Pub Date : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357310
Zakia Terki, F. Chebbara, A. Mezache
In radar systems, detection performance is always related to target and clutter models. The probability of detection is shown to be sensitive to the degree of estimation accuracy of clutter levels. In this work, the performances of logt-CFAR, zlog(z)-CFAR and Bayesian-CFAR detectors are investigated using both simulated and real data. The clutter is assumed to be log-normal, Weibull or Pareto type II distributed. The dependence of the false alarm probability is presented. From simulated data, CFAR detectors provide fully CFAR decision rules. From IPIX real data with different range resolutions, it is shown that the Bayesian-CFAR algorithm exhibits a small deviation of the false alarm probability.
{"title":"Analysis of non-coherent CFAR detectors in sea-clutter: A comparison","authors":"Zakia Terki, F. Chebbara, A. Mezache","doi":"10.1109/CiSt49399.2021.9357310","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357310","url":null,"abstract":"In radar systems, detection performance is always related to target and clutter models. The probability of detection is shown to be sensitive to the degree of estimation accuracy of clutter levels. In this work, the performances of logt-CFAR, zlog(z)-CFAR and Bayesian-CFAR detectors are investigated using both simulated and real data. The clutter is assumed to be log-normal, Weibull or Pareto type II distributed. The dependence of the false alarm probability is presented. From simulated data, CFAR detectors provide fully CFAR decision rules. From IPIX real data with different range resolutions, it is shown that the Bayesian-CFAR algorithm exhibits a small deviation of the false alarm probability.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130690130","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357067
Yassine Akhiat, Youssef Asnaoui, M. Chahhou, Ahmed Zinedine
Feature selection (FS) is a very important pre-processing technique in machine learning and data mining. It aims to select a small subset of relevant and informative features from the original feature space that may contain many irrelevant, redundant and noisy features. Feature selection usually leads to better performance, interpretability, and lower computational cost. In the literature, FS methods are categorized into three main approaches: Filters, Wrappers, and Embedded. In this paper we introduce a new feature selection method called graph feature selection (GFS). The main steps of GFS are the following: first, we create a weighted graph where each node corresponds to each feature and the weight between two nodes is computed using a matrix of individual and pairwise score of a Decision tree classifier. Second, at each iteration, we split the graph into two random partitions having the same number of nodes, then we keep moving the worst node from one partition to another until the global modularity is converged. Third, from the final best partition, we select the best ranked features according to a new proposed variable importance criterion. The results of GFS are compared to three well-known feature selection algorithms using nine benchmarking datasets. The proposed method shows its ability and effectiveness at identifying the most informative feature subset.
{"title":"A new graph feature selection approach","authors":"Yassine Akhiat, Youssef Asnaoui, M. Chahhou, Ahmed Zinedine","doi":"10.1109/CiSt49399.2021.9357067","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357067","url":null,"abstract":"Feature selection (FS) is a very important pre-processing technique in machine learning and data mining. It aims to select a small subset of relevant and informative features from the original feature space that may contain many irrelevant, redundant and noisy features. Feature selection usually leads to better performance, interpretability, and lower computational cost. In the literature, FS methods are categorized into three main approaches: Filters, Wrappers, and Embedded. In this paper we introduce a new feature selection method called graph feature selection (GFS). The main steps of GFS are the following: first, we create a weighted graph where each node corresponds to each feature and the weight between two nodes is computed using a matrix of individual and pairwise score of a Decision tree classifier. Second, at each iteration, we split the graph into two random partitions having the same number of nodes, then we keep moving the worst node from one partition to another until the global modularity is converged. Third, from the final best partition, we select the best ranked features according to a new proposed variable importance criterion. The results of GFS are compared to three well-known feature selection algorithms using nine benchmarking datasets. The proposed method shows its ability and effectiveness at identifying the most informative feature subset.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132966685","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357291
Kaoutar Belhoucine, M. Mourchid, A. Mouloudi, Samir Mbarki
Introducing ontology in information retrieval provides the obvious benefit of higher precision and addresses other common issues such as information quality and user adaptation. However, the main disadvantage is the costs (i.e., time and effort) of manually constructing an ontology and of its representativeness of the specified domain. This paper considers the ontology construction process and proposes a middle-out approach that allows the construction of a well-founded ontology speedily. The domain application that interests us is Moroccan commercial law. The ontology to be built aims to support users in describing a specific legal situation and retrieving the relevant legal articles and court decisions in similar cases. The proposed approach combines a top-down and bottom-up strategy. The first allows us to define an ontological model of the legal domain by reusing an existing core ontology, whereas the second populates and refines this model based on an ontology-learning process from Arabic texts.
