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Hybrid EEG-fNIRS Detection of MCI Subtypes Based on Transformer Network

SN Computer Science 2025年第4期6卷 1-13页

作者： Bouaziz, Bassem Chaabene, Siwar Mahdi, Walid Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) University of Sfax Sfax 3023 Tunisia

Preventing the progression of mild cognitive impairment (MCI) to dementia is of paramount importance in the field of healthcare. Furthermore, detecting the subtle indications of MCI in its early stages is difficult but necessary. MCI can appear as either amnestic (aMCI) or non-amnestic (naMCI), confounding the diagnosis even more. However, with the development of improved neuroimaging techniques, computational algorithms that analyze data acquired during clinical tests might provide a solution. Among them, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) offer invaluable insights into an individual’s cognitive state. In this paper, we present a novel approach for distinguishing individuals diagnosed with MCI, specifically aMCI and naMCI, from healthy controls (HC). We employ a hybrid fusion of EEG and fNIRS signals, acquired during a cognitive task. To address this complex classification problem, we propose a new method based on the transformer network for MCI detection. Our experimental results demonstrate a remarkable classification accuracy of 99.78%, highlighting the significant improvement in MCI detection capabilities achieved through the integration of multimodal neuroimaging data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cognitive task EEG FNIRS HC vs aMCI vs naMCI MCI detection Multi-modality Transformer network

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Deep Learning for Detecting Mental Disorder from User Content on Social Media

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SN Computer Science 2025年第2期6卷 1-29页

作者： Tounsi, Malek Ameur, Hanen Jamoussi, Salma Multimedia Information Systems and Advanced Computing Laboratory University of Sfax Technopole of Sfax Sfax Tunisia

The prevalence of mental health disorders is rising, with many individuals expressing their thoughts and feelings through social media posts. This presents an opportunity for early detection of mental conditions using natural language processing (NLP). This paper addresses the lack of effective tools for detecting mental disorders from Arabic social media content. We propose Ara-MentalBERT, a novel deep learning fusion model that combines AraBERT and MentalBERT using a deep joint autoencoder (DJAE). The DJAE model aims to integrate Arabic language understanding with specialized mental health domain knowledge by fusing Arabic and mental health embedding representations of text. Our proposed method involves three key steps: (1) fine-tuning pre-trained AraBERT and MentalBERT models, (2) employing a deep joint autoencoder for Ara-Mental text representation learning, and (3) utilizing a mental health disorder classifier with the fused Ara-Mental representations. We evaluate our model on two diverse datasets: a Reddit dataset and a Twitter dataset. Results demonstrate significant improvements over existing methods, achieving an accuracy of 81.12% on the Reddit dataset for combined titles and posts and 86.12% accuracy on the Arabic tweets. These findings highlight the potential of our model to enhance mental health detection in Arabic-speaking communities, offering a powerful tool for identifying at-risk individuals more effectively. Our proposed method makes valuable progress at the intersection of NLP, mental health, and deep learning, paving the way for Arabic social media monitoring to facilitate early intervention and improve mental healthcare. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ara-Mental representation Ara-MentalBERT AraBERT Deep joint autoencoder Deep learning Mental health disorders MentalBERT

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A framework for people re-identification in multi-camera surveillance systems 14

A framework for people re-identification in multi-camera sur...

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14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017

作者： Ammar, Sirine Zaghden, Nizar Neji, Mahmoud MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory Tunisia

ISBN: (纸本)9789898533685

People re-identification has been a very active research topic recently in computer vision. It is an important application in surveillance system with disjoint cameras. This paper is focused on the implementation of a human re-identification system. First the face of detected people is divided into three parts and some soft-biometric traits are extracted from each part. In second step, we can recognize people even if their faces are hidden or they are with back appearance. The features extraction will be carried out according to the overall characteristics of the complete images of different persons. An algorithm that identifies people from their body shape will be developed. A powerful representation of the person based on the characteristics of color, texture and shape as well as different soft-biometric features is suggested. The experiments are carried out on SAIVT-SoftBio database which consists of videos from disjoint surveillance cameras as well as some static image based datasets (MUCT, VIPeR, CVSRP). © 2017.

关键词： Security systems

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AViTExt: Automatic video text extraction

AViTExt: Automatic video text extraction

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2008 3rd International Conference on information and Communication Technologies: From Theory to Applications, ICTTA

作者： Bouaziz, Baseem Zlitni, Tarek Mahdi, Walid MIRACL: Multimedia Information systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (纸本)1424417511

In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps: potential text region detection and a filtering process. In the first step we divide dynamically each pair of consecutive video frames into sub block in order to detect change. A significant difference between homologous blocks implies the appearance of an important object which may be a text region. The temporal redundancy is then used to filter these regions and forms an effective text region. The experimentation driven on a variety of video sequences shows the effectiveness of our approach by obtaining a 89,39% as precision rate and 90,19 as recall.

关键词： Indexing (of information)

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The pervasive information system adaptation: Android device context 8

The pervasive information system adaptation: Android device ...

