Skip to main content
Sahraoui Dhelim on Muck Rack

Sahraoui Dhelim

As seen in: Nature, Frontiers, Future Internet, MDPI
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Sahraoui

Contact Sahraoui, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning

|
By Laouni Djafri, Sahraoui Dhelim, Huansheng Ning
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (583) Issue with article?

From Network Sensors to Intelligent Systems: A Decade-Long Review of Swarm Robotics Technologies

|
By Chaker Abdelaziz Kerrache, Sahraoui Dhelim, Fouad Chaouki Refis, Nassim Ahmed Mahammedi
| preprints.org
This version is not peer-reviewed. Fouad Chaouki Refis  * ,Nassim Ahmed Mahammedi,Chaker Abdelaziz Kerrache,Sahraoui Dhelim Fouad Chaouki Refis  * ,Nassim Ahmed Mahammedi,Chaker Abdelaziz Kerrache,Sahraoui Dhelim You are already at the latest version Swarm Robotics (SR) is a relatively new field, inspired by the collective intelligence of social insects. It involves using local rules to control and coordinate large groups (swarms) of relatively simple physical robots.
Open in Who Shared Issue with article?

Uncertainty aware domain incremental learning for cross domain depression detection - Scientific Reports

|
By Sahraoui Dhelim
| Nature Verified
Abstract Deep learning techniques have demonstrated significant promise for detecting Major Depressive Disorder (MDD) from textual data but they still face limitations in real-world scenarios. Specifically, given the limited data availability, some efforts have resorted to aggregating data from different domains to expand the data volume.
Open in Who Shared Issue with article?
See all 16 licensed articles (17 Total)