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Artificial Intelligence

Description

Aucune description disponible pour cet axe de recherche.

Membres

Zahra Kodia Zahra Kodia
Lamjed Ben Said Lamjed Ben Said
Hatem Ben Sta Hatem Ben Sta
Ines Ben Jaafar Ines Ben Jaafar
Nadia Ben Azzouna Nadia Ben Azzouna
Saoussen Bel Haj Kacem Saoussen Bel Haj Kacem
Kalthoum Rezgui Kalthoum Rezgui
Slim Bechikh Slim Bechikh
Jihene LATRECH Jihene LATRECH
Besma Ben Amara Besma Ben Amara
Alia Maaloul Alia Maaloul
Lilia Rejeb Lilia Rejeb
Zakia Zouaghia Zakia Zouaghia
Wiem Ben Ghozzi Wiem Ben Ghozzi
Abir Chaabani Abir Chaabani
Imen Oueslati Imen Oueslati
Thouraya Sakouhi Thouraya Sakouhi
Ahmed Yosreddin Samti Ahmed Yosreddin Samti
Moez Elarfaoui Moez Elarfaoui
Nadia Omri Nadia Omri
Meriam Jemel Meriam Jemel
Wassim Ayadi Wassim Ayadi
Rahma Ferjani Rahma Ferjani
Amel ZIDI Amel ZIDI
Chayma Sakrani Chayma Sakrani
Siwar Mejri Siwar Mejri
Manel Kridis Manel Kridis
Jihene Tounsi Jihene Tounsi
Hela OUESLATI Hela OUESLATI
Mouna Belhaj Mouna Belhaj
Feriel Sassi Feriel Sassi
Rihab Said Rihab Said
Samira Harrabi Samira Harrabi
Ameni Hedhli Ameni Hedhli
Abir Jendoubi Abir Jendoubi
Sami Rojbi Sami Rojbi
Hanen Lejmi Hanen Lejmi
Ibtissem Ben Ouhiba Ibtissem Ben Ouhiba
Fatma Dhaoui Fatma Dhaoui
Hajer Alaya Hajer Alaya
Marwa Chabbouh Marwa Chabbouh
Mohamed Laabidi Mohamed Laabidi
Ali Abdelghafour Bejaoui Ali Abdelghafour Bejaoui
Hiba Chaher Hiba Chaher
Souad Mallat Souad Mallat
Syrine Ferjani Syrine Ferjani
Sabeur Elkosantini Sabeur Elkosantini
Salah Ghodhbani Salah Ghodhbani
Islem Henane Islem Henane
Wiem Hammami Wiem Hammami

Publications

  • 2025
    Hajer Alaya, Lilia Rejeb, Lamjed Ben Said

    Explanable AI in automatic sleep scoring: A review

    Hajer ALAYA, Lilia Rejeb, Lamjed Ben Said, “Explainable AI in automatic sleep scoring: A review”, International Conference on Intelligence in Business and Industry 2025 (IBI'25) 24 et 25 avril 2025., 2025

    Résumé

    The application of Artificial Intelligence (AI) in
    automatic sleep scoring presents significant opportunities for
    enhancing sleep analysis and diagnosing sleep disorders.
    However, a major challenge lies in the lack of transparency in
    AI-driven decision-making, which can hinder trust and
    comprehension among sleep researchers and clinicians.
    Explainable Artificial Intelligence (XAI) has emerged as a key
    approach to addresss these concerns by providing insights into
    AI model predictions and improving interpretability. This
    review examines the role and effectiveness of Explainability and
    interpretability in automatic sleep scoring, analyzing key
    challenges, the impact of various methodologies, and commonly
    used algorithms. Based on a comprehensive analysis of 100
    recent studies, we bridge the gap between computer-readable
    data encodings and human-understandable information,
    enhancing model explainability and transparency. Ultimately,
    this review underscores the vital role of Explainability in
    refining sleep evaluation and decision-making, emphasizing the
    necessity of further research to address existing challenges and
    maximize its potential.

    Ali Abdelghafour Bejaoui, Meriam Jemel, Nadia Ben Azzouna

    Explainable AI Planning:literature review

    Automated planning systems have become indispensable tools in a wide range of applications, from robotics and healthcare to logistics and autonomous systems. However, as these systems grow in complexity, their decision-making processes often become opaque, 2025

    Résumé

    Explainable AI Planning (XAIP) is a pivotal research
    area focused on enhancing the transparency, interpretability,
    and trustworthiness of automated planning systems. This
    paper provides a comprehensive review of XAIP, emphasizing key
    techniques for plan explanation, such as contrastive explanations,
    hierarchical decomposition, and argumentative reasoning frameworks.
    We explore the critical role of argumentation in justifying
    planning decisions and address the challenges of replanning in
    dynamic and uncertain environments, particularly in high-stakes
    domains like healthcare, autonomous systems, and logistics.
    Additionally, we discuss the ethical and practical implications
    of deploying XAIP, highlighting the importance of human-AI
    collaboration, regulatory compliance, and uncertainty handling.
    By examining these aspects, this paper aims to provide a detailed
    understanding of how XAIP can improve the transparency,
    interpretability, and usability of AI planning systems across
    various domains.

    Boutheina JLIFI, Syrine Ferjani, Claude Duvallet

    A Genetic Algorithm based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM) for Predicting Electric Vehicles energy consumption

    Computers and Electrical Engineering, 123, 110185., 2025

    Résumé

    To overcome Climate Change, countries are turning to greener transportation systems. Therefore, the use of Electric Vehicles (EVs) is leveraging substantially since they present multiple advantages, like reducing hazardous emissions. Recently, the demand for EVs has increased, which means that more charging stations need to be available. By the year 2030, 15 million EVs will be accessible, and since the number of charging stations is limited, the charging needs should be defined for better management of the charging infrastructure. In this research, we aim to tackle this problem by efficiently predicting the energy consumption of EVs. We proposed a Genetic Algorithm (GA) based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM), which is an optimized LSTM model that incorporates a GA for Hyperparameter Tuning. After experimenting our methodology and performing a comparative analysis with previous studies from the literature, the obtained results showed the efficiency of our novel model, with Mean Squared Error (MSE) equals to 0.000112 and a Determination Coefficient (R) equals to 0.96470. It outperformed other models of the literature for predicting energy use based on real-world data collected from the campus of Georgia Tech in Atlanta, USA.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    CoD-MaF: toward a Context-Driven Collaborative Filtering using Contextual Biased Matrix Factorization

