All Outputs (15)

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..

A robust self-supervised approach for fine-grained crack detection in concrete structures. (2024)
Journal Article
SOHAIB, M., HASAN, M.J., SHAH, M.A. and ZHENG, Z. 2024. A robust self-supervised approach for fine-grained crack detection in concrete structures. Scientific reports [online], 14(1), article number 12646. Available from: https://doi.org/10.1038/s41598-024-63575-x

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies,... Read More about A robust self-supervised approach for fine-grained crack detection in concrete structures..

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..

The climate‐induced changes in the life history of the common cuttlefish in the English Channel. (2024)
Journal Article
LAPTIKHOVSKY, V., BARRETT, C.J., BARRY, P.J., FIRMIN, C., MACLEOD, E., STOTT, S. and VIEIRA, R. 2024. The climate-induced changes in the life history of the common cuttlefish in the English Channel. Marine ecology [online], Early View. Available from: https://doi.org/10.1111/maec.12810

The population of common cuttlefish Sepia officinalis in the English Channel recently developed two life cycles: annual (spawning 1 y.o.) and biennial (spawning 2 y.o.) instead of the biennial strategy known before, associated with increasing environ... Read More about The climate‐induced changes in the life history of the common cuttlefish in the English Channel..

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..

Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning. (2024)
Journal Article
HAQUE, R., AL SAKIB, A., HOSSAIN, M.F., ISLAM, F., AZIZ, F.I., AHMED, M.R., KANNAN, S., ROHAN, A. and HASAN, M.J. 2024. Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning. BioMedInformatics [online], 4(2), pages 966-991. Available from: https://doi.org/10.3390/biomedinformatics4020054

Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood... Read More about Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning..

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..

Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. (2024)
Journal Article
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J.A.W. 2024. Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. IEEE transactions on intelligent transportation systems, [online], Early Access. Available from: https://doi.org/10.1109/TITS.2024.3374550

The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transp... Read More about Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms..

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures. (2024)
Journal Article
SOHAIB, M., HASAN, M.J., CHEN, J. and ZHENG, Z. 2024. Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures. Measurement science and technology [online], 35(5): AI-driven measurement methods for resilient infrastructure and communities, article number 055402. Available from: https://doi.org/10.1088/1361-6501/ad296c

Identification of damage and selection of a restoration strategy in concrete structures is contingent upon automatic inspection for crack detection and assessment. Most research on deep learning models for autonomous inspection has focused solely on... Read More about Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures..

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..

TPAAD: two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network. (2024)
Journal Article
NISA, N., KHAN, A.S., AHMAD, Z. and ABDULLAH, J. 2024. TPAAD: two-phase authentication system for denial of service attack detection and mitigation using machine learning in software-defined network. International journal of network management [online], Early View, article number e2258. Available from: https://doi.org/10.1002/nem.2258

Software-defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of... Read More about TPAAD: two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network..

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..

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..