Pub Date : 2020-12-15DOI: 10.1109/ICCES51560.2020.9334660
A. Abdelrahman, H. Khaled, E. Shaaban, W. Elkilani
Nowadays, WPA/WPA2 are used for the authentication and encryption process of the most used WLANs in our daily life. WPA/WPA2 PSK is one of the authentication most used mechanisms. This paper presents the design and the implementation of our VRST PSK cracking tool (Vulnerability Research Study Tool). VRST represents a unique edge through illustrating the relations between the cracking steps input and 802.11 standards. To the best of our knowledge, the previous research contributions do not reveal the knowhow of extracting the cracking input parameters from the raw exchanged data between the Access Point and the client.
目前，我们日常生活中使用最多的无线局域网都采用WPA/WPA2进行认证和加密。WPA/WPA2 PSK是最常用的身份验证机制之一。本文介绍了我们的VRST PSK破解工具(Vulnerability Research Study tool)的设计与实现。VRST通过说明破解步骤输入和802.11标准之间的关系，代表了独特的优势。据我们所知，以前的研究贡献并没有揭示从接入点和客户端之间的原始交换数据中提取破解输入参数的诀窍。
{"title":"Detailed Study of WLAN PSK Cracking Implementation","authors":"A. Abdelrahman, H. Khaled, E. Shaaban, W. Elkilani","doi":"10.1109/ICCES51560.2020.9334660","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334660","url":null,"abstract":"Nowadays, WPA/WPA2 are used for the authentication and encryption process of the most used WLANs in our daily life. WPA/WPA2 PSK is one of the authentication most used mechanisms. This paper presents the design and the implementation of our VRST PSK cracking tool (Vulnerability Research Study Tool). VRST represents a unique edge through illustrating the relations between the cracking steps input and 802.11 standards. To the best of our knowledge, the previous research contributions do not reveal the knowhow of extracting the cracking input parameters from the raw exchanged data between the Access Point and the client.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"6 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":"128127830","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/ICCES51560.2020.9334678
N. Mekhiel
We propose using available silicon technology for simple implementation of quantum computing by decoupling its quantum bits in time and space then coupled back as analog signals and waves to present all possible superposition values of Q-Bits. Broadcasting waves allows Q-Bits to be available in space at different points at the same time creating an additional dimension for Q-Bits. The complexity of decoupling Q-Bits in space and time is evaluated and an optimized decoupling in both time and space is presented. We suggest using an analog computer to process the Q-bits implemented as analog signals or waves. The analog computer needs to be reconfigurable by a digital computer for initialization and reconfiguration to run quantum applications.
{"title":"Simple Quantum Computing with Quantum Bits Decoupled in Time and Space Implemented in Silicon and Coupled Back as Analog Signals and Waves Processed by Analog Computer","authors":"N. Mekhiel","doi":"10.1109/ICCES51560.2020.9334678","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334678","url":null,"abstract":"We propose using available silicon technology for simple implementation of quantum computing by decoupling its quantum bits in time and space then coupled back as analog signals and waves to present all possible superposition values of Q-Bits. Broadcasting waves allows Q-Bits to be available in space at different points at the same time creating an additional dimension for Q-Bits. The complexity of decoupling Q-Bits in space and time is evaluated and an optimized decoupling in both time and space is presented. We suggest using an analog computer to process the Q-bits implemented as analog signals or waves. The analog computer needs to be reconfigurable by a digital computer for initialization and reconfiguration to run quantum applications.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"23 6 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":"125892652","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/ICCES51560.2020.9334647
E. S. Shoukralla, B. Ahmed
A modified formula of the traditional Barycentric Lagrange interpolation is established and applied for solving the second kind Volterra integral equations. The main goal is improving the performance of the traditional formula to minimize the round-off error. For this goal, we expand each Barycentric function into Maclaurin polynomial so that the interpolant unknown function, the given function, and the kernel can be expressed through a monomial basis polynomial matrix. Moreover, by substituting the interpolant unknown function into both sides of the integral equation, the solution is reduced to an equivalent algebraic linear system in matrix form. Convergence in the mean and the maximum norm error estimation are studied. From the solution of illustrated four examples, we observed that the interpolant solutions equal to the exact solutions if the kernel and the given functions are analytic while extraordinarily converge to the exact solutions for non-algebraic functions, which ensures the accuracy and authenticity of the presented method.
