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7th International Conference on Advances in Mobile computing and Multimedia, MoMM2009

作者： Zlitni, Tarek Mahdi, Walid Ben Abdallah, Hanène Mir at cl Multimedia Information Systems and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia BP 3021 Sfax 69042 Tunisia

ISBN: (纸本)9781605586595

In this paper, we propose a new approach to identify programs in TV streams. In the first step of our approach, we construct a reference catalogue for video grammars of visual jingles. In the second step, we identify programs in TV streams by examining the similarity of the video signal to the visual grammars in the catalogue. After presenting our approach, we report the results of its experimental evaluation on several streams extracted from different channels and composed of several programs. Copyright 2009 ACM.

关键词： Indexing (of information)

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Computational Complexity Analysis of Ant Colony Clustering Algorithms: Application to Student's Grouping Problem

Computational Complexity Analysis of Ant Colony Clustering A...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

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Dynamic construction of dictionaries for sentiment classification

Dynamic construction of dictionaries for sentiment classific...

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International Conference on Cloud and Green computing (CGC)

作者： Salma Jamoussi Hanen Ameur Multimedia InfoRmation systems and Advanced Computing Laboratory

The sentiment classification is one of the new challenges emerged with the advence of social networks. Our purpose is to determine the sentimental orientation of a Facebook comment (positive or negative) by using the linguistic approach. In most of the sentiment analysis applications using this approach, the sentiment lexicon plays a key role. Thus, it is very important to create a lexicon covering several sentiment words. For this reason, we address in this paper the problem how to group and list words present in the corpus into two dictionaries. We proposed a new automatic technique to create the positive and negative dictionaries that exploits the emotions symbols (emoticons, acronyms and exclamation words) present in comments. More importantly, our idea allows to enlarge these dictionaries with an enrichment step. Finally, by using these prepared dictionaries, we predict the positive and negative polarities of the comment. We evaluate our approach by comparison to human classification. Our results are also effective and consistent.

关键词： Dictionaries Semantics Facebook Pragmatics Accuracy

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Svm for human identification using the ECG signal

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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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Speech Emotion Recognition with deep learning

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Procedia Computer Science 2020年 176卷 251-260页

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

This paper proposes an emotion recognition system based on speech signals in two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one, we extract an 42-dimensional vector of audio features including 39 coefficients of Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate(ZCR), Harmonic to Noise Rate (HNR) and Teager Energy Operator (TEO). And the second one, we propose the use of the method Auto-Encoder for the selection of pertinent parameters from the parameters previously extracted. Secondly, we use the Support Vector Machines (SVM) as a classifier method. Experiments are conducted on the Ryerson Multimedia laboratory (RML).

关键词： Emotion recognition MFCC ZCR TEO HNR SVM auto-encoder

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EEG and fMRI Artifact Detection Techniques: A Survey of Recent Developments

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SN Computer Science 2023年第5期4卷 528页

作者： Mili, Rahma Bouaziz, Bassem Maalel, Ahmed Gargouri, Faiez Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Faculty of Economics and Management of Sfax University of Sfax Sfax 3021 Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Higher Institute of Computer and Multimedia of Sfax Sfax 3021 Tunisia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba 2010 Tunisia Higher Institute of Applied Sciences and Technology University of Sousse Sousse 4003 Tunisia

The evolution of different techniques for exploring cerebral activity and the development of signal processing and analysis methods have enabled a better understanding of the dynamic cerebral mechanisms in favor of fine diagnosis and characterization of certain diseases such as dementia and epilepsy. Integration of functional magnetic resonance imaging (fMRI) with electroencephalography (EEG) has offered the possibility of understanding new insights into neuroscientific studies because of the higher temporal and spatial measurements of brain activity when compared with the use of each technique separately. However, the experimental limitations of fMRI/EEG are mainly the numerous artifacts, mainly due to switching of magnetic field gradients and cardiac activity. To provide a comprehensive overview of this research area, a systematic mapping study has been conducted. This mapping study aims to identify the topics studied within the artifacts’ detection in EEG and fMRI data, as well as examine the adopted research methodologies, identify the gaps in the current research and point to directions for future research. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artifacts Deep learning EEG fMRI Systematic mapping study

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Rocchio's model based on vector space basis change for pseudo relevance feedback

Rocchio's model based on vector space basis change for pseud...

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3rd Symposium on Languages, Applications and Technologies, SLATE 2014

作者： Mbarek, Rabeb Tmar, Mohamed Hattab, Hawete Sfax University Multimedia Information Systems and Advanced Computing Laboratory Sfax Tunisia Umm Al-qura University Department of Mathematics Makkah Saudi Arabia

ISBN: (纸本)9783939897682

Rocchio's relevance feedback model is a classic query expansion method and it has been shown to be effective in boosting information retrieval performance. The main problem with this method is that the relevant and the irrelevant documents overlap in the vector space because they often share same terms (at least the terms of the query). With respect to the initial vector space basis (index terms), it is difficult to select terms that separate relevant and irrelevant documents. The Vector Space Basis Change is used to separate relevant and irrelevant documents without any modification on the query term weights. In this paper, first, we study how to incorporate Vector Space Basis Change into the Rocchio's model. Second, we propose Rocchio's models based on Vector Space Basis Change, called VSBCRoc models. Experimental results on a TREC collection show that our proposed models are effective. © Rabeb Mbarek, Mohamed Tmar, and Hawete Hattab.

关键词： Vector spaces

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Deep support vector machines for speech emotion recognition 19th

Deep support vector machines for speech emotion recognition

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19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Aouani, Hadhami Ben Ayed, Yassine National School of Engineers ENIS University of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACL University of Sfax Sfax Tunisia

ISBN: (纸本)9783030493417

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: zero crossing rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Support vector machines

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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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Semantic multimedia search: the case of SMIL documents

Semantic multimedia search: the case of SMIL documents

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4th International Conference on Web and information Technologies, ICWIT 2012

作者： Chkiwa, Mounira Jedidi, Anis Université de Sfax MIRACL Multimedia Information Systems and Advanced Computing Laboratory B.P. 242 Sakiet Ezziet 3021 Sfax Tunisia

Since the first implementations of its principles, the Semantic Web presented a field of free work to ensure its integration and adaptation to the various domains of research. The application of Semantic Web technologies into the process of search in a collection of SMIL documents appears a promising initiative seen the evolution of this language through its various versions. In this paper we propose a semantic search tool in a collection of SMIL documents;this tool adopts a procedure composed of three modules: description, interrogation and representation of the results. We employ for the first module metadata commonly used to annotate information semantically, and for the second we solicit languages of Semantic Web such as RDF, OWL and SPARQL and seen the importance of collaboration of ontologies in the semantic description of multimedia resources, we also present the technique of concepts connection allowing to extend an initial ontology.

关键词： Metadata

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