2018 13th International Conference on Computer Engineering and Systems (ICCES)最新文献_第5页

ICCES 2018 Session CNS1: Computer Networks and Security I ICCES 2018会议CNS1:计算机网络与安全

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639493

引用次数: 0

ICCES 2018 Paper Statistics ICCES 2018论文统计

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639216

引用次数: 0

Deep Learning Algorithms for Detecting Fake News in Online Text 在线文本中假新闻检测的深度学习算法

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639198

Sherry Girgis, Eslam Amer, M. Gadallah

{"title":"Deep Learning Algorithms for Detecting Fake News in Online Text","authors":"Sherry Girgis, Eslam Amer, M. Gadallah","doi":"10.1109/ICCES.2018.8639198","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639198","url":null,"abstract":"Spreading of fake news is a social phenomenon that is pervasive at the social level between individuals, and also through social media such as Facebook and Twitter. Fake news that we are interested in is one of many kinds of deception in social media, but it’s more important one as it is created with dishonest intention to mislead people. We are concerned about this issue because we have noticed that this phenomenon has recently caused through the means of social communication to change the course of society and peoples and also their views, for example, during revolutions in some Arab countries have emerged some false news that led to the absence of truth and stirs up public opinion and also fake of news is one of the factors Trump successes in the presidential election. So we decided to face and reduce this phenomenon, which is still the main factor to choose most of our decisions. Techniques of fake news detection varied, ingenious, and often exciting. In this paper our objective is to build a classifier that can predict whether a piece of news is fake or not based only its content, thereby approaching the problem from a purely deep learning perspective by RNN technique models (vanilla, GRU) and LSTMs. We will show the difference and analysis of results by applying them to the dataset that we used called LAIR. We found that the results are close, but the GRU is the best of our results that reached (0.217) followed by LSTM (0.2166) and finally comes vanilla (0.215). Due to these results, we will seek to increase accuracy by applying a hybrid model between the GRU and CNN techniques on the same data set.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"9 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":"128774693","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: 83

Modeling Procedures for Breast Cancer Diagnosis based on Clinical Elastography Images 基于临床弹性成像的乳腺癌诊断建模程序

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639338

M. Naser, Ahmed M. Sayed, A. A. Wahba, M. Eldosoky

{"title":"Modeling Procedures for Breast Cancer Diagnosis based on Clinical Elastography Images","authors":"M. Naser, Ahmed M. Sayed, A. A. Wahba, M. Eldosoky","doi":"10.1109/ICCES.2018.8639338","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639338","url":null,"abstract":"Nowadays, breast cancer is considered the second cause common cancer type of women death. To determine the proper therapeutic procedures before cancer spreading, early detection of cancer is a definitive step. Ultrasound elastography is considered one of the early effective noninvasive diagnostic tools. It has many advantages as low cost, its safety and the highly increasing development in various medical imaging applications.In this work, 3D modelling and simulations using virtual phantoms that were designed based on realistic in-vivo experimental results. The models were constructed for each in-vivo individual case assuring the biomechanical features of the breast tissue. The models are integrated several breast tumor’s parameters including size, shape, and position. In particular, mathematical and computational analyses were used to compare this work’s results by assorted specifics of in-vivo elastograms. Tumor discrimination; either malignant or benign, was performed depending on the non-linear biomechanical properties of breast tumors. To calculate the main classification parameters, tissue deformations and strain differences among the suspected mass and the normal surrounding background tissue were analyzed and empirically fitted. The results show a kindly agreement between the model outputs and the in-vivo diagnostics elastograms. Generally, the introduced finite element modeling method can be considered as a non-invasive diagnostic procedure in an early stage to preceding classify breast tumors. The 3D simulation results can assure a more theoretical insight on the behavior of nonlinear biomechanical properties that might not be obvious or convenient using clinical experimentations.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"115 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":"116021322","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

ICCES 2018 Plenary Talk III ICCES 2018全体会谈三

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639223

引用次数: 0

Suspicious Human Activity Recognition using Statistical Features 利用统计特征识别可疑人类活动

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639457

Hanan Samir, Hossam E. Abd El Munim, G. Aly

{"title":"Suspicious Human Activity Recognition using Statistical Features","authors":"Hanan Samir, Hossam E. Abd El Munim, G. Aly","doi":"10.1109/ICCES.2018.8639457","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639457","url":null,"abstract":"This paper presents a new algorithm for suspicious human activity recognition in videos based on a combination of two different feature types. The first feature concerns the shape and is called shape moments. The second concerns the boundary coordinates and is called \"Histogram of Normalized Distances (HND) from Center of gravity of the object shape (COG) and it's contour points\" combining these features leads to the formation of a strong complementary feature vector that captures effective discriminate details of human action videos. The authors used two methods for classification, the Multi-class Support Vector Machine and Naive Bayes classifier. The classification by using the Multi-class SVM classifier verified recognition rate up to 95.6 %, but the Naive Bayes classifier verified 97.2%. The authors evaluated the suspicious activity recognition on 250 videos from HMDB data set. Five distinct suspicious human activities (e.g., Running, Punching, Kicking, Shooting guns and Falling floor, etc.) by 250 different persons. Experiments on HMDB show that the presented system can recognize suspicious activities effectively and accurately in surveillance videos.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"32 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":"115347573","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

Software Defined Networking: Attacks and Countermeasures 软件定义网络:攻击和对策

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639429

Nada Mostafa Abd Elazim, M. Sobh, Ayman M. Bahaa-Eldin

引用次数: 11

ICCES 2018 Plenary Talk I ICCES 2018全体会议演讲一

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639477

引用次数: 0

Predicting Personality Traits from Social Media using Text Semantics 利用文本语义预测社交媒体的人格特征

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639408

Mariam Hassanein, Wedad Hussein, S. Rady, Tarek F. Gharib

引用次数: 26

Extracting Valuable Associations among Textural Features of Medical Images 提取医学图像纹理特征之间有价值的关联

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639496

F. H. Ismail, Aboul ella Hassanien

引用次数: 1