Demo Data from SIGIR 2026 / reviewers in the wild / expert
Issam Eddine Lakhlaf
dblp:438/5196
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0001-3845-891XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Internet of things and sensor networks · 50% Routing and switching · 50% | |
| Artificial intelligence
1 paper |
Reinforcement learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › low-power wireless
low-power and lossy networks |
1.0 | 1 | 2026 | QRPL: Q-Learning-Based Routing Protocol for Low-Power and Lossy IoT Networks · IEEE Trans. Mob. Comput. 2026 |
Routing and switching
routing protocol |
1.0 | 1 | 2026 | QRPL: Q-Learning-Based Routing Protocol for Low-Power and Lossy IoT Networks · IEEE Trans. Mob. Comput. 2026 |
Machine learning › Reinforcement learning › value-based reinforcement learning
q-learning |
0.3 | 1 | 2026 | QRPL: Q-Learning-Based Routing Protocol for Low-Power and Lossy IoT Networks · IEEE Trans. Mob. Comput. 2026 |
Methods — techniques the papers use, named apart from their topics
q-learning · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | QRPL: Q-Learning-Based Routing Protocol for Low-Power and Lossy IoT NetworksabstractInternational audience Badis Djamaa, Mustapha Réda Senouci, Issam Eddine Lakhlaf, Abbas Bradai, Walid Moussaoui, Yacine Moussaoui |
IEEE Trans. Mob. Comput. | 3 |