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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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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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CrossViT Wide Residual Squeeze-and-Excitation Network for Alzheimer's disease classification with self attention ProGAN data augmentation

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International Journal of Hybrid Intelligent systems 2021年第3-4期17卷 163-177页

作者： Kadri, Rahma Bouaziz, Bassem Tmar, Mohamed Gargouri, Faiez Multimedia InfoRmation Systems and Advanced Computing Laboratory Miracl University of Sfax Tunisia Sfax Tunisia Faculty of Economics and Management of Sfax Fseg Sfax University of Sfax Tunisia Sfax Tunisia

Efficient and accurate early prediction of Alzheimer's disease (AD) based on the neuroimaging data has attracted interest from many researchers to prevent its progression. Deep learning networks have demonstrated an optimal ability to analyse large-scale multimodal neuroimaging for AD classification. The most widely used architecture of deep learning is the Convolution neural networks (CNN) that have shown great potential in AD detection. However CNN does not capture long range dependencies within the input image and does not ensure a good global feature extraction. Furthermore, increasing the receptive field of CNN by increasing the kernels sizes can cause a feature granularity loss. Another limitation is that CNN lacks a weighing mechanism of image features;the network doesn't focus on the relevant features within the image. Recently,vision transformer have shown an outstanding performance over the CNN and overcomes its main limitations. The vision transformer relies on the self-attention layers. The main drawbacks of this new technique is that it requires a huge amount of training data. In this paper, we combined the main strengths of these two architectures for AD classification. We proposed a new method based on the combination of the Cross ViT and Wide Residual Squeeze-and-Excitation Network. We acquired MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS). We also proposed a new data augmentation based on the self attention progressive generative adversarial neural network to overcome the limitation of the data. Our proposed method achieved 99% classification accuracy and outperforms CNN models. © 2021 - IOS Press. All rights reserved.

关键词： AD classification CNN CrossViT self attention ProGAN wide residual squeeze-and-excitation network

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E-health Application In IoMT Environment Deployed in An Edge And Cloud computing Platforms

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Procedia Computer Science 2024年 246卷 1019-1028页

作者： Ahlem Ayari Hassen Hamdi Khlil Ahmad Alsulbi Business Computing Department Higher Institute of Management of Sousse Sousse 4037 Tunisia Miracl (Multimedia InfoRmation systems and Advanced Computing Laboratory) FSEGS University of Sfax Sfax 3000 Tunisia Computers Department College of Engineering and Computers in Al-Qunfodah Umm Al-Qura University Makka 24211 Saudi Arabia

Different countries may use E-health in Health systems that include all the institutions, organizations, and resources that are devoted to promote, sustain, or restore health. The basic goal of E-health projects is to enhance the delay of the service. Healthcare data now are being moved and collected from Internet of Medical Things (IoMT) environment. Different researches shows that recent E-health application is deployed in distributed computing and especially cloud computing is being used more frequently. Most of these applications are based on intelligent algorithm’s such as KNN, SVM and deep learning algorithms etc… Which require more height power computing and storage capacity, but cloud computing with this delicate algorithm and with the sensitive health domain has some tradeoffs in handling the large amount of data from different IoMT devices, so it effects the delay which can cause risk. We propose Edge computing to address the limitations of cloud. By transferring some of the computing to the network’s edge, edge computing can significantly increase processing efficiency. In order to accomplish our goal, we first deploy our health application based on IoMT environment in a cloud. Then, we run it in an edge, and we analyze the response times in each of these situations. The experimental study proves that the proposed approach reduces delay in response time compared to the use of clouds.

关键词： E-health Cloud computing Edge computing IoMT

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Speech Emotion Recognition with deep learning 24

Speech Emotion Recognition with deep learning

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

作者： Aouani, Hadhami Ayed, Yassine Ben 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). © 2020 The Authors. Published by Elsevier B.V.

关键词： Emotion Recognition

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SVM for human identification using the ECG signal 24

SVM for human identification using the ECG signal

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

作者： Hamza, Sihem Ayed, Yassine Ben 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. © 2020 The Authors. Published by Elsevier B.V.

关键词： Electrocardiograms

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Improving Speech Emotion Recognition Using Data Augmentation and Balancing Techniques

Improving Speech Emotion Recognition Using Data Augmentation...

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International Conference on Cyberworlds

作者： Chawki Barhoumi Yassine Ben Ayed National School of Electronics and Telecommunications of Sfax Stax Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax Tunisia

Speech Emotion Recognition (SER) is a challenging task due to the complexity and variability of human emotions. In this paper, we propose an innovative approach to improve SER performance on the EMODB dataset. Our approach employs data augmentation techniques, such as noise addition and spectrogram shift, as well as balancing techniques, including random oversampling. We also extract five different features from the dataset samples: MFCC, Chroma, Mel Spectrogram, ZCR, and RMS. We compare the performance of four different classifiers - MLP, SVM, KNN, and CNN - with and without the use of our proposed approach. Our results demonstrate that the proposed approach significantly enhances the accuracy of speech emotion recognition compared to the approach without data augmentation and balancing techniques. Our experiments reveal that the proposed approach achieves higher accuracy and F1-score compared to other approaches, with MLP and CNN models achieving 100% accuracy. These findings highlight the effectiveness of data augmentation and balancing techniques in improving the performance of speech emotion recognition. Moreover, our approach holds great potential for application in various real-life scenarios, including mental health monitoring, human-robot interaction, and speech-based virtual assistants.

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Performance Analysis of Parkinson Detection Techniques

Performance Analysis of Parkinson Detection Techniques

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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Donia Ammous Hajer Bouzayen Yassine Ben Ayed National School of Engineers of Sfax Laboratory of Electronics and Information Technology (LETI) Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACLE) Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Parkinson’s Disease (PD) presents significant challenges in timely diagnosis and management, driving the need for advanced automatic recognition systems. This comparative study explores various methodologies for PD detection, leveraging machine learning and signal processing techniques across diverse modalities. Key phases, including data collection, preprocessing, feature extraction, selection, and classification, are scrutinized. Through comprehensive analysis of methodologies, datasets, and performance metrics, the study reveals promising accuracies in PD classification. However, challenges such as dataset availability and class imbalance persist. Future research avenues include multi-modal fusion, real-time monitoring, and telemedicine integration, aiming to enhance diagnostic accuracy and accessibility. Embracing these perspectives can accelerate the translation of research findings into clinical practice, improving outcomes for individuals with Parkinson’s disease.

关键词： Measurement Accuracy Telemedicine Machine learning Signal processing Real-time systems Performance analysis

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New Dynamic Bayesian Network for Time-to-Event Prediction

New Dynamic Bayesian Network for Time-to-Event Prediction

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ACS/IEEE International Conference on Computer systems and Applications

作者： Walid Ben Abdallah Hasna Njah Salma Jamoussi National Engineering School of Gabes Gabes University Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax University Tunisia Higher institute of Computer sciences and multimedia of Gabes Gabes University Tunisia Higher institute of Computer sciences and multimedia of Sfax Sfax university Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Time-to-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-to-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-to-event processes.

关键词： Multiple sclerosis Accuracy Precision medicine Probabilistic logic Bayes methods Timing Medical diagnostic imaging Context modeling

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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

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

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