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Publications

Cette page présente les publications du Centre Borelli déposées sur le serveur HAL.

Affichage des publications 431 à 440 sur 1260 au total

Computer Science

Detecting Virtual Harassment in Social Media Using Machine Learning

The escalating prevalence of cyberbullying stands as a significant challenge in our digital era, causing severe repercussions on individuals' mental health, well-being, and professional integrity. This paper introduces an innovative approach to…

Lina Feriel Benassou, Safa Bendaouia, Osman Salem, Ahmed Mehaoua

6th International Conference on Machine Learning for Networking (MLN'2023) -

Computer Science

Graph Neural Networks for Anomaly Detection in Internet of Medical Things

Recent advancements in the realm of Graph Neural Networks (GNN) have unveiled compelling new prospects for classifying and detecting anomalies within medical datasets. Within this study, we delve into the efficacy of GNNs by leveraging diverse…

Mickael Mohammed, Osman Salem, Ahmed Mehaoua

2023 IEEE International Conference on E-health Networking, Application & Services (IEEE Healthcom 23) -

Computer Science

Detecting Abnormal Authentication Delays in Identity and Access Management using Machine Learning

Authentication delay is an important metric for measuring the performance and responsiveness of Identity and Access Management (IAM) systems. A sudden increase in authentication delay can indicate several problems, such as performance degradation,…

Jiahui Xiang, Osman Salem, Ahmed Mehaoua

6th International Conference on Machine Learning for Networking (MLN'2023) -

Computer Science

Heart Disease Detection using the Internet of Medical Things

The deployment of Internet of Medical Things (IoMTs) for remote monitoring has grown exponentially as alternative for in-hospital diagnosis. The COVID-19 lockdown and the shortage of human and medical apparatus pushed the detection and diagnostics…

Osman Salem, Ahmed Mehaoua

"2023 IEEE International Conference on E-health Networking, Application & Services (IEEE Healthcom 23) -

Signal and Image processing

A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression

We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then…

Yanhao Li, Marina Gardella, Quentin Bammey, Tina Nikoukhah, Rafael Grompone von Gioi, Miguel Colom, Jean-Michel Morel

Image Processing On Line -

Computer Science

Using genetic algorithms to detect intrusions for IoT systems

The purpose of this paper is to develop an intrusion detection system for IoT systems using deep learning and the genetic algorithm to optimize (or, more precisely, compress) the model. To meet the security requirements of IoT systems, we aim to…

Riad Ziani, Amel Khamoum, Osman Salem, Ahmed Mehaoua

2023 IEEE International Conference on E-health Networking, Application & Services (IEEE Healthcom 23) -

Computer Science

Machine Learning for IoT Devices Security Reinforcement

As more lightweight objects connect to the Internet, the Internet of Things (IoT) is changing our linked environment. Thus, IoT intrusion detection research must be high-quality to provide solutions. Network intrusion datasets are essential for…

Philippe Ea, Jiahui Xiang, Osman Salem, Ahmed Mehaoua

6th International Conference on Machine Learning for Networking (MLN'2023) -

Computer Science

Security Assessment of Bluetooth Just Works Pairing Method: Vulnerabilities and Enhancements

Bluetooth Low Energy (BLE) is used by many devices and Internet of Things (IoT) smart applications in numerous fields such as medication, home automation, transportation, and agriculture. It turns classic Bluetooth into a technology that can be…

Mohamed Yanis Sadaoui, Osman Salem, Ahmed Mehaoua

2023 IEEE International Conference on E-health Networking, Application & Services (IEEE Healthcom 23) -

Computer Science

Analysis of Different Machine Learning Models for Diabetes Prediction

This paper presents a comparative analysis of various machine learning models applied to a diabetes dataset. The study evaluates a range of ML algorithms, employing exploratory data analysis and feature engineering techniques. The primary objective…

Rym Moussaoui, Osman Salem, Ahmed Mehaoua

2023 IEEE International Conference on E-health Networking, Application & Services (IEEE Healthcom 23) -