All Outputs (5)

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.