{"title":"The Barycentric Lagrange Interpolation via Maclaurin Polynomials for Solving the Second Kind Volterra Integral Equations","authors":"E. S. Shoukralla, B. Ahmed","doi":"10.1109/ICCES51560.2020.9334647","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334647","url":null,"abstract":"A modified formula of the traditional Barycentric Lagrange interpolation is established and applied for solving the second kind Volterra integral equations. The main goal is improving the performance of the traditional formula to minimize the round-off error. For this goal, we expand each Barycentric function into Maclaurin polynomial so that the interpolant unknown function, the given function, and the kernel can be expressed through a monomial basis polynomial matrix. Moreover, by substituting the interpolant unknown function into both sides of the integral equation, the solution is reduced to an equivalent algebraic linear system in matrix form. Convergence in the mean and the maximum norm error estimation are studied. From the solution of illustrated four examples, we observed that the interpolant solutions equal to the exact solutions if the kernel and the given functions are analytic while extraordinarily converge to the exact solutions for non-algebraic functions, which ensures the accuracy and authenticity of the presented method.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"32 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":"124488568","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/ICCES51560.2020.9334611
M. Badawy, N. Ismail, Samir Alamrity, K. Ismail
Electrical Impedance Tomography (EIT) is a new technique for industrial imaging that can be used to visualize the runtime changes of impedance entire along a pipeline to measure the industrial continuous processes of flow velocity profile distribution. This paper proposes a full three-dimensional (3D) pipeline sensing strategy that considers the 3D nature of the EIT sensing field. The proposed strategy includes a new 3D sensing system and an implementation of the fast-forward solver using Finite Element Modelling (FEM). In this paper, we have two different techniques of image reconstruction inverse solver. The implementation of the one-step Gauss-Newton solver reconstruction inverse solution algorithm is introduced firstly, and then the implementation of Graz consensus reconstruction algorithm for EIT (GREIT) solver reconstruction inverse solution algorithm is introduced. A comparison of the results of both techniques is introduced also. An application of auto/cross-correlation function for the reconstructed centered images obtained by the EIT system for the box moves from upstream sensing section to downstream sensing section on the outeredge of the pipeline are also introduced. According to the correlation test results of our proposal, the best fit of the correlation coefficient to images was distinguished with a higher correlation coefficient between 0.8687 and 0.998 for one-step Gauss-Newton solver and 0.5575 and 0.9115 for GREIT solver.
{"title":"A Comparative Study of Safe Tomography Techniques in Implementation of Industrial Process Measurements","authors":"M. Badawy, N. Ismail, Samir Alamrity, K. Ismail","doi":"10.1109/ICCES51560.2020.9334611","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334611","url":null,"abstract":"Electrical Impedance Tomography (EIT) is a new technique for industrial imaging that can be used to visualize the runtime changes of impedance entire along a pipeline to measure the industrial continuous processes of flow velocity profile distribution. This paper proposes a full three-dimensional (3D) pipeline sensing strategy that considers the 3D nature of the EIT sensing field. The proposed strategy includes a new 3D sensing system and an implementation of the fast-forward solver using Finite Element Modelling (FEM). In this paper, we have two different techniques of image reconstruction inverse solver. The implementation of the one-step Gauss-Newton solver reconstruction inverse solution algorithm is introduced firstly, and then the implementation of Graz consensus reconstruction algorithm for EIT (GREIT) solver reconstruction inverse solution algorithm is introduced. A comparison of the results of both techniques is introduced also. An application of auto/cross-correlation function for the reconstructed centered images obtained by the EIT system for the box moves from upstream sensing section to downstream sensing section on the outeredge of the pipeline are also introduced. According to the correlation test results of our proposal, the best fit of the correlation coefficient to images was distinguished with a higher correlation coefficient between 0.8687 and 0.998 for one-step Gauss-Newton solver and 0.5575 and 0.9115 for GREIT solver.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"30 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":"115139864","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/ICCES51560.2020.9334573
Reham Fathy M. Ahmed, Cherif R. Salama, Hani M. K. Mahdi
With the huge amount of published research papers, retrieving relevant information is a difficult task for any researcher. Effective clustering algorithms can help improve and simplify the retrieval process. Here, we propose an approach for automatic clustering for text document using a Self-Organizing Map (SOM). It is one of unsupervised artificial neural network that widely used for data analysis, data compression, clustering, and data mining. The quality and accuracy of a SOM algorithm depends on the selection of values for some of its parameters which are its initial learning rate, SOM matrix dimensions, and the number of iterations. Best values are typically selected using trial and error; however, in the current paper we suggest a more systematic approach to parameters optimization using the genetic algorithm. The proposed method is applied to cluster 3 scientific papers datasets using their keywords. Similar research papers were mapped closer to each other. Clustering results were validated using the Dunn index.
