Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification

Ali, R.; Hussain, A.; Abel, A.

IEEE Access Special Section Editorial: Health Informatics for the Developing World (2017)
Journal Article
Qadir, J., Mujeeb-U-Rahman, M., Rehmani, M., Pathan, A., Imran, M., Hussain, A., …Luo, B. (2017). IEEE Access Special Section Editorial: Health Informatics for the Developing World. IEEE Access, 5, 27818-27823. https://doi.org/10.1109/ACCESS.2017.278311

We live in a world with growing disparity in the quality of life available to people in the developed and developing countries. Healthcare in the developing world is fraught with numerous problems such as the lack of health infrastructure, and human... Read More about IEEE Access Special Section Editorial: Health Informatics for the Developing World.

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique (2017)
Journal Article
Wajid, S. K., Hussain, A., & Huang, K. (2018). Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique. Expert Systems with Applications, 112, 388-400. https://doi.org/10.1016/j.eswa.2017.11.057

In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an in... Read More about Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique.

Affective Reasoning for Big Social Data Analysis (2017)
Journal Article
Cambria, E., Hussain, A., & Vinciarelli, A. (2017). Affective Reasoning for Big Social Data Analysis. IEEE Transactions on Affective Computing, 8(4), 426-427. https://doi.org/10.1109/TAFFC.2017.2763218

This special section focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply affective reasoning tools and techniques for big social data analysis. A key motivation for this special section, in par... Read More about Affective Reasoning for Big Social Data Analysis.

Learning from Few Samples with Memory Network (2017)
Journal Article
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2018). Learning from Few Samples with Memory Network. Cognitive Computation, 10(1), 15-22. https://doi.org/10.1007/s12559-017-9507-z

Neural networks (NN) have achieved great successes in pattern recognition and machine learning. However, the success of a NN usually relies on the provision of a sufficiently large number of data samples as training data. When fed with a limited data... Read More about Learning from Few Samples with Memory Network.

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks (2017)
Journal Article
Anbar, M., Abdullah, R., Al-Tamimi, B. N., & Hussain, A. (2018). A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks. Cognitive Computation, 10(2), 201-214. https://doi.org/10.1007/s12559-017-9519-8

Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attack... Read More about A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks.

Semi-supervised learning for big social data analysis (2017)
Journal Article
Hussain, A., & Cambria, E. (2018). Semi-supervised learning for big social data analysis. Neurocomputing, 275, 1662-1673. https://doi.org/10.1016/j.neucom.2017.10.010

In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different... Read More about Semi-supervised learning for big social data analysis.

Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution (2017)
Journal Article
Ren, P., Sun, W., Luo, C., & Hussain, A. (2018). Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution. Cognitive Computation, 10(1), 165-178. https://doi.org/10.1007/s12559-017-9512-2

In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural categories, most existing deep learning models for image super-resolution tend to exploit an indiscriminate scheme for... Read More about Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution.

A Bayesian Assessment of Real-World Behavior During Multitasking (2017)
Journal Article
Bergmann, J., Fei, J., Green, D., Hussain, A., & Howard, N. (2017). A Bayesian Assessment of Real-World Behavior During Multitasking. Cognitive Computation, 9, 749-757. https://doi.org/10.1007/s12559-017-9500-6

Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monit... Read More about A Bayesian Assessment of Real-World Behavior During Multitasking.

A novel decision support system for the interpretation of remote sensing big data (2017)
Journal Article
Boulila, W., Farah, I. R., & Hussain, A. (2018). A novel decision support system for the interpretation of remote sensing big data. Earth Science Informatics, 11(1), 31-45. https://doi.org/10.1007/s12145-017-0313-7

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the incre... Read More about A novel decision support system for the interpretation of remote sensing big data.

A Review of Sentiment Analysis Research in Chinese Language (2017)
Journal Article
Peng, H., Cambria, E., & Hussain, A. (2017). A Review of Sentiment Analysis Research in Chinese Language. Cognitive Computation, 9(4), 423-435. https://doi.org/10.1007/s12559-017-9470-8

Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This... Read More about A Review of Sentiment Analysis Research in Chinese Language.

Dual-branch deep convolution neural network for polarimetric SAR image classification (2017)
Journal Article
Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., & Yang, E. (2017). Dual-branch deep convolution neural network for polarimetric SAR image classification. Applied Sciences, 7(5), https://doi.org/10.3390/app7050447

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the C... Read More about Dual-branch deep convolution neural network for polarimetric SAR image classification.

Group sparse regularization for deep neural networks (2017)
Journal Article
Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017). Group sparse regularization for deep neural networks. Neurocomputing, 241, 81-89. https://doi.org/10.1016/j.neucom.2017.02.029

In this paper, we address the challenging task of simultaneously optimizing (i) the weights of a neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i.e., feature selection). While these pr... Read More about Group sparse regularization for deep neural networks.

Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis (2017)
Journal Article
Poria, S., Peng, H., Hussain, A., Howard, N., & Cambria, E. (2017). Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing, 261, 217-230. https://doi.org/10.1016/j.neucom.2016.0

The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers... Read More about Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis.

A review of affective computing: From unimodal analysis to multimodal fusion (2017)
Journal Article
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98-125. https://doi.org/10.1016/j.inffus.2017.02.003

Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the pro... Read More about A review of affective computing: From unimodal analysis to multimodal fusion.

Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification (2017)
Journal Article
Ali, R., Hussain, A., & Abel, A. (2017). Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification. ARPN Journal of Engineering and Applied Sciences, 12(2), 310-316

In the 21st Century, a key challenge in both wild and cultured fish populations for control and management of disease is to securely and consistently perform pathogen identification. To provide automated accurate classification for the challeng... Read More about Ensemble based majority voting for point-to-point measurements of Gyrodactylus species identification.

All Outputs (15)