M. Laib, R. Aggoune, R. Crespo, P. Hubsch,
Steel Quality Monitoring Using Data-Driven Approaches: ArcelorMittal Case Study, 2022,
Lecture Notes in Computer Science, vol 13377.
Paper
M. Kanevski, M. Laib, Unsupervised Learning of High Dimensional Environmental Data
Using Local Fractality Concept. In: Del Bimbo A. et al. (eds) Pattern Recognition.
ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science,
vol 12666.
Paper
U. Iffat, E. Roseren, M. Laib, Dealing with High Dimensional Sequence Data in Manufacturing.
Procedia CIRP, 2021, 104, pp. 1298–1303.
Paper
F. Guignard, M. Laib, F. Amato, M. Kanevski, Advanced analysis of temporal data
using Fisher-Shannon information: theoretical development and application in geosciences, 2020,
Frontiers in Earth Science, 8:255.
arXiv:1912.02452/
Paper
F. Amato, M. Laib, F. Guignard, M. Kanevski, Analysis of air pollution time series using
complexity-invariant distance and information measures, 2020,
Physica A: Statistical Mechanics and its Applications, 547:124391.
arXiv:1909.11484/
Paper
M. Laib and M. Kanevski, A new algorithm for redundancy minimisation in geo-environmental
data, 2019. Computers & Geosciences, 133 104328.
Paper
M. Laib, F. Guignard, M. Kanevski, L. Telesca, Community detection analysis
in wind speed-monitoring systems using mutual information-based complex network, 2019/04.
Chaos: An Interdisciplinary Journal of Nonlinear Science, 29 (4) p. 043107.
arXiv:1809.00511/
Paper
L. Telesca, F. Guignard, M. Laib, M. Kanevski, Analysis of temporal properties of extremes of wind
measurements from 132 stations over Switzerland, 2019.
arXiv:1808.08847/
Paper
L. Telesca, M. Laib, F. Guignard, D. Mauree, M. Kanevski, Linearity versus non-linearity in high frequency multilevel
wind time series measured in urban areas, Chaos, Solitons & Fractals, 120 (2019), pp. 234-244.
arXiv:1808.07265 /
Paper
F. Guignard, M. Lovallo, M. Laib, J. Golay, M. Kanevski, N. Helbig, L. Telesca, Investigating the time dynamics of
wind speed in complex terrains by using the Fisher–Shannon method, 2019, Physica A: Statistical Mechanics and its
Applications, 523 pp. 611-621.
arXiv:1807.11849 /
Paper
M. Laib, M. Kanevski, A novel filter algorithm for unsupervised feature selection based on a space
filling measure. Proceedings of the 26rd European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), pp. 485-490, Bruges (Belgium), 2018.
Paper
M. Laib, L. Telesca, M. Kanevski, Long-range fluctuations and
multifractality in connectivity density time series of a wind speed monitoring network,
Chaos: An Interdisciplinary Journal of Nonlinear Science, 28 (2018) pp. 033108.
arXiv:1708.04216 /
Paper
M. Laib, J. Golay, L. Telesca, M. Kanevski, Multifractal analysis of the time series
of daily means of wind speed in complex regions, Chaos, Solitons & Fractals, 109 (2018)
pp. 118-127, arXiv:1710.01490 /
Paper
M. Laib, L. Telesca, M. Kanevski, Periodic fluctuations in correlation-based
connectivity density time series: application to wind speed-monitoring network in Switzerland, Physica A:
Statistical Mechanics and its Applications, 492 (2018) pp. 1555-1569
arXiv:1708.03782 /
Paper
M. Laib, M. Kanevski, Spatial Modelling of Extreme Wind Speed Distributions in Switzerland,
Energy Procedia, 97: 100-107, 2016.
Paper
I. Rezgui, Z. Gheribi-Aoulmi, M. Laib, La méthode combinatoire (s) pour la construction de
quelques types de plans en blocs incomplets partiellement équilibrés et le R-package "CombinS" associé,
Sciences & Technologie A– N°42, Décembre (2015), pp. 15-22.
Paper
A. Boudraa, Z. Gheribi-Aoulmi, M. Laib, Recursive Method for Construction of Resolvable Nested
Designs and Uniform Designs Associated, International Journal of Research and Reviews in Applied Sciences
17 (2), 167, 2013.
Paper
Conferences
R. Aggoune, M. Laib, A Genetic Algorithm for Feature Selection Applied to Data From
Multiples Sources: Application to Manufacturing Data. 23ème congrès annuel de la Société Française
de Recherche Opérationnelle et d’Aide à la Décision, INSA Lyon, Feb 2022, Villeurbanne -
Lyon. Abstract
M. Laib, F. Guignard, M. Kanevski, L. Telesca, Analysis of Wind Time Series Using Network Science and Multifractal Concept,
EGU General Assembly 2019, Vienna. Poster
M. Laib, J. Golay, F. Guignard, M. Kanevski, Deep Learning for Remote Sensing Scene Classification: A Simple and High-Performance Architecture,
EGU General Assembly 2018, Vienna. Poster
M. Laib, J. Golay, L. Telesca, M. Kanevski, Spatial mapping of the multifractal parameters of wind time series in Switzerland,
EGU General Assembly 2018, Vienna. Poster
J. Golay, M. Laib, M. Kanevski, IDmining: An R Package for Mining Large Datasets with the Morisita Estimator of Intrinsic Dimension,
EGU General Assembly 2018, Vienna. Poster
M. Kanevski, M. Laib, Analysis of high dimensional environmental data using local fractality concept and machine learning,
EGU General Assembly 2018, Vienna. Abstract
M. Laib, L. Telesca, M. Kanevski, Multifractal analysis of wind speed connectivity time series,
Swiss Geoscience Meeting, Davos 2017. Poster
M. Laib, L. Telesca, M. Kanevski, Modelling environmental data using unsupervised feature selection,
Spatial Statistics 2017, Lancaster. Poster
M. Laib, M. Kanevski, Network Analysis for High Frequency Wind Speed, EGU General Assembly 2017, Vienna.
M. Laib, M. Kanevski, Modelling Wind Data using Network and Time Series
Analysis, Swiss Geoscience Meeting, Geneva 2016. Poster
M. Laib, M. Kanevski, Analysis and Modelling of Extreme Wind Speed
Distribution in Mountainous Regions, EGU General Assembly 2016, Vienna. Poster