Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068153
Sara Salama, Rashed K. Salem, H. Abdel-Kader
Data are the representation of our world and our life. Data are increasing continuously, they come from different sources such as sensors, maps, climate informatics, smartphones, social media and/or medical data domains. Data are represented by different forms such as image, text, video and/or digital data. These incomprehensible data need an influential technique to be clustered and analyzed. This paper presents a hashing technique for the clustering process of unclassified and disorganized data. These clustered data are useful for decision-making process. The proposed technique is based on Golay error-correction code. The main concept is reversing the original Golay error-correction scheme and building Golay Code Addresses Hash Table (GCAHT). Simulation results stated that the proposed technique achieved high performance. Beta-CV, Dunn Index, C-index and Sum Square Error are used for measurements.
{"title":"Improving Golay Code Using Hashing Technique","authors":"Sara Salama, Rashed K. Salem, H. Abdel-Kader","doi":"10.1109/ICCES48960.2019.9068153","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068153","url":null,"abstract":"Data are the representation of our world and our life. Data are increasing continuously, they come from different sources such as sensors, maps, climate informatics, smartphones, social media and/or medical data domains. Data are represented by different forms such as image, text, video and/or digital data. These incomprehensible data need an influential technique to be clustered and analyzed. This paper presents a hashing technique for the clustering process of unclassified and disorganized data. These clustered data are useful for decision-making process. The proposed technique is based on Golay error-correction code. The main concept is reversing the original Golay error-correction scheme and building Golay Code Addresses Hash Table (GCAHT). Simulation results stated that the proposed technique achieved high performance. Beta-CV, Dunn Index, C-index and Sum Square Error are used for measurements.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114197561","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068178
Walaa Alkady, Walaa K. Gad, K. Bahnasy
The biological activity of molecules is usually measured in assays to establish the level of inhibition of signal transduction or metabolic pathways. Drug discovery involves the use of Quantitative Structure Activity Relationship (QSAR) to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity. QSAR has very complicated 3D structure. Therefore, the flower-based optimization model (FBOM) for molecules is proposed to solve the curse of dimensionality problems. Four performance measures: accuracy, precision, sensitivity and specificity are used to evaluate the proposed model. Molecules activity is predicted using support vector machine (SVM), Naive Bayesian (NB), K-Nearest Neighbor (KNN), Decision Tree (DT) and Neural Network (NN) Classifiers. The results of the proposed model are promising. The proposed model reduces the number of features to 8 features out of 1666 features. Moreover, the average classification accuracy reaches to 95%.
{"title":"Swarm Intelligence Optimization for Feature Selection of Biomolecules","authors":"Walaa Alkady, Walaa K. Gad, K. Bahnasy","doi":"10.1109/ICCES48960.2019.9068178","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068178","url":null,"abstract":"The biological activity of molecules is usually measured in assays to establish the level of inhibition of signal transduction or metabolic pathways. Drug discovery involves the use of Quantitative Structure Activity Relationship (QSAR) to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity. QSAR has very complicated 3D structure. Therefore, the flower-based optimization model (FBOM) for molecules is proposed to solve the curse of dimensionality problems. Four performance measures: accuracy, precision, sensitivity and specificity are used to evaluate the proposed model. Molecules activity is predicted using support vector machine (SVM), Naive Bayesian (NB), K-Nearest Neighbor (KNN), Decision Tree (DT) and Neural Network (NN) Classifiers. The results of the proposed model are promising. The proposed model reduces the number of features to 8 features out of 1666 features. Moreover, the average classification accuracy reaches to 95%.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115391812","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068124
Nada Shorim, Taraggy M. Ghanim, Ashraf AbdelRaouf
Cloud computing application for automatic recognition of Arabic handwritten is needed nowadays and of great importance especially when implemented on a mobile application. It is a type of applications that includes many challenging aspects. This paper introduces for the first time a cloud based mobile app that applies Arabic handwritten recognition for translation purposes and finding locations on Google maps. Proposing such a service for non-Arabic speakers is of great importance especially while visiting Arabic speaking countries. Our approach is the first to build a mobile app based on cloud computing that proposes a multi-phase hybrid classifier for Arabic Handwritten text recognition. Google Maps and Google Translate APIs are applied on the recognized text as part of the introduced cloud computing application. The recognition part of the proposed approach is a multi-stage classifier introduced to cope with big database and high computation complexities. The experiment applied on our approach shows better results of our Arabic handwritten recognition when compared with similar approaches.
