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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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/ICCES48960.2019.9068140
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":null,"pages":null},"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/ICCES48960.2019.9068111
Nesma El-Sokkary, A. Arafa, Ahmed H. Asad, H. Hefny
the majority cancer mortality among women is due to breast cancer over the world wide. Recent researches have shown the effectiveness of x-ray mammography in early detection of breast cancer. Unfortunately, the present systems for early detection are expensive and needs extremely complex algorithms. The crucial challenge in designing a computer-aided detection (CAD) systems for breast cancer are the segmentation phase, which requires highly complex computation. Hence, this paper proposes a CAD system to be utilized for breast cancer detection in mammographic datasets. The segmentation step is performed by a Particle Swarm Optimization Algorithm (PSO). Statistical, textural and shape feature are calculated over the segmented region. A non linear support vector machine (SVM) is exploited in the next phase in order to analyze the extracted features and classify the mammograms into normal, benign or malignant. For the sack of evaluating the performance, the experiment is performed on Mini-MIAS database. The obtained accuracy rates based on 10-folds cross validation are 85.4% for classifying normal from abnormal, 89.5% for classifying malignant from benign. The experiment shows that the classification accuracy is 81% when classifying normal, malignant or benign. The result compromises with recent researches concurs that the proposed algorithm compromises between the achieved accuracy to complexity cost.
{"title":"A Computer Aided Detection System for Breast Cancer in the MammogramsBased on Particle Swarm Optimization Algorithm","authors":"Nesma El-Sokkary, A. Arafa, Ahmed H. Asad, H. Hefny","doi":"10.1109/ICCES48960.2019.9068111","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068111","url":null,"abstract":"the majority cancer mortality among women is due to breast cancer over the world wide. Recent researches have shown the effectiveness of x-ray mammography in early detection of breast cancer. Unfortunately, the present systems for early detection are expensive and needs extremely complex algorithms. The crucial challenge in designing a computer-aided detection (CAD) systems for breast cancer are the segmentation phase, which requires highly complex computation. Hence, this paper proposes a CAD system to be utilized for breast cancer detection in mammographic datasets. The segmentation step is performed by a Particle Swarm Optimization Algorithm (PSO). Statistical, textural and shape feature are calculated over the segmented region. A non linear support vector machine (SVM) is exploited in the next phase in order to analyze the extracted features and classify the mammograms into normal, benign or malignant. For the sack of evaluating the performance, the experiment is performed on Mini-MIAS database. The obtained accuracy rates based on 10-folds cross validation are 85.4% for classifying normal from abnormal, 89.5% for classifying malignant from benign. The experiment shows that the classification accuracy is 81% when classifying normal, malignant or benign. The result compromises with recent researches concurs that the proposed algorithm compromises between the achieved accuracy to complexity cost.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115379256","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/ICCES48960.2019.9068176
Nora Shoaip, S. Barakat, Mohammed M Elmogy
In the near future, the elderly will occupy nearly a quarter of the world's population. Such a percentage poses a significant challenge to face diseases related to this age, including Alzheimer's disease (AD), which is responsible for the destruction of brain cells and memory. This paper is our first step toward comprehensive research in the Alzheimer management system. It related to AD knowledge structures and semantic reasoning by using ontology. There is a necessary need to coordinate strategies that reuse existing ontology to support and enhance knowledge resources around the world. So we aim to reuse existing ontology and ensure comprehensive integration between them to improve the accuracy of reasoning. We propose Alzheimer's Disease Integrated Ontology (ADIO) that is intended to integrate two important biomedical ontologies for AD researches (i) Alzheimer's disease ontology (ADO) and (ii) AD Map Ontology (ADMO). ADO was described in OWL format and related to clinical, preclinical, experimental, and molecular mechanisms. ADMO represents the complexity of AD pathophysiology and more specific for the description of biological systems. So ADO and ADMO are relevant complements with each other, and their integration can increase the satisfaction of AD knowledge resources. As a result, HermiT 1.4.3.456 reasoner in Protégé provides checking of AD I 0 consistency, and the results of DLQuery show that ADIO is reliable and effective.
