2020 15th International Conference on Computer Engineering and Systems (ICCES)最新文献

Comparative Study of Vehicular Proactive Caching Between Cellular and VLC Networks 蜂窝网络与VLC网络车辆主动缓存的比较研究

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334556

Mohamed E. Gad, T. Ismail, Sameh Hosny, B. Mokhtar

{"title":"Comparative Study of Vehicular Proactive Caching Between Cellular and VLC Networks","authors":"Mohamed E. Gad, T. Ismail, Sameh Hosny, B. Mokhtar","doi":"10.1109/ICCES51560.2020.9334556","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334556","url":null,"abstract":"The rapid growth of vehicle demand, such as information sharing, entertainment, and multimedia contents, overwhelms the back-haul network. Due to this nature of the network that suffers from high link disconnections and limited resources, it is challenging to develop a new strategy to satisfy users’ requirements. Proactive caching is a useful technique to mitigate the load on core networks, and determining the best caching placement of data time to enhance the network is a significant issue. However, radiofrequency is spreading and supporting multiple channels communication, and has a licensed bandwidth and low data rates. In this paper, a novel proactive caching scheme based on an optical communication system is proposed. We exploit the high data rate of visible light communication VLC in the vehicular networks to minimize total network cost. The optimal caching placement is determined by solving the optimization problem through a genetic algorithm according to vehicle demand profiles. Also, we present suitable analysis for caching effects in average blocking and data throughput. Compared with radio techniques, numerical results illustrate that the proposed solution has a significant caching gain, average user blocking, and network throughput at different conditions.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126171313","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: 1

Extensible Fault Secure Sift-Out Technique for FPGA-based Applications 基于fpga应用的可扩展故障安全筛选技术

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334653

Seif Shawkat, G. Alkady, H. Amer, R. Daoud, I. Adly

引用次数: 1

Evaluating the Modsecurity Web Application Firewall Against SQL Injection Attacks Modsecurity Web应用防火墙对SQL注入攻击的评估

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334626

Basem Ibrahim Mukhtar, Marianne A. Azer

引用次数: 4

ICCES 2020 Reviewer Statistics icces2020审稿人统计

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/icces51560.2020.9334590

引用次数: 0

Generalized Fractured Integer Multiplier Based on Divide and Conquer with Tree Pruning 基于树剪枝分治的广义断裂整数乘子

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334645

Rizk Tawfik, Cherif R. Salama, M. El-Kharashi

引用次数: 0

Comparative Study of Visual Question Answering Algorithms 视觉问答算法的比较研究

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334686

A. Mostafa, Hazem M. Abbas, M. Khalil

{"title":"Comparative Study of Visual Question Answering Algorithms","authors":"A. Mostafa, Hazem M. Abbas, M. Khalil","doi":"10.1109/ICCES51560.2020.9334686","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334686","url":null,"abstract":"Visual Question Answering (VQA) is a recent task that challenges algorithms to reason about the visual content of an image to be able to answer a natural language question. In this study, we compare the performance of state of the art VQA algorithms on different VQA benchmarks. Each benchmark is more effective at testing VQA algorithms on different levels. Some datasets challenge the algorithms to perform complex reasoning steps to arrive to an answer. Other datasets might challenge algorithms to retrieve external world knowledge to answer the posed questions. We categorize the algorithms by their main contributions into 4 categories. Firstly, the joint embedding approach which focuses on how to map the visual and textual data into a common embedding space. Secondly, attention based methods which focuses on relevant parts of the image or the question. Thirdly, compositional models which deal with composing a model from smaller modules. Finally, we introduce external-knowledge based algorithms which need external sources to be able to retrieve facts necessary to answer a question when those facts may not be present in the scene nor in the whole training data set. We also mention other algorithms that don’t specifically belong to the aforementioned categories, but offers performance competitive with the state of the art.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123500315","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

Optimal Control of Geothermal Power Plant by Using PI Controller Based on Genetic Algorithm 基于遗传算法的PI控制器对地热发电厂的最优控制

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334604

N. Azazy, W. Helmy, H. Hasanien

{"title":"Optimal Control of Geothermal Power Plant by Using PI Controller Based on Genetic Algorithm","authors":"N. Azazy, W. Helmy, H. Hasanien","doi":"10.1109/ICCES51560.2020.9334604","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334604","url":null,"abstract":"A Controlled Geothermal power plant model for low temperature geothermal plant is presented in this paper using the Simulink model, and MATLAB software which represents the whole power plant showing the electrical power, and it’s variation with the different geothermal temperatures. The model includes all the mechanical equations, and energy conversion equations related to the organic Rankine cycle, and the organic fluid used in the mechanical part of the plant, and their effect on the generated electrical power. The model shows clearly the variation, and unstable response of the electrical power output energy signals at different geothermal temperatures in Egypt’s promising geothermal regions. A PI controller based on the Genetic Algorithm is used to control the turbine to overcome the variation of the output power at so as to be able to be integrated into a distribution power system. The results of this research help strongly the governmental, and private sectors working on geothermal energy in Egypt to specify the best locations of the geothermal power plants to be integrated into the network, and predict easily the quantity of the electric power energy can be generated.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124292750","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

Fall Detection Based on RetinaNet and MobileNet Convolutional Neural Networks 基于RetinaNet和MobileNet卷积神经网络的跌倒检测

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334570

Hadir Abdo, K. M. Amin, Ahmad M. Hamad

{"title":"Fall Detection Based on RetinaNet and MobileNet Convolutional Neural Networks","authors":"Hadir Abdo, K. M. Amin, Ahmad M. Hamad","doi":"10.1109/ICCES51560.2020.9334570","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334570","url":null,"abstract":"The problem of falling is a major health problem resulting in serious injuries and sometimes lead to death especially for elderly. Elderly people aged over than 75 are exposed to accidental deaths due to falls. Approaches based on computer vision give a promising and an effective solution for detection human falls. This paper presented a method for fall detection which based on combining convolutional neural networks RetinaNet and Mobilenet in addition to handcrafted features. Traditional human detection methods may result in human shape deformation which affect the performance of fall detection frameworks. Therefore, the proposed framework depends on RetinaNet for detecting humans with shorter computing time and higher accuracy compared with the traditional human detection methods. Then, the proposed framework relies on handcrafted features to represent shape and motion properties of the detected human. The proposed framework extracts aspect ratio and head position as shape features and motion history image as a motion feature of the detected human to create the feature map. This feature map is used in training MobileNet network to classify the human motion into fall or not-fall. The proposed framework is evaluated using UR and FDD datasets and the experimental results proved the efficiency of the proposed framework achieving up to 98% accuracy compared with the state-of-the-art methods.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"157 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128179812","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: 14

Session AI: Artificial Intelligence 会议AI:人工智能

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/icces51560.2020.9334674

引用次数: 0

IoT Based AI and its Implementations in Industries 基于物联网的人工智能及其在工业中的实现

2020 15th International Conference on Computer Engineering and Systems (ICCES) Pub Date : 2020-12-15 DOI: 10.1109/ICCES51560.2020.9334627

Sherif El-Gendy

引用次数: 5