{"title":"Implementing Arabic Handwritten Recognition Approach using Cloud Computing and Google APIs on a mobile application","authors":"Nada Shorim, Taraggy M. Ghanim, Ashraf AbdelRaouf","doi":"10.1109/ICCES48960.2019.9068124","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068124","url":null,"abstract":"Cloud computing application for automatic recognition of Arabic handwritten is needed nowadays and of great importance especially when implemented on a mobile application. It is a type of applications that includes many challenging aspects. This paper introduces for the first time a cloud based mobile app that applies Arabic handwritten recognition for translation purposes and finding locations on Google maps. Proposing such a service for non-Arabic speakers is of great importance especially while visiting Arabic speaking countries. Our approach is the first to build a mobile app based on cloud computing that proposes a multi-phase hybrid classifier for Arabic Handwritten text recognition. Google Maps and Google Translate APIs are applied on the recognized text as part of the introduced cloud computing application. The recognition part of the proposed approach is a multi-stage classifier introduced to cope with big database and high computation complexities. The experiment applied on our approach shows better results of our Arabic handwritten recognition when compared with similar approaches.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129655736","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068122
A. Kamal, H. Dahshan, A. Elbayoumy
Network coding (NC) can effectively improve data delivery in a noisy network. It allows the nodes to combine multiple packets and deliver them out. The destination can then recover it. However, pollution attacks are the most common threat to NC. As malicious nodes can inject false Ethernet packets into the network to ban the receiver from decoding the packets properly, certain authentication information must be embedded in the packets to enable the receiver to authenticate received packets. In this paper, a new scheme to apply secure Message Authentication Code (MAC) with network coding is proposed. By applying this scheme, malicious packets could be rejected in intermediate nodes without waiting until verified and dropped by the receiving node. This technique is applied with the aid of a separate hardware device with an Altera Cyclone IV FPGA chip to generate the MAC and append it to the original ethernet packets. The proposed scheme can be integrated in the existing running environments without any changes in the network configuration. The performance of the proposed scheme is evaluated to measure its throughput.
网络编码(NC)可以有效地改善噪声网络中的数据传输。它允许节点组合多个数据包并将其发送出去。然后目的地可以恢复它。然而，污染袭击是NC最常见的威胁。由于恶意节点可以向网络中注入虚假的以太网报文，从而阻止接收方对报文进行正确的解码，因此必须在报文中嵌入一定的认证信息，使接收方能够对接收到的报文进行认证。本文提出了一种将安全消息认证码(MAC)与网络编码结合使用的新方案。通过应用该方案，可以在中间节点拒绝恶意数据包，而无需等待接收节点的验证和丢弃。该技术通过使用Altera Cyclone IV FPGA芯片的单独硬件设备来生成MAC并将其附加到原始以太网数据包中。该方案可以在不改变网络配置的情况下集成到现有的运行环境中。对所提方案的性能进行了评估，以衡量其吞吐量。
{"title":"Implementation of A Homomorphic MAC Scheme in a Transparent Hardware Appliance for Network Coding","authors":"A. Kamal, H. Dahshan, A. Elbayoumy","doi":"10.1109/ICCES48960.2019.9068122","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068122","url":null,"abstract":"Network coding (NC) can effectively improve data delivery in a noisy network. It allows the nodes to combine multiple packets and deliver them out. The destination can then recover it. However, pollution attacks are the most common threat to NC. As malicious nodes can inject false Ethernet packets into the network to ban the receiver from decoding the packets properly, certain authentication information must be embedded in the packets to enable the receiver to authenticate received packets. In this paper, a new scheme to apply secure Message Authentication Code (MAC) with network coding is proposed. By applying this scheme, malicious packets could be rejected in intermediate nodes without waiting until verified and dropped by the receiving node. This technique is applied with the aid of a separate hardware device with an Altera Cyclone IV FPGA chip to generate the MAC and append it to the original ethernet packets. The proposed scheme can be integrated in the existing running environments without any changes in the network configuration. The performance of the proposed scheme is evaluated to measure its throughput.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128931964","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068147
Manar Ramzy Dronky, W. Khalifa, M. Roushdy
Applying iris recognition systems in many sensitive security areas highlights the importance of developing liveness detection methods. These methods read the users physiological signs of life to verify if the iris pattern acquired for identification is fake or real. This paper explores the results of BSIF for solving the problem of iris liveness detection to combat presentation attacks. Four public datasets representing printed, plastic, synthetic and contact lens attacks were used for method evaluation in both scenarios segmented and unsegmented eye images. The results have showed that BSIF can efficiently detect plastic and synthetic attacks without segmentation with correct classification rate of 100%. In addition, unsegmented eye images achieved better results in detecting print attack on the tested datasets. While, segmentation is still required in the most challenging attack which is by contact lens.