{"title":"Clustering Research Papers Using Genetic Algorithm Optimized Self-Organizing Maps","authors":"Reham Fathy M. Ahmed, Cherif R. Salama, Hani M. K. Mahdi","doi":"10.1109/ICCES51560.2020.9334573","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334573","url":null,"abstract":"With the huge amount of published research papers, retrieving relevant information is a difficult task for any researcher. Effective clustering algorithms can help improve and simplify the retrieval process. Here, we propose an approach for automatic clustering for text document using a Self-Organizing Map (SOM). It is one of unsupervised artificial neural network that widely used for data analysis, data compression, clustering, and data mining. The quality and accuracy of a SOM algorithm depends on the selection of values for some of its parameters which are its initial learning rate, SOM matrix dimensions, and the number of iterations. Best values are typically selected using trial and error; however, in the current paper we suggest a more systematic approach to parameters optimization using the genetic algorithm. The proposed method is applied to cluster 3 scientific papers datasets using their keywords. Similar research papers were mapped closer to each other. Clustering results were validated using the Dunn index.","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":"114765531","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/ICCES51560.2020.9334680
A. Mahmoud, T. Rizk, M. Ahmed
This paper introduces analysis of mode dynamics in multiple-quantum well (MQW) laser as a promising device for high-speed photonics. The study is based on a multimode model of semiconductor laser under direct intensity modulation. The simulation results are used to investigate the influence of the injection current on the dynamics of the non-modulated multimode laser, as well as influence of the modulation parameters (modulation index and modulation frequency) on the dynamics of the laser. The modal oscillations and the associated multimode hopping that characterizes the long-wavelength laser are investigated in both the non-modulated and modulated laser. The coupling among the oscillating modes under both cases is evaluated in terms of their correlation coefficients. Dependence of the small-signal modulation response and bandwidth on the bias current is introduced. In addition, we present comparison of the modulation response of the total output with those of the strongest oscillating modes.
{"title":"Multimode Analysis of High-Speed Multiple-Quantum Well Semiconductor Laser","authors":"A. Mahmoud, T. Rizk, M. Ahmed","doi":"10.1109/ICCES51560.2020.9334680","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334680","url":null,"abstract":"This paper introduces analysis of mode dynamics in multiple-quantum well (MQW) laser as a promising device for high-speed photonics. The study is based on a multimode model of semiconductor laser under direct intensity modulation. The simulation results are used to investigate the influence of the injection current on the dynamics of the non-modulated multimode laser, as well as influence of the modulation parameters (modulation index and modulation frequency) on the dynamics of the laser. The modal oscillations and the associated multimode hopping that characterizes the long-wavelength laser are investigated in both the non-modulated and modulated laser. The coupling among the oscillating modes under both cases is evaluated in terms of their correlation coefficients. Dependence of the small-signal modulation response and bandwidth on the bias current is introduced. In addition, we present comparison of the modulation response of the total output with those of the strongest oscillating modes.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"55 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132241539","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/icces51560.2020.9334673
{"title":"ICCES 2020 Final Program","authors":"","doi":"10.1109/icces51560.2020.9334673","DOIUrl":"https://doi.org/10.1109/icces51560.2020.9334673","url":null,"abstract":"","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"22 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":"128639022","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/ICCES51560.2020.9334608
Mina Younan, E. H. Houssein, M. Elhoseny, A. Ali
Revolution of Smart Things (SThs) connected to the Internet to build Internet of Things (IoT) applications, causes a flood of data streams every moment. Main root causes of massive SThs integration for increasing accuracy of sensed features and for enabling fault tolerance. In general, resulting deluge of real-time data streams has the property of five V of the big data (i.e., volume, velocity, variety, veracity, and value). Such properties make mining and analysis of massive and heterogeneous data be challenging tasks. In our previous work, we present three novel data reduction models based on Dynamic Time Warping (DTW) for enabling balanced indexing in the IoT. This paper presents two extensions to improve the Hybrid algorithm (ClRe 3.0) using DTW warped path. First extension (ClRe 3.1): targets improving accuracy of indexed clusters representatives by taking the average of individual warped items and keeping only 50% of the warped items for each warped slot. Second extension (ClRe 3.2): targets decreasing size of indexed clusters representatives as possible by compensating every warped slot by its corresponding item keeping only common items with minimum distances. The proposed extensions are explained using real samples and evaluated using Szeged-weather dataset as well. The evaluation results proves that ClRe 3.1 could enhance the accuracy of ClRe 3.0 by approximate 9% in average, keeping indexes sizes as possible as fitted (i.e., < the average length of all datasets). In case of indexing only highly common readings, ClRe 3.2 out-performs other extensions in decreasing indexes sizes.
