All Outputs (842)

Towards design and implementation of Industry 4.0 for food manufacturing (2021)
Journal Article
Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2023). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, 35(33), 23753–23765. https://doi.org/10.1007/s00521-021-05726-z

Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transform... Read More about Towards design and implementation of Industry 4.0 for food manufacturing.

A divide-and-conquer approach to neural natural language generation from structured data (2021)
Journal Article
Dethlefs, N., Schoene, A., & Cuayáhuitl, H. (2021). A divide-and-conquer approach to neural natural language generation from structured data. Neurocomputing, 433, 300-309. https://doi.org/10.1016/j.neucom.2020.12.083

Current approaches that generate text from linked data for complex real-world domains can face problems including rich and sparse vocabularies as well as learning from examples of long varied sequences. In this article, we propose a novel divide-and-... Read More about A divide-and-conquer approach to neural natural language generation from structured data.

A Semantic Knowledge-Based Framework for Information Extraction and Exploration (2021)
Journal Article
Aljamel, A., Osman, T., & Thakker, D. (2021). A Semantic Knowledge-Based Framework for Information Extraction and Exploration. International Journal of Decision Support System Technology, 13(2), 85--109. https://doi.org/10.4018/IJDSST.2021040105

The availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from web data. This research proposes a novel comprehensive semantic knowledge-ba... Read More about A Semantic Knowledge-Based Framework for Information Extraction and Exploration.

Finding the most navigable path in road networks (2021)
Journal Article
Kaur, R., Goyal, V., & Gunturi, V. M. (2021). Finding the most navigable path in road networks. GeoInformatica, 25(1), 207-240. https://doi.org/10.1007/s10707-020-00428-5

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destinatio... Read More about Finding the most navigable path in road networks.

A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems (2021)
Journal Article
Khodayee, S. M., Chiacchio, F., & Papadopoulos, Y. (2021). A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems. IEEE Access, 9, 51029-51049. https://doi.org/10.1109/ACCESS.2021.3069807

Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not... Read More about A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems.

Nonlinear adaptive backstepping control of permanent magnet synchronous motor (2021)
Journal Article
Ali, N., Alam, W., Pervaiz, M., & Iqbal, J. (2021). Nonlinear adaptive backstepping control of permanent magnet synchronous motor. Revue Roumaine des Sciences Techniques : Série Électrotechnique et Énergétique, 66(1), 9-14

This paper addresses the speed tracking problem of a permanent magnet synchronous motor (PMSM) under the influence of parametric uncertainties and external load torque disturbances. The nonlinear dynamics associated with both PMSM and load is conside... Read More about Nonlinear adaptive backstepping control of permanent magnet synchronous motor.

Technologies for analysing and improving healthcare processes (2020)
Book Chapter
Gordon, N. (2020). Technologies for analysing and improving healthcare processes. In W. Leal Filho, T. Wall, A. Azul, L. Brandli, & P. Özuyar (Eds.), Good Health and Well-Being. Springer. https://doi.org/10.1007/978-3-319-95681-7

The right to a healthy life is a natural expectation and recognised as a human right (WHO, 2017) – and is further recognised as such in the United Nations Sustainable Development Goals, where Goal 3 focusses on “good health and well-being” (United Na... Read More about Technologies for analysing and improving healthcare processes.

Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J. (2020, August). Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines. Presented at 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), Online

As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catas... Read More about Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines.

Identifying Gaps in Cybersecurity Teaching and Learning (2020)
Presentation / Conference Contribution
Brayshaw, M., Gordon, N., & Karatazgianni, A. (2020, July). Identifying Gaps in Cybersecurity Teaching and Learning. Presented at INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century

This paper explores perceptions and expectations of privacy when using computer-mediated communication and social media. In this paper we present the results of an empirical survey into this topic and explore the pedagogic implications for the teachi... Read More about Identifying Gaps in Cybersecurity Teaching and Learning.

Refining the Teaching of Programming (2020)
Presentation / Conference Contribution
Gordon, N., Cargill, M., Grey, S., & Brayshaw, M. (2020, July). Refining the Teaching of Programming. Presented at INSPIRE XXV: e-Learning as a Solution during Unprecedented Times in the 21st Century, Online

This paper considers issues around the teaching of programming, a critical yet challenging part of the computing education at all levels. This paper begins by outlining some of the key concerns around computing education-from secondary school, throug... Read More about Refining the Teaching of Programming.

Learning technologies for learning in health and well-being (2020)
Book Chapter
Gordon, N. (2020). Learning technologies for learning in health and well-being. In P. Gökcin Özuyar, W. Leal Filho, T. Wall, A. Marisa Azul, & L. Brandli (Eds.), Good Health and Well-Being. Springer. https://doi.org/10.1007/978-3-319-95681-7

The United Nations (U.N.) Sustainable Goal 3, for healthy lives and promoting well-being, notes that 45% of all countries have less than one physician per 1000 people, that figure rising to 90% for less developed countries (United Nations, 2018). Giv... Read More about Learning technologies for learning in health and well-being.