    International Journal of Data Science and Analytics, 1-18., 2025

    Résumé

    Contextual recommendation has become attainable through the massive amounts of contextual information generated by smartphones and Internet of Things (IoT) devices. The availability of a huge amount of contextual data paves the way for a revolution in recommendation systems. It overcomes the static nature of personalization, which does not allow the discovery of new items and interests, toward a contextualization of the user’s tastes, which are in constant evolution. In this paper, we proposed CoD-MaF, a Context-Driven Collaborative Filtering using Contextual biased Matrix Factorization. Our approach employs feature selection methods to extract the most influential contextual features that will be used to cluster the users using K-means algorithm. The model then performs a collaborative filtering based on matrix factorization with improved contextual biases to suggest relevant personalized recommendations. We highlighted the performance of our method through experiments on four datasets (LDOS-CoMoDa, STS-Travel, IncarMusic and Frappe). Our model enhanced the accuracy of predictions and achieved competitive performance compared to baseline methods in metrics of RMSE and MAE.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    Machine Learning Based Collaborative Filtering Using Jensen-Shannon Divergence for Context-Driven Recommendations

    *, 2025

    Résumé

    This research presents a machine learning-based context-driven collaborative filtering approach with three
    steps: contextual clustering, weighted similarity assessment, and collaborative filtering. User data is clustered
    across 3 aspects, and similarity scores are calculated, dynamically weighted, and aggregated into a normalized
    User-User similarity matrix. Collaborative filtering is then applied to generate contextual recommendations.
    Experiments on the LDOS-CoMoDa dataset demonstrated good performance, with RMSE and MAE rates of
    0.5774 and 0.3333 respectively, outperforming reference approaches.

  • Alia Maaloul, Meriam Jemel, Nadia Ben Azzouna

    Feature selection for Gestational Diabetes Mellitus prediction using XAI based AutoML approach

    International Conference on Decision Aid and Artificial Intelligence 2024 (ICODAI), Tunis, Tunisia, 2024, 2024

    Résumé

    Predicting Gestational Diabetes Mellitus (GDM) is crucial for
    pregnant women to enable regular monitoring of their blood sugar levels and
    adherence to a healthy diet. Early intervention can significantly lower the risk
    of developing this condition. To assess this risk, Machine Learning (ML) and
    Deep Learning techniques are employed. However, traditional ML models often
    face challenges in accurately predicting GDM risk due to the complex
    processing required to optimize their hyperparameters for the best performance.
    This study presents a feature selection for GDM prediction using AutoML-XAI
    techniques (Automatic Machine Learning – eXplainable Artificial Intelligence
    techniques) approach, which aims to automatically predict GDM risk as
    accurate as possible while providing meaningful interpretations of the
    predictive results used in feature selection. The AutoML models generated
    utilize a Kaggle dataset and several combinations of features selected based on
    their scores of importance determinated with XAI techniques such as SHAP

    (Shapley Additive Explanations) and LIME (Local Interpretable Model-
    agnostic Explanations). The proposed approach of autoML and features

    selection with XAI techniques leads to the creation of a precise, efficient, and
    easily interpretable model which surpasses other machine learning models in
    predicting GDM risk without the need for human intervention. The scores of
    importance of features are involved in the feature selection process and
    multiple AutoML models are generated and assessed, with the optimal AutoML
    model being established automatically.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    A novel AutoCNN model for stock market index prediction

    Journal of Telecommunications and the Digital Economy, 12(1), 612-636., 2024

    Résumé

    Stock markets have an important impact on economic growth of countries. The prediction
    of stock market indexes has been a complex task for last years. Indeed, many researches and financial analysts are highly interested in the research area of stock market prediction. In this paper, we propose a novel framework, titled AutoCNN based on artificial intelligence techniques, to predict future stock market indexes. AutoCNN is composed mainly of three stages: (1) CNN for Automatic Feature Extraction, (2) The Halving Grid Search algorithm is combined to CNN model for stock indexes prediction and (3) Evaluation and recommendation. To validate our AutoCNN, we conduct experiments on two financial datasets that are extracted in the period between 2018 and 2023, which includes several events such as economic, health and geopolitical international crises. The performance of AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves strong prediction abilities. AutoCNN contributes to emerging technologies and innovation in the financial sector by automating decision-making, leveraging advanced pattern recognition, and enhancing the overall decision support system for investors in the digital economy.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Predicting the stock market prices using a machine learning-based framework during crisis periods

    Multimed Tools Appl 84, 28873–28907., 2024

    Résumé

    Stock markets are highly volatile, complex, non-linear, and stochastic. Therefore, predicting stock market behavior is one of finance’s most complex challenges. Recently, political, health, and economic crises have profoundly impacted global stock prices, leading researchers to use machine learning to predict prices and analyze financial data. This article delves into two primary facets: firstly, examining stock price responses to the Russia-Ukraine war and the COVID-19 pandemic by assessing volatility and draw-downs from 2018 to 2023. Secondly, we introduce a framework named StockPredCris, designed to predict future stock indices employing two machine learning algorithms: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost). Our experiments are conducted on four major stock market indices (NASDAQ, Russell 2000, DAX, and SSE) using a combination of historical stock index data and COVID-19 pandemic data. The performance of the implemented models is evaluated using five regression metrics along with the CPU time metric. The results of SVR demonstrates superior performance and signifcantly outperforms the other considered models for benchmarking. For instance, SVR achieved 0.0 MSE and 99.99% R for the four stock indices, with training times between 0.005 and 0.007 seconds. We recommend SVR for predicting future stock prices during crises. This study offers valuable insights for financial decision-makers, guiding them in making informed choices by understanding stock market reactions to major global crises, while addressing the uncertainty and fear of investors.

    Moez Elarfaoui, Nadia Ben Azzouna

    CLUSTERING BASED ON HYBRIDIZATION OF GENETIC ALGORITHM AND IMPROVED K-MEANS (GA-IKM) IN AN IOT NETWORK

    International Journal of Wireless & Mobile Networks (IJWMN), Vol.16, No.6, December 2024, 2024

    Résumé

    The continuous development of Internet infrastructures and the evolution of digital electronics, particularly Nano-computers, are making the Internet of Things (IoT) emergent. Despite the progress, these IoT objects suffer from a crucial problem which is their limited power supply. IoT objects are often deployed as an ad-hoc network. To minimize their consumption of electrical energy, clustering techniques are used. In this paper, a centralized clustering algorithm with single-hop routing based on a genetic algorithm and Improved k-means is proposed. The proposed approach is compared with the LEACH, K-means and OK-means algorithms. Simulation results show that the proposed algorithm performs well in terms of network lifetime and energy consumption.

