. Shokair

引用次数: 1