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All Outputs (10)

A novel refined track initiation algorithm for group targets based on group model (2014)
Journal Article
Gao, F., Ren, H., Wang, J., Hussain, A., & Durrani, T. S. (2014). A novel refined track initiation algorithm for group targets based on group model. Chinese Journal of Electronics, 23(4), 851-856

Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model... Read More about A novel refined track initiation algorithm for group targets based on group model.

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach (2014)
Journal Article
Gao, F., Zhang, Y., Wang, J., Sun, J., Yang, E., & Hussain, A. (2015). Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach. Cognitive Computation, 7(4), 434-444. https://doi.org/10.1007/s12559-014-931

The human visual system (HVS) possesses a remarkable ability of real-time complex scene analysis despite the limited neuronal hardware available for such tasks. The HVS successfully overcomes the problem of information bottleneck by selecting potenti... Read More about Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach.

Towards an intelligent framework for multimodal affective data analysis (2014)
Journal Article
Poria, S., Cambria, E., Hussain, A., & Huang, G. (2015). Towards an intelligent framework for multimodal affective data analysis. Neural Networks, 63, 104-116. https://doi.org/10.1016/j.neunet.2014.10.005

An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-mod... Read More about Towards an intelligent framework for multimodal affective data analysis.

AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network (2014)
Journal Article
Xia, Y., Cambria, E., & Hussain, A. (2015). AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network. Cognitive Computation, 7(2), 241-253. https://doi.org/10.1007/s12559-014-9305-9

Aspect-level opinion mining systems suffer from concept coverage problem due to the richness and ambiguity of natural language opinions. Aspects mentioned by review authors can be expressed in various forms, resulting in a potentially large number of... Read More about AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network.

Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features (2014)
Journal Article
Xia, Y., Cambria, E., Hussain, A., & Zhao, H. (2015). Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features. Cognitive Computation, 7(3), 369-380. https://doi.org/10.1007/s12559-014-9298-4

Contextual polarity ambiguity is an important problem in sentiment analysis. Many opinion keywords carry varying polarities in different contexts, posing huge challenges for sentiment analysis research. Previous work on contextual polarity disambigua... Read More about Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features.

EmoSenticSpace: A novel framework for affective common-sense reasoning (2014)
Journal Article
Poria, S., Gelbukh, A., Cambria, E., Hussain, A., & Huang, G. (2014). EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems, 69, 108-123. https://doi.org/10.1016/j.knosys.2014.06.011

Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text accurately. The need for knowledge bases that give access to semantics and sentics (the concep... Read More about EmoSenticSpace: A novel framework for affective common-sense reasoning.

A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data (2014)
Journal Article
Abdullah, A., & Hussain, A. (2015). A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data. Cognitive Computation, 7(1), 161-182. https://doi.org/10.1007/s12559-014-9281-0

Cluster extraction is a vital part of data mining; however, humans and computers perform it very differently. Humans tend to estimate, perceive or visualize clusters cognitively, while digital computers either perform an exact extraction, follow a fu... Read More about A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data.

Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data (2014)
Journal Article
Malik, Z. K., Hussain, A., & Wu, J. (2014). Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data. Cognitive Computation, 6(3), 595-607. https://doi.org/10.1007/s12559-014-9257-0

In this paper, we aim to develop novel learning approaches for extracting invariant features from time series. Specifically, we implement an existing method of solving the generalized eigenproblem and use this to firstly implement the biologically in... Read More about Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data.