Pub Date : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204174
Lina El Alaoui El Abidi, M. Hanine, B. Aksasse
Exponentially decaying signals occur in various parts of nature and affect the performance and flexibility of signals. In fact, that drives scientists to invest in a perpetual search for new solutions. To meet this challenge few methods are proposed. In this study, we focused on the use of the Prony Method and Bessel and Butterworth smoothing methods in the LabVIEW environment for estimating the parameters of a sum of real exponential signals in the presence of noise. The performances of the proposed method are illustrated using simulated data, clearly showing the improved performance of the Prony Method and especially with Butterworth filter.
{"title":"Smooth Filters for Improving Prony’s Method in Labview Environment","authors":"Lina El Alaoui El Abidi, M. Hanine, B. Aksasse","doi":"10.1109/ISCV49265.2020.9204174","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204174","url":null,"abstract":"Exponentially decaying signals occur in various parts of nature and affect the performance and flexibility of signals. In fact, that drives scientists to invest in a perpetual search for new solutions. To meet this challenge few methods are proposed. In this study, we focused on the use of the Prony Method and Bessel and Butterworth smoothing methods in the LabVIEW environment for estimating the parameters of a sum of real exponential signals in the presence of noise. The performances of the proposed method are illustrated using simulated data, clearly showing the improved performance of the Prony Method and especially with Butterworth filter.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125725662","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204185
Fatima Zohra El hlouli, J. Riffi, Mohamed Adnane Mahraz, Ali El Yahyaouy, H. Tairi
Due to the increasing digitalization of banking services and the predominance of mobile banking applications, the rate of credit card payments is increasing every year, among billions of transactions identified as fraudulent. Data mining algorithms have played a fundamental role in detecting fraudulent transactions, through combating fraudster’s attacks working around classical fraud prevention systems. In this paper, we try to detect fraudulent transactions using two artificial neural network classifiers, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM), applied on the credit card fraud dataset. The performance of these classifiers is evaluated based on accuracy, recall, precision, and classification time. The results show that the accuracy of MLP and ELM classifiers achieves respectively 97.84% and 95.46%. Otherwise, ELM is very fast for predicting new fraudulent transactions.
{"title":"Credit Card Fraud Detection Based on Multilayer Perceptron and Extreme Learning Machine Architectures","authors":"Fatima Zohra El hlouli, J. Riffi, Mohamed Adnane Mahraz, Ali El Yahyaouy, H. Tairi","doi":"10.1109/ISCV49265.2020.9204185","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204185","url":null,"abstract":"Due to the increasing digitalization of banking services and the predominance of mobile banking applications, the rate of credit card payments is increasing every year, among billions of transactions identified as fraudulent. Data mining algorithms have played a fundamental role in detecting fraudulent transactions, through combating fraudster’s attacks working around classical fraud prevention systems. In this paper, we try to detect fraudulent transactions using two artificial neural network classifiers, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM), applied on the credit card fraud dataset. The performance of these classifiers is evaluated based on accuracy, recall, precision, and classification time. The results show that the accuracy of MLP and ELM classifiers achieves respectively 97.84% and 95.46%. Otherwise, ELM is very fast for predicting new fraudulent transactions.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124517543","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204164
Abderrezzak Sebbah, B. Kadri
the internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme’s performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.
{"title":"A Privacy and Authentication Scheme for IoT Environments Using ECC and Fuzzy Extractor","authors":"Abderrezzak Sebbah, B. Kadri","doi":"10.1109/ISCV49265.2020.9204164","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204164","url":null,"abstract":"the internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme’s performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134522744","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204214
Hicham Lamtougui, H. E. Moubtahij, Hassan Fouadi, Ali Yahyaouy, K. Satori
By virtue of advances in machine learning, handwriting recognition is considered as one of the main research topics in this field. Many studies have been proposed to improve this recognition of handwritten texts for different languages such as Latin and Chinese. Yet, the processing of Arabic texts remains a particularly distinctive problem due to the complicated nature of the Arabic script compared to other scripts. In this work, we display a study and an evaluation of relevant articles recently published in conferences and indexed journals. The core of the problem is to relatively find out an efficient method capable of recognizing the handwritten text by any user via digital devices. In this article, we study the various works interested in the recognition of handwritten Arabic script implemented by deep learning. We thouroughly discuss different classification approaches like CNN, RNN and DBN. The pros and cons of each approach will be presented, as well as their different results.
