Conversation analysis for computational modelling of task-oriented dialogue (2023)
Thesis
Duran, N. Conversation analysis for computational modelling of task-oriented dialogue. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/10021806

Current methods of dialogue modelling for Conversational AI (CAI) bear little resemblance to the manner in which humans organise conversational interactions. The way utterances are represented, interpreted, and generated are determined by the necessi... Read More about Conversation analysis for computational modelling of task-oriented dialogue.

Chatbot in E-learning (2023)
Conference Proceeding
Hussain, S., Al-Hashmi, S. H., Malik, M. H., & Ali Kazmi, S. I. (2023). Chatbot in E-learning. In SHS Web of Conferences: International Conference on Teaching and Learning – Digital Transformation of Education and Employability (ICTL 2022). https://doi.org/10.1051/shsconf/202315601002

In many modern apps, especially those that provide the user intelligence help, the usage of chatbots is quite common. In reality, these systems frequently have chatbots that can read user inquiries and give the appropriate replies quickly and accurat... Read More about Chatbot in E-learning.

AI and Society: Behind AI Systems (2022)
Presentation / Conference
Ochu, E., & Aneja, U. (2022, October). AI and Society: Behind AI Systems. Presented at Behind AI Systems, Online

Artificial Intelligence, algorithmic systems and machine decision making are being embedded in many areas of society – policing, justice, finance, banking, shopping and navigation just to name a few. These systems are affecting people’s lives in ways... Read More about AI and Society: Behind AI Systems.

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.

A spiking neural network model of rodent head direction calibrated with landmark free learning (2022)
Journal Article
Stentiford, R., Knowles, T. C., & Pearson, M. J. (2022). A spiking neural network model of rodent head direction calibrated with landmark free learning. Frontiers in Neurorobotics, 16, -. https://doi.org/10.3389/fnbot.2022.867019

Maintaining a stable estimate of head direction requires both self-motion (idiothetic) information and environmental (allothetic) anchoring. In unfamiliar or dark environments idiothetic drive can maintain a rough estimate of heading but is subject t... Read More about A spiking neural network model of rodent head direction calibrated with landmark free learning.

Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue (2022)
Journal Article
Duran, N., Battle, S., & Smith, J. (2022). Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue. Communication Methods and Measures, 16(3), 182-214. https://doi.org/10.1080/19312458.2021.2020229

We present the Conversation Analysis Modeling Schema (CAMS), a novel dialogue labeling schema that combines the Conversation Analysis concept of Adjacency Pairs, with Dialogue Acts. The aim is to capture both the semantic and syntactic structure of d... Read More about Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue.

Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications (2020)
Journal Article
Souri, A., Mohammed, A. S., Yousif Potrus, M., Malik, M. H., Safara, F., & Hosseinzadeh, M. (2020). Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications. IEEE Access, 8, 23863-23874. https://doi.org/10.1109/ACCESS.2020.2967629

By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT... Read More about Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications.

Evolutionary n-level hypergraph partitioning with adaptive coarsening (2019)
Journal Article
Preen, R., & Smith, J. (2019). Evolutionary n-level hypergraph partitioning with adaptive coarsening. IEEE Transactions on Evolutionary Computation, 23(6), 962-971. https://doi.org/10.1109/TEVC.2019.2896951

Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel appro... Read More about Evolutionary n-level hypergraph partitioning with adaptive coarsening.

Effect of influential users on recommendation (2015)
Conference Proceeding
Oshodin, E., & Chiclana, F. (2015). Effect of influential users on recommendation. In 2015 SAI Intelligent Systems Conference (IntelliSys) (731-732). https://doi.org/10.1109/IntelliSys.2015.7361221

Relevant information stored in boundless pool of data source are required for the recommendation provided for users in recommender systems. Current recommender systems still suffer from inaccurate or erroneous predictions for users. This may be due t... Read More about Effect of influential users on recommendation.