All Outputs (166)

A new concept of bio-based prestress technology with experimental Proof-of-Concept on Bamboo-Timber composite beams (2023)
Journal Article
Zhang, H., Shen, M., Deng, Y., Andras, P., Sukontasukkul, P., Yuen, T. Y. P., Wong, S. H., Limkatanyu, S., Singleton, I., & Hansapinyo, C. (2023). A new concept of bio-based prestress technology with experimental Proof-of-Concept on Bamboo-Timber composite beams. Construction and Building Materials, 402, Article 132991. https://doi.org/10.1016/j.conbuildmat.2023.132991

This paper presents a pioneering experimental proof-of-concept study to validate a novel concept of prestress technology that used only pure bio-based composite materials while achieved consistent prestressed stress distribution within the structure... Read More about A new concept of bio-based prestress technology with experimental Proof-of-Concept on Bamboo-Timber composite beams.

A machine learning-based structural load estimation model for shear-critical RC beams and slabs using multifractal analysis (2023)
Journal Article
Osei, J. B., Adom-Asamoah, M., Owusu Twumasi, J., Andras, P., & Zhang, H. (2023). A machine learning-based structural load estimation model for shear-critical RC beams and slabs using multifractal analysis. Construction and Building Materials, 394, Article 132250. https://doi.org/10.1016/j.conbuildmat.2023.132250

This paper presents a machine learning model for load-level estimation for shear-critical reinforced concrete (RC) beams and slabs using multifractal features of their characteristic crack patterns to automate and provide well-informed decisions for... Read More about A machine learning-based structural load estimation model for shear-critical RC beams and slabs using multifractal analysis.

Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method (2023)
Journal Article
Yuan, H., Goh, K., Andras, P., Luo, W., Wang, C., & Gao, Y. (2024). Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method. Transactions of the Institute of Measurement and Control, 46(3), 453-462. https://doi.org/10.1177/01423312231181993

This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, manoeuvrability, and robustness in case of malfunction of the front steerin... Read More about Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method.

Structural Complexity and Performance of Support Vector Machines (2022)
Presentation / Conference Contribution
Olorisade, B. K., Brereton, P., & Andras, P. (2022, July). Structural Complexity and Performance of Support Vector Machines. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy

Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of str... Read More about Structural Complexity and Performance of Support Vector Machines.

Federated Learning for Short-term Residential Load Forecasting (2022)
Journal Article
Briggs, C., Fan, Z., & Andras, P. (2022). Federated Learning for Short-term Residential Load Forecasting. IEEE Open Access Journal of Power and Energy, 9, 573-583. https://doi.org/10.1109/oajpe.2022.3206220

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters wi... Read More about Federated Learning for Short-term Residential Load Forecasting.

Scalability resilience framework using application-level fault injection for cloud-based software services (2022)
Journal Article
Al-Said Ahmad, A., & Andras, P. (2022). Scalability resilience framework using application-level fault injection for cloud-based software services. Journal of cloud computing: advances, systems and applications, 11(1), Article 1. https://doi.org/10.1186/s13677-021-00277-z

This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework using the Application-Level Fault Injection (ALFI) to investigate how the fa... Read More about Scalability resilience framework using application-level fault injection for cloud-based software services.

A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things (2021)
Book Chapter
Farhad, A., Woolley, S. I., & Andras, P. (2021). A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things. In J. Mantas, L. Stoicu-Tivadar, C. Chronaki, A. Hasman, P. Weber, P. Gallos, M. Crişan-Vida, E. Zoulias, & O. Sorina Chirila (Eds.), Public Health and Informatics (504-505). IOS Press. https://doi.org/10.3233/SHTI210216

This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT – as standal... Read More about A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things.

Compounding barriers to fairness in the digital technology ecosystem (2021)
Presentation / Conference Contribution
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021, October). Compounding barriers to fairness in the digital technology ecosystem. Presented at 2021 IEEE International Symposium on Technology and Society (ISTAS), Waterloo, ON, Canada

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology inno... Read More about Compounding barriers to fairness in the digital technology ecosystem.

A review of privacy-preserving federated learning for the Internet-of-Things (2021)
Book Chapter
Briggs, C., Fan, Z., & Andras, P. (2021). A review of privacy-preserving federated learning for the Internet-of-Things. In M. Habib ur Rehman, & M. Medhat Gaber (Eds.), Federated Learning Systems: Towards Next-Generation AI (21-50). Springer. https://doi.org/10.1007/978-3-030-70604-3_2

The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is attributable to human activities and behavior. Collecting personal data and executing machine learning tasks on this data in a central location presents a significan... Read More about A review of privacy-preserving federated learning for the Internet-of-Things.

