Publications scientifiques

– Long short-term memory for predicting firemen interventions. In 6th 10 IEEE International Conference on Control, Decision and Information Technologies, pages ***–***, April 2019. Selene Leya Cerna Nahuis, Christophe Guyeux, Héber Hwang Arcolezi, Anna Diva Plasencia Lotufo, Raphaël Couturier, and Guillaume Royer.

–  Anonymously forecasting the number and nature of firefighting operations. In Proceedings of the 23rd International Database Applications & Engineering Symposium, pages 30 :1–30 :8. ACM, June 2019. Jean-François Couchot, Christophe Guyeux, and Guillaume Royer.

– Predicting the firemen interventions : a concrete case study. In European Network for Business and Industrial Statistics, pages ***–***, September 2019.Christophe Guyeux.

– Firemen prediction by using neural networks : a real case study. In Kapoor
S. Bi Y., Bhatia R., editor, Intelligent Systems Conference, volume
1037, pages 541–552. Springer, Cham, September 2019. Christophe Guyeux, Jean-Marc Nicod, Christophe Varnier, Zeina
Al Masry, Noureddine Zerhouni, Nabil Omri, and Guillaume Royer.

– A comparison of lstm and xgboost for predicting firemen interventions. In Reis L. Costanzo S. Orovic I. Moreira F. Rocha Á., Adeli H., editor, 8th World Conference on Information Systems and Technologies, volume 1160, pages 424–434. Springer (AISC series), April 2020. Selene Leya Cerna Nahuis, Christophe Guyeux, Héber Arcolezi, Raphaël Couturier, Guillaume Royer, and Anna Diva Lotufo.

–  Predicting fire brigades operational breakdowns : a real case study. Mathematics and Computer Science, 8(8), August 2020. S.I. New Trends in Machine Learning : Theory and Practice. Selene Leya Cerna Nahuis, Christophe Guyeux, Guillaume Royer, Céline Chevallier, and Guillaume Plumerel.

–  Forecasting the number of firefighters interventions per region with local- 9 differential-privacy-based data. Computers & Security, 96, September 2020. Heber H. Arcolezi, Jean-François Couchot, Selene Cerna, Christophe Guyeux, Guillaume Royer, Béchara Al Bouna, and Xiaokui Xiao.

– Artificial intelligence to assist and optimize fire brigade services. In The International Emergency Management Society, 2020 Annual Conference, pages ***–***, November 2020. Selene Leya Cerna Nahuis, Christophe Guyeux, and Guillaume Royer.

– Time series forecasting for the number of firefighters interventions. In The 35-th International Conference on Advanced Information Networking and Applications, pages ***–***. Springer Series « Advances in Intelligent Systems and Computing », May 2021. Roxane Mallouhy, Christophe Guyeux, Chady Abou Jaoude, and Abdallah Makhoul.

– Preserving geo-indistinguishability of the emergency scene to predict ambulance response time. Mathematical and Computational Applications, 26(3), September 2021. Héber Arcolezi, Selene Cerna, Christophe Guyeux, and Jean-François Couchot.

– Machine learning-based forecasting of firemen ambulances’ turnaround time in hospitals, considering the covid-19 impact. Applied Soft Computing Journal, 109, September 2021. Selene Cerna, Héber Arcolezi, Christophe Guyeux, Guillaume Royer Fey, and Céline Chevallier

–  Privacy-preserving prediction of victim’s mortality and their need for transportation to health facilities. IEEE Transactions on Industrial Informatics, 2021. Heber H. Arcolezi, Selena Cerna, Jean-françois Couchot, Christophe Guyeux, and Abdallah Makhoul.

–  The usefulness of nlp techniques for predicting firefighting responses. Neural Computing and Applications, 2021. Selene Cerna, Christophe Guyeux, and David Laiymani.

–  How to predict patient arrival in the emergency room. In 10th World Conference on Information Systems and Technologies, pages ***–***, April 2022. Christophe Guyeux and Jacques M. Bahi.

–  Anomalies and breakpoint detection for a dataset of firefighters’ operations during the covid-19 period in france. In 10th World Conference on Information Systems and Technologies, pages ***–***, April 2022. Roxane Mallouhy, Christophe Guyeux, Chady Abou Jaoude, and Abdallah
Makhoul.

–  Forecasting the number of firemen interventions using exponential smoothing methods : a case study. In 36-th International Conference on Advanced Information Networking and Applications, volume 449, pages ***–***. Springer LNNS, April 2022. Roxane Mallouhy, Christophe Guyeux, Chady Abou Jaoude, and Abdallah Makhoul. 

– Machine learning for predicting firefighters’ interventions per type of mission. In 8th International Conference on Control, Decision and Information Technologies, pages ***–***, May 2022. Roxane Mallouhy, Christophe Guyeux, Chady Abou Jaoude, and Abdallah Makhoul.

–  How to build an optimal and operational knowledge base to predict firefighters’ interventions. In Intelligent Systems Conference, pages ***–***, September 2022. Christophe Guyeux, Abdallah Makhoul, and Jacques Bahi