    Alia Maaloul, Meriam Jemel, Nadia Ben Azzouna

    XAI based feature selection for gestational diabetes Mellitus prediction

    10th International Conference on Control, Decision and Information Technologies (CoDIT), Vallette, Malta, 2024, pp. 1939-1944, 2024

    Résumé

    Gestational Diabetes Mellitus (GDM) is a type of diabetes that develops during pregnancy. It is important for pregnant women to monitor their blood sugar levels regularly and follow a healthy diet. However, early intervention can greatly reduce risk of this type of diabetes. Machine Learning and Deep Learning techniques are utilized to predict this risk based on an individual's symptoms, lifestyle, and medical history. By identifying key features such as age, insulin, body mass index, and glucose levels, machine learning models such as Random Forest and XGBoost are used in this research work to classify patients at risk of a gestational diabetes. In addition, we propose an explainable feature selection approach to improve the accuracy of machine learning models for GDM prediction. This method involves iteratively eliminating features that exhibit a negative contribution as determined by the SHAP (Shapley Additive explanations) feature attribution explanations for the model’s predictions

    Wided Oueslati, Siwar Mejri, Jalel Akaichi

    A comprehensive study on social networks analysis and mining to detect opinion leaders

    International Journal of Computers and Applications, 46(8), 641–650., 2024

    Résumé

    In today's society, social networks are vital for communication, allowing individuals to influence each other significantly. Opinion leaders play a crucial role in shaping opinions, attitudes, beliefs, motivations, and behaviors. Recognizing this, companies seek to identify influential users who resonate with their target audience to leverage their impact. Consequently, detecting opinion leaders in social networks has become essential. This paper aims to provide a comprehensive literature review on opinion leader detection. We present a detailed overview of various methods and approaches developed in this field, examining their strengths and weaknesses to identify the most effective strategies for different social networks. Additionally, we highlight key trends, challenges, and future directions in opinion leader detection. Our goal is to equip companies with the necessary knowledge to harness the power of opinion leaders for enhancing marketing and communication strategies. For researchers, this paper serves as a foundational resource, outlining the current state of the art and identifying gaps in the literature for future studies. Ultimately, we strive to advance the understanding of effective opinion leader detection and utilization within the dynamic landscape of social networks.

    Hamdi Ouechtati, Nadia Ben Azzouna

    Towards an Adaptive Trust Management Model Based on ANFIS in the SIoT

    SECRYPT 2024: 710-715, 2024

    Résumé

    The integration of social networking concepts into the IoT environment has led to the Social Internet of Things
    (SIoT) paradigm which enables connected devices and people to facilitate information sharing, interact, and
    enable a variety of attractive applications. However, with this emerging paradigm, people feel cautious and
    wary. They worry about violating their privacy and revealing their data. Without trustworthy mechanisms to
    guarantee the reliability of user’s communications and interactions, the SIoT will not reach enough popularity
    to be considered as a cutting-edge technology. Accordingly, trust management becomes a major challenge
    to improve security and provide qualified services. Therefore, we overcome these issues through proposing
    an adaptive trust management model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to
    estimate the trust level of objects in the Social Internet of Things. We formalized and implemented a new trust
    management model built ANFIS, to analyze different trust parameters, estimate the trust level of objects and
    distinguish malicious behavior from benign behaviors. Experimentation made on a real data set proves the
    performance and the resilience of our trust management model.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning.

    Journal of Supercomputing, 80(1), 2024

    Résumé

    The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario is crucial. This research proposes a new hybrid recommender system named CoDFi-DL which combines demographic and enhanced collaborative filtering. The demographic filtering is performed through a deep neural network (DNN) and used to solve the new user cold start problem. The enhanced collaborative filtering component of our model focuses on delivering personalized recommendations through a neighborhood-based method. The major contribution in this research is the DNN-based demographic filtering which overcomes the new user cold start problem and enhances the collaborative filtering process. Our system significantly improves the relevancy of the recommendation task and thus provides personalized recommended items to cold users. To evaluate the effectiveness of our approach, we conducted experiments on real multi-label datasets, 1M and 100K MovieLens. CoDFi-DL recommender system showed higher performance in comparison with baseline methods, achieving lower RMSE rates of 0.5710 on the 1M MovieLens dataset and 0.6127 on the 100K MovieLens dataset.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    Twit-CoFiD: a hybrid recommender system based on tweet sentiment analysis

    Social Network Analysis and Mining, 14(1), 123., 2024

    Résumé

    Internet users are overwhelmed by the vast number of services and products to choose from. This data deluge has led to the need for recommender systems. Simultaneously, the explosion of interactions on social networks is constantly increasing. These interactions produce a large amount of content that incites organizations and individuals to exploit it as a support for decision making. In our research, we propose, Twit-CoFiD, a hybrid recommender system based on tweet sentiment analysis which performs a demographic filtering to use its outputs in an enhanced collaborative filtering enriched with a sentiment analysis component. The demographic filtering, based on a Deep Neural Network (DNN), allows to overcome the cold start problem. The sentiment analysis of Twitter data combined with the enhanced collaborative filtering makes recommendations more relevant and personalized. Experiments were conducted on 1M and 100K Movielens datasets. Our system was compared to other existing methods in terms of predictive accuracy, assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. It yielded improved results, achieving lower RMSE and MAE rates of 0.4474 and 0.3186 on 100K Movielens dataset and of 0.3609 and 0.3315 on 1M Movielens dataset.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    Context-based Collaborative Filtering: K-Means Clustering and Contextual Matrix Factorization*

    In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1-5). IEEE., 2024

    Résumé

    The rapid expansion of contextual information from smartphones and Internet of Things (IoT) devices paved the way for Context-Aware Recommendation Systems (CARS). This abundance of contextual data heralds a transformative era for traditional recommendation systems. In alignment with this trend, we propose a novel model which provides personalized recommendations based on context. Our approach uses K-means algorithm to cluster users based on contextual features. Then, the model performs collaborative filtering based on matrix factorization with enhanced contextual biases to provide relevant recommendations. We demonstrated the performance of our method through experiments conducted on the movie recommender dataset LDOS-CoMoDa. The experimental results showed the effective performance of our proposal compared to reference methods, achieving an RMSE of 0.7416 and an MAE of 0.6183.

  • Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Hybrid machine learning model for predicting NASDAQ composite index

    2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, 2023, pp. 1-6, 2023

    Résumé

    Financial markets are dynamic and open systems. They are subject to the influence of environmental changes. For this reason, predicting stock market prices is a difficult task for investors due to the volatility of the financial stock markets nature. Stock market forecasting leads investors to make decisions with more confidence based on the prediction of stock market price behavior. Indeed, a lot of analysts are greatly focused in the research domain of stock market prediction. Generally, the stock market prediction tools are categorized into two types of algorithms: (1) linear models like Auto Regressive (AR), Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), and (2) non-linear models like Autoregressive Conditionally Heteroscedastic (ARCH), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and recently Neural Network (NN)). This paper aspires to crucially predict the stock index movement for National Association of Securities Dealers Automated Quotations (NASDAQ) based on deep learning networks. We propose a hybrid stock price prediction model using Convolutional Neural Network (CNN) for feature selection and Neural Network models to perform the task of prediction. To evaluate the performance of the proposed models, we use five regression evaluation metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-Square (R2), and the Execution Time (ET) metric to calculate the necessary time for running each hybrid model. The results reveal that error rates in the CNN-BGRU model are found to be lower compared to CNN-GRU, CNN-LSTM, CNN-BLSTM and the the existing hybrid models. This research work produces a practical experience for decision makers on financial time series data.

