Takwa OMRI

@innovcom.org

University of Tunis El Manar, National Engineering School of Tunis, Tunisia
Innov'COM Laboratory, SUPCOM

Takwa OMRI


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https://researchid.co/takwaomri

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Science
5

Scopus Publications

6

Scholar Citations

2

Scholar h-index

Scopus Publications

  • Evaluation of hybrid deep learning approaches for air pollution forecasting
    T. Omri, A. Karoui, D. Georges, M. Ayadi
    International Journal of Environmental Science and Technology, 2024
  • Multi-RAT integration in Heterogeneous Vehicular Communication Networks (HVCNets)
    Takwa Omri, Ridha Bouallegue
    2023 International Wireless Communications and Mobile Computing Iwcmc 2023, 2023
    The integration of multiple Radio Access Technology (RAT) in vehicular communication has gained interest in recent years. Indeed, a single RAT might be unable to satisfy all the requirements of vehicular communication networks. Particularly with the increasing amount of exchanged messages in vehicular communication networks in addition to the increasing number of connected autonomous vehicles. Although Multi-RAT Heterogeneous Vehicular Communication Networks (HVCNets) can provide increased network capacity to vehicle networks, they face several challenges. For instance, how to integrate multiple technologies that use different PHY and MAC protocols and techniques. Furthermore, how to select the most suitable RAT. This paper presents an in-depth study of what is currently the state of the art in HVCNets as well as RAT selection schemes in such networks.
  • Dynamic Mode Decomposition for the Environmental Forecasting Tasks
    Takwa Omri, Asma Karoui, Didier Georges, Mounir Ayadi
    9th 2023 International Conference on Control Decision and Information Technologies Codit 2023, 2023
    This paper introduces the dynamic mode decomposition (DMD) for the prediction of the air quality and the precipitation in the presence of high rain as an application of environment prediction tasks by forecasting the appropriate index parameters for each field. The forecasting procedure is based on the use of two real data bases containing the pollutant concentration, and the precipitation for the flooding forecasting. Moreover, the temporal evolution of the DMD modes, can be used to reconstruct the desired components and perform forecasting at the same time using the eigenvalues and eigenvectors. This task of the DMD is already known by the literature, the new in this paper is the application of the DMD for forecasting tasks on the environment issues which present appreciated results that are discussed and well analysed in this paper using different performance indexes to prove its efficiency.
  • WSN-based loT platform for smart monitoring
    Takwa OMRI, Kaouthar SETHOM, Ridha BOUALLEGUE
    2022 International Wireless Communications and Mobile Computing Iwcmc 2022, 2022
    Efficient surveillance system is required to be able to sense the whole monitored area in order to detect and track the target. In addition it should alert the controller as soon as possible for further actions. In this paper we discussed the WSN network deployment strategies and the efficient network deployment for a high level performance. In addition, we developed a dashboard that allows the responsible party to keep an eye on the border and on all the sensors that have been deployed. And we implemented an loT based platform as well.
  • Prediction of water flow depth with kinematic wave equations and NARMAX approach based on neural networks in overland flow model
    Takwa Omri, Asma Karoui, Didier Georges, Mounir Ayadi
    Proceedings of the International Conference on Advanced Systems and Emergent Technologies IC Aset 2020, 2020
    This paper deals with predict the water flow depth in presence of exceptional rain by two methods and to compare the performances of them based on error measurements. Two approaches are used and then compared: The knowledge modeling based on kinematic wave approach and the NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) neural networks approach, based on real data taken from the Tondi Kiboro catchment area of Niger by a group from the IGE laboratory. For the first approach, the aim is to minimize the error between the measured and calculated flow rate values and to use the values of the parameters estimated during the optimization problem to calculate the new water flow depth values. For the second approach which is an unconventional method based on neural networks, the attempt is made to estimate the flow rate values using a recursive relation of the NARMAX approach through the use of a supervised learning model.

RECENT SCHOLAR PUBLICATIONS

  • Multi-RAT integration in heterogeneous vehicular communication networks (HVCNets)
    T Omri, R Bouallegue
    2023 International Wireless Communications and Mobile Computing (IWCMC), 751-756 , 2023
    2023
    Citations: 4
  • WSN-based loT platform for smart monitoring
    O Takwa, K Sethom, R Bouallègue
    2022 International Wireless Communications and Mobile Computing (IWCMC), 666-671 , 2022
    2022
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Multi-RAT integration in heterogeneous vehicular communication networks (HVCNets)
    T Omri, R Bouallegue
    2023 International Wireless Communications and Mobile Computing (IWCMC), 751-756 , 2023
    2023
    Citations: 4
  • WSN-based loT platform for smart monitoring
    O Takwa, K Sethom, R Bouallègue
    2022 International Wireless Communications and Mobile Computing (IWCMC), 666-671 , 2022
    2022
    Citations: 2