Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334589
Sherif El-Gendy, Marianne A. Azer
The Internet of things technology provides people with new experiences through the interaction between devices, people and networks. Examples include smart grid, smart health, smart home, smart workplace, e-commerce, smart industrial management, and e-governance. More and more devices are connected every day, resulting in greater security threats and problems. An extensive IoT security model is needed in order to aid resource-based IoT devices and end security. In this paper, we focus on the IoT devices applications and networks, in addition to the attack vectors and security requirements for IoT systems, as well as the organizational approach towards IoT security. We also propose a security architecture to provide security enabled IoT services, and provide a baseline for security deployment.
{"title":"Security Framework for Internet of Things (IoT)","authors":"Sherif El-Gendy, Marianne A. Azer","doi":"10.1109/ICCES51560.2020.9334589","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334589","url":null,"abstract":"The Internet of things technology provides people with new experiences through the interaction between devices, people and networks. Examples include smart grid, smart health, smart home, smart workplace, e-commerce, smart industrial management, and e-governance. More and more devices are connected every day, resulting in greater security threats and problems. An extensive IoT security model is needed in order to aid resource-based IoT devices and end security. In this paper, we focus on the IoT devices applications and networks, in addition to the attack vectors and security requirements for IoT systems, as well as the organizational approach towards IoT security. We also propose a security architecture to provide security enabled IoT services, and provide a baseline for security deployment.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"34 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":"134364442","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}
Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334566
Aml Mostafa, Walaa K. Gad, T. Abdelkader, N. Badr
Geographical information is important in several applications, such as, advertising and recommending. Despite the availability and the existence of social media, especially twitter, the geographical coordinates are often hidden according to privacy reasons. In this paper, a new model is proposed to predict the tweet location based on the KNN-Sentimental Analysis (KNNSA) model. Predicting the tweet location based on the KNN-sentiment analysis (KNNSA) model extracts text features from the tweet in addition to the date and time features. Then, applying sentimental analysis and classifying the data by K-nearest neighbors (KNN) classifier. The (KNNSA) model is evaluated and compared to the previous work and it achieves better performance in terms of root mean squared error (RMSE) and of the mean absolute error (MAE).
{"title":"Predicting the Tweet Location Based on KNN-Sentimental Analysis","authors":"Aml Mostafa, Walaa K. Gad, T. Abdelkader, N. Badr","doi":"10.1109/ICCES51560.2020.9334566","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334566","url":null,"abstract":"Geographical information is important in several applications, such as, advertising and recommending. Despite the availability and the existence of social media, especially twitter, the geographical coordinates are often hidden according to privacy reasons. In this paper, a new model is proposed to predict the tweet location based on the KNN-Sentimental Analysis (KNNSA) model. Predicting the tweet location based on the KNN-sentiment analysis (KNNSA) model extracts text features from the tweet in addition to the date and time features. Then, applying sentimental analysis and classifying the data by K-nearest neighbors (KNN) classifier. The (KNNSA) model is evaluated and compared to the previous work and it achieves better performance in terms of root mean squared error (RMSE) and of the mean absolute error (MAE).","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"29 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":"134035774","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}
Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334560
E. T. Sadek, Noha A. Seada, S. Ghoniemy
Autism is a mental disorder appearing in children as a delay in their social and communicational skills. ASD causes are still mysterious but scientists believe that they are caused by genetic defects. Diagnosing autism has been an exhaustive process that attracts several researchers’ attentions. In this work, an overall description of autism its classes, signs and diagnosing protocols are covered. As computational technologies helped in assisting almost every field it added advantages in detecting and recognizing autism In this work, deep investigation of computer vision-based protocols in cooperation with machine learning technologies are discussed to propose autism diagnosing solution.
