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All Outputs (315)

Computational Intelligence for Changing Environments [Guest Editorial] (2015)
Journal Article
Hussain, A., Tao, D., Wu, J., & Zhao, D. (2015). Computational Intelligence for Changing Environments [Guest Editorial]. IEEE Computational Intelligence Magazine, 10(4), 10-11. https://doi.org/10.1109/MCI.2015.2472119

The articles in this special section focus on the growing interest in biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors.

Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns (2015)
Journal Article
Poria, S., Cambria, E., Gelbukh, A., Bisio, F., & Hussain, A. (2015). Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns. IEEE Computational Intelligence Magazine, 10(4), 26-36. https://doi.org/10.1109/MCI.2015.2471215

Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligen... Read More about Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns.

A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering (2015)
Journal Article
Abdullah, A., Khan, I. H., Basuhail, A., & Hussain, A. (2015). A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering. Cognitive Computation, 7(6), 693-705. https://doi.org/10.1007/s12559-015-9358-4

In this study, we investigate how the two hemispheres of the brain are involved spatiotemporally in a cognitive-based setup when people relate different colors with different concepts (for example, the color ‘blue’ associated with the word ‘dependabl... Read More about A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering.

An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data (2015)
Journal Article
Malik, Z. K., Hussain, A., & Wu, J. (2016). An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data. Neurocomputing, 173(2), 127-136. https://doi.org/10.1016/j.neucom.2014.12.119

This paper presents a generalized incremental Laplacian Eigenmaps (GENILE), a novel online version of the Laplacian Eigenmaps, one of the most popular manifold-based dimensionality reduction techniques which solves the generalized eigenvalue problem.... Read More about An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data.

Fusing audio, visual and textual clues for sentiment analysis from multimodal content (2015)
Journal Article
Poria, S., Cambria, E., Howard, N., Huang, G., & Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174(Part A), 50-59. https://doi.org/10.1016/j.neucom.2015.01.095

A huge number of videos are posted every day on social media platforms such as Facebook and YouTube. This makes the Internet an unlimited source of information. In the coming decades, coping with such information and mining useful knowledge from it w... Read More about Fusing audio, visual and textual clues for sentiment analysis from multimodal content.

A novel classification algorithm based on incremental semi-supervised support vector machine (2015)
Journal Article
Gao, F., Mei, J., Sun, J., Wang, J., Yang, E., & Hussain, A. (2015). A novel classification algorithm based on incremental semi-supervised support vector machine. PLOS ONE, 10(8), Article e0135709. https://doi.org/10.1371/journal.pone.0135709

For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In th... Read More about A novel classification algorithm based on incremental semi-supervised support vector machine.

Integrating SUMO and kalman filter models towards a social network based approach of public transport arrival time prediction (2015)
Journal Article
Abidin, A. F., Kolberg, M., & Hussain, A. (2015). Integrating SUMO and kalman filter models towards a social network based approach of public transport arrival time prediction. International Journal of Simulation: Systems, Science & Technology, 16(3), 5.1-5.9. https://doi.org/10.5013/IJSSST.a.16.03.05

Bus arrival time is a key service for improving public transport attractiveness by providing users with an accurate arrival time. In this research, a model of bus arrival time prediction, which aims to improve arrival time accuracy, is proposed. The... Read More about Integrating SUMO and kalman filter models towards a social network based approach of public transport arrival time prediction.

Local energy-based shape histogram feature extraction technique for breast cancer diagnosis (2015)
Journal Article
Wajid, S. K., & Hussain, A. (2015). Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. Expert Systems with Applications, 42(20), 6990-6999. https://doi.org/10.1016/j.eswa.2015.04.057

This paper proposes a novel local energy-based shape histogram (LESH) as the feature set for recognition of abnormalities in mammograms. It investigates the implication of this technique on mammogram datasets of the Mammographic Image Analysis Societ... Read More about Local energy-based shape histogram feature extraction technique for breast cancer diagnosis.

A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes (2015)
Journal Article
Tu, Z., Zheng, A., Yang, E., Luo, B., & Hussain, A. (2015). A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes. Cognitive Computation, 7(5), 539-551. https://doi.org/10.1007/s12559-015-9318-z

In the human brain, independent components of optical flows from the medial superior temporal area are speculated for motion cognition. Inspired by this hypothesis, a novel approach combining independent component analysis (ICA) with principal compon... Read More about A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes.

Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach (2015)
Journal Article
Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., & Hussain, A. (2015). Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach. Cognitive Computation, 7(4), 487-499. https://doi.org/10.1007/s12559-014-9316-6

Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challeng... Read More about Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach.

Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm (2015)
Journal Article
Khan, A., Xue, L. Z., Wei, W., Qu, Y. P., Hussain, A., & Vencio, R. Z. N. (2015). Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm. Cognitive Computation, 7(4), 477-486. https://doi.org/10.1007/s12559-014-9315-7

The self-organizing map (SOM) approach has been used to perform cognitive and biologically inspired computing in a growing range of cross-disciplinary fields. Recently, the SOM based neural network framework was adapted to solve continuous derivative... Read More about Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm.

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/s12559-014-9312-x

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.