All Outputs (114)

Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. (2020)
Journal Article
CHAI, Y., REN, J., HWANG, B., WANG, J., FAN, D., YAN, Y. and ZHU, S. 2021. Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 577-586. Available from: https://doi.org/10.1109/jstars.2020.3040614

Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods su... Read More about Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images..

Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images. (2020)
Journal Article
MA, P., REN, J., ZHAO, H., SUN, G., MURRAY, P. and ZHENG, J. 2021. Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 1233-1245. Available from: https://doi.org/10.1109/JSTARS.2020.3040699

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss fr... Read More about Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images..

Applying acceptance requirements to requirements modeling tools via gamification: a case study on privacy and security. (2020)
Conference Proceeding
PIRAS, L., CALABRESE, F. and GIORGINI, P. 2020. Applying acceptance requirements to requirements modeling tools via gamification: a case study on privacy and security. In Grabis, J. and Bork, D. (eds.) The practice of enterprise modeling: proceedings of 13th International Federation for Information Processing (IFIP) Practice of enterprise modelling working conference 2020 (Poem 2020), 25-27 November 2020, Riga, Latvia. Lecture notes in business information processing, 400. Cham: Springer [online], pages 366-376. Available from: https://doi.org/10.1007/978-3-030-63479-7_25

Requirements elicitation, analysis and modeling are critical activities for software success. However, software systems are increasingly complex, harder to develop due to an ever-growing number of requirements from numerous and heterogeneous stakehol... Read More about Applying acceptance requirements to requirements modeling tools via gamification: a case study on privacy and security..

Toward an ensemble of object detectors. (2020)
Conference Proceeding
DANG, T., NGUYEN, T.T. and MCCALL, J. 2020. Toward an ensemble of object detectors. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 458-467. Available from: https://doi.org/10.1007/978-3-030-63823-8_53

The field of object detection has witnessed great strides in recent years. With the wave of deep neural networks (DNN), many breakthroughs have achieved for the problems of object detection which previously were thought to be difficult. However, ther... Read More about Toward an ensemble of object detectors..

A homogeneous-heterogeneous ensemble of classifiers. (2020)
Conference Proceeding
LUONG, A.V., VU, T.H., NGUYEN, P.M., VAN PHAM, N., MCCALL, J., LIEW, A.W.-C. and NGUYEN, T.T. 2020. A homogeneous-heterogeneous ensemble of classifiers. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 251-259. Available from: https://doi.org/10.1007/978-3-030-63823-8_30

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each ot... Read More about A homogeneous-heterogeneous ensemble of classifiers..

Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. (2020)
Journal Article
FU, H., SUN, G., REN, J., ZHANG, A. and JIA, X. 2020. Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. IEEE transactions on geoscience and remote sensing [online], 60, article 5500214. Available from: https://doi.org/10.1109/TGRS.2020.3034656

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral–... Read More about Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery..

Iterative enhanced multivariance products representation for effective compression of hyperspectral images. (2020)
Journal Article
TUNA, S., TÖREYIN, B.U., REN, J., ZHAO, H. and MARSHALL, S. 2021. Iterative enhanced multivariance products representation for effective compression of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 59(11), pages 9569-9584. Available from: https://doi.org/10.1109/TGRS.2020.3031016

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method b... Read More about Iterative enhanced multivariance products representation for effective compression of hyperspectral images..

Efficient task optimization algorithm for green computing in cloud. (2020)
Journal Article
G, T., CH, D.C., VARMA, G.P.S. and MEKALA, M.S. 2023. Efficient task optimization algorithm for green computing in cloud. Internet technology letters [online] 6(1): ubiquitous clouds and social Internet of Things, article e254. Available from: https://doi.org/10.1002/itl2.254

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techn... Read More about Efficient task optimization algorithm for green computing in cloud..

Computing students learning outcomes in learning by developing action model. (2020)
Conference Proceeding
LINTILÄ, T. and ZARB, M. 2020. Computing students learning outcomes in learning by developing action model. In Gómez Chova, L., López Martínez, A. and Candel Torres, I. (eds.) Proceedings of 13th International conference of education, research and innovation 2020 (ICERI2020), 9-10 November 2020, [virtual conference]. Valencia: IATED [online], pages 1936-1945. Available from: https://doi.org/10.21125/iceri.2020.0477

The purpose of this paper is to present the results of research aimed at finding out the learning outcomes of computing students with a study module implementation based on the Learning by Developing (LbD) Action Model used in Laurea University of Ap... Read More about Computing students learning outcomes in learning by developing action model..

Contextualisation of data flow diagrams for security analysis. (2020)
Conference Proceeding
FAILY, S., SCANDARIATO, R., SHOSTACK, A., SION, L. and KI-ARIES, D. 2020. Contextualisation of data flow diagrams for security analysis. In Eades, H. III and Gadyatskaya, O. (eds.) Graphical models for security: revised selected papers from the proceedings of the 7th International workshop on graphical models for security (GraMSec 2020), 22 June 2020, Boston, USA. Lecture notes in computer science, 12419. Cham: Springer [online], pages 186-197. Available from: https://doi.org/10.1007/978-3-030-62230-5_10

Data flow diagrams (DFDs) are popular for sketching systems for subsequent threat modelling. Their limited semantics make reasoning about them difficult, but enriching them endangers their simplicity and subsequent ease of take up. We present an appr... Read More about Contextualisation of data flow diagrams for security analysis..