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8th International Conference on Mobile Ubiquitous computing, systems, Services and Technologies, UBICOMM 2014

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

The conceptual step in the pervasive adaptation system is mostly aimed at the success of such a system. It is for this reason that the development of a conceptual adaptation system helps developers to implement their adaptation systems. Accordingly, several categories of contextual information can be presented in a pervasive information system;as such, the network context, the location context, the service context, the application context, the device context, and the person context. Again, a detailed description of each of these contextual information categories allows achieving a better adaptation of the applications to the contexts of use. However, the device context shows the focal point of any information system. In fact, the adaptation of the pervasive information systems, when using contexts, rests on the existing physical constraints in this context. In this paper, we present the architecture of adapting the applications to the pervasive system based on the semantic web services. Specifically, we are interested in the adaptation of the device context. The device contextual data are collected by using an Android program. The adaptation rules are also created using the Jena toolkit.

关键词： information use

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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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Novelty detection in data stream clustering using the artificial immune system 13

Novelty detection in data stream clustering using the artifi...

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13th European, Mediterranean and Middle Eastern Conference on information systems, EMCIS 2016

作者： Abid, Amai Jamoussi, Salma Multimedia Information systems and Advanced Computing Laboratory MIRACL Sfax University Tunisia

ISBN: (纸本)9789606897092

Data stream clustering poses many challenges to the data mining community where traditional data mining techniques become infeasible to mine such continuous flow of data. This paper presents a novel method for data stream clustering. In particular, we address the problem of novelty detection in data streams. For this purpose, the proposed method uses the Artificial Immune System meta-heuristic. The latter has been widely used for unsupervised data mining tasks and it owns the property of adaptability required by data stream clustering algorithms. Our method, called AIS-Clus (Artificial Immune System for data stream Clustering), is able to detect novel concepts using the performance of the learning process of AIS meta-heuristic. Furthermore, AIS-Clus has the ability to adapt its model to handle novel arriving items in textual data streams. Experimental results are given for two textual datasets and promising results are obtained. © Proceedings of the 13th European, Mediterranean and Middle Eastern Conference on information systems, EMCIS 2016. All rights reserved.

关键词： Clustering algorithms

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A New Method for Sentiment Analysis Using Contextual Auto-Encoders

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Journal of Computer Science & Technology 2018年第6期33卷 1307-1319页

作者： Hanen Ameur Salma Jamoussi Abdelmajid Ben Hamadou Multimedia Information Systems and Advanced Computing Laboratory Sfax University Sfax Technopole Sfa 3021 Tunisia

Sentiment analysis, a hot research topic, presents new challenges for understanding users＇ opinions and judg-ments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In this paper, weintroduce a novel machine learning framework using auto-encoders network to predict the sentiment polarity label at theword level and the sentence level. Inspired by the dimensionality reduction and the feature extraction capabilities of theauto-encoders, we propose a new model for distributed word vector representation ＂PMI-SA＂ using as input pointwise-mutual-information ＂PMI＂ word vectors. The resulted continuous word vectors are combined to represent a sentence. Anunsupervised sentence embedding method, called Contextual Recursive Auto-Encoders ＂CoRAE＂, is also developed forlearning sentence representation. Indeed, CoRAE follows the basic idea of the recursive auto-encoders to deeply composethe vectors of words constituting the sentence, but without relying on any syntactic parse tree. The CoRAE model consistsin combining recursively each word with its context words （neighbors＇ words： previous and next） by considering the wordorder. A support vector machine classifier with fine-tuning technique is also used to show that our deep compositionalrepresentation model CoRAE improves significantly the accuracy of sentiment analysis task. Experimental results demon-strate that CoRAE remarkably outperforms several competitive baseline methods on two databases, namely, Sanders twittercorpus and Facebook comments corpus. The CoRAE model achieves an efficiency of 83.28% with the Facebook dataset and97.57% with the Sanders dataset.

关键词： sentiment analysis recursive auto-encoder stacked auto-encoder pointwise mutual information deep embed-ding representation

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A spatiotemporal text localization and identification approach for content-based video browsing 09

A spatiotemporal text localization and identification approa...

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

作者： Bouaziz, Bassem Mahdi, Walid Zlitni, Tarek Benhamadou, Abdelmajid MIRACL: Multimedia Information Systems and Advanced Computing Laboratory CP 1030 Sfax Tunisia

ISBN: (纸本)9781605586595

Text in videos contains much semantic information that can be used for video indexing and browsing. In this paper, we propose a spatiotemporal video-text localization and identification approach which proceeds in two main steps: text region localization and text region identification. In the first step we detect the significant appearance of the new objects in a frame by a split and merge processes applied on binarized edge frame pair differences. Detected objects are, a priori, considered as text. They are then filtered according to both local contrast and texture criteria in order to get the effective ones. The resulted text regions are identified based on a visual grammar descriptor containing a set of semantic text class regions characterized by visual features. A visual table of content is generated based on extracted text regions occurring within video sequence enriched by a semantic identification. The experimentation performed on a variety of video sequences shows the efficiency of our. Copyright 2009 ACM.

关键词： Semantics

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