{"title":"A Middle-out Approach for Building a Legal domain ontology in Arabic","authors":"Kaoutar Belhoucine, M. Mourchid, A. Mouloudi, Samir Mbarki","doi":"10.1109/CiSt49399.2021.9357291","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357291","url":null,"abstract":"Introducing ontology in information retrieval provides the obvious benefit of higher precision and addresses other common issues such as information quality and user adaptation. However, the main disadvantage is the costs (i.e., time and effort) of manually constructing an ontology and of its representativeness of the specified domain. This paper considers the ontology construction process and proposes a middle-out approach that allows the construction of a well-founded ontology speedily. The domain application that interests us is Moroccan commercial law. The ontology to be built aims to support users in describing a specific legal situation and retrieving the relevant legal articles and court decisions in similar cases. The proposed approach combines a top-down and bottom-up strategy. The first allows us to define an ontological model of the legal domain by reusing an existing core ontology, whereas the second populates and refines this model based on an ontology-learning process from Arabic texts.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134142208","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357317
Imane Aboutajedyne, Mouna Squalli Houssaini, A. Aboutajeddine, Yassine Salih Alj, M. E. Mohajir
As a result of technological and societal change, an important demand for new job skills is growing, emphasizing the need to design new educational solutions beyond the school settings. This paper aims to propose a design model for the development of non-traditional educational activities. The proposed model leverages on the combined strengths of design-thinking approach and learning theories principles. To illustrate the application of the suggested design process, a case study of an educational activity that is designed for a non-profit organization is presented. This activity, conceived for kids of ages between 8 and 14, is developed based on insights from the considered local community and the learning outcomes of the intended job skills. Overall, this initiative can be considered as a novel model from which we can inspire innovative design of nonconventional learning activities.
{"title":"A design model for the development of non-traditional educational activities","authors":"Imane Aboutajedyne, Mouna Squalli Houssaini, A. Aboutajeddine, Yassine Salih Alj, M. E. Mohajir","doi":"10.1109/CiSt49399.2021.9357317","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357317","url":null,"abstract":"As a result of technological and societal change, an important demand for new job skills is growing, emphasizing the need to design new educational solutions beyond the school settings. This paper aims to propose a design model for the development of non-traditional educational activities. The proposed model leverages on the combined strengths of design-thinking approach and learning theories principles. To illustrate the application of the suggested design process, a case study of an educational activity that is designed for a non-profit organization is presented. This activity, conceived for kids of ages between 8 and 14, is developed based on insights from the considered local community and the learning outcomes of the intended job skills. Overall, this initiative can be considered as a novel model from which we can inspire innovative design of nonconventional learning activities.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792892","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357253
Chakir Fri, Rachid Elouahbi
E-learning has been one of the major trends in education and its becoming an attracting topic in the field of artificial intelligence and its subfields like machine learning and deep learning, that are considered the most promising technologies in our era where its application score is almost unlimited. Many researchers are showing interest in the topic with significant research results. The aim of this paper is to extract the applications of machine learning and deep learning in E-learning systems. In this work we collected research papers from five research databases: Springer Link, Science Direct, Scopus, IEEE Digital Library, and Web of Science for a topic modeling application using a machine learning technique known as Latent Dirichlet Allocation (LDA).
电子学习已经成为教育的主要趋势之一，在人工智能及其子领域，如机器学习和深度学习领域，它成为一个吸引人的话题，被认为是我们这个时代最有前途的技术，它的应用分数几乎是无限的。许多研究人员对这一主题表现出兴趣，并取得了重要的研究成果。本文的目的是提取机器学习和深度学习在电子学习系统中的应用。在这项工作中，我们从五个研究数据库中收集了研究论文:施普林格Link, Science Direct, Scopus, IEEE数字图书馆和Web of Science，用于使用被称为潜在狄利let分配(LDA)的机器学习技术的主题建模应用程序。
{"title":"Machine Learning and Deep Learning applications in E-learning Systems: A Literature Survey using Topic Modeling Approach","authors":"Chakir Fri, Rachid Elouahbi","doi":"10.1109/CiSt49399.2021.9357253","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357253","url":null,"abstract":"E-learning has been one of the major trends in education and its becoming an attracting topic in the field of artificial intelligence and its subfields like machine learning and deep learning, that are considered the most promising technologies in our era where its application score is almost unlimited. Many researchers are showing interest in the topic with significant research results. The aim of this paper is to extract the applications of machine learning and deep learning in E-learning systems. In this work we collected research papers from five research databases: Springer Link, Science Direct, Scopus, IEEE Digital Library, and Web of Science for a topic modeling application using a machine learning technique known as Latent Dirichlet Allocation (LDA).","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130470228","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357186
B. Vogel‐Heuser, K. Land, Fandi Bi
The digitalization of teaching due to the Covid-19 pandemic offers new challenges, yet also new opportunities. To assist and encourage students in their self-study of the unified modeling language (UML), modeling tasks were provided; then student solutions were analyzed and discussed in web meetings. This way, earlier and more in-depth insights into typical faults in the students' modeling solutions could be achieved. Two groups of students were considered, and it was examined whether students make fewer or different faults in modeling depending on their maturity and pre-knowledge.