A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease (2020)
Presentation / Conference Contribution
Rana, S. S., Ma, X., Pang, W., & Wolverson, E. (2020, December). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. Presented at 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, United Kingdom

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utili... Read More about A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease.

A Fast and Compact 3-D CNN for Hyperspectral Image Classification (2020)
Journal Article
Ahmad, M., Khan, A. M., Mazzara, M., Distefano, S., Ali, M., & Sarfraz, M. S. (2022). A Fast and Compact 3-D CNN for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2020.3043710

Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI classification (HSIC) is a challenging task due to high interclass similarity, high intraclass variability, overlapping, and nested regions. The 2-D convolutional... Read More about A Fast and Compact 3-D CNN for Hyperspectral Image Classification.

A Model-based RCM Analysis Method (2020)
Presentation / Conference Contribution
Mian, Z., Jia, S., Shi, X., Tang, C., Chen, J., & Gao, Y. (2020, December). A Model-based RCM Analysis Method. Presented at 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Macau, China

The reliability-centered maintenance (RCM) is one of the most advanced maintenance plan generating technologies for equipments. At present, the key technologies such as FMEA and FMECA supporting the RCM analysis remains in the manual stage in some en... Read More about A Model-based RCM Analysis Method.

Computational Intelligence for Safety Assurance of Cooperative Systems of Systems (2020)
Journal Article
Kabir, S., & Papadopoulos, Y. (2020). Computational Intelligence for Safety Assurance of Cooperative Systems of Systems. Computer, 53(12), 24-34. https://doi.org/10.1109/MC.2020.3014604

Cooperative systems of systems (CSoSs) form a new technological frontier for their enormous economic and societal potentials in various domains. This article presents a novel framework for dynamic safety assurance of CSoSs that integrates design time... Read More about Computational Intelligence for Safety Assurance of Cooperative Systems of Systems.

Rough video conceptualization for real-time event precognition with motion entropy (2020)
Journal Article
Chakraborty, D. B., & Pal, S. K. (2021). Rough video conceptualization for real-time event precognition with motion entropy. Information Sciences, 543, 488-503. https://doi.org/10.1016/j.ins.2020.09.021

This article defines a new methodology for pre-recognition of events with object motion analysis in a video without any prior knowledge. This unsupervised application is named as ‘conceptualization’. This conceptualization technique is also tested wi... Read More about Rough video conceptualization for real-time event precognition with motion entropy.

Monte Carlo simulations of two-dimensional electron gasses in gallium nitride high electron mobility transistors via general-purpose computing on graphics processing units (2020)
Thesis
Smith, L. Monte Carlo simulations of two-dimensional electron gasses in gallium nitride high electron mobility transistors via general-purpose computing on graphics processing units. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4223241

The work in this thesis covers two main topics: successfully porting an Ensemble Monte Carlo (EMC) focused on bulk III-V semiconductors on to the graphics processing unit (GPU) and investigating carrier transport in a two-dimensional electron gas (2D... Read More about Monte Carlo simulations of two-dimensional electron gasses in gallium nitride high electron mobility transistors via general-purpose computing on graphics processing units.

Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050 (2020)
Journal Article
Golizadeh Akhlaghi, Y., Aslansefat, K., Zhao, X., Sadati, S., Badiei, A., Xiao, X., Shittu, S., Fan, Y., & Ma, X. (2021). Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050. Applied energy, 281, Article 116062. https://doi.org/10.1016/j.apenergy.2020.116062

The empirical success of the Artificial Intelligence (AI), has enhanced importance of the transparency in black box Machine Learning (ML) models. This study pioneers in developing an explainable and interpretable Deep Neural Network (DNN) model for a... Read More about Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050.

Effect of IDT position parameters on SAW yarn tension sensor sensitivity (2020)
Journal Article
Lei, B., Lu, W., Mian, Z., & Bao, W. (2020). Effect of IDT position parameters on SAW yarn tension sensor sensitivity. Measurement and Control, 53(9-10), 2055-2062. https://doi.org/10.1177/0020294020965620

In this paper, the effect of the interdigital transducer (IDT) position parameters on the surface acoustic wave (SAW) yarn tension sensor sensitivity is investigated. The stress–strain characteristic of substrate was studied by the combination of fin... Read More about Effect of IDT position parameters on SAW yarn tension sensor sensitivity.

Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation (2020)
Journal Article
Ramirez Rivera, A., Khan, A., Bekkouch, I. E. I., & Sheikh, T. S. (2022). Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation. IEEE Transactions on Neural Networks and Learning Systems, 33(1), 281-291. https://doi.org/10.1109/TNNLS.2020.3027667

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality o... Read More about Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation.