Prof. Nedra Mellouli is a full Professor in computer sciences. Her research interests mainly focus on multimodal big data-driven approaches for time series Big Data analytics, as precipitation monitoring, agriculture of precision, Intelligent systems of irrigation, energy optimization, healthcare decision systems and recommendation systems.
Gaël Marec; Nédra Mellouli
Symmetric non negative matrices factorization applied to the detection of communities in graphs and forensic image analysi Article de journal
Dans: Data & Knowledge Engineering, vol. 157, p. 102411, 2025.
@article{marec_3442,
title = {Symmetric non negative matrices factorization applied to the detection of communities in graphs and forensic image analysi},
author = {Gaël Marec and Nédra Mellouli},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0169023X25000060},
year = {2025},
date = {2025-05-01},
journal = {Data & Knowledge Engineering},
volume = {157},
pages = {102411},
abstract = {With the proliferation of data, particularly on social networks, the accuracy of the information becomes uncertain. In this context, a major challenge lies in detecting image manipulations, where alterations are made to deceive observers. Aligning with the anomaly detection issue, recent methods approach the detection of image transformations as a community detection problem within graphs associated with the images. In this study, we propose using a community clustering method based on non-negative symmetric matrix factorization. By examining several experiments detecting alterations in manipulated images, we assess the method's robustness and discuss potential enhancements. We also present a process for automatically generating visually and semantically coherent forged images. Additionally, we provide a web application to demonstrate this process.},
keywords = {},
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Christophe Rodrigues; Marius Ortega; Aurélien Bossard; Nédra Mellouli
REDIRE: Extreme REduction DImension for extRactivE Summarization Article de journal
Dans: Data & Knowledge Engineering, vol. 157, p. 102407, 2025.
@article{rodrigues_3621,
title = {REDIRE: Extreme REduction DImension for extRactivE Summarization},
author = {Christophe Rodrigues and Marius Ortega and Aurélien Bossard and Nédra Mellouli},
url = {http://dx.doi.org/10.1016/j.datak.2025.102407},
year = {2025},
date = {2025-01-01},
journal = {Data & Knowledge Engineering},
volume = {157},
pages = {102407},
abstract = {This paper presents an automatic unsupervised summarization model capable of extracting the most important sentences from a corpus. The unsupervised aspect makes it possible to do away
with large corpora, made up of documents and their reference summaries, and to directly process documents potentially made up of several thousand words. To extract sentences in a summary, we use pre-entrained word embeddings to represent the documents. From this thick cloud of word vectors, we apply an extreme dimension reduction to identify important words, which we group by proximity. Sentences are extracted using linear constraint solving
to maximize the information present in the summary. We evaluate the approach on large documents and present very encouraging initial results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Akram Hakiri; Sadok Ben Yahia; Aniruddha GOKHALE; Nédra Mellouli
Special Issue on Digital Twin for Future Networks and Emerging IoT Applications Article de journal
Dans: Future Generation Computer Systems-The International Journal Of Escience, vol. 161, p. 81-84, 2024.
@article{hakiri_3112,
title = {Special Issue on Digital Twin for Future Networks and Emerging IoT Applications},
author = {Akram Hakiri and Sadok Ben Yahia and Aniruddha GOKHALE and Nédra Mellouli},
url = {https://www.sciencedirect.com/special-issue/10FFXQK3NWS},
year = {2024},
date = {2024-12-01},
journal = {Future Generation Computer Systems-The International Journal Of Escience},
volume = {161},
pages = {81-84},
abstract = {The rapid evolution of digital technologies has given rise to the concept of Digital Twin, a dynamic, virtual representation of physical systems, processes, and environments. This special issue delves into the transformative potential of Digital Twins in the realm of future networks and emerging Internet of Things (IoT) applications. By integrating advanced simulation, real-time data analytics, and machine learning, Digital Twins offer unprecedented opportunities for optimizing network performance, enhancing predictive maintenance, and enabling smarter IoT solutions.
The articles in this issue explore a variety of topics, including the development and implementation of Digital Twins for next-generation communication networks, the role of artificial intelligence in enhancing the fidelity and utility of Digital Twins, and the application of these technologies in diverse IoT domains such as smart cities, healthcare, industrial automation, and environmental monitoring. Emphasis is placed on innovative methodologies, case studies, and experimental results that highlight the practical benefits and challenges associated with deploying Digital Twins in real-world scenarios.
