Pub Date : 2020-06-01DOI: 10.1109/ISCV49265.2020.9204324
M. A. Sabri, Y. Filali, Hasnae El Khoukhi, A. Aarab
In recent years skin cancer is becoming more and more threatening because of its fast and significant spread worldwide. This evidence has increased interest and efforts in the development of automatic diagnostic computational systems to assist early diagnosis. Several approaches have been proposed to assist in skin lesion diagnosis which used machine learning and ensemble learning. In some cases, a classifier can correctly predict the output class while others fail and vice versa. So the idea is to use different machine learning and ensemble learning to classify skin cancer. In this paper, we propose an improved ensemble learning method to classify skin cancer. Features used are the best combination of extracted features from different characteristics, i.e., shape, color, texture, and skeleton of the lesion, then we classify these features using different algorithms to predict the classes. Globally, the experimented results show a promoting result.
{"title":"Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning model","authors":"M. A. Sabri, Y. Filali, Hasnae El Khoukhi, A. Aarab","doi":"10.1109/ISCV49265.2020.9204324","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204324","url":null,"abstract":"In recent years skin cancer is becoming more and more threatening because of its fast and significant spread worldwide. This evidence has increased interest and efforts in the development of automatic diagnostic computational systems to assist early diagnosis. Several approaches have been proposed to assist in skin lesion diagnosis which used machine learning and ensemble learning. In some cases, a classifier can correctly predict the output class while others fail and vice versa. So the idea is to use different machine learning and ensemble learning to classify skin cancer. In this paper, we propose an improved ensemble learning method to classify skin cancer. Features used are the best combination of extracted features from different characteristics, i.e., shape, color, texture, and skeleton of the lesion, then we classify these features using different algorithms to predict the classes. Globally, the experimented results show a promoting result.","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":"128156211","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/ISCV49265.2020.9204271
Jaewon Moon, Seungwoo Kum, Youngkee Kim, V. Stankovski, Uroš Paščinski, Petar Kochovski
Federated Learning is a distributed machine learning approach which enables model training without sharing private locally produced data. It has been actively researched for several years as a means to utilize big data while protecting personal information. However, the server must decide which clients to participate in and what results to be used for aggregation each round. Besides, since the server needs to maintain the connection with the client directly, device overload and the processing delay may cause due to changes in the system environment such as network condition. In this paper, we propose a data management system that efficiently addresses the problem of general Federated Learning by improvements of the data management process on the connection between the Federated Learning server and the client. Additionally, it is shown that the proposed system can perform tasks independently and scales for increasing number of devices participating in the Federated Learning tasks.
{"title":"A Decentralized AI Data Management System In Federated Learning","authors":"Jaewon Moon, Seungwoo Kum, Youngkee Kim, V. Stankovski, Uroš Paščinski, Petar Kochovski","doi":"10.1109/ISCV49265.2020.9204271","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204271","url":null,"abstract":"Federated Learning is a distributed machine learning approach which enables model training without sharing private locally produced data. It has been actively researched for several years as a means to utilize big data while protecting personal information. However, the server must decide which clients to participate in and what results to be used for aggregation each round. Besides, since the server needs to maintain the connection with the client directly, device overload and the processing delay may cause due to changes in the system environment such as network condition. In this paper, we propose a data management system that efficiently addresses the problem of general Federated Learning by improvements of the data management process on the connection between the Federated Learning server and the client. Additionally, it is shown that the proposed system can perform tasks independently and scales for increasing number of devices participating in the Federated Learning tasks.","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":"132881202","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/ISCV49265.2020.9204118
Mohamed Amine Tahiri, H. Karmouni, M. Sayyouri, H. Qjidaa
In this paper, we propose a new algorithm for computing Hahn polynomial coefficients (HPCs) for higher polynomial order, which greatly reduces the spread of numerical defects associated with Hahn polynomials (HPs) using conventional methods. The proposed method is used to reconstruct large 2D images. The reliability and effectiveness of the new approach were often linked to standard repetition algorithms. The findings show that the method proposed is efficient and effective in terms of calculation accuracy and stability of high order Hahn moments.