{"title":"A Review on Computer Vision-Based Techniques for Autism Symptoms Detection and Recognition","authors":"E. T. Sadek, Noha A. Seada, S. Ghoniemy","doi":"10.1109/ICCES51560.2020.9334560","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334560","url":null,"abstract":"Autism is a mental disorder appearing in children as a delay in their social and communicational skills. ASD causes are still mysterious but scientists believe that they are caused by genetic defects. Diagnosing autism has been an exhaustive process that attracts several researchers’ attentions. In this work, an overall description of autism its classes, signs and diagnosing protocols are covered. As computational technologies helped in assisting almost every field it added advantages in detecting and recognizing autism In this work, deep investigation of computer vision-based protocols in cooperation with machine learning technologies are discussed to propose autism diagnosing solution.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"75 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":"134041921","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}
Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334677
H. Fahmy
Networking is a field of integration, hardware and software, protocols and standards, simulation and testbeds, wired and wireless, VLSI and communication, energy harvesting and management, an orchestrated harmony that collaborates dependably, all for the good of a connected well-performing network.To tackle the limited energy resources in Wireless Sensor Networks (WSNs), two approaches may be used separately or jointly to maintain or increase network lifetime; specifically, energy harvesting and/or energy management:Energy harvesting refers to harnessing energy from the surrounding nature or other energy sources such as human body and subsequently converting it to electrical energy. The harnessed electrical energy powers the sensor nodes.Energy management schemes take into account several interacting factors that jointly effect the power consumption of a wireless sensor node. Typically, these factors are the specific sensor type, data transmission, radio energy consumption and the sensing subsystem.Based on the WSN architecture and power expenditure, several approaches for energy management have to be embraced, even simultaneously, to reduce power consumption. Generally, three main techniques might be identified; explicitly, duty-cycling, data-driven, and mobility approaches.This talk provides an insight into the highly important topic of increasing or maintaining the lifetime of WSNs through managing the energy reservoir of sensor nodes.
{"title":"Plenary Talk I : Energy Harvesting and Management Techniques for Wireless Sensor Networks","authors":"H. Fahmy","doi":"10.1109/ICCES51560.2020.9334677","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334677","url":null,"abstract":"Networking is a field of integration, hardware and software, protocols and standards, simulation and testbeds, wired and wireless, VLSI and communication, energy harvesting and management, an orchestrated harmony that collaborates dependably, all for the good of a connected well-performing network.To tackle the limited energy resources in Wireless Sensor Networks (WSNs), two approaches may be used separately or jointly to maintain or increase network lifetime; specifically, energy harvesting and/or energy management:Energy harvesting refers to harnessing energy from the surrounding nature or other energy sources such as human body and subsequently converting it to electrical energy. The harnessed electrical energy powers the sensor nodes.Energy management schemes take into account several interacting factors that jointly effect the power consumption of a wireless sensor node. Typically, these factors are the specific sensor type, data transmission, radio energy consumption and the sensing subsystem.Based on the WSN architecture and power expenditure, several approaches for energy management have to be embraced, even simultaneously, to reduce power consumption. Generally, three main techniques might be identified; explicitly, duty-cycling, data-driven, and mobility approaches.This talk provides an insight into the highly important topic of increasing or maintaining the lifetime of WSNs through managing the energy reservoir of sensor nodes.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"235 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":"132274981","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}
Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334613
Abdelrahman S. Moustafa, H. Omran, K. Sharaf
An automated design and optimization tool for all digital phase locked-loop (ADPLL) system is presented in this paper. The ADPLL system design goal is to determine the digital loop filter (DLF) coefficients using analytic noise models and phase noise simulation results for each ADPLL block, then the DLF coefficients are optimized using evolutionary optimization method to achieve the optimum locking time and phase noise of the ADPLL. The system simulation results of the ADPLL designed by the tool show good agreement with the system’s required specifications.
{"title":"Automatic All-Digital Phase-Locked Loop System Design and optimization Tool","authors":"Abdelrahman S. Moustafa, H. Omran, K. Sharaf","doi":"10.1109/ICCES51560.2020.9334613","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334613","url":null,"abstract":"An automated design and optimization tool for all digital phase locked-loop (ADPLL) system is presented in this paper. The ADPLL system design goal is to determine the digital loop filter (DLF) coefficients using analytic noise models and phase noise simulation results for each ADPLL block, then the DLF coefficients are optimized using evolutionary optimization method to achieve the optimum locking time and phase noise of the ADPLL. The system simulation results of the ADPLL designed by the tool show good agreement with the system’s required specifications.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"7 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":"116272082","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}
Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334619
M. H. Saad, M. Khalil, Hazem M. Abbas
In this paper, seven End-To-End deep learning models, including three sequence models, were trained for driver distractions recognition. One of these models (Convolutional GRU) achieved 95.48% test-accuracy. The performance of each model was analyzed using different techniques like confusion matrix, t-SNE representation, and saliency map. In addition, the largest driver distractions dataset, to date, was presented. This dataset contains ten classes and comes with temporal information. Using the NoIR technology the dataset was captured, that makes the dataset to be able to contain samples at different lighting conditions in case of using IR LEDs. The dataset contains 70 drivers either males or females.