    Samira Harrabi, Ines Ben Jaafar, Khaled Ghedira

    Survey on IoV Routing Protocols

    Wireless Personal Communications 128(1), 2023

    Résumé

    Internet of vehicles (IoV) can be considered as a superset of vehicular ad-hoc networks (VANETs). It extends VANET’s structure, applications and scale. Unlike, the traditional intelligent transportation system (ITS), IoV focus more on information interactions between vehicles, roadside units (RSU) and humans. The principal aim is to make people obtain road traffic information easily and in real-time, to ensure the travel convenience, and to increase the travel comfort. The goal behind the Internet of vehicles is essentially to be used in urban traffic environment to ensure network access for passengers and drivers. The environment of the IoV is the combination of different wireless network environment as well as road conditions. Despite its continuing expansion, the IOV contains different radio access technologies that lead to a heterogeneous network, and make it more crucial than the VANET. These drawbacks pose numerous challenges, especially the routing one. In IoV environment, the routing protocol must cope with events such as link failure and to find the best route to propagate the data toward the desired destination. In this paper, we mainly focus on surveying the IoV routing protocols, hence we present and compare unicast, multicast and broadcast protocols.

    Chayma sakrani, Boutheina Jlifi

    Towards a soft three-level voting model (Soft T-LVM) for fake news detection

    Journal of Intelligent Information Systems, 61(1), 249-269., 2023

    Résumé

    Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

    Wided Oueslati, Siwar Mejri, Shaha Al-Otaibi, Sarra Ayouni

    Recognition of opinion leaders in social networks using text posts’ trajectory scoring and users’ comments sentiment analysis

    IEEE Access, vol. 11, pp. 123589-123609, 2023, 2023

    Résumé

    Identifying opinion leaders in social networks, particularly in social media, is a crucial marketing strategy. These individuals have a considerable influence on the purchasing decisions of their communities. Companies can benefit from collaborating with relevant opinion leaders in their market as this can increase their visibility, establish their credibility, and gain consumer trust, leading to increased sales, improved brand perception, and an expanded market share. Additionally, by gaining a comprehensive understanding of opinion leaders, companies can better comprehend the trends and preferences of their target audience. This allows them to tailor their marketing and product strategies more effectively. Identifying suitable influencers to endorse their products or services is a significant challenge for companies. The identification of opinion leaders is complicated by their informal and unstructured nature, as well as the varying selection criteria depending on the marketing campaign’s goals. While numerous research studies have focused on detecting opinion leaders in social networks based on content, interactions, or a combination of both, few have explored sentiment analysis of post content, received interactions, and user comments in relation to published posts. The purpose of this paper is to present an hybrid approach to detect opinion leaders in Facebook. This approach involves analyzing the trajectory of post content by examining interactions on the post, as well as mining the text content of the post itself and analyzing the users’comments sentiments.

    Chayma sakrani, Boutheina Jlifi

    Towards a soft three-level voting model (Soft T-LVM) for fake news detection

    Journal of Intelligent Information Systems, 61(1), 249-269., 2023

    Résumé

    Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

  • Zahra Kodia, Lamjed Ben Said

    Stock market prediction of Nifty 50 index applying machine learning techniques

    Applied Artificial Intelligence 36:1, 2022

    Résumé

    The stock market is viewed as an unpredictable, volatile, and competitive market. The prediction of stock prices has been a challenging task for many years. In fact, many analysts are highly interested in the research area of stock price prediction. Various forecasting methods can be categorized into linear and non-linear algorithms. In this paper, we offer an overview of the use of deep learning networks for the Indian National Stock Exchange time series analysis and prediction. The networks used are Recurrent Neural Network, Long Short-Term Memory Network, and Convolutional Neural Network to predict future trends of NIFTY 50 stock prices. Comparative analysis is done using different evaluation metrics. These analysis led us to identify the impact of feature selection process and hyper-parameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric. These errors in the LSTM model are found to be lower compared to RNN and CNN models.

    Meriem Sebai, Lilia Rejeb, Mohamed-ali Denden, Yasmine Amor, Lassaad Baati, Lamjed Ben Said

    Optimal electric vehicles route planning with traffic flow prediction and real-time traffic incidents

    International Journal of Electrical and Computer Engineering Research, 2(1), 1–12. doi:10.53375/ijecer.2022.93, 2022

    Résumé

    Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.

    Nada Mohammed Murad, Lilia Rejeb, Lamjed Ben Said

    The use of DCNN for road path detection and segmentation

    Iraqi Journal for Computer Science and Mathematics: Vol. 3: Iss. 2, Article 13. DOI: https://doi.org/10.52866/ijcsm.2022.02.01.013, 2022

    Résumé

    In this study, various organizations that have participated in several road path-detecting experimentsare analyzed. However, the majority of techniques rely on attributes or form models built by humans to identifysections of the path. In this paper, a suggestion was made regarding a road path recognition structure that is dependenton a deep convolutional neural network. A tiny neural network has been developed to perform feature extraction toa massive collection of photographs to extract the suitable path feature. The parameters obtained from the model ofthe route classification network are utilized in the process of establishing the parameters of the layers that constitutethe path detection network. The deep convolutional path discovery network’s production is pixel-based and focuseson the identification of path types and positions. To train it, a detection failure job is provided. Failure in pathclassification and regression are the two components that make up a planned detection failure function. Instead oflaborious postprocessing, a straightforward solution to the problem of route marking can be found using observedpath pixels in conjunction with a consensus of random examples. According to the findings of the experiments, theclassification precision of the network for classifying every kind is higher than 98.3%. The simulation that was trainedusing the suggested detection failure function is capable of achieving an accuracy of detection that is 85.5% over atotal of 30 distinct scenarios on the road

  • Nabil Morri, Sameh Hadouaj, Lamjed Ben Said

    An approach to intelligent control public transportation system using a multi-agent system

    Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2020. Lecture Notes in Business Information Processing, vol 417. Springer, Cham., 2020

