Outputs (62)

mmFruit: A Contactless and Non-Destructive Approach for Fine-Grained Fruit Moisture Sensing Using Millimeter-Wave Technology (2024)
Journal Article
Niaz, F., Zhang, J., Khalid, M., Younas, M., & Niaz, A. (online). mmFruit: A Contactless and Non-Destructive Approach for Fine-Grained Fruit Moisture Sensing Using Millimeter-Wave Technology. IEEE Transactions on Mobile Computing (TMC), https://doi.org/10.1109/TMC.2024.3520914

Wireless sensing offers a promising approach for non-destructive and contactless identification of the moisture content in fruits. Traditional methods assess fruit quality based on external features such as color, shape, size, and texture. However, f... Read More about mmFruit: A Contactless and Non-Destructive Approach for Fine-Grained Fruit Moisture Sensing Using Millimeter-Wave Technology.

Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback (2024)
Journal Article
Grey, S., & Gordon, N. (2024). Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback. New Directions in the Teaching of Physical Sciences, 19(1), 1-9. https://doi.org/10.29311/ndtns.vi19.4103

It is widely recognised that feedback is an important part of learning: effective feedback should result in a meaningful change in student behaviour (Morris et al., 2021). However, individual feedback takes time to produce, and for large cohorts-typi... Read More about Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback.

HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients (2024)
Journal Article
Petch, L., Moustafa, A., Ma, X., & Yasser, M. (2025). HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients. Applied Intelligence, 55(2), Article 170. https://doi.org/10.1007/s10489-024-05924-x

This paper introduces a novel approach for training generative adversarial networks using federated machine learning. Generative adversarial networks have gained plenty of attention in the research community especially with their abilities to produce... Read More about HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients.

Social Media Data: Challenges, Mitigation Strategies, and Opportunities for Disaster Management (2024)
Presentation / Conference Contribution
Abid, S., Mishra, B. K., Younis, U., Thakker, D., & Mishra, N. (2024, December). Social Media Data: Challenges, Mitigation Strategies, and Opportunities for Disaster Management. Presented at 2024 International Conference on Frontiers of Information Technology, FIT 2024, Islamabad, Pakistan

For disaster management, the accurate and timely availability of factual information is crucial for effective decision-making and response. Traditional communication channels are either costly or do not provide real-time data. Here comes the role of... Read More about Social Media Data: Challenges, Mitigation Strategies, and Opportunities for Disaster Management.

Tailored Risk Assessment and Forecasting in Intermittent Claudication: A Proof of Concept Decision Support Tool (2024)
Presentation / Conference Contribution
Ravindhran, B., Prosser, J., Lim, A., Mishra, B., Lathan, R., Hitchman, L., Smith, G., Carradice, D., Thakker, D., Pymer, S., & Chetter, I. (2024, June). Tailored Risk Assessment and Forecasting in Intermittent Claudication: A Proof of Concept Decision Support Tool. Presented at The European Society for Vascular Surgery Translational Spring Meeting 2024, Stockholm, Sweden

Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction (2024)
Journal Article
Mishra, B. K., Preniqi, V., Thakker, D., & Feigl, E. (2024). Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction. Discover Internet of Things, 4(1), Article 24. https://doi.org/10.1007/s43926-024-00075-4

Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation... Read More about Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction.

Progression of risk in heart failure using dynamic risk modelling (2024)
Thesis
Kazmi, S. (2024). Progression of risk in heart failure using dynamic risk modelling. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/5086141

Heart failure (HF) is a prevalent condition affecting a significant number of individuals in the UK, leading to substantial healthcare utilisation and adverse outcomes. Despite advancements in treatment and management, the prognosis for hospitalised... Read More about Progression of risk in heart failure using dynamic risk modelling.

Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing (2024)
Presentation / Conference Contribution
Wuraola, I., Dethlefs, N., & Marciniak, D. (2024, November). Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing. Presented at EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Miami, FLorida, USA

In the realm of social media discourse, the integration of slang enriches communication, reflecting the sociocultural identities of users. This study investigates the capability of large language models (LLMs) to paraphrase slang within climate-relat... Read More about Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing.

Computing for Social Good in Education (2024)
Journal Article
Ellis, H., Hislop, G. W., Goldweber, M., Rebelsky, S., Pearce, J., Ordonez, P., Pias, M., & Gordon, N. (2024). Computing for Social Good in Education. ACM Inroads, 15(4), 47-57. https://doi.org/10.1145/3699719

Computing for Social Good in Education (CSG-Ed) engages students with the positive potential of computing to benefit society. It can introduce students to aspects of professional responsibility, something computing students need more than ever given... Read More about Computing for Social Good in Education.

A Fuzzy-Immune-Regulated Single-Neuron Proportional–Integral–Derivative Control System for Robust Trajectory Tracking in a Lawn-Mowing Robot (2024)
Journal Article
Saleem, O., Hamza, A., & Iqbal, J. (2024). A Fuzzy-Immune-Regulated Single-Neuron Proportional–Integral–Derivative Control System for Robust Trajectory Tracking in a Lawn-Mowing Robot. Computers, 13(11), Article 301. https://doi.org/10.3390/computers13110301

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–... Read More about A Fuzzy-Immune-Regulated Single-Neuron Proportional–Integral–Derivative Control System for Robust Trajectory Tracking in a Lawn-Mowing Robot.