{"title":"End-To-End Driver Distraction Recognition Using Novel Low Lighting Support Dataset","authors":"M. H. Saad, M. Khalil, Hazem M. Abbas","doi":"10.1109/ICCES51560.2020.9334619","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334619","url":null,"abstract":"In this paper, seven End-To-End deep learning models, including three sequence models, were trained for driver distractions recognition. One of these models (Convolutional GRU) achieved 95.48% test-accuracy. The performance of each model was analyzed using different techniques like confusion matrix, t-SNE representation, and saliency map. In addition, the largest driver distractions dataset, to date, was presented. This dataset contains ten classes and comes with temporal information. Using the NoIR technology the dataset was captured, that makes the dataset to be able to contain samples at different lighting conditions in case of using IR LEDs. The dataset contains 70 drivers either males or females.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 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":"124866618","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}
Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334567
Somaya A. Aboulrous, A. El-Moursy, M. Saad, Amany Abdelsamea, S. Nassar, Hazem M. Abbas
Fifth generation (5G) systems are considered as the future of telecommunications and data processing. 5G systems are envisaged to increase current network capacity by 1000-fold. In addition to increasing spectral efficiency and utilizing wider bandwidths, ultra network densification with wireless small cells is considered a significant capacity-enhancing method for meeting the ever-increasing demand of 5G data traffic requirements. The enormous amounts of data traffic generated in inter-small-cell 5G backhaul networks require efficient routing protocols to speed up the routing decisions, while ensuring high data rates, low latency, and low power consumption requirements. Therefore, a parallel routing protocol is proposed to speed up routing decisions in 5G backhaul networks, using high performance computing (HPC). We explore the efficiency of utilizing HPC to manage and speed up the parallel routing protocol using different communication network sizes. Our numerical results indicate that our HPC implementation achieves a routing speed-up of 37x for large network size (2048).
{"title":"Parallel Multi-hop Routing Protocol for 5G Backhauling Network Using HPC Platform","authors":"Somaya A. Aboulrous, A. El-Moursy, M. Saad, Amany Abdelsamea, S. Nassar, Hazem M. Abbas","doi":"10.1109/ICCES51560.2020.9334567","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334567","url":null,"abstract":"Fifth generation (5G) systems are considered as the future of telecommunications and data processing. 5G systems are envisaged to increase current network capacity by 1000-fold. In addition to increasing spectral efficiency and utilizing wider bandwidths, ultra network densification with wireless small cells is considered a significant capacity-enhancing method for meeting the ever-increasing demand of 5G data traffic requirements. The enormous amounts of data traffic generated in inter-small-cell 5G backhaul networks require efficient routing protocols to speed up the routing decisions, while ensuring high data rates, low latency, and low power consumption requirements. Therefore, a parallel routing protocol is proposed to speed up routing decisions in 5G backhaul networks, using high performance computing (HPC). We explore the efficiency of utilizing HPC to manage and speed up the parallel routing protocol using different communication network sizes. Our numerical results indicate that our HPC implementation achieves a routing speed-up of 37x for large network size (2048).","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 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":"126204277","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}
Pub Date : 2020-12-15DOI: 10.1109/icces51560.2020.9334681
{"title":"[Title page]","authors":"","doi":"10.1109/icces51560.2020.9334681","DOIUrl":"https://doi.org/10.1109/icces51560.2020.9334681","url":null,"abstract":"","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"3 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":"117065285","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}
Pub Date : 2020-12-15DOI: 10.1109/icces51560.2020.9334617
{"title":"[ICCES 2020 Copyright notice]","authors":"","doi":"10.1109/icces51560.2020.9334617","DOIUrl":"https://doi.org/10.1109/icces51560.2020.9334617","url":null,"abstract":"","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"17 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":"134536843","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}
Pub Date : 2020-12-15DOI: 10.1109/icces51560.2020.9334602
{"title":"ICCES 2020 Paper Statistics","authors":"","doi":"10.1109/icces51560.2020.9334602","DOIUrl":"https://doi.org/10.1109/icces51560.2020.9334602","url":null,"abstract":"","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"181 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":"133503072","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}
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