    Résumé

    Traffic congestion has increased globally during the last decade representing an undoubted menace to the quality of urban life. A significant contribution can be made by the public transport system in reducing the problem intensity if it provides high-quality service. However, public transportation systems are highly complex because of the modes involved, the multitude of origins and destinations, and the amount and variety of traffic. They have to cope with dynamic environments where many complex and random phenomena appear and disturb the traffic network. To ensure good service quality, a control system should be used in order to maintain the public transport scheduled timetable. The quality service should be measured in terms of public transport key performance indicators (KPIs) for the wider urban transport system and issues. In fact, in the absence of a set of widely accepted performance measures and transferable methodologies, it is very difficult for public transport to objectively assess the effects of specific regulation system and to make use of lessons learned from other public transport systems. Moreover, vehicle traffic control tasks are distributed geographically and functionally, and disturbances might influence on many itineraries and occur simultaneously. Unfortunately, most existing traffic control systems consider only a part of the performance criteria and propose a solution without man-aging its influence on neighboring areas of the network. This paper sets the context of performance measurement in the field of public traffic management and presents the regulation support system of public transportation (RSSPT). The aim of this regulation support system is (i) to detect the traffic perturbation by distinguishing a critical performance variation of the current traffic, (ii) and to find the regulation action by optimizing the performance of the quality service of the public transportation. We adopt a multi-agent approach to model the system, as their distributed nature, allows managing several disturbances concurrently. The validation of our model is based on the data of an entire journey of the New York City transport system in which two perturbation scenarios occur. This net-work has the nation’s largest bus fleet and more subway and commuter rail cars than all other U.S. transit systems combined. The obtained results show the efficiency of our system especially in case many performance indicators are needed to regulate a disturbance situation. It demonstrates the advantage as well of the multiagent approach and shows how the agents of different neighboring zones on which the disturbance has an impact, coordinate and adapt their plans and solve the issue.

    Oussama Kebir, Issam Nouaouri, Mouna Belhaj, Lamjed Ben Said, Kamel Akrout

    A multi-agent model for countering terrorism

    In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques (pp. 260-271). IOS Press., 2020

    Résumé

    The rise of terrorism over the past decade did not only hinder the development of some countries, but also it continues to destroy humanity. To face this concept of an emerging crisis, every country and every citizen is responsible for the fight against terrorism. As conventional plans became useless against terrorism, governments are required to establish innovative concepts and technologies to support units in this asymmetric war. In this paper, we propose a new multi-agent model for
    counter-terrorism characterized by a methodical process and a flexibility to handle different contingency scenarios. The division of labour in our multi-agent model improves decision making and the structuring of organisational plans.

    Oussama Kebir, Issam Nouaouri, Mouna Belhaj, Lamjed Ben Said, Kamel Akrout

    A multi-agent architecture for modeling organizational planning against terrorist attacks in urban areas

    2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA), Tunis, Tunisia, 2020, pp. 1-8, doi: 10.1109/OCTA49274.2020.9151843., 2020

    Résumé

    Nowadays the world is suffering from the emergence of a new concept of war, it is the asymmetric warfare created by the terrorists' new combat doctrine. As the plans to face classic enemies have become unusual against terrorism, this calls for innovative concepts and technologies to support the units and to improve the capability of leaders and structure their choices. In this paper, we propose a multi agent architecture for action planning against terrorist attacks. It is characterized by rapid decisive responses and methodical steps to handle the situation, and by the flexibility to adapt a contingency scenario. We aim to create a multi-agent model that describes the relation between actors during the terrorist attack in order to find the best possible units distribution to neutralize the enemy.

    Kalthoum Rezgui, Hédia Sellemi

    A blockchain-based smart contracts platform to competency assessment and validation

    -, 2020

    Résumé

    During last years, several competency management systems (CMSs) have been proposed to support the acquisition, allocation, and improvement of competencies. However, competency information and associated proofs are still not tracked and shared in a trustworthy and immutable way. In this context, blockchain technology provides a prominent manner to keep track of competencies and achievements and to ensure their sharing in a secure and transparent manner. Particularly, given the decentralized nature of immutable and distributed ledgers enabled by blockchain, the potential for using this revolutionary new technology for lifelong competency tracking and assessment is tremendous. In this paper, a functional architecture using smart contracts and blockchain is proposed to support competency tracking and assessment in learning networks. Thus, by implementing the proposed architecture, all the different stakeholders involved can be connected, namely, learners, authors, and assessors. Besides, a full traceability of acquired proofs of competencies and competency profiles is warranted, while ensuring their authenticity and integrity.

  • Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift

    book-chapter in learning from data streams in evolving environments, pp 39-61. Springer International Publishing, January 2019., 2019

    Résumé

    Recent advances in Computational Intelligent Systems have focused on addressing complex problems related to the dynamicity of the environments. Generally in dynamic environments, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift through ensemble classifiers has received a great interest in last decades. The success of these ensemble methods relies on their diversity. Accordingly, various diversity techniques can be used like block-based data, weighting-data or filtering-data. Each of these diversity techniques is efficient to handle certain characteristics of drift. However, when the drift is complex, they fail to efficiently handle it. Complex drifts may present a mixture of several characteristics (speed, severity, influence zones in the feature space, etc.) which may vary over time. In this case, drift handling is more complicated and requires new detection and updating tools. For this purpose, a new ensemble approach, namely EnsembleEDIST2, is presented. It combines the three diversity techniques in order to take benefit from their advantages and outperform their limits. Additionally, it makes use of EDIST2, as drift detection mechanism, in order to monitor the ensemble’s performance and detect changes. EnsembleEDIST2 was tested through different scenarios of complex drift generated from synthetic and real datasets. This diversity combination allows EnsembleEDIST2 to outperform similar ensemble approaches in terms of accuracy rate, and present stable behaviors in handling different scenarios of complex drift.

    Mouna Belhaj, Hanen Lejmi, Lamjed Ben Said

    Studying emotions at work using agent-based modeling and simulation

    In IFIP international conference on artificial intelligence applications and innovations (pp. 571-583). Cham: Springer International Publishing., 2019

    Résumé

    Emotions in workplace is a topic that has increasingly at
    tracted attention of both organizational practitioners and academics. This is due to the fundamental role emotions play in shaping human resources behaviors, performance, productivity, interpersonal relationships and engagement at work. In the current research, a computational social simulation approach is adopted to replicate and study the emotional experiences of employees in organizations. More speci cally, an emotional
    agent-based model of an employee at work is proposed. The developed model is used in a computer simulator WEMOS (Workers EMotions in Organizations Simulator) to conduct certain analyzes in relation to the most likely emotions-evoking stimuli as well as the emotional content of several work-related stimuli. Simulation results can be employed to gain deeper understanding about emotions in the work life.

    Hanen Lejmi, Mouna Belhaj, Lamjed Ben Said

    Studying Emotions at Work Using Agent-Based Modeling and Simulation

    Studying Emotions at Work Using Agent-Based Modeling and Simulation, 2019

    Résumé

    Emotions in workplace is a topic that has increasingly attracted attention of both organizational practitioners and academics. This is due to the fundamental role emotions play in shaping human resources behaviors, performance, productivity, interpersonal relationships and engagement at work. In the current research, a computational social simulation approach is adopted to replicate and study the emotional experiences of employees in organizations. More specifically, an emotional agent-based model of an employee at work is proposed. The developed model is used in a computer simulator WEMOS (Workers EMotions in Organizations Simulator) to conduct certain analyzes in relation to the most likely emotions-evoking stimuli as well as the emotional content of several work-related stimuli. Simulation results can be employed to gain deeper understanding about emotions in the work life.

