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Richard LabibDepartment of Mathematics and Industrial Engineering
Profile in the Directory of Experts of Polytechnique Montréal
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Brault, J.-M., Labib, R., Perrier, M., & Stuart, P. R. (2011). Prediction of activated sludge filamentous bulking using ATP data and neural networks. Canadian Journal of Chemical Engineering, 89(4), 901-913. External link
Bernier, M., Labib, R., Pinel, P., & Paillot, R. (2004). A Multiple load aggregation algorithm for annual hourly simulations of GCHP systems. HVAC and R Research, 10(4), 471-487. External link
Birikundavyi, S., Labib, R., Trung, H. T., & Rousselle, J. (2002). Performance of Neural Networks in Daily Streamflow Forecasting. Journal of Hydrologic Engineering, 7(5), 392-398. External link
Connolly, J.-F., & Labib, R. (2009). A multiscale scheme for approximating the Quantron's discriminating function. IEEE Transactions on Neural Networks, 20(8), 1254-1266. External link
Denis, H. L., Alpaugh, M., Alvarez, C. P., Fenyi, A., Barker, R. A., Chouinard, S., Arrowsmith, C. H., Melki, R., Labib, R., Harding, R. J., & Cicchetti, F. (2023). Detection of antibodies against the huntingtin protein in human plasma. Cellular and Molecular Life Sciences, 80(2), 45 (11 pages). External link
De Montigny, S., & Labib, R. (2011, July). Learning Algorithms for a Specific Configuration of the Quantron [Paper]. International Joint Conference on Neural Networks (IJCNN 2011), San Jose, California. External link
Hamel, É., & Labib, R. (2019). Modeling biological refractory periods and synaptic depression in an artificial neuron. Biomedical Physics & Engineering Express, 5(2), 025038 (16 pages). External link
Kolus, A., Dube, P. A., Imbeau, D., Labib, R., & Dubeau, D. (2014). Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches. Applied Ergonomics, 45(6), 1475-1483. External link
Lamontagne-Proulx, J., St-Amour, I., Labib, R., Pilon, J., Denis, H. L., Cloutier, N., Roux-Dalvai, F., Vincent, A. T., Mason, S. L., Williams-Gray, C., Duchez, A.-C., Droit, A., Lacroix, S., Dupre, N., Langlois, M., Chouinard, S., Panisset, M., Barker, R. A., Boilard, E., & Cicchetti, F. (2019). Portrait of blood-derived extracellular vesicles in patients with Parkinson's disease. Neurobiology of Disease, 124, 163-175. External link
Labib, R., & Montigny, S. (2016). Closed-Form Evaluation of Integrals Involving the Gamma Function. Communications in Statistics - Theory and Methods, 46(17), 8328-8342. External link
L'Espérance, P.-Y., & Labib, R. (2013). Model of an Excitatory Synapse Based on Stochastic Processes. IEEE Transactions on Neural Networks and Learning Systems, 24(9), 1449-58. External link
Labib, R., & De Montigny, S. (2012). On the learning potential of the approximated quantron. International Journal of Neural Systems, 22(3). External link
Labib, R., & Khattar, K. (2010). Mlp Bilinear Separation. Neural Computing and Applications, 19(2), 305-315. External link
Labib, R., & Assadi, R. (2007). Modified Multi-Layered Perceptron Applied to Packing and Covering Problems. Neural Computing and Applications, 16(2), 173-186. External link
Labib, R., de Montigny, S., & Adadji, M. (2006). Le grand livre des lois : parcours dans le monde des probabilités. External link
Labib, R., Audette, F., Fortin, A., & Assadi, R. (2005). Hardware implementation of a new artificial neuron. International Journal of Neural Systems, 15(6), 427-433. External link
Labib, R. (2004). Closure to "Performance of Neural Networks in Daily Streamflow Forecasting" by S. Birikundavyi, R. Labib, H. T. Trung, and J. Rousselle. Journal of Hydrologic Engineering, 9(6), 557-558. External link
Labib, R., Lefebvre, M., Ribeiro, J., Rousselle, J., & Trung, H. T. (2000). Application of diffusion processes to runoff estimation. Journal of Hydrologic Engineering, 5(1), 1-7. External link
Labib, R. (2000). Processus de diffusion : outils de modélisation, de prévision et de contrôle [Ph.D. thesis, École Polytechnique de Montréal]. Available
Labib, R. (1999, July). New single neuron structure for solving nonlinear problems [Paper]. International Joint Conference on Neural Networks (IJCNN 1999), Washington, DC, USA. External link
Lefebvre, M., & Labib, R. (1996, June). Risk sensitive optimal control of wear processes [Paper]. AMS/SIAM Seminar on Mathematics of Stochastic Manufacturing Systems, Williamsburg, VA, USA. Unavailable
Lefebvre, M., & Labib, R. (1997, July). Risk sensitive optimal control of wear processes: the vector case [Paper]. 18th IFIP TC7 Conference on Systems Modelling and Optimization, Boca Raton, FL, USA. External link
Lefebvre, M., & Labib, R. (1997). Temps d'atteinte de cercles pour des processus de Bessel bidimensionnels. Annales des sciences mathématiques du Québec, 21(2), 123-131. External link
Lefebvre, M., & Labib, R. (1996). Hitting lines and circles with diffusion processes. Australian Journal of Statistics, 38(2), 213-222. External link
Labib, R., Lefebvre, M., Ribeiro, J., & Rousselle, J. (1996). Modèles utilisant les processus de diffusion comme outils de prévision. (Technical Report). Unavailable
Lefebvre, M., & Labib, R. (1996, August). Modelling and controlling the flow of a river [Presentation]. In 16th Nordic Conference on Mathematical Statistics, Lahti, Finlande. Unavailable
Labib, R. (1995). Problèmes de premier passage pour des processus de Bessel [Master's thesis, École Polytechnique de Montréal]. Unavailable
Pasquier, P., Zarrella, A., & Labib, R. (2018). Application of artificial neural networks to near-instant construction of short-term g-functions. Applied Thermal Engineering, 143, 910-921. External link
Ratle, F., Lecarpentier, B., Labib, R., & Trochu, F. (2004, September). Multi-objective optimization of a composite material spring design using an evolutionary algorithm [Paper]. 8th International Conference on Parallel Problem Solving from Nature (PPSN 2004), Birmingham, UK. External link
Sharif Azadeh, S., Labib, R., & Savard, G. (2013). Railway demand forecasting in revenue management using neural networks. International Journal of Revenue Management, 7(1), 18-36. External link
Zagre, G. E., Gamache, M., Labib, R., & Shlenchak, V. (2023). Machine learning algorithms for real-time coal recognition using monitor-while-drilling data. International Journal of Mining, Reclamation and Environment, 26 pages. External link