{"title":"Challenges for Students of Mechanical Engineering Using UML - Typical Questions and Faults","authors":"B. Vogel‐Heuser, K. Land, Fandi Bi","doi":"10.1109/CiSt49399.2021.9357186","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357186","url":null,"abstract":"The digitalization of teaching due to the Covid-19 pandemic offers new challenges, yet also new opportunities. To assist and encourage students in their self-study of the unified modeling language (UML), modeling tasks were provided; then student solutions were analyzed and discussed in web meetings. This way, earlier and more in-depth insights into typical faults in the students' modeling solutions could be achieved. Two groups of students were considered, and it was examined whether students make fewer or different faults in modeling depending on their maturity and pre-knowledge.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116821918","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357170
Marouane Yassine, David Beauchemin, François Laviolette, Luc Lamontagne
Address parsing consists of identifying the segments that make up an address such as a street name or a postal code. Because of its importance for tasks like record linkage, address parsing has been approached with many techniques. Neural network methods defined a new state-of-the-art for address parsing. While this approach yielded notable results, previous work has only focused on applying neural networks to achieve address parsing of addresses from one source country. We propose an approach in which we employ subword embeddings and a Recurrent Neural Network architecture to build a single model capable of learning to parse addresses from multiple countries at the same time while taking into account the difference in languages and address formatting systems. We achieved accuracies around 99% on the countries used for training with no pre-processing nor post-processing needed. We explore the possibility of transferring the address parsing knowledge obtained by training on some countries' addresses to others with no further training in a zero-shot transfer learning setting. We achieve good results for 80% of the countries (33 out of 41), almost 50% of which (20 out of 41) is near state-of-the-art performance. In addition, we propose an open-source Python implementation of our trained models11https://githuh.com/GRAAL-Research/deepparse.
{"title":"Leveraging Subword Embeddings for Multinational Address Parsing","authors":"Marouane Yassine, David Beauchemin, François Laviolette, Luc Lamontagne","doi":"10.1109/CiSt49399.2021.9357170","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357170","url":null,"abstract":"Address parsing consists of identifying the segments that make up an address such as a street name or a postal code. Because of its importance for tasks like record linkage, address parsing has been approached with many techniques. Neural network methods defined a new state-of-the-art for address parsing. While this approach yielded notable results, previous work has only focused on applying neural networks to achieve address parsing of addresses from one source country. We propose an approach in which we employ subword embeddings and a Recurrent Neural Network architecture to build a single model capable of learning to parse addresses from multiple countries at the same time while taking into account the difference in languages and address formatting systems. We achieved accuracies around 99% on the countries used for training with no pre-processing nor post-processing needed. We explore the possibility of transferring the address parsing knowledge obtained by training on some countries' addresses to others with no further training in a zero-shot transfer learning setting. We achieve good results for 80% of the countries (33 out of 41), almost 50% of which (20 out of 41) is near state-of-the-art performance. In addition, we propose an open-source Python implementation of our trained models11https://githuh.com/GRAAL-Research/deepparse.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660712","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357250
Samira Lafraxo, Mohamed El Ansari
The novel Coronavirus (COVID19) is an infectious epidemic declared in March 2020 as Pandemic. Because of its easy and rapid transmission, Coronavirus has caused thousands of deaths around the world. Thus, developing new systems for accurate and fast COVID19 detection is becoming crucial. X-ray imaging is used by radiology doctors for the diagnosis of coron-avirus. However, this process requires considerable time. Therefore, artificial intelligence systems can help in reducing pressure on health care systems. In this paper, we propose CoviNet a deep learning network to automatically detect COVID19 presence in chest X-ray images. The suggested architecture is based on an adaptive median filter, histogram equalization, and a convolutional neural network. It is trained end-to-end on a publicly available dataset. Our model achieved an accuracy of 98.62% for binary classification and 95.77% for multi-class classification. As the early diagnosis may limit the spread of the virus, this framework can be used to assist radiologists in the initial diagnosis of COVID19.
