All Outputs (6)

Maintenance automation using deep learning methods: A case study from the aerospace industry (2023)
Conference Proceeding
Mayhew, P. J., Ihshaish, H., Deza, I., & Del Amo, A. (2023). Maintenance automation using deep learning methods: A case study from the aerospace industry. In Artificial Neural Networks and Machine Learning – ICANN 2023 (295-307). https://doi.org/10.1007/978-3-031-44204-9_25

In this study, state-of-the-art AI models are employed to classify aerospace maintenance records into categories based on the fault descriptions of avionic components. The classification is performed using short natural language text descriptions pro... Read More about Maintenance automation using deep learning methods: A case study from the aerospace industry.

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.

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.

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.

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.