Federated Learning for Short-term Residential Energy Demand Forecasting (2021)
Preprint / Working Paper
Briggs, C., Fan, Z., & Andras, P. Federated Learning for Short-term Residential Energy Demand Forecasting

Energy demand forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart... Read More about Federated Learning for Short-term Residential Energy Demand Forecasting.

Where do successful populations originate from? (2021)
Journal Article
Andras, P., & Stanton, A. (2021). Where do successful populations originate from?. Journal of Theoretical Biology, 524, Article 110734. https://doi.org/10.1016/j.jtbi.2021.110734

In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these populations. Here we focus on the role of geographical factors, such as land... Read More about Where do successful populations originate from?.

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (2020)
Presentation / Conference Contribution
Briggs, C., Fan, Z., & Andras, P. (2020, December). Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. Presented at NeurIPS 2020 Workshop: Tackling Climate Change with Machine Learning, Online

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer’s ho... Read More about Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters.

Federated learning with hierarchical clustering of local updates to improve training on non-IID data (2020)
Presentation / Conference Contribution
Briggs, C., Fan, Z., & Andras, P. (2020, July). Federated learning with hierarchical clustering of local updates to improve training on non-IID data. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow

Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion - as is typic... Read More about Federated learning with hierarchical clustering of local updates to improve training on non-IID data.

Composition of Games as a Model for the Evolution of Social Institutions (2020)
Presentation / Conference Contribution
Andras, P. (2020, July). Composition of Games as a Model for the Evolution of Social Institutions. Presented at ALIFE 2020: The 2020 Conference on Artificial Life, Online

The evolution of social institutions (e.g. institutions of political decision making or joint resource administration) is an important question in the context of understanding of how societies develop and evolve. In principle, social institutions can... Read More about Composition of Games as a Model for the Evolution of Social Institutions.

Scalability analysis comparisons of cloud-based software services (2019)
Journal Article
Ahmad, A. A.-S., & Andras, P. (2019). Scalability analysis comparisons of cloud-based software services. Journal of cloud computing: advances, systems and applications, 8, Article 10. https://doi.org/10.1186/s13677-019-0134-y

Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software servi... Read More about Scalability analysis comparisons of cloud-based software services.

Environmental Harshness and Fitness Improving Innovations (2019)
Presentation / Conference Contribution
Andras, P. (2019, July). Environmental Harshness and Fitness Improving Innovations. Presented at ALIFE 2019: The 2019 Conference on Artificial Life, Newcastle-upon-Tyne

Fitness improving innovations occur in populations of organisms as genetic changes (mutations) that allow better fit with the environmental niche of the organisms. Similarly, fitness improving innovations may occur in the context of human communities... Read More about Environmental Harshness and Fitness Improving Innovations.

Cloud-based software services delivery from the perspective of scalability (2019)
Journal Article
Al-Said Ahmad, A., & Andras, P. (2021). Cloud-based software services delivery from the perspective of scalability. International Journal of Parallel, Emergent and Distributed Systems, 36(2), 53-68. https://doi.org/10.1080/17445760.2019.1617864

Measuring and testing the scalability and performance of cloud-based software services is critical for the delivery of such services, and the development of cloud computing. There are three interconnected Cloud-based software services’ performance as... Read More about Cloud-based software services delivery from the perspective of scalability.

The use of bibliography enriched features for automatic citation screening (2019)
Journal Article
Olorisade, B. K., Brereton, P., & Andras, P. (2019). The use of bibliography enriched features for automatic citation screening. Journal of Biomedical Informatics, 94, Article 103202. https://doi.org/10.1016/j.jbi.2019.103202

Context
Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalan... Read More about The use of bibliography enriched features for automatic citation screening.

User perception of Bitcoin usability and security across novice users (2019)
Journal Article
Alshamsi, A., & Andras, P. (2019). User perception of Bitcoin usability and security across novice users. International Journal of Human-Computer Studies, 126, 94-110. https://doi.org/10.1016/j.ijhcs.2019.02.004

This paper investigates users’ perceptions and experiences of an anonymous digital payment system (Bitcoin) and its influence on users in terms of usability and security in comparison to other non-anonymous payment systems such as credit/debit cards.... Read More about User perception of Bitcoin usability and security across novice users.

Measuring and testing the scalability of cloud-based software services (2019)
Presentation / Conference Contribution
Al-Said Ahmad, A., & Andras, P. (2018, October). Measuring and testing the scalability of cloud-based software services. Presented at 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), Amman, Jordan

Performance and scalability testing and measurements of cloud-based software services are critically important in the context of rapid growth of cloud computing and supporting the delivery of these services. Cloud-based software services performance... Read More about Measuring and testing the scalability of cloud-based software services.