{"title":"Offline Arabic Handwriting Recognition Using Deep Learning: Comparative Study","authors":"Hicham Lamtougui, H. E. Moubtahij, Hassan Fouadi, Ali Yahyaouy, K. Satori","doi":"10.1109/ISCV49265.2020.9204214","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204214","url":null,"abstract":"By virtue of advances in machine learning, handwriting recognition is considered as one of the main research topics in this field. Many studies have been proposed to improve this recognition of handwritten texts for different languages such as Latin and Chinese. Yet, the processing of Arabic texts remains a particularly distinctive problem due to the complicated nature of the Arabic script compared to other scripts. In this work, we display a study and an evaluation of relevant articles recently published in conferences and indexed journals. The core of the problem is to relatively find out an efficient method capable of recognizing the handwritten text by any user via digital devices. In this article, we study the various works interested in the recognition of handwritten Arabic script implemented by deep learning. We thouroughly discuss different classification approaches like CNN, RNN and DBN. The pros and cons of each approach will be presented, as well as their different results.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134408853","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204250
Abderrahim Ezzine, H. Satori, Mohamed Hamidi, K. Satori
The main aim of an Automatic Speech Recognition system (ASR) is to produce a system that is able to simulate the human listener based on the learning approach and speech data of a studied language. In this paper, we describe the Darija Moroccan Dialect speech recognition system that is implemented to recognize the ten first Arabic digits spoken in Moroccan dialect (Darija) collected from 20 speakers including both males and females. This system is designed based on the CMU Sphinx tools through the ASR Hidden Markov Model method with small data and the Mel frequency spectral coefficients (MFCCs) that are used in the feature extraction phase. Our best-obtained accuracy is 96.27 % found with 8 GMMs.
{"title":"Moroccan Dialect Speech Recognition System Based on CMU SphinxTools","authors":"Abderrahim Ezzine, H. Satori, Mohamed Hamidi, K. Satori","doi":"10.1109/ISCV49265.2020.9204250","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204250","url":null,"abstract":"The main aim of an Automatic Speech Recognition system (ASR) is to produce a system that is able to simulate the human listener based on the learning approach and speech data of a studied language. In this paper, we describe the Darija Moroccan Dialect speech recognition system that is implemented to recognize the ten first Arabic digits spoken in Moroccan dialect (Darija) collected from 20 speakers including both males and females. This system is designed based on the CMU Sphinx tools through the ASR Hidden Markov Model method with small data and the Mel frequency spectral coefficients (MFCCs) that are used in the feature extraction phase. Our best-obtained accuracy is 96.27 % found with 8 GMMs.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126805367","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204054
Souhaila Ben Haddi, A. Zugari, A. Zakriti, Soufiane Achraou
In this paper, we introduce a compact wideband microstrip band pass filter for 3.5GHz, with excellent performance for the next generation mobile standards “5G”. The frequency band also includes the frequencies of the WIMAX (Worldwide Interoperability for Microwave Access) and WLAN (wireless local area network) applications. The proposed band pass filter is based on a rectangular T-Shaped resonator. Their dimensions are equal to 9×5mm2. The proposed compact microstrip band pass filter has been designed by software CST Microwave Studio using FR4 substrate having relative permittivity (εr) of 4.3. This filter has a center frequency of 4,75GHz and 3dB bandwidth from 3 to 6GHz, an insertion loss low than 1dB, a return loss better than 30dB and also a fractional bandwidth more than 70%. This results are in good agreement with those accomplished by Advanced Design System “ADS”.