Through this special issue, we aim to provide a comprehensive overview of the current state of research and development in Digital Twins, underscore the technological advancements driving their adoption, and discuss future directions and open research questions. This collection of works serves as a valuable resource for researchers, practitioners, and policymakers interested in harnessing the power of Digital Twins to revolutionize network infrastructures and IoT ecosystems.},
note = {Numéro spécial dans le journal Future Generation Computer Systems},
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Akram Hakiri; Aniruddha GOKHALE; Sadok Ben Yahia; Nédra Mellouli
A comprehensive survey on digital twin for future networks and emerging Internet of Things industry Article de journal
Dans: Computer Networks, vol. 244, p. 110350, 2024.
@article{hakiri_3101,
title = {A comprehensive survey on digital twin for future networks and emerging Internet of Things industry},
author = {Akram Hakiri and Aniruddha GOKHALE and Sadok Ben Yahia and Nédra Mellouli},
url = {https://www.sciencedirect.com/science/article/pii/S1389128624001828},
year = {2024},
date = {2024-05-01},
journal = {Computer Networks},
volume = {244},
pages = {110350},
abstract = {The rapid growth of industrial digitalization in the Industry 4.0 era is fundamentally transforming the industrial
sector by connecting products, machines, and people, offering real-time digital models to allow self-diagnosis,
self-optimization and self-configuration. However, this uptake in such a digital transformation faces numerous
obstacles. For example, the lack of real-time data feeds to perform custom closed-loop control and realize
common, powerful industrial systems, the complexity of traditional tools and their inability in finding effective
solutions to industry problems, lack of capabilities to experiment rapidly on innovative ideas, and the absence
of continuous real-time interactions between physical objects and their simulation representations along with
reliable two-way communications, are key barriers towards the adoption of such a digital transformation.
Digital twins hold the promise of improving maintainability and deployability, enabling flexibility, auditability,
and responsiveness to changing conditions, allowing continuous learning, monitoring and actuation, and
allowing easy integration of new technologies in order to deploy open, scalable and reliable Industrial Internet
of Things (IIoT).
A critical understanding of this emerging paradigm is necessary to address the multiple dimensions of
challenges in realizing digital twins at scale and create new means to generate knowledge in the industrial
IoT. To address these requirements, this paper surveys existing digital twin along software technologies,
standardization efforts and the wide range of recent and state-of-the-art digital twin-based projects; presents
diverse use cases that can benefit from this emerging technology; followed by an in-depth discussion of the
major challenges in this area drawing upon the research status and key trends in Digital Twins.},
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pubstate = {published},
tppubtype = {article}
}
Nzamba Bignoumba; Nédra Mellouli; Sadok Ben Yahia
A new efficient ALignment-driven Neural Network for Mortality Prediction from Irregular Multivariate Time Series data Article de journal
Dans: Expert Systems With Applications, vol. 238, no. Part E, p. 122148, 2024.
