Publications

2020

Articles de revue

  1. Baltres, A.; Al Masry, Z.; Zemouri, R.; Valmary-Degano, S.; Arnould, L.; Zerhouni, N. and Devalland, C. Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer. In Breast Cancer, 2020. doi  www 
  1. Zemouri, R. Semi-Supervised Adversarial Variational Autoencoder. In Machine Learning and Knowledge Extraction, 2 (3): 361-378, 2020. doi  www 
  1. Zemouri, R.; Levesque, M.; Amyot, N.; Hudon, C.; Kokoko, O. and Tahan, S. A. Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators. In IEEE Access, 8: 5438-5454, 2020. doi  www 
  1. Proteau, A.; Zemouri, R.; Tahan, A. and Thomas, M. Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach. In International Journal of Advanced Manufacturing Technology, 111 (11-12): 3597-3611, 2020. doi  www 

Chapitres d'ouvrage

  1. Remadna, I.; Terrissa, S. L.; Zemouri, R.; Ayad, S. and Zerhouni, N. Unsupervised Feature Reduction Techniques with Bidirectional GRU Neural Network for Aircraft Engine RUL Estimation. In Advanced Intelligent Systems for Sustainable Development (AI2SD'2019), pages 496-506, 2020. doi  www 

Articles de conférence

  1. Remadna, I.; Terrissa, S. L.; Zemouri, R.; Ayad, S. and Zerhouni, N. Leveraging the Power of the Combination of CNN and Bi-Directional LSTM Networks for Aircraft Engine RUL Estimation. In 2020 Prognostics and Health Management Conference (PHM-Besanc con), pages 116-121, IEEE, Besancon, France, 2020. doi  www 
  1. Zemouri, R.; Levesque, M.; Amyot, N.; Hudon, C. and Kokoko, O. Deep Variational Autoencoder: An Efficient Tool for PHM Frameworks. In 2020 Prognostics and Health Management Conference (PHM-Besanc con), pages 235-240, IEEE, Besancon, France, 2020. doi  www 

2019

Articles de revue

  1. Zemouri, R.; Devalland, C.; Valmary-Degano, S. and Zerhouni, N. Intelligence artificielle~: quel avenir en anatomie pathologique~?. In Annales de Pathologie, 39 (2): 119-129, 2019. doi  www 
  1. Zemouri, R.; Omri, N.; Fnaiech, F.; Zerhouni, N. and Fnaiech, N. A new growing pruning deep learning neural network algorithm (GP-DLNN). In Neural Computing and Applications, 2019. doi  www 
  1. Zemouri, R.; Zerhouni, N. and Racoceanu, D. Deep Learning in the Biomedical Applications: Recent and Future Status. In Applied Sciences, 9 (8): 1526, 2019. doi  www 

2018

Articles de conférence

  1. Zemouri, R.; Omri, N.; Morello, B.; Devalland, C.; Arnould, L.; Zerhouni, N. and Fnaiech, F. Constructive Deep Neural Network for Breast Cancer Diagnosis. In 10th IFAC Symposium on Biological and Medical Systems BMS 2018, pages 98-103, S~ao Paulo, Brazil, 2018. doi  www 
  1. Remadna, I.; Terrissa, S. L.; Zemouri, R. and Ayad, S. An overview on the deep learning based prognostic. In 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pages 196-200, IEEE, Hammamet, France, 2018. doi  www 
  1. Zemouri, R.; Omri, N.; Devalland, C.; Arnould, L.; Morello, B.; Zerhouni, N. and Fnaiech, F. Breast cancer diagnosis based on joint variable selection and Constructive Deep Neural Network. In 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), pages 159-164, IEEE, Tunis, France, 2018. doi  www 

2017

Chapitres d'ouvrage

  1. Zemouri, R. and Patic, P. Mobile Robot Used to Collect Data from a Difficult Access Area. In New Advances in Mechanisms, Mechanical Transmissions and Robotics, pages 287-295, Springer Link, 2017. doi  www 

Articles de conférence

  1. Zemouri, R. An evolutionary building algorithm for Deep Neural Networks. In 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), pages 1-7, IEEE, Nancy, France, 2017. doi  www 

2012

Articles de revue

  1. Zemouri, R. and Zerhouni, N. Autonomous and adaptive procedure for cumulative failure prediction. In Neural Computing and Applications, 21 (2): 319-331, 2012. doi  www 

Articles de conférence

  1. Javed, K.; Gouriveau, R.; Zerhouni, N.; Zemouri, R. and Li, X. Robust, reliable and applicable tool wear monitoring and prognostic : approach based on an Improved-Extreme Learning Machine. In IEEE International Conference on Prognostics and Health Management, PHM'12., pages 1-9, I, Denver, Colorado, United States, 2012. www 
  1. Javed, K.; Gouriveau, R.; Zemouri, R. and Zerhouni, N. Features Selection Procedure for Prognostics: An Approach Based on Predictability. In 8th IFAC International Symposium on Fault Dectection, Supervision and Safety for Technical Processes, SAFEPROCESS'12., pages 25-30, Mexico City, Mexico, 2012. www 

2011

Articles de conférence

  1. Javed, K.; Gouriveau, R.; Zemouri, R. and Zerhouni, N. Improving data-driven prognostics by assessing predictability of features. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM'11., pages 555-560, Montréal, Québec, Canada, 2011. www 