{"title":"A Compact Microstrip T-Shaped Resonator Band Pass Filter for 5G Applications","authors":"Souhaila Ben Haddi, A. Zugari, A. Zakriti, Soufiane Achraou","doi":"10.1109/ISCV49265.2020.9204054","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204054","url":null,"abstract":"In this paper, we introduce a compact wideband microstrip band pass filter for 3.5GHz, with excellent performance for the next generation mobile standards “5G”. The frequency band also includes the frequencies of the WIMAX (Worldwide Interoperability for Microwave Access) and WLAN (wireless local area network) applications. The proposed band pass filter is based on a rectangular T-Shaped resonator. Their dimensions are equal to 9×5mm2. The proposed compact microstrip band pass filter has been designed by software CST Microwave Studio using FR4 substrate having relative permittivity (εr) of 4.3. This filter has a center frequency of 4,75GHz and 3dB bandwidth from 3 to 6GHz, an insertion loss low than 1dB, a return loss better than 30dB and also a fractional bandwidth more than 70%. This results are in good agreement with those accomplished by Advanced Design System “ADS”.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116445676","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204288
S. Iftikhar, F. Jabeen, Hira Nasir, Shahwana Fida
The tagging process involves use of labels to provide content with further information using a set of keywords (tags). A tag is a keyword or phrase used to provide metadata about a resource. Resource can be image, web page, video, audio etc., which are available on Web. In this research article the issue of polysemy (a word or phrase with multiple meanings) in tagging is analyzed because it creates a lot of confusion and is cause of ambiguity, redundancy and incorrect search results. In particular if a user looks at URLs the user can’t guess about what sort of resource it refers. By looking at tag set associated with a resource a user can guess about the resource, but the guess can be ambiguous if tag set contains polysemy tags. A novel solution is proposed for the detection of polysemy tags in folk tag set. The capabilities of Word Net, Wikipedia and visual tag dictionary are exploited. Experiments are performed on tag sets that is taken from Delicious. This approach can detect many common types of polysemy that appears in folksonomies like contrastive and complementary polysemy.
{"title":"WordNet and Wiki Based Approach for Finding Polysemy Tags in a Tag Set","authors":"S. Iftikhar, F. Jabeen, Hira Nasir, Shahwana Fida","doi":"10.1109/ISCV49265.2020.9204288","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204288","url":null,"abstract":"The tagging process involves use of labels to provide content with further information using a set of keywords (tags). A tag is a keyword or phrase used to provide metadata about a resource. Resource can be image, web page, video, audio etc., which are available on Web. In this research article the issue of polysemy (a word or phrase with multiple meanings) in tagging is analyzed because it creates a lot of confusion and is cause of ambiguity, redundancy and incorrect search results. In particular if a user looks at URLs the user can’t guess about what sort of resource it refers. By looking at tag set associated with a resource a user can guess about the resource, but the guess can be ambiguous if tag set contains polysemy tags. A novel solution is proposed for the detection of polysemy tags in folk tag set. The capabilities of Word Net, Wikipedia and visual tag dictionary are exploited. Experiments are performed on tag sets that is taken from Delicious. This approach can detect many common types of polysemy that appears in folksonomies like contrastive and complementary polysemy.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781407","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204237
M. El bakkali, Said Elkhaldi, H. Elftouh, N. Touhami
In this paper 16 elements small-signal equivalent circuit for GaAs pHEMT is presented. ED02AH process based on III-V materiel is chosen. This process is used in the improvement of telecommunication systems, space and defense applications. This work presents the results of direct extraction of the small signal or linear model based on an analytical method and measurements of the dispersion parameters [S]. The extraction of the parameters of the small signal equivalent scheme is done for an ED02AH (6x15$mu$m) process of GaAs technology, a transistor of 6 gate fingers, each with a width of 15 $mu$m. A good agreement between the simulated and measured parameters S confirms the validity of the proposed method.
{"title":"Small-Signal Modeling of GaAs – pHEMT Using Direct Extraction Method","authors":"M. El bakkali, Said Elkhaldi, H. Elftouh, N. Touhami","doi":"10.1109/ISCV49265.2020.9204237","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204237","url":null,"abstract":"In this paper 16 elements small-signal equivalent circuit for GaAs pHEMT is presented. ED02AH process based on III-V materiel is chosen. This process is used in the improvement of telecommunication systems, space and defense applications. This work presents the results of direct extraction of the small signal or linear model based on an analytical method and measurements of the dispersion parameters [S]. The extraction of the parameters of the small signal equivalent scheme is done for an ED02AH (6x15$mu$m) process of GaAs technology, a transistor of 6 gate fingers, each with a width of 15 $mu$m. A good agreement between the simulated and measured parameters S confirms the validity of the proposed method.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115164039","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204022
F. Ngom, Ibrahima Fall, M. Camara, A. Bah
Heart disease causes millions of deaths worldwide. Many approaches have been proposed for the prediction of heart disease. Several machine learning, deep learning, and data mining algorithms are used in the detection and diagnosis of heart disease based on parameters or risk factors. The most used algorithms are Naïve Bayes, Machine Vector Support, decision tree, KNNs, and artificial neural networks. The most frequently used parameters or risk factors are the 14 attributes of the UCI Cleveland standard. In this article, a study on these different approaches is carried out. This study shows diversity in relation to the choices and the use of different attributes in the prediction of cardiovascular diseases.
{"title":"A study on predicting and diagnosing non-communicable diseases: case of cardiovascular diseases","authors":"F. Ngom, Ibrahima Fall, M. Camara, A. Bah","doi":"10.1109/ISCV49265.2020.9204022","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204022","url":null,"abstract":"Heart disease causes millions of deaths worldwide. Many approaches have been proposed for the prediction of heart disease. Several machine learning, deep learning, and data mining algorithms are used in the detection and diagnosis of heart disease based on parameters or risk factors. The most used algorithms are Naïve Bayes, Machine Vector Support, decision tree, KNNs, and artificial neural networks. The most frequently used parameters or risk factors are the 14 attributes of the UCI Cleveland standard. In this article, a study on these different approaches is carried out. This study shows diversity in relation to the choices and the use of different attributes in the prediction of cardiovascular diseases.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124491825","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 : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204167
Azdad Nabila, E. Mohamed
In this paper, we consider an IEEE 802.15.4-based Wireless Body Area Network (WBAN), where different biomedical sensors are distributed on a human body and have to send the measured data to a coordinator node. Focusing on the Slotted Carrier Sense Multiple Access with Collision Avoidance algorithm (Slotted CSMA/CA) defined by the IEEE 802.15.4 norm in the beacon-enabled mode, we propose an enhanced backoff strategy to provide nodes an equitable access to the communication medium. Then we analyze its performance over realistic requirements of the considered sensors using the latest version of Castalia Simulator. The obtained results reveal the efficiency of our proposal as compared to the traditional profile of the norm in terms of reliability, timeliness and throughput.
{"title":"An enhanced backoff strategy for fair channel access in WBAN-based health monitoring systems","authors":"Azdad Nabila, E. Mohamed","doi":"10.1109/ISCV49265.2020.9204167","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204167","url":null,"abstract":"In this paper, we consider an IEEE 802.15.4-based Wireless Body Area Network (WBAN), where different biomedical sensors are distributed on a human body and have to send the measured data to a coordinator node. Focusing on the Slotted Carrier Sense Multiple Access with Collision Avoidance algorithm (Slotted CSMA/CA) defined by the IEEE 802.15.4 norm in the beacon-enabled mode, we propose an enhanced backoff strategy to provide nodes an equitable access to the communication medium. Then we analyze its performance over realistic requirements of the considered sensors using the latest version of Castalia Simulator. The obtained results reveal the efficiency of our proposal as compared to the traditional profile of the norm in terms of reliability, timeliness and throughput.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124242692","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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