All Outputs (17)

SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection. (2024)
Journal Article
LI, Y., REN, J., YAN, Y., SUN, G. and MA, P. 2024. SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection. Remote sensing [online], 16(3), article number 2353. Available from: https://doi.org/10.3390/rs16132353

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attrib... Read More about SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection..

A multimodel-based screening framework for C-19 using deep learning-inspired data fusion. (2024)
Journal Article
SHANKAR, A., RIZWAN, P., MEKALA, M.S., ELYAN, E., GANDOMI, A.H., MAPLE, C. and RODRIGUES, J.J.P.C. 2024. A multimodel-based screening framework for C-19 using deep learning-inspired data fusion. IEEE journal of biomedical and health informatics [online], Early Access. Available from: https://doi.org/10.1109/JBHI.2024.3400878

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field... Read More about A multimodel-based screening framework for C-19 using deep learning-inspired data fusion..

Prompting-to-distill semantic knowledge for few-shot learning. (2024)
Journal Article
JI, H., GAO, Z., REN, J., WANG, X.-A., GAO, T., SUN, W. and MA, P. 2024. Prompting-to-distill semantic knowledge for few-shot learning. IEEE geoscience and remote sensing letters [online], 21, article 6011605. Available from: https://doi.org/10.1109/lgrs.2024.3414505

Recognizing visual patterns in low-data regime necessitates deep neural networks to glean generalized representations from limited training samples. In this paper, we propose a novel few-shot classification method, namely ProDFSL, leveraging multi-mo... Read More about Prompting-to-distill semantic knowledge for few-shot learning..

ABBD: accumulated band-wise binary distancing for unsupervised parameter-free hyperspectral change detection. (2024)
Journal Article
LI, Y., REN, J., YAN, Y., MA, P., ASSAAD, M. and GAO, Z. 2024. ABBD: accumulated band-wise binary distancing for unsupervised parameter-free hyperspectral change detection. IEEE journal of selected topics in applied earth observations and remote sensing [online], 17, pages 9880-9893. Available from: https://doi.org/10.1109/JSTARS.2024.3407212

As a fundamental task in remote sensing earth observation, hyperspectral change detection (HCD) aims to identify the changed pixels in bi-temporal hyperspectral images (HSIs). However, the water-absorption effect, poor weather conditions, noise and i... Read More about ABBD: accumulated band-wise binary distancing for unsupervised parameter-free hyperspectral change detection..

A review of deep learning methods for digitisation of complex documents and engineering diagrams. (2024)
Journal Article
JAMIESON, L., MORENO-GARCIA, C.F. and ELYAN, E. 2024. A review of deep learning methods for digitisation of complex documents and engineering diagrams. Artificial intelligence review [online], 57(6), article number 136. Available from: https://doi.org/10.1007/s10462-024-10779-2

This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines)... Read More about A review of deep learning methods for digitisation of complex documents and engineering diagrams..

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. (2024)
Journal Article
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. [2024]. Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. International journal of information security [online], Latest Articles. Available from: https://doi.org/10.1007/s10207-024-00855-7

Embedded systems, including the Internet of Things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performa... Read More about Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things..

Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning. (2024)
Journal Article
YAN, Y., REN, J., SUN, H. and WILLIAMS, R. 2024. Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning. IEEE transactions on industrial informatics [online], Early Access. Available from: https://doi.org/10.1109/TII.2024.3384609

Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus... Read More about Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning..

CIA security for internet of vehicles and blockchain-AI integration. (2024)
Journal Article
HAI, T., AKSOY, M., IWENDI, C., IBEKE, E. and MOHAN, S. 2024. CIA security for internet of vehicles and blockchain-AI integration. Journal of grid computing [online], 22(2), article number 43. Available from: https://doi.org/10.1007/s10723-024-09757-3

The lack of data security and the hazardous nature of the Internet of Vehicles (IoV), in the absence of networking settings, have prevented the openness and self-organization of the vehicle networks of IoV cars. The lapses originating in the areas of... Read More about CIA security for internet of vehicles and blockchain-AI integration..

DICAM: deep inception and channel-wise attention modules for underwater image enhancement. (2024)
Journal Article
FARHADI TOLIE, H., REN, J. and ELYAN, E. 2024. DICAM: deep inception and channel-wise attention modules for underwater image enhancement. Neurocomputing [online], 584, article number 127585. Available from: https://doi.org/10.1016/j.neucom.2024.127585

In underwater environments, imaging devices suffer from water turbidity, attenuation of lights, scattering, and particles, leading to low quality, poor contrast, and biased color images. This has led to great challenges for underwater condition monit... Read More about DICAM: deep inception and channel-wise attention modules for underwater image enhancement..

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..

Overcoming the barriers to teaching teamwork to undergraduates in STEM. (2024)
Journal Article
GOLDSMITH, G.R., AIKEN, M.L., CAMARILLO-ABAD, H.M., DIKI, K., GARDNER, D.L., STIPČIĆ, M. and ESPELETA, J.F. 2024. Overcoming the barriers to teaching teamwork to undergraduates in STEM. CBE-life sciences education [online], 23(2), es2. Available from: https://doi.org/10.1187/cbe.23-07-0128

There is widespread recognition that undergraduate students in the life sciences must learn how to work in teams. However, instructors who wish to incorporate teamwork into their classrooms rarely have formal training in how to teach teamwork. This i... Read More about Overcoming the barriers to teaching teamwork to undergraduates in STEM..

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..

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..

Hyperspectral imagery quality assessment and band reconstruction using the Prophet model. (2024)
Journal Article
MA, P., REN, J., GAO, Z., LI, Y. and CHEN, R. [2024]. Hyperspectral imagery quality assessment and band reconstruction using the Prophet model. CAAI transactions on intelligence technology [online], (accepted).

In Hyperspectral Imaging (HSI), the detrimental influence of noise and distortions on data quality is profound, which has severely affected the following-on analytics and decision-making such as land mapping. This study presents an innovative framewo... Read More about Hyperspectral imagery quality assessment and band reconstruction using the Prophet model..

Piecing together performance: collaborative, participatory research-through-design for better diversity in games. (2024)
Journal Article
GARDNER, D.L., BOYD, L. and GARDNER, R.T. 2024. Piecing together performance: collaborative, participatory research-through-design for better diversity in games. IEEE transactions on games [online], Early Access. Available from: https://doi.org/10.1109/TG.2023.3349369

Digital games are a multi-billion-dollar industry whose production and consumption extend globally. Representation in games is an increasingly important topic. As those who create and consume the medium grow ever more diverse, it is essential that pl... Read More about Piecing together performance: collaborative, participatory research-through-design for better diversity in games..

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..