{"title":"Improved Models for Time Series Cluster Representation Based Dynamic Time Warping","authors":"Mina Younan, E. H. Houssein, M. Elhoseny, A. Ali","doi":"10.1109/ICCES51560.2020.9334608","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334608","url":null,"abstract":"Revolution of Smart Things (SThs) connected to the Internet to build Internet of Things (IoT) applications, causes a flood of data streams every moment. Main root causes of massive SThs integration for increasing accuracy of sensed features and for enabling fault tolerance. In general, resulting deluge of real-time data streams has the property of five V of the big data (i.e., volume, velocity, variety, veracity, and value). Such properties make mining and analysis of massive and heterogeneous data be challenging tasks. In our previous work, we present three novel data reduction models based on Dynamic Time Warping (DTW) for enabling balanced indexing in the IoT. This paper presents two extensions to improve the Hybrid algorithm (ClRe 3.0) using DTW warped path. First extension (ClRe 3.1): targets improving accuracy of indexed clusters representatives by taking the average of individual warped items and keeping only 50% of the warped items for each warped slot. Second extension (ClRe 3.2): targets decreasing size of indexed clusters representatives as possible by compensating every warped slot by its corresponding item keeping only common items with minimum distances. The proposed extensions are explained using real samples and evaluated using Szeged-weather dataset as well. The evaluation results proves that ClRe 3.1 could enhance the accuracy of ClRe 3.0 by approximate 9% in average, keeping indexes sizes as possible as fitted (i.e., < the average length of all datasets). In case of indexing only highly common readings, ClRe 3.2 out-performs other extensions in decreasing indexes sizes.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"157 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":"132791088","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/ICCES51560.2020.9334625
Enas A. M. Khalil, Enas M. F. El Houby, H. K. Mohamed
Sentiment Analysis (SA) is the field that combines Natural Language Processing (NLP), Computational Linguistics (CL) and text analysis to study people’s opinions through, by extracting and analyzing subjective information from different resources as the Web, social media and similar sources and so help in drawing public’s sentiments or attitude toward certain people, products or ideas and extracting the contextual polarity of the information. This review focuses on recent work in SA using Deep Learning (DL)techniques in the sentiment classification process, it is based on the articles published through ScienceDirect and Springer databases in the interval from 2016 to 2020.It sheds the light on different DL algorithms used, different applications of SA systems. 58 articles studied in ScienceDirect While 26 articles in Springer satisfying the same criteria with the total of 84 articles studied and analyzed in this review. The review concerns with DL techniques, language, domain, and performance results.
{"title":"Deep Learning Approach in Sentiment Analysis: A Review","authors":"Enas A. M. Khalil, Enas M. F. El Houby, H. K. Mohamed","doi":"10.1109/ICCES51560.2020.9334625","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334625","url":null,"abstract":"Sentiment Analysis (SA) is the field that combines Natural Language Processing (NLP), Computational Linguistics (CL) and text analysis to study people’s opinions through, by extracting and analyzing subjective information from different resources as the Web, social media and similar sources and so help in drawing public’s sentiments or attitude toward certain people, products or ideas and extracting the contextual polarity of the information. This review focuses on recent work in SA using Deep Learning (DL)techniques in the sentiment classification process, it is based on the articles published through ScienceDirect and Springer databases in the interval from 2016 to 2020.It sheds the light on different DL algorithms used, different applications of SA systems. 58 articles studied in ScienceDirect While 26 articles in Springer satisfying the same criteria with the total of 84 articles studied and analyzed in this review. The review concerns with DL techniques, language, domain, and performance results.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"104 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":"124649157","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/ICCES51560.2020.9334600
Ahmed Mabrouk Fangoh, Sahar Selim
At the time of writing, the COVID-19 pandemic is one of the lead causes of death worldwide and has caused significant changes to everyone’s lives. While a vaccine is still unavailable, early screenings and detection of the disease can significantly help in managing the healthcare system’s capacity as well as allow radiologists and clinicians better assign their priorities. With deep learning’s rapid advancements over the last few years, its application in solving this issue is only natural. This paper aims to outline the works of a few major developments in the field of using deep learning to classify COVID-19 cases, illustrating common techniques and issues faced. Following this, a deep learning architecture is proposed and tested, then compared to the findings of the mentioned papers.
{"title":"Using CNN-XGBoost Deep Networks for COVID-19 Detection in Chest X-ray Images","authors":"Ahmed Mabrouk Fangoh, Sahar Selim","doi":"10.1109/ICCES51560.2020.9334600","DOIUrl":"https://doi.org/10.1109/ICCES51560.2020.9334600","url":null,"abstract":"At the time of writing, the COVID-19 pandemic is one of the lead causes of death worldwide and has caused significant changes to everyone’s lives. While a vaccine is still unavailable, early screenings and detection of the disease can significantly help in managing the healthcare system’s capacity as well as allow radiologists and clinicians better assign their priorities. With deep learning’s rapid advancements over the last few years, its application in solving this issue is only natural. This paper aims to outline the works of a few major developments in the field of using deep learning to classify COVID-19 cases, illustrating common techniques and issues faced. Following this, a deep learning architecture is proposed and tested, then compared to the findings of the mentioned papers.","PeriodicalId":247183,"journal":{"name":"2020 15th International Conference on Computer Engineering and Systems (ICCES)","volume":"148 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":"128953147","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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