Skip to main content

Research Repository

Advanced Search

Browse


Problem classification for tailored help desk auto replies (2022)
Conference Proceeding
Nicholls, R., Fellows, R., Battle, S., & Ihshaish, H. (2022). Problem classification for tailored help desk auto replies. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 (445-454). https://doi.org/10.1007/978-3-031-15937-4_37

IT helpdesks are charged with the task of responding quickly to user queries. To give the user confidence that their query matters, the helpdesk will auto-reply to the user with confirmation that their query has been received and logged. This auto-re... Read More about Problem classification for tailored help desk auto replies.

Analysing the predictivity of features to characterise the search space (2022)
Conference Proceeding
Durgut, R., Aydin, M. E., Ihshaish, H., & Rakib, A. (2022). Analysing the predictivity of features to characterise the search space. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (1-13). https://doi.org/10.1007/978-3-031-15937-4_1

Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised... Read More about Analysing the predictivity of features to characterise the search space.

Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models (2022)
Presentation / Conference
Sharp, R., Ihshaish, H., & Deza, J. I. (2022, August). Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models. Paper presented at International Conference for Sustainable Ecological Engineering Design for Society, SEEDS 22, UWE Bristol and online

The forecasting of large ramps in wind power output known as ramp events is crucial for the incorporation of large volumes of wind energy into national electricity grids. Large variations in wind power supply must be compensated by ancillary energy s... Read More about Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models.

Task-oriented dialogue systems: Performance vs. quality-optima, a review (2022)
Conference Proceeding
Fellows, R., Ihshaish, H., Battle, S., Haines, C., Mayhew, P., & Deza, J. I. (2022). Task-oriented dialogue systems: Performance vs. quality-optima, a review. In David C. Wyld et al. (Eds): SIPP, NLPCL, BIGML, SOEN, AISC, NCWMC, CCSIT - 2022 pp. 69-87, 2022. CS & IT - CSCP 2022 (69-87). https://doi.org/10.5121/csit.2022.121306

Task-oriented dialogue systems (TODS) are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS are not yet reaching their full pote... Read More about Task-oriented dialogue systems: Performance vs. quality-optima, a review.

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.

Classification of eye-state using EEG recordings: Speed-up gains using signal epochs and mutual information measure (2019)
Conference Proceeding
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.

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.

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.

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. (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. (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.