Skip to main content

Research Repository

Advanced Search

All Outputs (4204)

A Novel Time-Slotted LoRa MAC Protocol for Scalable IoT Networks (2022)
Journal Article
Alahmadi, H., Bouabdallah, F., & Al-Dubai, A. (2022). A Novel Time-Slotted LoRa MAC Protocol for Scalable IoT Networks. Future Generation Computer Systems, 134, 287-302. https://doi.org/10.1016/j.future.2022.04.003

Long Range (LoRa) networks provide long range, cost-effective and energy-efficient communications by utilising the free unlicensed ISM band, which makes them appealing for Internet of Things (IoT) applications. However, in high density networks, reli... Read More about A Novel Time-Slotted LoRa MAC Protocol for Scalable IoT Networks.

Has the collective experience of the pandemic reset our relationship with the future? (2022)
Journal Article
Smyth, M. (2022). Has the collective experience of the pandemic reset our relationship with the future?. DOC - Design and Society, 1(1),

The fusion of architecture and cultures that comprised the cityscape of Blade Runner (1982) was a key part in how the narrative humanised the vision of the future, it provided a backdrop in which characters displaying human emotions played out a stor... Read More about Has the collective experience of the pandemic reset our relationship with the future?.

Opportunities and risks in the use of AI in career development practice (2022)
Journal Article
Wilson, M., Robertson, P., Cruickshank, P., & Gkatzia, D. (2022). Opportunities and risks in the use of AI in career development practice. Journal of the National Institute for Career Education and Counselling, 48(1), 48-57. https://doi.org/10.20856/jnicec.4807

The Covid-19 pandemic required many aspects of life to move online. This accelerated a broader trend for increasing use of ICT and AI, with implications for both the world of work and career development. This article explores the potential benefits a... Read More about Opportunities and risks in the use of AI in career development practice.

Sensitive Pictures: Emotional Interpretation in the Museum (2022)
Presentation / Conference Contribution
Benford, S. D., Sundnes Løvlie, A., Ryding, K., Rajkowska, P., Bodiaj, E., Paris Darzentas, D., Cameron, H., Spence, J., Egede, J., & Spanjevic, B. (2022, April). Sensitive Pictures: Emotional Interpretation in the Museum. Presented at CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional vis... Read More about Sensitive Pictures: Emotional Interpretation in the Museum.

Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning (2022)
Journal Article
Ghaleb, F. A., Alsaedi, M., Saeed, F., Ahmad, J., & Alasli, M. (2022). Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning. Sensors, 22(9), Article 3373. https://doi.org/10.3390/s22093373

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are ei... Read More about Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.

Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications (2022)
Journal Article
Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., & Sedik, A. (2022). Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. Computers and Electrical Engineering, 100, Article 108011. https://doi.org/10.1016/j.compeleceng.2022.108011

In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D for... Read More about Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications.

Identifying factors influencing study skills engagement and participation for online learners in higher education during Covid-19 (2022)
Journal Article
Fabian, K., Smith, S., Taylor-Smith, E., & Meharg, D. (2022). Identifying factors influencing study skills engagement and participation for online learners in higher education during Covid-19. British Journal of Educational Technology, 53(6), 1915-1936. https://doi.org/10.1111/bjet.13221

The Covid-19 pandemic disrupted education across the world as campuses closed to restrict the spread of the virus. UK universities swiftly migrated to online delivery. The experiences of students and staff during this transition can inform our return... Read More about Identifying factors influencing study skills engagement and participation for online learners in higher education during Covid-19.

Research In Times Of Crisis: Adaptations Of Research Due To The COVID-19 Pandemic (2022)
Presentation / Conference Contribution
Milosheva, M., & Salzano, R. (2022, April). Research In Times Of Crisis: Adaptations Of Research Due To The COVID-19 Pandemic. Paper presented at ASIST 24-Hour Global Conference, Online

During the COVID-19 global pandemic, many researchers have had to adapt, delay, or halt, their research completely. However, decisions related to adaptations, and the “hidden work” fundamental to such adaptations, often go unreported in research outp... Read More about Research In Times Of Crisis: Adaptations Of Research Due To The COVID-19 Pandemic.

A Participative Approach To Understanding The Hidden Curriculum (2022)
Presentation / Conference Contribution
Brazier, D., & Milosheva, M. (2022, April). A Participative Approach To Understanding The Hidden Curriculum. Paper presented at ASIST 24-Hour Global Conference, Online

Information needs are fundamental building blocks of the information behaviour and information retrieval literature. However, the concept of an “information need” is rarely discussed or defined, particularly in the context of everyday life informatio... Read More about A Participative Approach To Understanding The Hidden Curriculum.

A Study of Online Safety and Digital Literacy of Academic Researchers Working from Home during the COVID-19 Pandemic (2022)
Presentation / Conference Contribution
Haynes, D., & Salzano, R. (2022, April). A Study of Online Safety and Digital Literacy of Academic Researchers Working from Home during the COVID-19 Pandemic. Paper presented at ASIS&T Global 24-hour Conference, 2022, Online

Universities in the UK responded to the COVID-19 pandemic by moving teaching to an online environment and requiring staff to work from home, as far as possible. Researchers face particular challenges of security and privacy where their work involves... Read More about A Study of Online Safety and Digital Literacy of Academic Researchers Working from Home during the COVID-19 Pandemic.