{"title":"Impact of segmentation on iris liveness detection","authors":"Manar Ramzy Dronky, W. Khalifa, M. Roushdy","doi":"10.1109/ICCES48960.2019.9068147","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068147","url":null,"abstract":"Applying iris recognition systems in many sensitive security areas highlights the importance of developing liveness detection methods. These methods read the users physiological signs of life to verify if the iris pattern acquired for identification is fake or real. This paper explores the results of BSIF for solving the problem of iris liveness detection to combat presentation attacks. Four public datasets representing printed, plastic, synthetic and contact lens attacks were used for method evaluation in both scenarios segmented and unsegmented eye images. The results have showed that BSIF can efficiently detect plastic and synthetic attacks without segmentation with correct classification rate of 100%. In addition, unsegmented eye images achieved better results in detecting print attack on the tested datasets. While, segmentation is still required in the most challenging attack which is by contact lens.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124269063","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068143
Mohamed Mounir, Mohamed Abdelsalam, M. Safar, A. Salem
Today's car manufacturers are racing in deploying new innovative functionalities in modern cars like human-machine interface (HMI) technologies, cloud-based services, vehicle ad-hoc networks (V ANET) and autonomous driving. Such new technologies increase the complexity of vehicle's E/E architecture and adds new requirements on automotive software systems. This can specially be seen in cockpit domain units like Advanced Driver Assistance Systems (ADAS), Infotainment Head Units (IHU) and Telematics (TEM). The software applications of such units now exhibit large variations in requirements in terms of safety, security and connectivity as they are involved in both in-Vehicle network communication and cellular vehicle communication (V2X). In addition to that, Original Equipment Manufacturers (OEMs) are heading towards consolidating multiple units into single high computing platform. Although this simplifies the networking model of the vehicle, it adds more challenges on the architecture of automotive software systems. This paper focuses on utilizing hardware-assisted virtualization techniques to allow consolidating these heterogeneous applications on the same hardware. The performance of the proposed approach is evaluated to proof meeting the requirements of such applications.
{"title":"Hardware-Assisted Virtualization for Heterogeneous Automotive Applications","authors":"Mohamed Mounir, Mohamed Abdelsalam, M. Safar, A. Salem","doi":"10.1109/ICCES48960.2019.9068143","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068143","url":null,"abstract":"Today's car manufacturers are racing in deploying new innovative functionalities in modern cars like human-machine interface (HMI) technologies, cloud-based services, vehicle ad-hoc networks (V ANET) and autonomous driving. Such new technologies increase the complexity of vehicle's E/E architecture and adds new requirements on automotive software systems. This can specially be seen in cockpit domain units like Advanced Driver Assistance Systems (ADAS), Infotainment Head Units (IHU) and Telematics (TEM). The software applications of such units now exhibit large variations in requirements in terms of safety, security and connectivity as they are involved in both in-Vehicle network communication and cellular vehicle communication (V2X). In addition to that, Original Equipment Manufacturers (OEMs) are heading towards consolidating multiple units into single high computing platform. Although this simplifies the networking model of the vehicle, it adds more challenges on the architecture of automotive software systems. This paper focuses on utilizing hardware-assisted virtualization techniques to allow consolidating these heterogeneous applications on the same hardware. The performance of the proposed approach is evaluated to proof meeting the requirements of such applications.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128600998","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068128
A. Fekry, Georgios A. Dafoulas, Manal A. Ismail
This paper suggests a model for automatic detection of student behavior to be used in student presentations. The proposed approach is based on the combined use of computer vision libraries and machine learning algorithms to help and support in student assessment using video content. This paper is a part of a research study focusing on investigating and analysing, human behaviours and finding relations between human behaviours and their personal modalities using pattern recognition techniques. For the purpose of this study a group of specific behaviours expressed by students during group presentations in higher education level, are selected. The study proceeds with the detection of the occurrences of those behaviours and comparative analysis of the model's suggested behavioural patterns against those observed through the manual analysis of observations. Both approaches are based on the same set of video files.