  • Samira Harrabi, Ines Ben Jaafar, Khaled ghedira

    A Swarm Intelligence-based Routing Protocol for Vehicular Networks

    International Journal of Vehicle Information and Communication Systems (IJVICS), 2018

    Résumé

    Vehicular Ad hoc Networks (VANETs) are a particular case of Mobile Ad hoc Networks (MANETs). They are applied to exchange information among vehicles and between vehicles and a nearby fixed infrastructure. Unlike the MANETs, the VANETs have highly mobile nodes that cause a dynamic topology, a disconnected network, etc. Consequently, these features pose numerous challenges. One of them is routing. In a vehicular environment, the routing protocol needs to cope with events like link failure and to find an effective path to propagate the information toward the desired destination. In this context, we assume in this paper that the vehicles are intelligent and have a knowledge base about their communication environment. Our aim is to carry out the routing of the data based on swarm intelligence. The optimum route is explored using the Particle Swarm Optimisation (PSO). The proposed approach is called the Optimised Agent-based AODV Protocol for VANET (OptA2PV).

    Hanen Lejmi, Thouraya Daouas

    Emotions recognition in an intelligent elearning environment.

    Emotions recognition in an intelligent elearning environment., 2018

    Résumé

    For the purpose of improving the quality in Elearning process and overcoming the limitations of the current online educational environments, we propose to take into consideration the emotional states of students during Elearning sessions. Our objective is to ensure the ability of emotional intelligence: Emotion Recognition, in an eLearning environment. Thus, we present an architecture of Emotionally Intelligent Elearning System (EIES). Within the development of a computational probabilistic model of emotions, we proposed a Bayesian Network (BN) model to deal with emotions in Elearning environments and handle the uncertain nature of emotion recognition process. In a second phase, we focus on the incorporation of the emotion recognition in the Elearning systems by developing a simulation of EIES based on the BN model, able to predict the students’ affects. Consequently, we reached positive and promising results related to the fact that simulated EIES based on the BN model of emotions predicts correctly the student’s emotion when an event occurs during an Elearning session.

  • Mouna Belhaj, Fahem Kebair, Lamjed Ben Said

    Emotional dynamics and coping mechanisms to generate human-like agent behaviors

    Applied Artificial Intelligence, 31(5-6), 472-492., 2017

    Résumé

    Emotion mechanisms represent an important moderating factor of human behavior. Thus, they are necessary to produce realistic behavioral simulations. This work addresses this challenging issue by incorporating emotional processes into an agent model. We intend to show the potential of emotions and coping mechanisms to produce fast and human-like emotional behaviors, particularly, in emergency situations. We focus on the interplay of emotions and goals and its impact on agent behavior. Emotions constitute heuristics to agent decision making. They induce emotion-specific goals that orient agent goal adoption mechanisms and fasten its behavior selection.

    Samira Harrabi, Ines Ben Jaafar, khaled ghedira

    Message Dissemination in Vehicular Networks on the Basis of Agent Technology

    An International Journal of Wireless Personal Communications, 2017

    Résumé

    Vehicular Ad hoc Network (VANET) is a sub-family of Mobile Ad hoc Network (MANET). The principal goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road and varying node density of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs, but most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another. In this paper, we propose a novel clustering algorithm based on agent technology to improve routing in VANET.

  • Nabil Morri, Sameh Hadouaj, Lamjed Ben Said

    Agent Technology for Multi-criteria Regulation in Public Transportation.

    International Journal of Machine Learning and Computing, 6(2), 105., 2016

    Résumé

    This paper provides an agent technology for
    a decision support system. This system is designed to
    detect and regulate the traffic of multimodal public
    transport when many disturbances come simultaneously.
    The objective of this system is to optimize the regulation
    action by learning technique of regulator. The goal of this
    research is to improve the quality of public transport
    service provided to users and respect the use rules (safety
    rules, business rules, commercial rules, etc.). So, to
    improve the quality service of the user, we have to
    optimize simultaneously several criteria like punctuality,
    regularity and correspondence in disturbance case. In
    this paper, we focus primarily on a multi agent system for
    optimizing and learning of Regulation Support System of
    a Multimodal Public Transport (RSSPT). We have
    validated our strategy by simulating situation related to
    existing transportation system.

    Imen Khammamssi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    Discussion and review on evolving data streams and concept drift adapting

    Evolving Systems, An Interdisciplinary Journal for Advanced Science and Technology Volume 9, pages 1–23, (2018), 2016

    Résumé

    Recent advances in computational intelligent systems have focused on addressing complex problems related to the dynamicity of the environments. In increasing number of real world applications, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift is becoming an attractive topic of research that concerns multidisciplinary domains such that machine learning, data mining, ubiquitous knowledge discovery, statistic decision theory, etc... Therefore, a rich body of the literature has been devoted to the study of methods and techniques for handling drifting data. However, this literature is fairly dispersed and it does not define guidelines for choosing an appropriate approach for a given application. Hence, the main objective of this survey is to present an ease understanding of the concept drift issues and related works, in order to help researchers from different disciplines to consider concept drift handling in their applications. This survey covers different facets of existing approaches, evokes discussion and helps readers to underline the sharp criteria that allow them to properly design their own approach. For this purpose, a new categorization of the existing state-of-the-art is presented with criticisms, future tendencies and not-yet-addressed challenges.

    Mouna Belhaj, Fahem Kebair, Lamjed Ben Said

    Modeling and simulation of coping mechanisms and emotional behavior during emergency situations

    In Agent and Multi-Agent Systems: Technology and Applications: 10th KES International Conference, KES-AMSTA 2016 Puerto de la Cruz, Tenerife, Spain, June 2016 Proceedings (pp. 163-176). Cham: Springer International Publishing., 2016

    Résumé

    Emotions shape human behaviors particularly during stressful situations. This paper addresses this challenging issue by incorporating coping mechanisms into an emotional agent. Indeed, coping refers to cognitive and behavioral efforts employed by humans to overcome stressful situations. In our proposal, we intend to show the potential of the integration of coping strategies to produce fast and human-like behavioral responses in emergency situations. Particularly, we propose a coping model that reveals the effect of agent emotions on their action selection processes.

    Samira Harrabi, Ines ben Jaafar, Khaled Ghédira

    A Novel Clustering Algorithm Based on Agent Technology for VANET

    International Journal of Network Protocols and Algorithms, Vol 8, N2, pp1-19, 2016., 2016

    Résumé

    Vehicular Ad-hoc Network (VANET) is a sub-family of Mobile Ad-hoc Network (MANET).The means goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road, varying node density and locations of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs. But most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another (such as traffic congestion, looking for free parking space, etc.). In this paper, we propose a novel clustering algorithm based on agent technology to solve the problems mentioned above and improve routing in VANET. Experimental part show promising results regarding the adoption of the proposed approach.

  • Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    Self-Adaptive Windowing Approach for Handling Complex Concept Drift

    Cognitive Computation Journal, Springer. vol.7, pages 772–790, issue.6 (2015), Evolving Systems, Springer-Verlag Berlin Heidelberg 2016, 2015

    Résumé

    Detecting changes in data streams attracts major attention in cognitive computing systems. The challenging issue is how to monitor and detect these changes in order to preserve the model performance during complex drifts. By complex drift, we mean a drift that presents many characteristics in the sometime. The most challenging complex drifts are gradual continuous drifts, where changes are only noticed during a long time period. Moreover, these gradual drifts may also be local, in the sense that they may affect a little amount of data, and thus make the drift detection more complicated. For this purpose, a new drift detection mechanism, EDIST2, is proposed in order to deal with these complex drifts. EDIST2 monitors the learner performance through a self-adaptive window that is autonomously adjusted through a statistical hypothesis test. This statistical test provides theoretical guarantees, regarding the false alarm rate, which were experimentally confirmed. EDIST2 has been tested through six synthetic datasets presenting different kinds of complex drift, and five real-world datasets. Encouraging results were found, comparing to similar approaches, where EDIST2 has achieved good accuracy rate in synthetic and real-world datasets and has achieved minimum delay of detection and false alarm rate.

    Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled ghedira

    Self-Adaptive Windowing Approach for Handling Complex Concept Drift

    Cognitive Computation Journal 7, 772–790 (2015). https://doi.org/10.1007/s12559-015-9341-0, 2015

    Résumé

    Detecting changes in data streams attracts major attention in cognitive computing systems. The challenging issue is how to monitor and detect these changes in order to preserve the model performance during complex drifts. By complex drift, we mean a drift that presents many characteristics in the sometime. The most challenging complex drifts are gradual continuous drifts, where changes are only noticed during a long time period. Moreover, these gradual drifts may also be local, in the sense that they may affect a little amount of data, and thus make the drift detection more complicated. For this purpose, a new drift detection mechanism, EDIST2, is proposed in order to deal with these complex drifts. EDIST2 monitors the learner performance through a self-adaptive window that is autonomously adjusted through a statistical hypothesis test. This statistical test provides theoretical guarantees, regarding the false alarm rate, which were experimentally confirmed. EDIST2 has been tested through six synthetic datasets presenting different kinds of complex drift, and five real-world datasets. Encouraging results were found, comparing to similar approaches, where EDIST2 has achieved good accuracy rate in synthetic and real-world datasets and has achieved minimum delay of detection and false alarm rate.

    Hanen Lejmi, Lamjed Ben Said, Fahem Kebaier

    Agent-based modeling and simulation of the emotional experiences of employees within organizations

    Agent-based modeling and simulation of the emotional experiences of employees within organizations, 2015

    Résumé

    In line with the multi-disciplinary growing interest in emotions and the scientific proof of their usefulness for taking decisions, scholars, in agent-oriented systems, start to account for emotions when building upon intelligence and realism in rational agents. As a result, several computational models of emotions were developed and new architectures for emotional artificial agents were proposed, in particular the Emotional Belief Desire Intention (EBDI) agents. In this paper, we provide a comprehensive description of two computational models which are used to generate immediate and expected emotions. These models will be incorporated within an EBDI agent architecture that takes into consideration these two types of emotions.

    Hanen Lejmi, Lamjed Ben Said, Fahem Kebaeir

    Agent-based modeling and simulation of the emotional experiences of employees within organizations

    Agent-based modeling and simulation of the emotional experiences of employees within organizations, 2015

    Résumé

    Agent-Based Modeling and Simulation (ABMS) have been used to study a wide range of complex systems and several emergent behaviors across a variety of disciplines. However, very limited works have adopted these paradigms to provide insights to organizational psychology in general and to researches dealing with emotions at work in particular. The current research uses ABMS to study the emotions experienced in the organizational context; it focuses specifically on their impact on the quality of decisions made as a key factor of organizations success. In this paper, the emphasis is set on the emotion generation process. The proposed work introduces an agent-based model of the emotional experiences of employees within organizations. It adopts a cross-disciplinary approach and it brings another theoretical perspective to agent-based modeling of emotions at work. In fact, this model is based on the OCC appraisal theory to generate artificial emotions, but it also takes advantage of theoretical foundations from organization behavior and organization psychology. Simulation results can bring new insights to organizational researches. Moreover, the simulated system can serve as a human resources development tool used by employees at work to enhance their emotional awareness.

    Hanen Lejmi, Lamjed Ben Said, Fahem Kebaeir

    Computational Models of Immediate and Expected Emotions for Emotional BDI Agents

    Computational Models of Immediate and Expected Emotions for Emotional BDI Agents, 2015

    Résumé

    In line with the multi-disciplinary growing interest in emotions and the scientific proof of their usefulness for taking decisions, scholars, in agent-oriented systems, start to account for emotions when building upon intelligence and realism in rational agents. As a result, several computational models of emotions were developed and new architectures for emotional artificial agents were proposed, in particular the Emotional Belief Desire Intention (EBDI) agents. In this paper, we provide a comprehensive description of two computational models which are used to generate immediate and expected emotions. These models will be incorporated within an EBDI agent architecture that takes into consideration these two types of emotions.

  • Mouna Belhaj, Fahem Kebair, Lamjed Ben Said

    A computational model of emotions for the simulation of human emotional dynamics in emergency situations

    International Journal of Computer Theory and Engineering, 6(3), 227., 2014

    Résumé

    Emotions have a considerable effect on human
    behaviors and cognitive processes, especially during crisis
    situations. Emotion modeling is therefore a key solution to
    generate realistic social simulations in crisis situations. In this
    context, we intend to model human emotional dynamics and to
    study their effect on individual and collective behaviors during
    emergency situations. In this paper, we focus on the first part of
    this research work which consists in the modeling of emotion
    generation in emergency situations. Thus, we provide first a
    modeling of the disaster space in a rescue simulation context.
    Then, we propose a computational model of the generated
    human emotions, basing on the emergency environment. This
    model uses the appraisal theories of emotions.

    Mouna Belhaj, Fahem Kebair, Lamjed Ben Said

    An emotional agent model for the simulation of realistic civilian behaviors during emergency situations

    IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, 2014, pp. 262-269, doi: 10.1109/WI-IAT.2014.176., 2014

    Résumé

    Analyzing human behaviors during emergency situations contributes to build efficient emergency management plans. Indeed, research shows that emotions have a major influence on human behavior particularly to respond to highly emotive events such as those happening in emergency situations. Therefore, it is recognized that they are necessary to produce human-like behaviors in artificial agents. In this paper, we present an emotional agent model of human civilians in an emergency context. The aim is to model and to simulate the emotion generation process and the impact of the elicited emotions on civilian behaviors in an emergency situation during a disaster.