Evaluation Of Inequalities Of Access In UK Online Digital Collections: A Systematic Review (2022)
Presentation / Conference Contribution
Brazier, D., Ryan, B., & Gooding, P. (2022, April). Evaluation Of Inequalities Of Access In UK Online Digital Collections: A Systematic Review. Paper presented at ASIST 24-Hour Global Conference, Online

This paper presents the results of a systematic literature review into how UK Cultural Heritage Institutions (CHIs) deal with issues of Equality, Diversity, and Inclusion (EDI). In recent years, researchers have addressed the fragmented nature of Cul... Read More about Evaluation Of Inequalities Of Access In UK Online Digital Collections: A Systematic Review.

A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement (2022)
Journal Article
Hussain, T., Wang, W., Gogate, M., Dashtipour, K., Tsao, Y., Lu, X., Ahsan, A., & Hussain, A. (2022). A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement. IEEE Transactions on Artificial Intelligence, 3(5), 833-842. https://doi.org/10.1109/TAI.2022.3169995

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms... Read More about A novel temporal attentive-pooling based convolutional recurrent architecture for acoustic signal enhancement.

Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform (2022)
Journal Article
Abd, M. H., Al-Suhail, G. A., Tahir, F. R., Ali Ali, A. M., Abbood, H. A., Dashtipour, K., Jamal, S. S., & Ahmad, J. (2022). Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform. Remote Sensing, 14(9), Article 1984. https://doi.org/10.3390/rs14091984

There is no doubt that chaotic systems are still attractive issues in various radar applications and communication systems. In this paper, we present a new 0.3 GHz mono-static microwave chaotic radar. It includes a chaotic system based on a time-dela... Read More about Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform.

Artificial Intelligence: Practical and Ethical Challenges (2022)
Book Chapter
Rice, S., & Maglaras, L. (2022). Artificial Intelligence: Practical and Ethical Challenges. In M. Ahmed, S. Rabiul Islam, A. Anwar, N. Moustafa, & A. Khan Pathan (Eds.), Explainable Artificial Intelligence for Cyber Security (59-71). Springer. https://doi.org/10.1007/978-3-030-96630-0_3

Artificial Intelligence (AI) & Machine Learning (ML) is used everywhere in daily life from speech recognition on our phones, targeted marketing strategies & face recognition without the majority of society even realising it. The power of technology a... Read More about Artificial Intelligence: Practical and Ethical Challenges.

Optimal Pitch Angle Controller for DFIG-Based Wind Turbine System Using Computational Optimization Techniques (2022)
Journal Article
Khurshid, A., Mughal, M. A., Othman, A., Al-Hadhrami, T., Kumar, H., Khurshid, I., Arshad, & Ahmad, J. (2022). Optimal Pitch Angle Controller for DFIG-Based Wind Turbine System Using Computational Optimization Techniques. Electronics, 11(8), Article 1290. https://doi.org/10.3390/electronics11081290

With the advent of high-speed and parallel computing, the applicability of computational optimization in engineering problems has increased, with greater validation than conventional methods. Pitch angle is an effective variable in extracting maximum... Read More about Optimal Pitch Angle Controller for DFIG-Based Wind Turbine System Using Computational Optimization Techniques.

A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos-Based Quantum Map (2022)
Journal Article
Alharbi, A. R., Ahmad, J., Arshad, Shaukat, S., Masood, F., Ghadi, Y. Y., Pitropakis, N., & Buchanan, W. J. (2022). A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos-Based Quantum Map. Complexity, 2022, Article 7047282. https://doi.org/10.1155/2022/7047282

With the increasing volume of data transmission through insecure communication channels, big data security has become one of the important concerns in the cybersecurity domain. To address these concerns and keep data safe, a robust privacy-preserving... Read More about A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos-Based Quantum Map.

Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers (2022)
Presentation / Conference Contribution
Cardoso, R. P., Hart, E., Burth Kurka, D., & Pitt, J. (2022, April). Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers. Presented at EvoSTAR, Madrid

Using Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. However, using gradient descent to train evolved architectures during the search can be comput... Read More about Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers.

Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (2022)
Book
Jiménez Laredo, J. L., Hidalgo, J. I., & Babaagba, K. O. (Eds.). (2022). Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings. Springer. https://doi.org/10.1007/978-3-031-02462-7

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and Ev... Read More about Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings.

Speculative and Critical Design: approaches and influences in education (2022)
Journal Article
Helgason, I., Encinas, E., Mitrovic, I., & Smyth, M. (2022). Speculative and Critical Design: approaches and influences in education. Interaction Design and Architecture(s) IxDetA, 5-7. https://doi.org/10.55612/s-5002-051-001psi

As guest editors of this special edition we are delighted to present this selection of papers responding to our call about Speculative and Critical Design in education. The response to the call demonstrates that interest in this approach is increasin... Read More about Speculative and Critical Design: approaches and influences in education.

Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn (2022)
Book Chapter
Hart, E. (2022). Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn. In A. E. Smith (Ed.), Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics (187-203). Springer. https://doi.org/10.1007/978-3-030-79092-9_9

Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to impro... Read More about Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn.