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Svm for human identification using the ECG signal

Procedia Computer Science 2020年 176卷 430-439页

作者： Sihem Hamza Yassine Ben Ayed Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax Tunisia

In this paper, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics. In this work, we propose a two-phase method to conduct human identification using the ECG signal, which are the feature extraction and the classification. In the first phase, it makes a fusion of three new types of characteristics: cepstral coefficients, ZCR, and entropy. In the second phase, the support vector machines (SVM) has been applied for the classification system. The proposed methods are evaluated using two public databases namely MIT-BIH arrhythmia and ECG-ID database obtained from the Physionet database. Experimental results show that our features can achieve high subject identification accuracy of 100% on ECG signals that are from the MIT-BIH database, ECG-ID (Five recording), and ECG-ID (Two recording), indicating that our features makes it possible to improve the efficiency of our identification system.

关键词： Biometric identification ECG signal Cepstral coefficients ZCR Entropy SVM

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Establishing an Interactive Virtual Library for Medical Manuscript Preservation Using KNN/SVM and an Amazon Elastic MapReduce Model 21

Establishing an Interactive Virtual Library for Medical Manu...

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2024 IEEE/ACS 21st International Conference on Computer systems and Applications, AICCSA 2024

作者： Hamdi, Hassen Zarrouk, Rim Mahmoudi, Ramzi Benameur, Narjes University of Sfax Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACL Tunisia FSG University of Gafsa University of Monastir Laboratoire Technologie Imagerie Médicale LTIM-LR12ES06 FMM Monastir5019 Tunisia Université Paris-Est Laboratoire d'Informatique Gaspard-Monge Unité Mixte CNRS-UMLV-ESIEE UMR8049 ESIEE Paris Cité Descartes Noisy Le Grand93162 France Laboratoire Technologie Imagerie Médicale-LTIM-LR12ES06 Tunisia University of Tunis ElManar Higher Institute of Medical Technologies of Tunis Laboratory of Biophysics and Medical Technologies Tunisia

ISBN: (纸本)9798331518240

The objective of this study was to assess the viability of establishing an interactive Virtual National Library of Medicine (VNL-M). This research proposes a solution that leverages machine learning algorithms and cloud computing technologies, including advanced techniques such as Hadoop, MapReduce, and Cascading, to virtualize a significant number of medical manuscripts from the National Library of Tunisia. The proposed solution has two phases: •The first presents a hybrid (KNN/SVM) approach for an optical character recognition (OCR) system. Because this technology necessitates high bandwidth and computational capacity, spreading it via distributed architecture or platforms may be a viable alternative for improving performance. •The second entails considering cloud computing as an infrastructure (IaaS) to deploy virtualization techniques for the National Library of Tunisia's 'medicine and health' subject area, which belongs to the 'natural sciences and mathematics' class in the Dewey Decimal Classification (DDC) system. Furthermore, Cloud storing as a Service (SaaS) is employed for the storing and retrieval of enormous amounts of medical manuscript information. The proposed solution was evaluated by conducting experiments using S3 and Amazon EC2 Elstic Map Reduce with an interesting-scale dataset from a database of manuscript heritage. Lastly, the VNL-M is published as a Web service (VNL-Mweb Service). © 2024 IEEE.

关键词： Cloud platforms

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A recursive methodology for radical communities' detection on social networks 24

A recursive methodology for radical communities' detection o...

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24th KES International Conference on Knowledge-Based and Intelligent information and Engineering systems, KES 2020

作者： Rekik, Amal Jamoussi, Salma Hamadou, Abdelmajid Ben Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax 3021 Tunisia Digital Research Center of Sfax DRCS Sfax3021 Tunisia

In the recent years, radical communities have become very aware of the enormous impact of social networks around the world. Thus, these latter are being frequently explored by these groups. Therefore, penetrating into these communities by analyzing both their interactions and their shared content is a considerably challenging task that serves to counter the online radicalization. For this purpose, in this paper, we propose a new recursive methodology for radical communities' detection on social networks based on the analysis and extraction of their violent used vocabulary. Our methodology consists mainly on extracting recursively a set of dangerous profiles from Twitter based on their suspicious interactions. Then, we analyze their textual shared data in order to construct a rich glossary containing their violent used vocabulary. This glossary is exploited and enriched to detect recursively radical communities. Finally, in order to evaluate the performance of our methodology, we resort to an expert who verifies both the list of the dangerous extracted profiles and the violent constructed glossary. The given results show the effectiveness and efficiency of our method. © 2020 The Authors. Published by Elsevier B.V.

关键词： Glossaries

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AugmentedBook: A collaborative e-learning augmented reality platform 1st

AugmentedBook: A collaborative e-learning augmented reality ...

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1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019

作者： Alzahrani, Nouf M. Lajmi, Sonia Information Technology Department Faculty of Computer Science and Information Technology Albaha University Albaha Saudi Arabia MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax Technological Pole of Sfax Sakiet Ezzit Sfax3021 Tunisia

ISBN: (纸本)9783030256289

This paper introduces an augmented reality-based framework (called AugmentedBook) for e-learning that allows the creation of collaborative notes, illustrative media (i.e. video, 2D or 3D image, audio) for mobile devices or Google glass. The augmented content can be added to real-world educational support to make it more comprehensive, interactive and collaborative. In this platform, students and teachers can add collaborative notes to any part of the educational support system. They can also find illustrative media and indicate the pertinence of the result. Using our AugmentedBook platform, students can also download the enriched support using a mobile device. Our framework solves the problem of standard integration of augmented reality applications in education, offering a distributed framework which is e-learning compliant. © 2020, Springer Nature Switzerland AG.