@article{bignoumba_2463,
title = {A new efficient ALignment-driven Neural Network for Mortality Prediction from Irregular Multivariate Time Series data},
author = {Nzamba Bignoumba and Nédra Mellouli and Sadok Ben Yahia},
url = {https://www.sciencedirect.com/science/article/pii/S0957417423026507?via%3Dihub},
year = {2024},
date = {2024-03-01},
journal = {Expert Systems With Applications},
volume = {238},
number = {Part E},
pages = {122148},
abstract = {The irregularity of the time interval between observations in and across the stream is a key factor that leads to a drop in performance when classical machine learning or deep learning models are used for a downstream task requiring multivariate time series. Indeed, irregular multivariate time series not only increase the rate of missing values but also lead to data sparsity, which consequently makes the data almost unleverageable and/or ineffective for models. To tackle this scorching challenge, most of the pioneering approaches apply imputation or interpolation in their core, which might lead to embedding data with noise. To especially address this irregular multivariate time series issue, we introduce, in this paper, a new deep neural network model called ALignment-driven Neural Network. The innovative idea of our model is to transform the irregular multivariate time series into pseudo-aligned (or pseudo-regular) latent values. The latter are shown as a matrix, where the coefficients are the latent values of each feature at user-defined reference time points that are evenly spaced. They are obtained through a duplication process driven by an exponential decay mechanism. The obtained output is then passed to a Recurrent Neural Network model, which is undoubtedly the must-use model for regular time series data. To show that our model added value, we looked at the Intensive Care Unit mortality prediction task. In this unit, the physiological measurements used to make decisions have a problem with time irregularity. Leveraging the publicly available MIMIC-III, we compare the performance of our model to that of flagship models. In addition, we also performed extensive ablation studies to highlight the importance of specific components in our model. Interestingly enough, whenever data is collected 24 and 48 h after a patient's admission, we outperform our pioneering competitors, i.e., +1.1% and +1.5% for the AUC score, +2.3% and for the AUPRC score and +0.6% and +1.7%
for the F1-score.},
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pubstate = {published},
tppubtype = {article}
}
Zhihui Ren; Yan-Fang Sang; Peng Cui; Deliang Chen; Yichi Zhang; Tongliang Gong; Shao Sun; Nédra Mellouli
Temporal Scaling Characteristics of Sub?Daily Precipitation in Qinghai?Tibet Plateau Article de journal
Dans: Earths Future, vol. 12, no. 3, p. e2024EF004417, 2024.
@article{ren_3111,
title = {Temporal Scaling Characteristics of Sub?Daily Precipitation in Qinghai?Tibet Plateau},
author = {Zhihui Ren and Yan-Fang Sang and Peng Cui and Deliang Chen and Yichi Zhang and Tongliang Gong and Shao Sun and Nédra Mellouli},
url = {https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004417},
year = {2024},
date = {2024-03-01},
journal = {Earths Future},
volume = {12},
number = {3},
pages = {e2024EF004417},
abstract = {The Qinghai?Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and
related natural disasters. However, the lack of sub?daily precipitation observations in this region has hindered
our understanding of rainstorm?related hazards and their societal impacts. To address this data gap, a new
approach is devised to estimate sub?daily precipitation in QTP using daily precipitation data and geographical
information. The approach involves establishing a statistical relationship between daily and sub?daily
precipitation based on data from 102 observation sites. This process results in a set of functions with six
associated parameters. These parameters are then modeled using local geographical and climatic information
through a machine learning algorithm called support vector regression. The results indicated that the temporal
scaling characteristics of sub?daily precipitation can be accurately described using a logarithmic function. The
uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are
estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be
closely linked to geographical conditions, enabling the creation of a 1?km parameters data set. This data set can
be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum
precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set
holds significant potential for various applications, including risk analysis, forecasting, and early warning for
rainstorm?related natural disasters in QTP. The innovative method developed in this study proves to be an
effective approach for estimating sub?daily precipitation and assessing its uncertainty in ungauged regions},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hanen Balti; Ali Ben Abbes; Yanfang Sang; Nédra Mellouli; Imed Riadh Faraha
Spatio-temporal Heterogeneous Graph using Multivariate Earth Observation Time Series: Application for drought forecasting Article de journal
Dans: Computers & Geosciences, vol. 179, p. 105435, 2023.
@article{balti_2397,
title = {Spatio-temporal Heterogeneous Graph using Multivariate Earth Observation Time Series: Application for drought forecasting},
author = {Hanen Balti and Ali Ben Abbes and Yanfang Sang and Nédra Mellouli and Imed Riadh Faraha},
url = {https://www.sciencedirect.com/science/article/pii/S0098300423001395?via%3Dihub},
year = {2023},
date = {2023-10-01},
journal = {Computers & Geosciences},
volume = {179},
pages = {105435},
note = {Accurate forecasting is required for the effective risk management of drought disasters. Many machine learning- and deep learning-based models have been proposed for drought forecasting, however, they cannot handle the temporal and/or spatial dependencies in the input data, causing unexpected forecasting results. In order to solve the challenging issue, in this paper we proposed the Heterogeneous Spatio-Temporal Graph (HetSPGraph), for drought forecasting. It includes three major layers: spatial aggregations including inter and intra aggregations, temporal aggregation, and a forecasting network. The main function of HetSPGraph is to learn the dynamic spatiotemporal correlations between the regions and to further predict the drought in different regions, based on which accurate drought forecasting can be achieved. Experimental forecasting results of the Standardized Precipitation Evapotranspiration Index (SPEI) in China indicated that the HetSPGraph model outperformed the traditional baseline methods including the Long Short-Term Memory model (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), Gated Recurrent Unit (GRU), Spatio-Temporal Graph Convolutional Networks (STGCN) and Geographic-Semantic-Temporal Hypergraph Convolutional Network (GST-HCN). Even for long-term forecasting (12 months), more accurate forecasting results, with the coefficient of determination
higher than 0.89, can also be obtained by HetSPGraph compared to the other three models. The proposed HetSPGraph model has the potential for wider use in forecasting drought and other natural disasters.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nesrine Waga; Hichem Kallel; Nédra Mellouli
Analytical and Deep Learning Approaches for Solving the Inverse kinematic Problem of a High Degrees of Freedom Robotic Arm Article de journal
Dans: Engineering Applications Of Artificial Intelligence, vol. 123, no. Part B, p. 106301, 2023.
