Machine Learning in 5G Challenge, ITU 2022
1st place among over 29 research teams on the Problem Statement “Federated Traffic Prediction for 5G and Beyond”. 4th place in the grand challenge among the winners of every problem statement.
Euclid Team presented a benchmark for Federated Learning for 5G Base Station Traffic Forecasting, where various optimizations to existing methods were introduced.
IFMBE Scientific Challenge 2022
1st place among over 20 research teams.
Predicting Early Dropouts of an Active and Healthy Ageing App. (Machine Learning)
The official results: https://ifmbe.org/announcements/awards/2022-awards-recipients/#1654417287668-85e944b7-747a
1st PLACE: EUCLID team (DUTH and Athena RC), Score: 0.8635
2nd PLACE:UBU team (Ubon Ratchathani University, Dept. of Electrical and Electronic Engineering, Thailand), Score: 0.7970
3rd PLACE:GIB-Team (University of Valladolid, Grupo de Ingeniería Biomédica, Spain), Score: 0.75
TREC 2013
2nd place among 15 research teams.
Algorithms for contextual recommendation in the Text Retrieval Conference (TREC) Contextual Suggestion track, November 2013.