2010

Articles de revue

  1. Zemouri, R.; Gouriveau, R. and Ciprian Patic, P. Improving the prediction accuracy of recurrent neural network by a PID controller. In International Journal of Systems Applications, Engineering & Development., 4 (2): 19-34, 2010. www 
  1. Zemouri, R.; Gouriveau, R. and Zerhouni, N. Defining and applying prediction performance metrics on a recurrent NARX time series model. In Neurocomputing, 73 (13-15): 2506-2521, 2010. doi  www 
  1. Patic, P.; Zemouri, R. and DUŢu a, L. Recurrent Neural Networks in Linear Systems Controlling. In Studies in Informatics and Control, 19 (2), 2010. doi  www 

Articles de conférence

  1. Zemouri, R. and Gouriveau, R. Towards accurate and reproducible predictions for prognostic : an approach combining a RRBF Network and an AutoRegressive Model. In 1st IFAC Workshop on Advanced Maintenance Engineering, Services and Technology, IFAC A-MEST'10., pages 163-168, Lisbonne, Portugal, 2010. www 

2009

Articles de revue

  1. Minca, E.; Filip, F. G.; Zemouri, R.; Dragomir, F. and Dragomir, O. Advanced methods for modeling the discrets hierarchical systems. In IFAC Proceedings Volumes, 42 (4): 1667-1672, 2009. doi  www 

Articles de conférence

  1. Zemouri, R.; Gouriveau, R. and Zerhouni, N. Combining a recurrent neural network and a PID controller for prognostic purpose. In PENTOM'09 - PErformances et Nouvelles TechnolOgies en Maintenance., pages 1-14, Autrans, France, 2009. www 
  1. Zemouri, R.; Racoceanu, D.; Zerhouni, N.; Minca, E. and Filip, F. G. Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction. In Intelligent systems and automation: 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA'09), pages 85-90, AIP, Zarzis (Tunisia), France, 2009. doi  www 
  1. Minca, E.; Zemouri, R.; Dragomir, F. and Dragomir, O. E. Hierarchical systems monitoring using Recurrent Synchronized Fuzzy Petri Nets. In 2009 European Control Conference (ECC), pages 4775-4779, IEEE, Budapest, France, 2009. doi  www 

2006

Articles de revue

  1. Zemouri, R. and Faure, J-M. Comparative Study Between the Timed Automata and the Recurrent Radial Basis Function for Discrete Event System Diagnosis. In IFAC Proceedings Volumes, 39 (13): 1455-1460, 2006. doi  www 

Articles de conférence

  1. Zemouri, R. and Faure, J-M. Comparative Study Between the Timed Automata and the Recurrent Radial Basis Function for Discrete Event System Diagnosis. In 6th IFAC Symposium, SAFEPROCESS 2006, pages 1455-1460, Elsevier, Beijing, China, 2006. doi  www 
  1. Zemouri, R. and Faure, J. M. Diagnosis of discrete event system by stochastic timed automata. In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, pages 1861-1866, IEEE, Munich, Germany, 2006. doi  www 

2005

Articles de conférence

  1. Limal, S.; Denis, B.; ZEMOURI, R. and Lesage, J-J. Réglage du routeur d'un sous réseau dédié au contr^ole commande temps réel : Approche expérimentale avec apprentissage par réseau de neurones. In 3ème Conférence Internationale sur la Productique (CIP'05), pages paper #44, 7 pages, Tlemcen, Algeria, 2005. www 

2003

Articles de revue

  1. Zemouri, R.; Racoceanu, D. and Zerhouni, N. Réseaux de neurones récurrents `a fonctions de base radiales : RRFR Application au pronostic. In Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, 16 (3): 307-338, 2003. doi  www 
  1. Zemouri, R.; Racoceanu, D. and Zerhouni, N. Réseaux de neurones récurrents `a fonctions de base radiales. Application `a la surveillance dynamique. In Journal Européen des Systèmes Automatisés (JESA), 37 (1): 49-81, 2003. doi  www 
  1. Zemouri, R.; Racoceanu, D. and Zerhouni, N. Recurrent radial basis function network for time-series prediction. In Engineering Applications of Artificial Intelligence, 16 (5-6): 453-463, 2003. doi  www 

Thèses

  1. ZEMOURI, R. Contribution `a la surveillance des systèmes de production `a l'aide des réseaux de neurones dynamiques : Application `a la e-maintenance. Ph.D. Thesis, Université de Franche-Comté, 2003.

2002

Articles de revue

  1. Zemouri, R.; Daniel, R. and Noureddine, Z. APPLICATION OF THE DYNAMIC RBF NETWORK IN A MONITORING PROBLEM OF THE PRODUCTION SYSTEMS. In IFAC Proceedings Volumes, 35 (1): 295-300, 2002. doi  www 

Articles de conférence

  1. Zemouri, R.; Racoceanu, D. and Zerhouni, N. From the spherical to an elliptic form of the dynamic RBF neural network influence field. In 2002 International Joint Conference on Neural Networks (IJCNN), pages 107-112, IEEE, Honolulu, United States, 2002. doi  www 

2001

Articles de conférence

  1. Zemouri, R.; Ryad, Z.; Daniel, R. and Noureddine, Z. The RRBF. Dynamic representation of time in radial basis function network. In ETFA 2001. 2001 8th International Conference on Emerging Technologies and Factory Automation., pages 737-740, IEEE, Antibes-Juan les Pins, France, 2001. doi  www 
  1. Zemouri, R.; Racoceanu, D. and Zerhouni, N. A Petri nets graphic method of reduction using birth-death processes. In 2001 ICRA. IEEE International Conference on Robotics and Automation, pages 46-51, IEEE, Seoul, South Korea, 2001. doi  www 
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