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

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

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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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Detection of Heart Diseases Using CNN-LSTM 22nd

Detection of Heart Diseases Using CNN-LSTM

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22nd International Conference on Hybrid Intelligent systems, HIS 2022 and the 18th International Conference on information Assurance and Security, IAS 2022

作者： Karoui, Hend Hamza, Sihem Ayed, Yassine Ben Multimedia Information Systems and Advanced Computing Laboratory MIRACL University of Sfax Sfax Tunisia

ISBN: (纸本)9783031274084

The ElectroCardioGram (ECG) is one of the most used signals for the prediction of heart disease. Lots of research are based on the ECG signal for the detection of cardiac diseases. In this research, we propose a four phases method to the recognition of cardiac disease. The first phase is to remove noise and detect QRS complex using a pass-band filter. In the second phase, we segment the filtered signal. For third phase is to make a fusion of three types of characteristics such as Zero Crossing Rate (ZCR), entropy and cepstral coefficients. Those features extracted considered as input in the next step which is the combination between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) proposed for multi-class classification of the ECG signal into five classes: Normal beat (N), Left bundle-branch block beat (L), Right bundle-branch block beat (R), premature Ventricular contraction (V), and Paced beat (P). The proposed model was evaluated using the MITBIH arrhythmia database and achieve an accuracy equal to 95.80%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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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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New Dynamic Bayesian Network for Time-To-Event Prediction 21

New Dynamic Bayesian Network for Time-To-Event Prediction

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

作者： Ben Abdallah, Walid Njah, Hasna Jamoussi, Salma National Engineering School of Gabes Gabes University Tunisia Sfax University Multimedia InfoRmation Systems and Advanced Computing Laboratory 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: (纸本)9798331518240

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. © 2024 IEEE.

关键词： Prediction models

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The Application of Artificial Intelligence in Predicting the Tribological Performance of Brake Pads: Effect of Sliding Speed and Normal Load on Friction Responses 8th

The Application of Artificial Intelligence in Predicting the...

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8th International Conference on Advances in Mechanical Engineering and Mechanics, ICAMEM 2024

作者： Sellami, Amira Rekik, Mouna Elleuch, Riadh Higher Institute of Transport and Logistics of Sousse University of Sousse Sousse Tunisia University of Sfax Sfax Tunisia Laboratory of Multimedia Information Systems and Advanced Computing Sfax Tunisia

ISBN: (纸本)9783031897320

During braking, it is crucial to comprehend and regulate both the sliding speed and normal load to optimize the performance and durability of braking systems. The primary aim of this study is to investigate the influence of sliding speed and normal load on the tribological behavior of brake lining materials. Three experimental tests were conducted by varying the load from 92 N to 139 N and the sliding speed from 3 m/s to 4.5 m/s. Experimental findings indicate that sliding speed affect the friction coefficient stability. Subsequently, machine learning and explainable algorithms are developed to establish a friction database model, shedding light on the relationship between brake solicitation and the evolution of the friction coefficient. The Shapley Additive explanation (SHAP) model reveals that sliding speed emerges as the most influential factor, positively affecting the friction coefficient level of brake lining materials. These findings align well with experimental results, underscoring the importance of understanding and managing sliding speed and normal load in optimizing braking system performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tribology

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Evaluation of acoustic features for early diagnosis of Alzheimer disease 19th

Evaluation of acoustic features for early diagnosis of Alzhe...

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

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030493417

Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilities of affected peoples. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The purpose of this article is extracting a set of acoustic features from Dementia Bank conversations from subjects with and without Alzheimer’s disease. Extracted features will be trained with Machine Learning (ML) algorithms to testing the detection accuracy. Obtained results indicate that the proposed features extracted from the speech samples can be used to distinguish between the patients with Alzheimer’s disease and the healthy control group. Classification accuracy over 90% was obtained with Support Vector Machine (SVM) classifier. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Diagnosis

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A withdrawal prediction model of at-risk learners based on behavioural indicators

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International Journal of Web-Based Learning and Teaching Technologies 2021年第2期16卷 32-53页

作者： Hlioui, Fedia Aloui, Nadia Gargouri, Faiez Multimedia Information System and Advanced Computing Laboratory University of Sfax Sfax Tunisia CCSE Department SWE Jeddah University Saudi Arabia and University of Sfax Tunisia and ISIMS Sfax Tunisia

Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates of occurrence. Therefore, the early prediction of learner withdrawal has become a major challenge, as well as identifying the factors, which contribute to this increasingly occurring phenomenon. In that regard, this manuscript presents a framework for withdrawal prediction model for the data from The Open university, one of the largest distance learning institutions. For that purpose, we start by pre-processing the dataset and tackling the challenge of discretization process and unbalanced data. Secondly, this paper identifies the semantical issues of raw data by introducing new behavioural indicators. Finally, we reckon on machine learning algorithms for withdrawal prediction model to understand the lack of learners’ commitment at an early stage. Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

关键词： Learning algorithms

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Automatic detection of Parkinson’s disease from speech using acoustic, prosodic and phonetic features 19th

Automatic detection of Parkinson’s disease from speech usin...

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

作者： Khaskhoussy, Rania Ayed, Yassine Ben MIRACL: Multimedia InfoRmation System and Advanced Computing Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030493417

Parkinson’s disease (PD) is a neurodegenerative disease ranked second after Alzheimer’s disease. It affects the central nervous system and causes a progressive and irreversible loss of neurons in the dopaminergic system, that insidiously leads to cognitive, emotional and language disorders. But until day there is no specific medication for this disease, the drug treatments that exist are purely symptomatic, that’s what encourages researchers to consider non-drug techniques. Among these techniques, speech processing becomes a relevant and innovative field of investigation and the use of machine-learning algorithms that provide promising results in the distinction between PD and healthy people. Otherwise many other factors such as feature extraction, number of feature, type of features and the classifiers used they all influence on the prediction accuracy evaluation. The aim of this study is to show the importance of this last factor, a model is suggested which include feature extraction from 3 types of features (acoustic, prosodic and phonetic) and classification is achieved using several machine learning classifiers and the results show that the proposed model can be highly recommended for classifying PD in healthy individuals with an accuracy of 99.50% obtained by Support Vector Machine (SVM). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Speech processing

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HybridCRS-TMS: Integrating Collaborative Recommender system and TOPSIS for Optimal Transport Mode Selection

HybridCRS-TMS: Integrating Collaborative Recommender System ...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Rekik, Mouna Grati, Rima Benmohamed, Ichrak Boukadi, Khouloud Higher Institute of Transport and Logistics of Sousse Tunisia University of Sfax Multimedia Information systems and Advanced Computing Laboratory Tunisia Zayed University College of Technological Innovation Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897587061

The pervasive influence of smartphones and mobile internet has revolutionized journey planning, particularly transportation. With navigation applications delivering real-time information, recommender systems have emerged as crucial tools for enhancing the travel experience. This paper introduces HybridCRS-TMS, a unique Hybrid Collaborative Recommender system for Transport Mode Selection, leveraging a dataset of 260 passengers. Through advanced data mining techniques, specifically k-Nearest Neighbors (k-NN) for collaborative recommendations and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis for objective evaluation, the system provides personalized transportation mode recommendations. The model not only demonstrates exceptional performance but also showcases the synergy between collaborative and objective decision-making approaches, contributing to efficient, personalized, and well-informed travel solutions. This study underscores the system’s versatility, illustrating its ability to optimize travel choices through a hybrid recommendation framework that integrates both collaborative and objective criteria. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Nearest neighbor search

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