smart.lab.tunisia@gmail.com +216 71 588 443
Connexion

Business Intelligence

Description

Aucune description disponible pour cet axe de recherche.

Membres

Siwar Mejri Siwar Mejri
Marwa Chabbouh Marwa Chabbouh

Publications

  • 2024
    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.

  • Marwa Chabbouh, Slim Bechikh, Lamjed Ben Said, Chih-Cheng Hung

    Multi-objective evolution of oblique decision trees for imbalanced data binary classification

    Swarm Evol. Comput. 49: 1-22 (2019), 2019

    Résumé

    Imbalanced data classification is one of the most challenging problems in data mining. In this kind of problems, we have two types of classes: the majority class and the minority one. The former has a relatively high number of instances while the latter contains a much less number of instances. As most traditional classifiers usually assume that data is evenly distributed for all classes, they may considerably fail in recognizing instances in the minority class due to the imbalance problem. Several interesting approaches have been proposed to handle the class imbalance issue in the literature and the Oblique Decision Tree (ODT) is one of them. Nevertheless, most standard ODT construction algorithms use a greedy search process; while only very few works have addressed this induction problem using an evolutionary approach and this is done without really considering the class imbalance issue. To cope with this limitation, we propose in this paper a multi-objective evolutionary approach to find optimized ODTs for imbalanced binary classification. Our approach, called ODT-Θ-NSGA-III (ODT-based-Θ-Nondominated Sorting Genetic Algorithm-III), is motivated by its abilities: (a) to escape local optima in the ODT search space and (b) to maximize simultaneously both Precision and Recall. Thanks to these two features, ODT-Θ-NSGA-III provides competitive and better results when compared to many state-of-the-art classification algorithms on commonly used imbalanced benchmark data sets.
  • Thouraya Sakouhi, Hadhami Ounissi, Marwa Manaa, Yasser Al Mashhour

    Immersive Analytics for Floods Management Semantic Trajectory Data Warehouse Ontology

    Immersive Analytics for Floods Management Semantic Trajectory Data Warehouse Ontology. iLRN 2018 Montana, 169., 2018

    Résumé

    Semantic Immersive Analytics is a new paradigm that has the capability for visualizing ontologies and meta-data including annotated web-documents, images, and digital media such as audio and video clips in a synthetic three-dimensional semi-immersive environment. More importantly, it supports visual semantic analytics, whereby an analyst can interactively investigate complex relationships between heterogeneous information and supports query processing and semantic association discovery. In our previous work we proposed a Semantic Trajectory Data Warehouse Ontology (STrDWO) [15], a tool supporting designers at the modeling of ontology-based trajectory data warehouses. In here, we intend to integrate our aforementioned tool with augmented Reality (AR) technologies to provide multi-sensory interfaces that support collaboration and allow users to immerse themselves in their data in a way that supports real-world geo-space analytics tasks. To do so, we present a Semantic trajectory data warehouse having an ontology-based multidimensional model. We illustrate our approach by a case study dealing with floods management.

  • Thouraya Sakouhi, Jalel Akaichi, Usman Ahmed

    Computing Semantic Trajectories: Methods and Used Techniques

    In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_39, 2017

    Résumé

    The widespread use of mobile devices generates huge amount of location data. The generated data is useful for many applications, including location-based services such as outdoor sports forums, routine prediction, location-based activity recognition and location-based social networking. Sharing individuals’ trajectories and annotating them with activities, for example a tourist transportation mode during his trip, helps bringing more semantics to the GPS data. Indeed, this provides a better understanding of the user trajectories, and then more interesting location-based services. To address this issue, diverse range of novel techniques in the literature are explored to enrich this data with semantic information, notably, machine learning and statistical algorithms. In this work, we focused, at a first level, on exploring and classifying the literature works related to semantic trajectory computation. Secondly, we capitalized and discussed the benefits and limitations of each approach.

  • Thouraya Sakouhi, Jalel Akaichi, Jamel Malki, Alain Bouju, Roua Wannous

    Inference on Semantic Trajectory Data Warehouse Using an Ontological Approach

    In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_47, 2014

    Résumé

    Using location aware devices is getting more and more spread, generating then a huge quantity of mobility data. The latter describes the movement of mobile objects and is called as well Trajectory data. In fact, these raw trajectories lack contextual information about the moving object goals and his activity during the travel. Therefore, the former must be enhanced with semantic information to be called then Semantic Trajectory. The semantic models proposed in the literature are in many cases ontology-based, and are composed of thematic, temporal and spatial ontologies and rules to support inference and reasoning tasks on data. Thus, calculating inference on moving objects trajectories considering all thematic, spatial, and temporal rules can be very long depending on the amount of data involved in this process. On the other side, TDW is an efficient tool for analyzing and extracting valuable information from raw mobility data. For that we propose throughout this work a TDW design, inspired from an ontology model. We will emphasis the trajectory to be seen as a first class semantic concept. Then we apply the inference on the proposed model to see if we can enhance it and make the complexity of this mechanism manageable.