    Mouna Belhaj, Fahem Kebair, Lamjed Ben Said

    Agent-based modeling and simulation of the emotional and behavioral dynamics of human civilians during emergency situations

    In: Müller, J.P., Weyrich, M., Bazzan, A.L.C. (eds) Multiagent System Technologies. MATES 2014. Lecture Notes in Computer Science(), vol 8732. Springer, Cham. https://doi.org/10.1007/978-3-319-11584-9_18, 2014

    Résumé

    Agent based social simulations are becoming prevailing tools in the context of human behavior studies. Researchers in psychology, cognitive science and neuroscience have proved the prominent role of emotion on cognition and behavior. Particularly, during emergency situations, human emotional dynamics have a major effect on behavior. In this context, we aim to study the role of emotions in reproducing human-like emotional civilian agents. The objective of the current research work is to model and to simulate human emotional dynamics and their effect on the behaviors of civilians in emergencies. In this article, we describe an emotional agent model that integrates a computational model of emotions. Agent perceptions are subject to a cognitive appraisal process to generate agent emotions. These have an effect on the generation of agent behavior.

    Hanen Lejmi, Lamjed Ben Said, Fahem Kebaeir

    Agent Decision-Making under Uncertainty: Towards a New E-BDI Agent Architecture Based on Immediate and Expected Emotions

    Agent Decision-Making under Uncertainty: Towards a New E-BDI Agent Architecture Based on Immediate and Expected Emotions, 2014

    Résumé

    Over the last decade, emotions have received considerable attention among scholars in agent oriented systems. In fact a large amount of computational models of emotions has been developed and a new generation of artificial agents has emerged to give rise to emotional agents, in particular the Emotional BDI (EBDI) agents. However, in spite of the several interesting studies that have been conducted to underline the role of emotions in decision-making, few works in the agent community have shed the light on the influences of both immediate and expected emotions to drive decision-making. In this context, we intend to propose a new conceptual model of EBDI agency that involves the interplay among immediate emotions, expected emotions and rational decisions of artificial agents.

    Islem Henane, Sameh Hadouaj, Khaled Ghédira, Ali Ferchichi

    Towards a generic approach for multi-level modeling of renewable resources management systems

    Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, 1471–1472. Presented at the Paris, France. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems., 2014

    Résumé

    Multi-agent systems are widely used in renewable and natural resources management. Multi-agent systems are able to manage the complexity of such systems characterized by a large number of interacting entities with different levels of granularity and including dynamics of different contexts (ecological, economic, social). In this work, we propose a generic multi-level architecture for renewable and natural resources management.

  • Hammadi Ghazouani, Moez Hammami, Ouajdi Korbaa

    Ensemble classifiers for drift detection and monitoring in dynamical Environments

    Annual Conference of the Prognostics and Health Management Society 2013, 2013

    Résumé

    Detecting and monitoring changes during the learning process are important areas of research in many industrial applications. The challenging issue is how to diagnose and analyze these changes so that the accuracy of the learning model can be preserved. Recently, ensemble classifiers have achieved good results when dealing with concept drifts. This paper presents two ensembles learning algorithms BagEDIST and BoostEDIST, which respectively combine the Online Bagging and the Online Boosting with the drift detection method EDIST. EDIST is a new drift detection method which monitors the distance between two consecutive errors of classification. The idea behind this combination is to develop an ensemble learning algorithm which explicitly handles concept drifts by providing useful descriptions about location, speed and severity of drifts. Moreover, this paper presents a new drift diversity measure in order to study the diversity of base classifiers and see how they cope with concept drifts. From various experiments, this new measure has provided a clearer vision about the ensemble’s behavior when dealing with concept drifts.

    imen khamassi, Mohamed Sayed Mouchaweh, Moez Hammami

    Nouvelle méthode de détection de dérive basée sur la distance entre les erreurs de classification

    5e Journées Doctorales Journées Nationales MACS, Strasbourg : France (2013), 2013

    Résumé

    La classification dynamique s’intéresse au traitement des données non-stationnaires issues des environnements évolutifs dans le temps. Ces données peuvent présenter des dérives, qui affectent la performance du modèle d’apprentissage initialement construit. Aujourd’hui, beaucoup d’intérêts sont portés sur la surveillance, la mise à jour et le diagnostic de ces dérives afin d’améliorer la performance du modèle d’apprentissage. Dans ce contexte, une nouvelle méthode de détection de dérive basée sur la distance entre les erreurs de classification est présentée. Cette méthode, nommée EDIST, surveille la distribution des distances des erreurs de classification entre deux fenêtres de données afin de détecter une différence à travers un test d’hypothèse statistique. EDIST a été testée à travers des bases de données artificielles et réelles. Des résultats encourageants ont été trouvés par rapport à des méthodes similaires. EDIST a pu trouver les meilleurs taux d’erreur de classification dans la plupart des cas et a montré une robustesse envers le bruit et les fausses alarmes.

    Kalthoum Rezgui, Khaled Ghédira

    Theoretical formulas of semantic measure: a survey

    -, 2013

    Résumé

    In recent years, several semantic similarity and relatedness measures have been developed and applied in many domains including linguistics, biomedical informatics, GeoInformatics, and Semantic Web. This paper discusses different semantic measures which compute similarity and relatedness scores between concepts based on a knowledge representation model offered by ontologies and semantic networks. The benchmarks and approaches used for the evaluation of semantic similarity methods are also described. The aim of this paper is to give a comprehensive view of these measures which helps researchers to choose the best semantic similarity or relatedness metric for their needs.

  • Sami Rojbi, Makram Soui

    User modeling and Web-based customazation techniques: An examination of the published literature

    2011 4th International Conference on Logistics, 2011

    Résumé

    This paper proposes a state of the art of the user modeling and interfaces customization techniques. It presents and discusses techniques intended to be used by application's designers and also end-user based techniques. It specifies the characteristics modeled in these systems as well as the used technologies.

  • Nabil Belgasmi, Lamjed Ben Said, Khaled Ghédira

    Genetic optimization of the multi-location transshipment problem with limited storage capacity

    ECAI 2008 (pp. 563-567). IOS Press., 2008

    Résumé

    Lateral Transshipments afford a valuable mechanism for compensating unmet demands only with on-hand inventory. In this paper we investigate the case where locations have a limited storage capacity. The problem is to determine how much to replenish each period to minimize the expected global cost while satisfying storage capacity constraints. We propose a Real-Coded Genetic Algorithm (RCGA) with a new crossover operator to approximate the optimal solution. We analyze the impact of different structures of storage capacities on the system behaviour. We find that Transshipments are able to correct the discrepancies between the constrained and the unconstrained locations while ensuring low costs and system-wide inventories. Our genetic algorithm proves its ability to solve instances of the problem with high accuracy.

Projets