{"title":"Automatic detection for students behaviors in a group presentation","authors":"A. Fekry, Georgios A. Dafoulas, Manal A. Ismail","doi":"10.1109/ICCES48960.2019.9068128","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068128","url":null,"abstract":"This paper suggests a model for automatic detection of student behavior to be used in student presentations. The proposed approach is based on the combined use of computer vision libraries and machine learning algorithms to help and support in student assessment using video content. This paper is a part of a research study focusing on investigating and analysing, human behaviours and finding relations between human behaviours and their personal modalities using pattern recognition techniques. For the purpose of this study a group of specific behaviours expressed by students during group presentations in higher education level, are selected. The study proceeds with the detection of the occurrences of those behaviours and comparative analysis of the model's suggested behavioural patterns against those observed through the manual analysis of observations. Both approaches are based on the same set of video files.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131884719","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068140
W. Gomaa
Sensor-based human activity recognition HAR has become increasingly more important in our daily lives for a number of reasons. Advances in the sensing capabilities of personal devices have seen unprecedented growth over the past decade. HAR systems have many applications especially in health monitoring, intelligent environments, and smart spaces. Wearable sensors are particularly suited in these areas. This is due to the fact that they have small size, their cost has been steadily decreasing, and they are currently embedded in almost all commodity mobile devices such as smart phones, smart watches, sensory gloves, hand straps, and shoes. In this paper we focus on analyzing sensory accelerometer data collected from wearable devices. And in particular, we study activities of daily living (ADL) which are the activities ordinary people have the ability for doing on a daily basis like eating, moving, individual hygiene, and dressing. To the best of our knowledge most HAR systems are based on supervised machine learning techniques and algorithms, In this paper we widens the scope of techniques that can be used for the automatic analysis of human activities and provide a valuation of the relative effectiveness and efficiency of a potentially myriad pool of techniques. Specifically, we apply two approaches. The first approach is time-aware treating the incoming data in its natural form as a sequential temporal sequence of measurements. The techniques we used are based on time series analysis. The other approach is time-neglectful. It is based on using statistical methods based on goodness-of-fit tests. Our comparative assessment shows that the latter approach has some potential in classification accuracy, though needs further investigation. The time-aware approach gives much better results, though the computational resources required can be prohibitive, so also needs further investigation from that perspective.
{"title":"Statistical and Time Series Analysis of Accelerometer Signals for Human Activity Recognition","authors":"W. Gomaa","doi":"10.1109/ICCES48960.2019.9068140","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068140","url":null,"abstract":"Sensor-based human activity recognition HAR has become increasingly more important in our daily lives for a number of reasons. Advances in the sensing capabilities of personal devices have seen unprecedented growth over the past decade. HAR systems have many applications especially in health monitoring, intelligent environments, and smart spaces. Wearable sensors are particularly suited in these areas. This is due to the fact that they have small size, their cost has been steadily decreasing, and they are currently embedded in almost all commodity mobile devices such as smart phones, smart watches, sensory gloves, hand straps, and shoes. In this paper we focus on analyzing sensory accelerometer data collected from wearable devices. And in particular, we study activities of daily living (ADL) which are the activities ordinary people have the ability for doing on a daily basis like eating, moving, individual hygiene, and dressing. To the best of our knowledge most HAR systems are based on supervised machine learning techniques and algorithms, In this paper we widens the scope of techniques that can be used for the automatic analysis of human activities and provide a valuation of the relative effectiveness and efficiency of a potentially myriad pool of techniques. Specifically, we apply two approaches. The first approach is time-aware treating the incoming data in its natural form as a sequential temporal sequence of measurements. The techniques we used are based on time series analysis. The other approach is time-neglectful. It is based on using statistical methods based on goodness-of-fit tests. Our comparative assessment shows that the latter approach has some potential in classification accuracy, though needs further investigation. The time-aware approach gives much better results, though the computational resources required can be prohibitive, so also needs further investigation from that perspective.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125199105","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068136
H. Hussien
A combination of DNA computing and a genetic algorithm is announced for RGB image encryption. The model is strong based on the scrambling technique of DNA computing operations using the crossover and mutation process and establishing a dynamic key based on a genetic algorithm, including a set of parameters such as population size, number of generation and mutation probability. First, the decoding of the image GA selected DNA sequence encoding process and the random key for the three R G B channels were followed by the DNA addition process. The decoded DNA added to the matrix of the output. Finally, conduct the XOR-mod procedure on the decoded matrix and the random number of the genetic algorithm to obtain the encrypted image. The paper includes countless experimental steps to confirm that the model has a high degree of safety and strength against different types of attacks.
