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International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014

作者： Zarrouk, Elyes Benayed, Yassine Gargouri, Faïez MIRACL Multimedia InfoRmation system and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia ISIMS University of Sfax Tunisia

Hidden Markov Models (HMM) are currently widely used in Automatic Speech Recognition (ASR) as being the most effective models. In addition, the HMM are just a special case of graphical models which are dynamic Bayesian Networks (DBN). These are modeling tools more sophisticated because they allow to include several specific variables in the problem of automatic speech recognition other than the one used in HMM. The use of DBNs in speech recognition beyond has generated much interest in recent years. This paper deals a comparative study between DBN and HMM systems for multi-dialect isolated Arabic words. Performance using DBNs is found to exceed that of HMMs trained on an identical task, giving higher recognition accuracy for four different Arabic dialects. In fact, the average of recognition rates for the four dialects with DBN was 83.01% while 74.17% with HMM standards. © (2014) by Society of Digital information and Wireless Communication (SDIWC). All rights reserved.

关键词： Hidden Markov models

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Particle swarm optimization based method for Bayesian Network structure learning

Particle swarm optimization based method for Bayesian Networ...

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2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013

作者： Aouay, Saoussen Jamoussi, Salma Ayed, Yassine Ben MIRACL Multimedia InfoRmation System and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia Sfax BP 1030 Tunisia

ISBN: (纸本)9781467358149

Bayesian Networks (BNs) are good tools for representing knowledge and reasoning under conditions of uncertainty. In general, learning Bayesian Network structure from a data-set is considered a NP-hard problem, due to the search space complexity. A novel structure-learning method, based on PSO (Particle Swarm Optimization) and the K2 algorithm, is presented in this paper. To learn the structure of a bayesian network, PSO here is used for searching in the space of orderings. Then the fitness of each ordering is calculated by running the K2 algorithm and returning the score of the network consistent with it. The experimental results demonstrate that our approach produces better performance compared to others BN structure learning algorithms. © 2013 IEEE.

关键词： Bayesian networks

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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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A new video images text localization approach based on a fast hough transform

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3rd International Conference on Image Analysis and Recognition, ICIAR 2006

作者： Bouaziz, Bassem Mahdi, Walid Hamadou, Abdelmajid Ben MIRACL Multimedia InfoRmation System and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia BP 1030 Sfax 69042 Tunisia

ISBN: (纸本)3540448918

The amount of digital video data is increasing over the world. It highlights the need for efficient algorithms that can retrieve this data by content. The full use of this media is currently limited by the opaque nature of the video which prevents content-based access. To facilitate video indexing and browsing, it is essential to allow non-linear access, especially for long programs. This can be achieved by identifying semantic description captured automatically from video story structure. Among these descriptions, text within video frames is considered as rich features that enable a good way for browsing video. In this paper we propose a fast Hough transformation based approach for automatic video frames text localization. Experimental results, that we drove on a large sample of video images issued from portions of news broadcasts, sports program, advertisements and movies, shows that our method is very efficient, capable to locate text regions with different character sizes, directions and styles even in case of complex image background. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Image analysis

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Biometric individual identification system based on the ECG signal 19th

Biometric individual identification system based on the ECG ...

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

作者： Hamza, Sihem Ben Ayed, Yassine Higher Institute of Computer Science and Multimedia ISIMS University of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACL University of Sfax Sfax Tunisia

ISBN: (纸本)9783030493417

Human biometric identification based on the ElectroCardioGram (ECG) is relatively new. This domain is intended to recognize individuals since the ECG has unique characteristics for each individual. These features are robust against forgery. In this study, feature extraction from ECG signals was performed using a combination of three new types of characteristics: MFCC, ZCR, and entropy. We proposed to apply classification methods: K Nearest Neighbors (KNN) and support vector machines (SVM) for human biometric identification. For evaluation we used two bases, namely MIT-BIH arrhythmia and normal sinus rhythm obtained from the Physionet database. For the MIT-BIH database, we used 47 individuals, each recording contains ECG data recorded for 15 s and in the SNR database, we used 18 individuals, the duration of each recording is 10 s. The analysis of the results obtained shows that the combination of all the features proposed makes it possible to improve the efficiency of our identification system to reach a performance rate equal to 100% for the two bases. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Electrocardiograms

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Handling uncertainty in semantic information retrieval process 9

Handling uncertainty in semantic information retrieval proce...

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9th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2013 - Co-located with the 12th International Semantic Web Conference, ISWC 2013

作者： Mounira, Chkiwa Anis, Jedidi Gargouri, Faiez Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax University Tunisia

This position paper proposes a collaboration method between Semantic Web and Fuzzy Logic aiming to handle uncertainty in the information retrieval process in order to cover more relevant items in result of search process. The collaboration method employs OWL ontology in query enhancement, RDF in annotation process and fuzzy rules in ranking enhancement.

关键词： information retrieval

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A two-leveled semantic web service description of the pervasive information system

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10th International Conference on Mobile Web information systems, MobiWIS 2013

作者： Fatma, Achour Anis, Jedidi Faiez, Gargouri Multimedia Information Systems and Advanced Computing Laboratory Sfax University Tunisia

ISBN: (纸本)9783642402753

To begin with, the semantic web service adaptation is based on the addition of contextual information to this web service description. So the technique of adapting the web service allows the user to establish reusable, interoperable, flexible and context-aware applications. The basic feature of the pervasive applications is the context-awareness. What is more, the pervasive systems are based on the NNN paradigm (aNywhere, aNytime, with aNything). Therefore, these systems must be used in different contexts depending on the user environment, his profile and the used terminal. Indeed, this type of system is dynamically adapted. Furthermore, we seek to offer a dynamic description of the web services. This objective, however, can be achieved by adding contextual information structures to the web service description. In the literature, more research works strive to combine the contextual description with the web service description by using the OWL-S structure. In fact, the main objective of this paper is to propose structures with two levels for the contextual description of the pervasive system. The first level is used to select the adapted web service to respond to the users needs, while the second level is aimed to offer the user the adapted description to respond to these needs. © 2013 Springer-Verlag.

关键词： Web services

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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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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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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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