@article{waga_2351,
title = {Analytical and Deep Learning Approaches for Solving the Inverse kinematic Problem of a High Degrees of Freedom Robotic Arm},
author = {Nesrine Waga and Hichem Kallel and Nédra Mellouli},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623004852?via%3Dihub},
year = {2023},
date = {2023-08-01},
journal = {Engineering Applications Of Artificial Intelligence},
volume = {123},
number = {Part B},
pages = {106301},
abstract = {Inverse kinematics is the basis for controlling the motion of robotic manipulators. It defines the required joint variables for the robotic end-effector accurately reach the desired location. Due to the derivation difficulty, computation complexity, singularity problem, and redundancy, analytical Inverse kinematics solutions pose numerous challenges to the operation of many robotic arms, especially for a manipulator with a high degree of freedom. This paper develops different Deep Learning networks for solving the Inverse kinematics problem of six- Degrees of Freedom robotic manipulators. The implemented neural architectures are Artificial Neural Network, Convolutional Neural Network, Long-Short Term Memory, Gated Recurrent Unit, and Bidirectional Long-Short Term Memory. In this context, we associate the proposed results with a specific tuning of Deep Learning network hyper-parameters (number of hidden layers, learning rate, Loss function, optimization algorithm, number of epochs, etc.). The Bidirectional Long-Short Term Memory network outperformed all proposed architectures. To be close as possible to the experimental results, we have included two types of noise in the training data set to validate which of the five proposed neural networks is more efficient. Furthermore, in this study, we compare the performance of analytical and soft computing solutions in generating robots' trajectories. We include this scenario, focusing on the advantage of implementing neural networks in avoiding the singularity problem that can occur using the analytical approach. In addition, we used the RoboDK simulator to show simulation results with real-world meaning. The performance of Deep Learning models depends on the complexity of the posed problem. Moreover, the complexity of the Inverse Kinematics problem is related to the number of Degrees of Freedom. At the end of this work, we evaluate the influence of the complexity of robotic manipulators on the proposed Deep Learning networks' performance. The results show that the implemented Deep Learning mechanisms performed well in reaching the desired pose of the end-effector. The proposed inverse kinematics strategies apply to other manipulators with different numbers of Degrees of Freedom.},
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pubstate = {published},
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Saber Zahhar; Nédra Mellouli; Christophe Rodrigues
Leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval Proceedings Article
Dans: International Conference on Web Information Systems Engineering, p. 101-110, Springer, Singapore, Doha, Qatar, 2025, ISBN: 978-981-96-1482-0.