Detection of false command and response injection attacks for cyber physical systems security and resilience. (2020)
Conference Proceeding
EKE, H., PETROVSKI, A. and AHRIZ, H. 2020. Detection of false command and response injection attacks for cyber physical systems security and resilience. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 10, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433615

The operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of... Read More about Detection of false command and response injection attacks for cyber physical systems security and resilience..

Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. (2020)
Conference Proceeding
MAJDANI, F., BATIK, L., PETROVSKI, A. and PETROVSKI, S. 2020. Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 4, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433613

This paper looks at potential vulnerabilities of the Smart Grid energy infrastructure to data injection cyber-attacks and the means of addressing these vulnerabilities through intelligent data analysis. Efforts are being made by multiple groups to pr... Read More about Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data..

Heterogeneous ensemble selection for evolving data streams. (2020)
Journal Article
LUONG, A.V., NGUYEN, T.T., LIEW, A.W.-C. and WANG, S. 2021. Heterogeneous ensemble selection for evolving data streams. Pattern recognition [online], 112, article ID 107743. Available from: https://doi.org/10.1016/j.patcog.2020.107743

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model.... Read More about Heterogeneous ensemble selection for evolving data streams..

Heterogeneous ensemble selection for evolving data streams. [Dataset] (2020)
Dataset
LUONG, A.V., NGUYEN, T.T., LIEW, A.W.-C. and WANG, S. 2021. Heterogeneous ensemble selection for evolving data streams. [Dataset]. Pattern recognition [online], 112, article ID 107743. Available from: https://www.sciencedirect.com/science/article/pii/S003132032030546X#sec0023

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model.... Read More about Heterogeneous ensemble selection for evolving data streams. [Dataset].

Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification. (2020)
Thesis
VUTTIPITTAYAMONGKOL, P. 2020. Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://openair.rgu.ac.uk

Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not d... Read More about Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification..

Exploring the use of conversational agents to improve cyber situational awareness in the Internet of Things (IoT). (2020)
Thesis
MCDERMOTT, C.D. 2020. Exploring the use of conversational agents to improve cyber situational awareness in the Internet of Things (IoT). Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://openair.rgu.ac.uk

The Internet of Things (IoT) is an emerging paradigm, which aims to extend the power of the Internet beyond computers and smartphones to a vast and growing range of "things" - devices, processes and environments. The result is an interconnected world... Read More about Exploring the use of conversational agents to improve cyber situational awareness in the Internet of Things (IoT)..

A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production. (2020)
Journal Article
ZAVOIANU, A.-C., LUGHOFER, E., POLLAK, R., EITZINGER, C. and RADAUER, T. 2021. A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production. Applied soft computing [online], 98, article ID 106827. Available from: https://doi.org/10.1016/j.asoc.2020.106827

We describe a general strategy for optimizing the quality of products of industrial batch processes that comprise multiple production stages. We focus on the particularities of applying this strategy in the field of micro-fluidic chip production. Our... Read More about A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production..

Generic wavelet‐based image decomposition and reconstruction framework for multi‐modal data analysis in smart camera applications. (2020)
Journal Article
YAN, Y., LIU, Y., YANG, M., ZHAO, H., CHAI, Y. and REN, J. 2020. Generic wavelet-based image decomposition and reconstruction framework for multi-modal data analysis in smart camera applications. IET computer vision [online], 14(7): computer vision for smart cameras and camera networks, pages 471-479. Available from: https://doi.org/10.1049/iet-cvi.2019.0780

Effective acquisition, analysis and reconstruction of multi-modal data such as colour and multi-/hyper-spectral imagery is crucial in smart camera applications, where wavelet-based coding and compression of images are highly demanded. Many existing d... Read More about Generic wavelet‐based image decomposition and reconstruction framework for multi‐modal data analysis in smart camera applications..

Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. Sensors [online], 20(20), article 5786. Available from: https://doi.org/10.3390/s20205786

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim... Read More about Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss..

Forecasting meteorological analysis using machine learning algorithms. (2020)
Conference Proceeding
PAVULURI, B.L., VEJENDLA, R.S., JITHENDRA, P., DEEPIKA, T. and BANO, S. 2020. Forecasting meteorological analysis using machine learning algorithms. In Proceedings of the 2020 International conference on smart electronics and communication (ICOSEC 2020), 10-12 September 2020, Trichy, India. Piscataway: IEEE [online], pages 456-461. Available from: https://doi.org/10.1109/ICOSEC49089.2020.9215440

Weather prediction is gaining up ubiquity quickly in the current period of Machine learning and Technologies. It is fundamental to foresee the temperature of the climate for quite a while. Decision trees, K-NN, Random Forest algorithms are an integra... Read More about Forecasting meteorological analysis using machine learning algorithms..