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Feature-based search space characterisation for data-driven adaptive operator selection (2023)
Journal Article
Aydin, M. E., Durgut, R., Rakib, A., & Ihshaish, H. (2024). Feature-based search space characterisation for data-driven adaptive operator selection. Evolving Systems, 15(1), 99-114. https://doi.org/10.1007/s12530-023-09560-7

Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide t... Read More about Feature-based search space characterisation for data-driven adaptive operator selection.

qNoise: A generator of non-Gaussian colored noise (2022)
Journal Article
Deza, J. I., & Ihshaish, H. (2022). qNoise: A generator of non-Gaussian colored noise. SoftwareX, 18, Article 101034. https://doi.org/10.1016/j.softx.2022.101034

We introduce a software generator for a class of colored (self-correlated) and non-Gaussian noise, whose statistics and spectrum depend on two param- eters, q and τ. Inspired by Tsallis’ nonextensive formulation of statistical physics, the so-called... Read More about qNoise: A generator of non-Gaussian colored noise.

AgroSupportAnalytics: A cloud-based complaints management and decision support system for sustainable farming in Egypt (2021)
Journal Article
Munir, K., Ghafoor, M., Khafagy, M., & Ihshaish, H. (2022). AgroSupportAnalytics: A cloud-based complaints management and decision support system for sustainable farming in Egypt. Egyptian Informatics Journal, 23(1), 73-82. https://doi.org/10.1016/j.eij.2021.06.002

Sustainable Farming requires up-to-date advice on crop diseases, patterns, and adequate prevention actions to face developing circumstances. Currently, in developing countries like Egypt, farmers’ access to such information is extremely limited due t... Read More about AgroSupportAnalytics: A cloud-based complaints management and decision support system for sustainable farming in Egypt.

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

© 2015 Author(s). 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 106) and edges (up to at least 1012). The key innovation is an efficient set... Read More about Par@Graph-a parallel toolbox for the construction and analysis of large complex climate networks.

Towards improving numerical weather predictions by evolutionary computing techniques (2012)
Journal Article
Senar, M. A., Cortés, A., & Ihshaish, H. (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.

Parallel multi-level genetic ensemble for numerical weather prediction enhancement (2012)
Journal Article
Senar, M. A., Ihshaish, H., & Cortés, A. (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.