{"title":"CoviNet: Automated COVID-19 Detection from X-rays using Deep Learning Techniques","authors":"Samira Lafraxo, Mohamed El Ansari","doi":"10.1109/CiSt49399.2021.9357250","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357250","url":null,"abstract":"The novel Coronavirus (COVID19) is an infectious epidemic declared in March 2020 as Pandemic. Because of its easy and rapid transmission, Coronavirus has caused thousands of deaths around the world. Thus, developing new systems for accurate and fast COVID19 detection is becoming crucial. X-ray imaging is used by radiology doctors for the diagnosis of coron-avirus. However, this process requires considerable time. Therefore, artificial intelligence systems can help in reducing pressure on health care systems. In this paper, we propose CoviNet a deep learning network to automatically detect COVID19 presence in chest X-ray images. The suggested architecture is based on an adaptive median filter, histogram equalization, and a convolutional neural network. It is trained end-to-end on a publicly available dataset. Our model achieved an accuracy of 98.62% for binary classification and 95.77% for multi-class classification. As the early diagnosis may limit the spread of the virus, this framework can be used to assist radiologists in the initial diagnosis of COVID19.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"83 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116410346","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357235
I. Šimonová, Ludmila Faltýnková, K. Kostolányová
The paper introduces results of research in which potential increase in learner's knowledge is considered from the view of four motivation types (Accurators, Coordinators, Directors, Explorers) within the process of smart instruction applied at two topics (Career Development, Healthy Living) of the English for Specific Purposes course. The main research objective is to find out whether learners of all motivation types can succeed in this process. Totally, 119 students, prospective teachers from the Faculty of Education and Faculty of Science, participated in the research. The SAMR (Substitution, Augmentation, Modification, Redefinition) model was applied within the smart instruction using smart devices to approach electronic sources and smart methods towards acquiring the learning content. The smart instruction was conducted for 12 weeks (one semester). Two hypotheses were set, and the quasi-experiment and ex-post-facto method were applied. Data referring to learners' motivation types were collected through the standardized Motivation Type Inventory (MTI) by Plaminek. The increase in learners' knowledge was calculated as the difference between entrance and final didactic tests scores. The results did not show statistically significant difference between single motivation types in the topic of Career Development. However, in Healthy Living, the difference was discovered in the group of Coordinators compared to other three types.
{"title":"Learners'Motivation Types in the Smart Instruction of English for Specific Purposes","authors":"I. Šimonová, Ludmila Faltýnková, K. Kostolányová","doi":"10.1109/CiSt49399.2021.9357235","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357235","url":null,"abstract":"The paper introduces results of research in which potential increase in learner's knowledge is considered from the view of four motivation types (Accurators, Coordinators, Directors, Explorers) within the process of smart instruction applied at two topics (Career Development, Healthy Living) of the English for Specific Purposes course. The main research objective is to find out whether learners of all motivation types can succeed in this process. Totally, 119 students, prospective teachers from the Faculty of Education and Faculty of Science, participated in the research. The SAMR (Substitution, Augmentation, Modification, Redefinition) model was applied within the smart instruction using smart devices to approach electronic sources and smart methods towards acquiring the learning content. The smart instruction was conducted for 12 weeks (one semester). Two hypotheses were set, and the quasi-experiment and ex-post-facto method were applied. Data referring to learners' motivation types were collected through the standardized Motivation Type Inventory (MTI) by Plaminek. The increase in learners' knowledge was calculated as the difference between entrance and final didactic tests scores. The results did not show statistically significant difference between single motivation types in the topic of Career Development. However, in Healthy Living, the difference was discovered in the group of Coordinators compared to other three types.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126868633","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 : 2020-06-05DOI: 10.1109/cist49399.2021.9357242
{"title":"6th International Congress on Information Science and Technology","authors":"","doi":"10.1109/cist49399.2021.9357242","DOIUrl":"https://doi.org/10.1109/cist49399.2021.9357242","url":null,"abstract":"","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126337810","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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