{"title":"Stable Computation of Hahn Polynomials for Higher Polynomial Order","authors":"Mohamed Amine Tahiri, H. Karmouni, M. Sayyouri, H. Qjidaa","doi":"10.1109/ISCV49265.2020.9204118","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204118","url":null,"abstract":"In this paper, we propose a new algorithm for computing Hahn polynomial coefficients (HPCs) for higher polynomial order, which greatly reduces the spread of numerical defects associated with Hahn polynomials (HPs) using conventional methods. The proposed method is used to reconstruct large 2D images. The reliability and effectiveness of the new approach were often linked to standard repetition algorithms. The findings show that the method proposed is efficient and effective in terms of calculation accuracy and stability of high order Hahn moments.","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":"134560471","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/ISCV49265.2020.9204337
Fadwa Mahiri, Aouatif Najoua, S. B. Souda
The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today’s manufacturing systems and increased the growth of data generated from manufacturing. Big data as a driver key of intelligent manufacturing empowered companies to adopt data driven approaches to enhance competitiveness, efficiency and sustainability. In this paper a historical evolution of data in manufacturing is overviewed, the smart manufacturing its key technologies and sustainability are related, and a conceptual framework from lifecycle product perspective was proposed to show potential applications of big data analytics in sustainable smart manufacturing.
{"title":"Data-driven sustainable smart manufacturing: A conceptual framework","authors":"Fadwa Mahiri, Aouatif Najoua, S. B. Souda","doi":"10.1109/ISCV49265.2020.9204337","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204337","url":null,"abstract":"The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today’s manufacturing systems and increased the growth of data generated from manufacturing. Big data as a driver key of intelligent manufacturing empowered companies to adopt data driven approaches to enhance competitiveness, efficiency and sustainability. In this paper a historical evolution of data in manufacturing is overviewed, the smart manufacturing its key technologies and sustainability are related, and a conceptual framework from lifecycle product perspective was proposed to show potential applications of big data analytics in sustainable smart manufacturing.","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":"132492379","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/ISCV49265.2020.9204226
M. Youssfi, O. Bouattane, Kaburlasos Vassilis, G. Papakostas
This paper presents a new distributed polymorphic learning model for a community of heterogeneous cyber physical robots operating in a multi agent environment. This model allows a community of intelligent physical agents to exchange their minds represented by configured and trained neural net-works. The training operation of the neural networks is performed, using machines and deep learning techniques, in a distributed way based on special agents deployed in machines having high-performance computing resources based on GPUs. Each mind, specialized in a specific field, is initially affected to an agent. Depending on the event context, robots can automatically select the trained and appropriate trained network to resolve the situation either by using their own training models, or by collaborating with other agents specialized to perform the context event. In this article, we present results of a model implementation based on DeepLearning4J Framework and a multi-agent system middleware
{"title":"Generic distributed polymorphic learning model for a community of heterogeneous cyber physical social robots in MAS Environment and GPU Architecture","authors":"M. Youssfi, O. Bouattane, Kaburlasos Vassilis, G. Papakostas","doi":"10.1109/ISCV49265.2020.9204226","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204226","url":null,"abstract":"This paper presents a new distributed polymorphic learning model for a community of heterogeneous cyber physical robots operating in a multi agent environment. This model allows a community of intelligent physical agents to exchange their minds represented by configured and trained neural net-works. The training operation of the neural networks is performed, using machines and deep learning techniques, in a distributed way based on special agents deployed in machines having high-performance computing resources based on GPUs. Each mind, specialized in a specific field, is initially affected to an agent. Depending on the event context, robots can automatically select the trained and appropriate trained network to resolve the situation either by using their own training models, or by collaborating with other agents specialized to perform the context event. In this article, we present results of a model implementation based on DeepLearning4J Framework and a multi-agent system middleware","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":"115025783","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/ISCV49265.2020.9204013
Aberbach Hicham, Adil Jeghal, Abdelouahed Sabri, H. Tairi
Nowadays Data Mining is used in many application areas enabling large data streams and algorithms for analysis and extraction of powerful data. On their side, the Computer Environments for Human Learning (EIAH) offer TEL devices (Technology-enhanced learning) such as simulators, serious games, MOOCs (massive online open courses), or educational platforms. These devices provide data that are traces of the activities of students or teachers. The data produced are cognitive information of very fine levels (student knowledge, skills, and errors) and require specific analysis and processing tools, we talk here about educational data mining methods, Educational data processing (EDM) is rising as a notion of research and analysis with a set of machine and psychological ways and research approaches for understanding however students learn. EDM uses machine approaches to research instructional knowledge so as to review instructional queries. For this knowledge exploration, several tools were used like personal learning environments, recommender systems, Context learning, and Course management systems. These tools offer numerous edges for instructional data processing. In this survey, we have a tendency to focus and supply numerous tools of analysis trends exploitation EDM Tools to explore data and knowledge, and explaining the process of EDM application, the goal is not only to transform the data into knowledge but also to filter the extracted knowledge to know how to modify the educational environment to improve learners’ learning. This paper surveys the foremost relevant studies administrated during this field up to date.
{"title":"A Survey on Educational Data Mining [2014-2019]","authors":"Aberbach Hicham, Adil Jeghal, Abdelouahed Sabri, H. Tairi","doi":"10.1109/ISCV49265.2020.9204013","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204013","url":null,"abstract":"Nowadays Data Mining is used in many application areas enabling large data streams and algorithms for analysis and extraction of powerful data. On their side, the Computer Environments for Human Learning (EIAH) offer TEL devices (Technology-enhanced learning) such as simulators, serious games, MOOCs (massive online open courses), or educational platforms. These devices provide data that are traces of the activities of students or teachers. The data produced are cognitive information of very fine levels (student knowledge, skills, and errors) and require specific analysis and processing tools, we talk here about educational data mining methods, Educational data processing (EDM) is rising as a notion of research and analysis with a set of machine and psychological ways and research approaches for understanding however students learn. EDM uses machine approaches to research instructional knowledge so as to review instructional queries. For this knowledge exploration, several tools were used like personal learning environments, recommender systems, Context learning, and Course management systems. These tools offer numerous edges for instructional data processing. In this survey, we have a tendency to focus and supply numerous tools of analysis trends exploitation EDM Tools to explore data and knowledge, and explaining the process of EDM application, the goal is not only to transform the data into knowledge but also to filter the extracted knowledge to know how to modify the educational environment to improve learners’ learning. This paper surveys the foremost relevant studies administrated during this field up to date.","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":"120896648","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/ISCV49265.2020.9204097
Jabrane Kachaoui, A. Belangour
Today, Ontologies have a major place in knowledge representation and modeling. They are used to formalize a domain knowledge and add a semantic layer to current systems and applications. Ontologies make it possible to explicitly represent the knowledge of a domain by means a formal language so that they can be manipulated automatically and shared easily. They are widely used in various fields of research such as Knowledge Representation (KR) and Data Integration (DI). However, the effectiveness to interoperate learning objects among various learning object repositories is often decreased because of using different ontological schemes for annotating learning objects into every learning object repository. Hence, semantic heterogeneity and structural differences between ontologies need to be resolved so as to generate common ontology to expedite learning object reusability. This paper focused on automated ontology mapping and merging concept. The study significance lies in an algorithmic approach for mapping attributes of learning objects/concepts and merging them based on mapped attributes; identifying suitable threshold value for mapping and merging.
{"title":"Local Ontologies Merging in Data Ponds","authors":"Jabrane Kachaoui, A. Belangour","doi":"10.1109/ISCV49265.2020.9204097","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204097","url":null,"abstract":"Today, Ontologies have a major place in knowledge representation and modeling. They are used to formalize a domain knowledge and add a semantic layer to current systems and applications. Ontologies make it possible to explicitly represent the knowledge of a domain by means a formal language so that they can be manipulated automatically and shared easily. They are widely used in various fields of research such as Knowledge Representation (KR) and Data Integration (DI). However, the effectiveness to interoperate learning objects among various learning object repositories is often decreased because of using different ontological schemes for annotating learning objects into every learning object repository. Hence, semantic heterogeneity and structural differences between ontologies need to be resolved so as to generate common ontology to expedite learning object reusability. This paper focused on automated ontology mapping and merging concept. The study significance lies in an algorithmic approach for mapping attributes of learning objects/concepts and merging them based on mapped attributes; identifying suitable threshold value for mapping and merging.","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":"124843981","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/ISCV49265.2020.9204120
H. Benbrahim, A. Merzouki, K. Minaoui
The purpose of this study is to quantify soil moisture variability in agriculture fields at field scale resolution using the Sentinel data (Sentinel-1 and Sentinel-2) based on a change detection technique. For calibration and validation of our model, ground measurements at 40 sampling sites in southern Manitoba, Canada, were carried out during the field campaign of SMAP Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The developed method is based on modelling soil moisture change by combining the difference in backscattered signal with that of NDVI observed on two consecutive acquisition days. This approach makes the assumption that the change in Normalized Difference Vegetation Index (NDVI) could better represent the attenuation of the backscattered signal resulting from the vegetation. Our model was evaluated over mature crop fields (canola, soybeans, wheat, corn and oats) using ground measurements and the agreement between satellite estimates and ground measurements was found satisfactory (RMSE lower than 0.093 m3/m3).