{"title":"DNA Computing for RGB image Encryption with Genetic Algorithm","authors":"H. Hussien","doi":"10.1109/ICCES48960.2019.9068136","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068136","url":null,"abstract":"A combination of DNA computing and a genetic algorithm is announced for RGB image encryption. The model is strong based on the scrambling technique of DNA computing operations using the crossover and mutation process and establishing a dynamic key based on a genetic algorithm, including a set of parameters such as population size, number of generation and mutation probability. First, the decoding of the image GA selected DNA sequence encoding process and the random key for the three R G B channels were followed by the DNA addition process. The decoded DNA added to the matrix of the output. Finally, conduct the XOR-mod procedure on the decoded matrix and the random number of the genetic algorithm to obtain the encrypted image. The paper includes countless experimental steps to confirm that the model has a high degree of safety and strength against different types of attacks.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133863101","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 : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068112
Hussein M. Fawzy, H. El Sherif, A. Khamis
There is a considerable increase in the number of skyscrapers in the world due modern development of construction technology. Current maintenance work for high-rise buildings mostly uses conventional ropes and scaffolds that pose a high risk of accidents and exhibit poor performance and efficiency. There is a demand to develop an automated cleaning system that can reduce accidents and improve the maintenance efficiency of the conventional high-rise building façade maintenance systems. In this paper, we demonstrate an automated façade cleaning system that can reduce accidents and decrease labor costs. We propose a new technique of cleaning mechanism for façade cleaning in high-rise buildings; the system consists of two Robots working for the cleaning process; the lifting robot or the Roof Top Robot (RTR) and the Cleaning Robot (CR). The RTR is designed to lift the CR vertically on the façade in the upper and lower directions; the horizontal direction is also performed by the end of each vertical motion. The CR acts as the main cleaning unit, which utilizes different cleaning modules that is required for the cleaning quality. The performance of the proposed cleaning system is evaluated experimentally; however, additional study should be necessary for more complicated facades architect designs.
{"title":"Robotic Façade Cleaning System for High-Rise Building","authors":"Hussein M. Fawzy, H. El Sherif, A. Khamis","doi":"10.1109/ICCES48960.2019.9068112","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068112","url":null,"abstract":"There is a considerable increase in the number of skyscrapers in the world due modern development of construction technology. Current maintenance work for high-rise buildings mostly uses conventional ropes and scaffolds that pose a high risk of accidents and exhibit poor performance and efficiency. There is a demand to develop an automated cleaning system that can reduce accidents and improve the maintenance efficiency of the conventional high-rise building façade maintenance systems. In this paper, we demonstrate an automated façade cleaning system that can reduce accidents and decrease labor costs. We propose a new technique of cleaning mechanism for façade cleaning in high-rise buildings; the system consists of two Robots working for the cleaning process; the lifting robot or the Roof Top Robot (RTR) and the Cleaning Robot (CR). The RTR is designed to lift the CR vertically on the façade in the upper and lower directions; the horizontal direction is also performed by the end of each vertical motion. The CR acts as the main cleaning unit, which utilizes different cleaning modules that is required for the cleaning quality. The performance of the proposed cleaning system is evaluated experimentally; however, additional study should be necessary for more complicated facades architect designs.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552169","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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