2018 13th International Conference on Computer Engineering and Systems (ICCES)最新文献_第10页

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2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639197

引用次数: 0

Amelio-rater: Detection and Classification of Driving Abnormal Behaviours for Automated Ratings and Real-Time Monitoring Amelio-rater:自动评级和实时监控驾驶异常行为的检测和分类

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639398

Noha El Masry, Passant El-Dorry, Mariam El Ashram, Ayman Atia, Jiro Tanaka

引用次数: 4

Web Service-based System for Hepatobiliary System Diseases Prognosis and Treatment 基于Web服务的肝胆系统疾病预后与治疗系统

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639324

Galal Al-Marzoqi, Marco Alfonse, I. Moawad, Mohamed Roushdy

{"title":"Web Service-based System for Hepatobiliary System Diseases Prognosis and Treatment","authors":"Galal Al-Marzoqi, Marco Alfonse, I. Moawad, Mohamed Roushdy","doi":"10.1109/ICCES.2018.8639324","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639324","url":null,"abstract":"Hepatobiliary system is one of the most important systems in the human body. It is responsible for many processes, which are necessary to keep body regulated and healthy. In our previous research, we exploited the existing Medical Ontologies for building a new Hepatobiliary System Diseases (HSD) Ontology in pathology domain. This Ontology is represented in the Web Ontology Language (OWL) that has recently become the standard language for the semantic web. In its current format, the HSD Ontology can be accessed only by the computer science specialists. In this paper, we present a system for Hepatobiliary system diseases prognosis and treatment. The system shares the Ontology knowledge by replying the inquiries of both physicians and medical students. The presented system is a web service-based, thus it can be integrated with intelligent systems. The proposed system utilizes the causal relations among diseases to predict the incoming diseases. During the patient visits, the system supports the physician by diagnosing the case, suggesting a treatment plan, and expecting the patient status progress. The system has been evaluated using a real dataset of 40 anonymous patients, and the diagnosis accuracy of the system is 92.5%.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 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":"133145802","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

Digital Design using CMOS and Hybrid CMOS/Memristor Gates: A Comparative Study 采用CMOS和混合CMOS/忆阻门的数字设计:比较研究

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639192

N. Ibrahim, S. Salah, M. Safar, M. El-Kharashi

引用次数: 3

Multi-Temporal-Resolution Technique for Action Recognition using C3D: Experimental Study 基于C3D的多时间分辨率动作识别技术:实验研究

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639245

Bassel S. Chawky, M. Marey, Howida A. Shedeed

引用次数: 0

An Accelerated Path Planning Approach 一种加速路径规划方法

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639491

E. Ahmed, Hossam E. Abd El Munim, Hassan M. Shehata Bedour

引用次数: 0

ICCES 2018 Session CNS2: Computer Network and Security II ICCES 2018会议CNS2:计算机网络与安全II

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639441

引用次数: 0

Design of a Backstepping Controller based on an Adaptive Elman Neural Network for a Two-Link Robot System 基于自适应Elman神经网络的双连杆机器人反步控制器设计

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639194

A. M. Sadek, Wael Mohamed Elawady, A. Sarhan

引用次数: 2

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2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/icces.2018.8639393

引用次数: 0

Comparison of CNNs for Remote Sensing Scene Classification 遥感场景分类cnn的比较

2018 13th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2018-12-01 DOI: 10.1109/ICCES.2018.8639467

Mayar A. Shafaey, M. A. Salem, H. M. Ebeid, M. Al-Berry, M. Tolba

{"title":"Comparison of CNNs for Remote Sensing Scene Classification","authors":"Mayar A. Shafaey, M. A. Salem, H. M. Ebeid, M. Al-Berry, M. Tolba","doi":"10.1109/ICCES.2018.8639467","DOIUrl":"https://doi.org/10.1109/ICCES.2018.8639467","url":null,"abstract":"Nowadays, deep learning are used widely in many applications related to remote sensing i.e. earth observation, urban planning, earth’s scene classification, and so on. The deep learning manner, especially CNNs, has proved its accuracy for these practical applications. Hence, in this article, CNNs models are reviewed and its five different architectures are applied for comparisons; namely, AlexNet, VGGNet, GoogleNet, Inception-V3, and ResNet-101. These models are carried out on seven different remote-sensing image datasets for image scene classification purpose; namely, WHU-RS19, UC-Merced Land Use, SIRI-WHU, RSSCN7, AID, PatternNet, and NWPU-RESISC45. These datasets have different spatial resolutions, ranging from 0.2 to 30, to differentiate the classification accuracy of the low and high resolution images. As well, the classification accuracy of each model is assessed by trying five different classifiers; namely, Naïve Bayes, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The best accuracy credits to ResNet-101 model with SVM classifier; it has reached about 98.6±0.02 % of the high resolution dataset, PatternNet.","PeriodicalId":113848,"journal":{"name":"2018 13th International Conference on Computer Engineering and Systems (ICCES)","volume":"69 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":"127031438","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