@inproceedings{zahhar_3624,
title = {Leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval},
author = {Saber Zahhar and Nédra Mellouli and Christophe Rodrigues},
url = {https://link.springer.com/chapter/10.1007/978-981-96-1483-7_8},
issn = {978-981-96-1482-0},
year = {2025},
date = {2025-02-01},
booktitle = {International Conference on Web Information Systems Engineering},
volume = {15463},
pages = {101-110},
publisher = {Springer, Singapore},
address = {Doha, Qatar},
abstract = {Meeting the Sustainable Development Goals (SDGs) established by the United Nations, presents a large-scale challenge for all countries. To monitor progress towards these goals, there is a need to develop key performance indicators using existing data and metadata. The computation of the indicators requires integrating and analyzing heterogeneous datasets, in particular web open data. This approach aims to highlight the positive impact of the web on the society. However, the diversity of web data sources and formats raises major issues in terms of structuring and integration. Despite the abundance of open data and metadata, its exploitation remains limited, leaving untapped potential for guiding urban policies towards sustainability. We have so far introduced a novel approach for SDG indicator computation, leveraging the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs). We have proposed a method that combines rule-based filtering with LLM-powered schema mapping to establish semantic correspondences between diverse data sources and SDG indicators, including disaggregated attributes. Our approach integrated these mappings into a KG, which enables indicator computation by querying graph's topology. Finally, we have evaluated our method through a case study focusing on the SDG Indicator 11.7.1 about accessibility of public open spaces. Our experimental results are promising showing significant improvements compared to traditional schema matching techniques.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Yahjeb Bouha Khatraty; Nédra Mellouli; Mamadou Tourad Diallo; Mohamedade Farouk Nanne
Smart Agriculture Framework with Digital Twin: A Monitoring Model Based on Clustering and prediction of Multivariate Time Series Proceedings Article
Dans: MIDD4DT '24, p. 13 - 18, Association for Computing Machinery, Hong Kong, China, 2024, ISBN: 979-8-4007-1338-5.
@inproceedings{khatraty_3440,
title = {Smart Agriculture Framework with Digital Twin: A Monitoring Model Based on Clustering and prediction of Multivariate Time Series},
author = {Yahjeb Bouha Khatraty and Nédra Mellouli and Mamadou Tourad Diallo and Mohamedade Farouk Nanne},
url = {https://dl.acm.org/doi/10.1145/3702636.3703759},
issn = {979-8-4007-1338-5},
year = {2024},
date = {2024-12-01},
booktitle = {MIDD4DT '24},
pages = {13 - 18},
publisher = {Association for Computing Machinery},
address = {Hong Kong, China},
edition = {MIDDLEWARE '24: 25th International Middleware Conference},
abstract = {The current trend in agricultural field management research emphasizes the use of the digital twin due to its importance in digital resource management. Our paper proposes a digital twin architecture for intelligent rice field control by combining IoT sensors, satellite data, and deep learning models to predict yield, weather, and soil conditions to achieve a predefined yield. This approach enables more comprehensive management of agricultural resources, helping farmers make informed decisions to improve yields while reducing costs and environmental impact.},
note = {MIDD4DT '24, December 2-6,},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nzamba Bignoumba; Sadok Ben Yahia; Nédra Mellouli
Deep Padding and Alignment Strategies for Irregular Multivariate Clinical Time Series Proceedings Article
Dans: 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, p. Pages 3275-3284, Elsevier's Procedia Computer Science open access journal, Seville, Spain, 2024, ISBN: (https://creativecommons.org/licenses/by-nc-nd/4.0.
@inproceedings{bignoumba_3439,
title = {Deep Padding and Alignment Strategies for Irregular Multivariate Clinical Time Series},
author = {Nzamba Bignoumba and Sadok Ben Yahia and Nédra Mellouli},
url = {http://kes2024.kesinternational.org/},
issn = {(https://creativecommons.org/licenses/by-nc-nd/4.0},
year = {2024},
date = {2024-09-01},
booktitle = {28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems},
volume = {Volume 246},
pages = {Pages 3275-3284},
publisher = {Elsevier's Procedia Computer Science open access journal},
address = {Seville, Spain},
abstract = {To improve the accuracy of an RNN when processing sparse and irregular multivariate clinical time series, we introduce two stacked deep learning models built on top of it, namely Padd-GRU and Alignment-driven Neural Network (ALNN). The Padd-GRU performs data-driven padding and imputation to obtain equal-length univariate and fill-in missing values, respectively. Then, the ALNN component transforms the resulting padded irregular multivariate clinical time series into a pseudo-aligned (or pseudo-regular) latent multivariate time series. We use the MIMIC-3 and PhysioNet databases to evaluate and compare our model to the state-of-the-art models on the mortality prediction task.},
note = {11 au 13/09/2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
El Moundir Faraoun; Nédra Mellouli; Stephane Millot; Myriam Lamolle
Contextual kNN Ensemble Retrieval Approach for Semantic Postal Address Matching. Proceedings Article
Dans: IAL@PKDD/ECML 2024, p. https://ceur-ws.org/Vol-3770/, Vilnius, Lithuania, 2024.
