Outputs (1096)

Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. (2024)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., PIRAS, L. and AL-KADRI, M.O. 2024. Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of information security and applications [online], 82, article number 103741. Available from: https://doi.org/10.1016/j.jisa.2024.103741

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to redu... Read More about Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI..

Factors influencing mobile app user experience: an analysis of education app user reviews. (2024)
Conference Proceeding
ARAMBEPOLA, N., MUNASINGHE, L. and WARNAJITH, N. 2024. Factors influencing mobile app user experience: an analysis of education app user reviews. In 4th International conference on advanced research in computing 2024 (ICARC 2024), 21-24 February 2024, Belihuloya, Sri Lanka. Piscataway: IEEE [online], pages 223-228. Available from: https://doi.org/10.1109/ICARC61713.2024.10499727

In the competitive digital world, user reviews considered as the most vital source of user feedback, provide valuable insights that reflect the success of software applications in terms of user experience (UX). As user-generated content grows exponen... Read More about Factors influencing mobile app user experience: an analysis of education app user reviews..

Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. (2024)
Journal Article
HAYNES, S.C., JOHNSTON, P. and ELYAN, E. 2024. Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. Multimedia tools and applications [online], Latest Articles. Available from: https://doi.org/10.1007/s11042-024-18543-y

The generalisability of deep neural network classifiers is emerging as one of the most important challenges of our time. The recent COVID-19 pandemic led to a surge of deep learning publications that proposed novel models for the detection of COVID-1... Read More about Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers..

Steps towards a philosophy of computing education. (2024)
Conference Proceeding
MCDERMOTT, R., DANIELS, M. and FREZZA, S.T. 2024. Steps towards a philosophy of computer education. In Mühling, A. and Jormanainen, I. (eds.) Proceedings of the 23rd Koli calling international conference on computing education research 2023, 13-18 November 2024, Koli, Finland. New York: ACM [online], article 20. Available from: https://doi.org/10.1145/3631802.3631817

Is it meaningful to talk about the philosophy of computing education? What is its subject matter and methods? Is it different from, or a subfield of, the philosophy of science education or the philosophy of technology education or the philosophy of e... Read More about Steps towards a philosophy of computing education..

Two-layer ensemble of deep learning models for medical image segmentation. (2024)
Journal Article
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Two-layer ensemble of deep learning models for medical image segmentation. Cognitive computation [online], In Press. Available from: https://doi.org/10.1007/s12559-024-10257-5

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation al... Read More about Two-layer ensemble of deep learning models for medical image segmentation..

Detection-driven exposure-correction network for nighttime drone-view object detection. (2024)
Journal Article
XI, Y., JIA, W., MIAO, Q., FENG, J., REN, J. and LUO, H. 2024. Detection-driven exposure-correction network for nighttime drone-view object detection. IEEE transactions on geoscience and remote sensing [online], 62, article number 5605014. Available from: https://doi.org/10.1109/TGRS.2024.3351134

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detecti... Read More about Detection-driven exposure-correction network for nighttime drone-view object detection..

Feature aggregation and region-aware learning for detection of splicing forgery. (2024)
Journal Article
XU, Y., ZHENG, J., REN, J. and FANG, A. 2024. Feature aggregation and region-aware learning for detection of splicing forgery. IEEE signal processing letters [online], 31, pages 696-700. Available from: https://doi.org/10.1109/LSP.2023.3348689

Detection of image splicing forgery become an increasingly difficult task due to the scale variations of the forged areas and the covered traces of manipulation from post-processing techniques. Most existing methods fail to jointly multi-scale local... Read More about Feature aggregation and region-aware learning for detection of splicing forgery..

Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19. (2023)
Conference Proceeding
JOHNSTON, P., ZARB, M. and MORENO-GARCIA, C.F. 2023. Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Frontiers in education conference (FIE 2023),18-21 October 2023, College Station, TX, USA. Piscataway: IEEE [online], article number 10343048. Available from: https://doi.org/10.1109/fie58773.2023.10343048

This paper presents an experience report of online attendance and associated behavioural patterns during a module in the first complete semester undertaken fully online in the autumn of 2020, and the corresponding module deliveries in 2021 and 2022.... Read More about Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19..

A weighted ensemble of regression methods for gross error identification problem. (2023)
Conference Proceeding
DOBOS, D., DANG, T., NGUYEN, T.T., MCCALL, J., WILSON, A., CORBETT, H. and STOCKTON, P. 2023. A weighted ensemble of regression methods for gross error identification problem. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Symposium series on computational intelligence (SSCI 2023), 5-8 December 2023, Mexico City, Mexico. Piscataway: IEEE [online], pages 413-420. Available from: https://doi.org/10.1109/SSCI52147.2023.10371882

In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (EoR) obtaine... Read More about A weighted ensemble of regression methods for gross error identification problem..

MicroConceptBERT: concept-relation based document information extraction framework. (2023)
Conference Proceeding
SILVA, K., SILVA, T. and NANAYAKKARA, G. 2023. MicroConceptBERT: concept-relation based document information extraction framework. In Proceedings of the 7th SLAAI (Sri Lanka Association for Artificial Intelligence) International conference on artificial intelligence 2023 (SLAAI-ICAI 2023), 23-24 November 2023, Kelaniya, Sri Lanka. Piscataway: IEEE [online], article number 10365022. Available from: https://doi.org/10.1109...ICAI59257.2023.10365022

Extracting information from documents is a crucial task in natural language processing research. Existing information extraction methodologies often focus on specific domains, such as medicine, education or finance, and are limited by language constr... Read More about MicroConceptBERT: concept-relation based document information extraction framework..