关键词： Augmented reality

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A recursive methodology for radical communities’ detection on social networks

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Procedia Computer Science 2020年 176卷 2010-2019页

作者： Amal Rekik Salma Jamoussi Abdelmajid Ben Hamadou Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax 3021 Tunisia Digital Research Center of Sfax DRCS Sfax 3021 Tunisia

In the recent years, radical communities have become very aware of the enormous impact of social networks around the world. Thus, these latter are being frequently explored by these groups. Therefore, penetrating into these communities by analyzing both their interactions and their shared content is a considerably challenging task that serves to counter the online radicalization. For this purpose, in this paper, we propose a new recursive methodology for radical communities’ detection on social networks based on the analysis and extraction of their violent used vocabulary. Our methodology consists mainly on extracting recursively a set of dangerous profiles from Twitter based on their suspicious interactions. Then, we analyze their textual shared data in order to construct a rich glossary containing their violent used vocabulary. This glossary is exploited and enriched to detect recursively radical communities. Finally, in order to evaluate the performance of our methodology, we resort to an expert who verifies both the list of the dangerous extracted profiles and the violent constructed glossary. The given results show the effectiveness and efficiency of our method.

关键词： Social network analysis Violent glossary construction Suspicious interaction analysis Radical communities’ detection

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Intelligent Prediction-Intervention approach to Support Students' Success in Web-based Learning Environments: A Case Study in Higher Education

Intelligent Prediction-Intervention approach to Support Stud...

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2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Tesnim Khelifi Nourhène Ben Rabah Ibtissem Daoudi Bénédicte Le Grand Farah Barika Ktata Centre de Recherche en Informatique Université Paris 1 Panthéon Sorbonne Paris France ESIEE-IT France LA Recherche en Intelligence Artificielle ENSI University of Manouba Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory

Due to the COVID-19 pandemic, the demand for distance learning has significantly increased in higher education institutions. This type of learning is usually supported by Web-based learning systems such as Massive Open Online Courses (Coursera, edX, etc.) and Learning Management systems (Moodle, Blackboard-Learn, etc.). However, in this remote context, students often lack feedback and support from educational staff, especially when they face difficulties or challenges. For that reason, this work presents a Prediction-Intervention approach that (a) predicts students who present difficulties during an online learning course, based on two main learning indicators, namely engagement and performance rates, and (b) offers immediate support to students, tailored to the problem they are facing. To predict students’ issues, our approach considers ten machine learning algorithms of different types (standalone, ensemble, and deep learning) which are compared to determine the best performing ones. It has been experimented with a dataset collected from the Blackboard-Learn platform utilized in an engineering school called ESIEE-IT in France during 2021-2022 academic year, showing thus quite promising results.

关键词： Deep learning COVID-19 Learning management systems Machine learning algorithms Electronic learning Pandemics Education

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A survey on hand gesture recognition for mobile devices

A survey on hand gesture recognition for mobile devices

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作者： Lahiani, Houssem Neji, Mahmoud National School of Electronics and Telecommunications University of Sfax Tunisia Tunisia Multimedia Information Systems and Advanced Computing Laboratory Faculty of Economics and Management University of Sfax Sfax Tunisia

As smartphones and mobile devices become ubiquitous in the daily life of human beings, facilitating interaction with them will have a positive impact on their use. This paper presents an emerging and important area for research on human-machine interaction, involving interaction with mobile devices. Hand gesture recognition is already a quite mature field and its integration on advanced technologies like smartphones makes a survey an interesting and needed contribution. The design of interactive mobile applications should differ from traditional desktop applications. For this purpose, the paper discusses some concepts that help to understand new challenges and introduces techniques that may be useful to properly exploit these devices. This paper gives also an overview of recent works done on vision-based and contact-based hand gesture recognition systems for mobile devices. Used techniques and approaches are also discussed. At the end, we conclude with some reflections. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Palmprint recognition

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St-Keys: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

SSRN

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SSRN 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia SM@RTS: Laboratory of Signals systeMs aRtificial Intelligence and neTworkS MIR@CL: Multimedia InfoRmation systems and Advanced Computing Laboratory Computer Vision Center Computer Science Department Universitat Aut`onoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluationon three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

arXiv

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arXiv 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Laboratory of Signals systems Artificial Intelligence and networks Sfax Tunisia Multimedia Information systems and Advanced Computing Laboratory Tunisia Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluation on three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, CC BY.

关键词： Image enhancement

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Online Contentâ€™s Popularity Metrics: RDF-Based Normalization 20th

Online Contentâ€™s Popularity Metrics: RDF-Based Normaliz...

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20th International Conference on Enterprise information systems, ICEIS 2018

作者： Sebei, Hiba Hadj Taib, Mohamed Ali Ben Aouicha, Mohamed Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (纸本)9783030261689

Social network websites are mainly constructed around the notion of user identities, as set up on the bases of their profiles, and online generated contents such as texts, videos, photos. Still, while some profiles gain an important position in the network, others do not. Similarly, some online generated contents appear to gain a great deal of attention from the part of users, whereas others are completely ignored. In this context, the notion of profile and online content related popularity has come to the forefront. Additionally, several studies turn out to be focused on the area of popularity associated analysis and prediction. Noteworthy, however, is that the popularity evaluative metrics prove to vary from a social network to another. In this respect, the present work is conceived to deal with such challenges through an advanced proposal whereby a unified presentation of popularity metrics related to each social entity, across several social networks, is put forward. Accordingly, a hierarchical structure of popularity metrics, as enhanced with a particular RDF presentation, is suggested, along with a brief summary of wide range of methods used to analyze such entities related popularities. © 2019, Springer Nature Switzerland AG.

关键词： Social networking (online)

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