@inproceedings{faraoun_3441,
title = {Contextual kNN Ensemble Retrieval Approach for Semantic Postal Address Matching.},
author = {El Moundir Faraoun and Nédra Mellouli and Stephane Millot and Myriam Lamolle},
url = {https://ceur-ws.org/Vol-3770/},
year = {2024},
date = {2024-09-01},
booktitle = {IAL@PKDD/ECML 2024},
volume = {3770},
pages = {https://ceur-ws.org/Vol-3770/},
address = {Vilnius, Lithuania},
edition = {IAL@ECML-PKDD 2024},
abstract = {The biggest challenge today regarding courier services (delivery of small to medium-sized parcels) is the problem
of Address Matching. With the expansion of geographical data and the diversity of formats in which it is received,
traditional matching methods are becoming increasingly obsolete due to the lack of conformity of delivery
information with postal address writing standards. These new constraints are affecting parcel delivery quality
in terms of deliverables, cost and environmental impact. This research focuses on courier delivery data (i.e.
postal addresses of recipients) in the context of matching French postal addresses. We introduce a new ensemble
retrieval approach to the problem through a voting system leveraging multiple k-Nearest Neighbors search
algorithms, called ?NN-vote which effectively transform the Address Matching task to an Address Retrieval task.
?NN-vote returns the top best normalized addresses similar to a given query (a non-normalized delivery address).
The system takes advantage of several address representations, in particular Pre-trained Transformers-Based
Sentence Embeddings. The system has been tested on a real database of French delivery addresses. The method
meets high expectations, returning exactly matched addresses with a success rate of up to 96% in top 10 as well
as 86% in top 1.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Marwa SAID; Karim HOUIDI; Akram Hakiri; Nédra Mellouli; Hella KAFFEL
Performance Evaluation of LoRaWAN Propagation Models for Large-Scale IoT Deployments Proceedings Article
Dans: IEEE, (Ed.): 27th IEEE International Symposium On Real-Time Distributed Computing, p. 6, Tunis, Tunisie, 2024, ISBN: 979-8-3503-7128-4.
@inproceedings{said_3113,
title = {Performance Evaluation of LoRaWAN Propagation Models for Large-Scale IoT Deployments},
author = {Marwa SAID and Karim HOUIDI and Akram Hakiri and Nédra Mellouli and Hella KAFFEL},
editor = {IEEE},
url = {https://isorc.github.io/2024/html/program.html},
issn = {979-8-3503-7128-4},
year = {2024},
date = {2024-05-01},
booktitle = {27th IEEE International Symposium On Real-Time Distributed Computing},
pages = {6},
address = {Tunis, Tunisie},
note = {22-25 mai 2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Marius Ortega; Nédra Mellouli; Aurélien Bossard; Christophe Rodrigues
REDIRE : Réduction Extrême de DImension pour le Résumé Extractif Proceedings Article
Dans: 24ème conférence francophone sur l'Extraction et la Gestion des Connaissances, Dijon, France, 2024.
@inproceedings{ortega_2654,
title = {REDIRE : Réduction Extrême de DImension pour le Résumé Extractif},
author = {Marius Ortega and Nédra Mellouli and Aurélien Bossard and Christophe Rodrigues},
url = {https://iutdijon.u-bourgogne.fr/egc2024/articles-acceptes/},
year = {2024},
date = {2024-01-01},
booktitle = {24ème conférence francophone sur l'Extraction et la Gestion des Connaissances},
address = {Dijon, France},
abstract = {This paper presents an unsupervised automatic summarization model capable of extracting the most important sentences from a corpus. To extract sentences in a summary, we use
pre-entrained word embeddings to represent the documents. From this thick cloud of word vectors,
we apply an extreme dimension reduction to identify important words, which we group
by proximity. Sentences are extracted using linear optimization to maximize the information
present in the summary. We evaluate the approach on large documents and present very encouraging initial results.},
note = {22-26/01/2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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