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Classification of eye-state using EEG recordings: Speed-up gains using signal epochs and mutual information measure (2019)
Conference Proceeding
Asquith, P. M., Asquith, P. M., & Ihshaish, H. (2019). Classification of eye-state using EEG recordings: Speed-up gains using signal epochs and mutual information measure. https://doi.org/10.1145/3331076.3331095

© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. The classification of electroencephalography (EEG) signals is useful in a wide range of applications such as seizure detection/prediction, motor imagery classification,... Read More about Classification of eye-state using EEG recordings: Speed-up gains using signal epochs and mutual information measure.

The construction of complex networks from linear and nonlinear measures - Climate networks (2015)
Conference Proceeding
Deza, J. I., & Ihshaish, H. (2015). The construction of complex networks from linear and nonlinear measures - Climate networks. https://doi.org/10.1016/j.procs.2015.05.260

© The Authors. Published by Elsevier B.V. During the last decade the techniques of complex network analysis have found application in climate research. The main idea consists in embedding the characteristics of climate variables, e.g., temperature, p... Read More about The construction of complex networks from linear and nonlinear measures - Climate networks.

Par@Graph – A parallel toolbox for the construction and analysis of large complex climate networks (2015)
Journal Article
Ihshaish, H., Tantet, A., Dijkzeul, J. C. M., & Dijkstra, H. A. (2015). Par@Graph – A parallel toolbox for the construction and analysis of large complex climate networks. Geoscientific Model Development, 8(10), 3321-3331. https://doi.org/10.5194/gmd-8-3321-2015

In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 10^6) and edges (up to at least 10^12). The key innovation is an efficient set of parallel soft... Read More about Par@Graph – A parallel toolbox for the construction and analysis of large complex climate networks.

Parallel software package for the construction and analysis of complex networks (2013)
Presentation / Conference
Ihshaish, H., & Dijkzeul, J. (2013, November). Parallel software package for the construction and analysis of complex networks. Poster presented at LINC Mid-Term Review

In climate research, big and complex networks could be generated by the big climate data produced by high resolution climate models, and also observations. To analyze such complex networks, there are two main computational challenges concerning both... Read More about Parallel software package for the construction and analysis of complex networks.

Parallel multi-level genetic ensemble for numerical weather prediction enhancement (2012)
Journal Article
Senar, M. A., Ihshaish, H., Cortés, A., & Senar, M. (2012). Parallel multi-level genetic ensemble for numerical weather prediction enhancement. Procedia Computer Science, 9, 276-285. https://doi.org/10.1016/j.procs.2012.04.029

The need for reliable predictions in environmental modelling is well-known. Particularly, the predicted weather and meteorological information about the future atmospheric state is crucial and necessary for almost all other areas of environmental mod... Read More about Parallel multi-level genetic ensemble for numerical weather prediction enhancement.

Tuning G-ensemble to improve forecast skill in numerical weather prediction models (2012)
Book Chapter
Ihshaish, H., Cortes, A., & Senar, M. (2012). Tuning G-ensemble to improve forecast skill in numerical weather prediction models. In H. R. Arabnia, H. Ishii, M. Ito, K. Joe, & H. Nishikawa (Eds.), Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications PDPTA'12, 869-875. WORLDCOMP'12

The process of weather forecasting produced by numerical weather prediction (NWP) models is complex and not always accurate. Moreover, it is generally defined by its very nature as a process that has to deal with uncertainties. In previous works, a n... Read More about Tuning G-ensemble to improve forecast skill in numerical weather prediction models.

Towards improving numerical weather predictions by evolutionary computing techniques (2012)
Journal Article
Senar, M. A., Cortés, A., Ihshaish, H., Cortes, A., & Senar, M. (2012). Towards improving numerical weather predictions by evolutionary computing techniques. Procedia Computer Science, 9, 1056-1063. https://doi.org/10.1016/j.procs.2012.04.114

Weather forecasting is complex and not always accurate, moreover, it is generally defined by its very nature as a process that has to deal with uncertainties. In a previous work, a new weather prediction scheme was presented, which uses evolutionary... Read More about Towards improving numerical weather predictions by evolutionary computing techniques.

Genetic ensemble (G-Ensemble) for meteorological prediction enhancement (2011)
Presentation / Conference
Ihshaish, H., Cortes, A., & Senar, M. (2011, July). Genetic ensemble (G-Ensemble) for meteorological prediction enhancement. Paper presented at The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2011)

The need for reliable predictions in environmental modelling is long known. Particularly, the predicted weather and meteorological information about the future atmospheric state is crucial and necessary for almost all other areas of environmental mod... Read More about Genetic ensemble (G-Ensemble) for meteorological prediction enhancement.