{"title":"Alzheimer's Disease Integrated Ontology (ADIO)","authors":"Nora Shoaip, S. Barakat, Mohammed M Elmogy","doi":"10.1109/ICCES48960.2019.9068176","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068176","url":null,"abstract":"In the near future, the elderly will occupy nearly a quarter of the world's population. Such a percentage poses a significant challenge to face diseases related to this age, including Alzheimer's disease (AD), which is responsible for the destruction of brain cells and memory. This paper is our first step toward comprehensive research in the Alzheimer management system. It related to AD knowledge structures and semantic reasoning by using ontology. There is a necessary need to coordinate strategies that reuse existing ontology to support and enhance knowledge resources around the world. So we aim to reuse existing ontology and ensure comprehensive integration between them to improve the accuracy of reasoning. We propose Alzheimer's Disease Integrated Ontology (ADIO) that is intended to integrate two important biomedical ontologies for AD researches (i) Alzheimer's disease ontology (ADO) and (ii) AD Map Ontology (ADMO). ADO was described in OWL format and related to clinical, preclinical, experimental, and molecular mechanisms. ADMO represents the complexity of AD pathophysiology and more specific for the description of biological systems. So ADO and ADMO are relevant complements with each other, and their integration can increase the satisfaction of AD knowledge resources. As a result, HermiT 1.4.3.456 reasoner in Protégé provides checking of AD I 0 consistency, and the results of DLQuery show that ADIO is reliable and effective.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114404265","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/ICCES48960.2019.9068107
Mohamed Ismail Ibrahim, Dina M. Ellaithy
This paper exploits the efficient performing of the Multiple Input Multiple Output (MIMO)-Alamouti scheme for spectrum sensing in cognitive radio (CR). Consequently, enhancement in the overall performance and the detection probability by using the MIMO-Alamouti scheme is achieved. Moreover, at low signal-to-noise ratio (SNR), the cooperative spectrum distinguishing algorithm among the different spectrum distinguishing techniques is employed to raise the probability of detection and also solving the hidden node problem. Matlab software is used to simulate the detection probability versus SNR for different schemes. Up to 50% enhancement in detection probability (Pd) as compared with the conventional technique under signal to noise ratio (SNR) equals −15 dB and false alarm probability (Pf) equals 0.1. As compared with the common spectrum sensing approach in case of the majority rule, at least 10% advance in the probability of detection at false alarm probability equals 0.1 under SNR equals −10 dB and the number of secondary user (SU) equals 5.
{"title":"Improvement the Performing of Spectrum Distinguishing in Cognitive Radio using MIMO-Alamouti Scheme","authors":"Mohamed Ismail Ibrahim, Dina M. Ellaithy","doi":"10.1109/ICCES48960.2019.9068107","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068107","url":null,"abstract":"This paper exploits the efficient performing of the Multiple Input Multiple Output (MIMO)-Alamouti scheme for spectrum sensing in cognitive radio (CR). Consequently, enhancement in the overall performance and the detection probability by using the MIMO-Alamouti scheme is achieved. Moreover, at low signal-to-noise ratio (SNR), the cooperative spectrum distinguishing algorithm among the different spectrum distinguishing techniques is employed to raise the probability of detection and also solving the hidden node problem. Matlab software is used to simulate the detection probability versus SNR for different schemes. Up to 50% enhancement in detection probability (Pd) as compared with the conventional technique under signal to noise ratio (SNR) equals −15 dB and false alarm probability (Pf) equals 0.1. As compared with the common spectrum sensing approach in case of the majority rule, at least 10% advance in the probability of detection at false alarm probability equals 0.1 under SNR equals −10 dB and the number of secondary user (SU) equals 5.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124037094","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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