{"title":"Quantification of soil moisture variability over agriculture fields using Sentinel imagery","authors":"H. Benbrahim, A. Merzouki, K. Minaoui","doi":"10.1109/ISCV49265.2020.9204120","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204120","url":null,"abstract":"The purpose of this study is to quantify soil moisture variability in agriculture fields at field scale resolution using the Sentinel data (Sentinel-1 and Sentinel-2) based on a change detection technique. For calibration and validation of our model, ground measurements at 40 sampling sites in southern Manitoba, Canada, were carried out during the field campaign of SMAP Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The developed method is based on modelling soil moisture change by combining the difference in backscattered signal with that of NDVI observed on two consecutive acquisition days. This approach makes the assumption that the change in Normalized Difference Vegetation Index (NDVI) could better represent the attenuation of the backscattered signal resulting from the vegetation. Our model was evaluated over mature crop fields (canola, soybeans, wheat, corn and oats) using ground measurements and the agreement between satellite estimates and ground measurements was found satisfactory (RMSE lower than 0.093 m3/m3).","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":"128328340","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/ISCV49265.2020.9204243
Zainab Oufqir, A. El Abderrahmani, K. Satori
Many libraries are available in the development world to create augmented reality applications, their functionality differs depending on the technology used to detect and track an object, points or features in a scene. In this article, we will discover ARKit, ARCore two open source libraries that display a virtual object in the real world. Their goal is to merge digital content and information with the real world. They can interact with the components of the device (camera and screen) to detect and track characteristics of the scene in order to insert virtual content. This study implements and concretizes the different functionalities available in augmented reality to enrich the real world with additional information.
{"title":"ARKit and ARCore in serve to augmented reality","authors":"Zainab Oufqir, A. El Abderrahmani, K. Satori","doi":"10.1109/ISCV49265.2020.9204243","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204243","url":null,"abstract":"Many libraries are available in the development world to create augmented reality applications, their functionality differs depending on the technology used to detect and track an object, points or features in a scene. In this article, we will discover ARKit, ARCore two open source libraries that display a virtual object in the real world. Their goal is to merge digital content and information with the real world. They can interact with the components of the device (camera and screen) to detect and track characteristics of the scene in order to insert virtual content. This study implements and concretizes the different functionalities available in augmented reality to enrich the real world with additional information.","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":"114763118","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/ISCV49265.2020.9204043
S. Gazzah, O. Bencharef
The coronavirus first outbreak in Wuhan city of China by December 2019. Due to its highly contagious power, they spread promptly in the four continents. Moreover, it devastating our daily lives and cause huge economic damage. Therefore, it is urgent to detect the positive cases at the earliest and put then under isolation. Automatic virus detection using Machine Learning will be a valuable contribution to prevent the spread of this epidemic. The purpose of this paper is to present short reviews on the coronavirus detection. In reviewing the existing works, we summarized and compared some related works performed on a collection of CT and X-ray images provided from infected patients. We conclude the paper with some discussions on how computer vision can response to urgent need to contribute in pandemics and to investigate many aspects of new viral replication and pathogenesis.
{"title":"A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics","authors":"S. Gazzah, O. Bencharef","doi":"10.1109/ISCV49265.2020.9204043","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204043","url":null,"abstract":"The coronavirus first outbreak in Wuhan city of China by December 2019. Due to its highly contagious power, they spread promptly in the four continents. Moreover, it devastating our daily lives and cause huge economic damage. Therefore, it is urgent to detect the positive cases at the earliest and put then under isolation. Automatic virus detection using Machine Learning will be a valuable contribution to prevent the spread of this epidemic. The purpose of this paper is to present short reviews on the coronavirus detection. In reviewing the existing works, we summarized and compared some related works performed on a collection of CT and X-ray images provided from infected patients. We conclude the paper with some discussions on how computer vision can response to urgent need to contribute in pandemics and to investigate many aspects of new viral replication and pathogenesis.","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":"114267418","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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