Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs (2019)
Journal Article
Alsarhan, A., Kilani, Y., Al-Dubai, A., Zomaya, A. Y., & Hussain, A. (2020). Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs. IEEE Transactions on Vehicular Technology, 69(2), 1568-1581. https://doi.org/10.1109/TVT.2019.2956228

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintain... Read More about Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs.

Visual attention model with a novel learning strategy and its application to target detection from SAR images (2016)
Presentation / Conference Contribution
Gao, F., Xue, X., Wang, J., Sun, J., Hussain, A., & Yang, E. (2016, November). Visual attention model with a novel learning strategy and its application to target detection from SAR images. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and man... Read More about Visual attention model with a novel learning strategy and its application to target detection from SAR images.

Predicting insulin resistance in children using a machine-learning-based clinical decision support system (2016)
Presentation / Conference Contribution
Hall, A. J., Hussain, A., & Shaikh, M. G. (2016, November). Predicting insulin resistance in children using a machine-learning-based clinical decision support system. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

This study proposes a new diagnostic approach based on application of machine learning techniques to anthropometric patient features in order to create a predictive model capable of diagnosing insulin resistance (HOMA-IR).

As part of the study, a... Read More about Predicting insulin resistance in children using a machine-learning-based clinical decision support system.

Modified cat swarm optimization for clustering (2016)
Presentation / Conference Contribution
Razzaq, S., Maqbool, F., & Hussain, A. (2016, November). Modified cat swarm optimization for clustering. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat S... Read More about Modified cat swarm optimization for clustering.

Distributed Reservoir Computing with Sparse Readouts [Research Frontier] (2016)
Journal Article
Scardapane, S., Panella, M., Comminiello, D., Hussain, A., & Uncini, A. (2016). Distributed Reservoir Computing with Sparse Readouts [Research Frontier]. IEEE Computational Intelligence Magazine, 11(4), 59-70. https://doi.org/10.1109/MCI.2016.2601759

In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development... Read More about Distributed Reservoir Computing with Sparse Readouts [Research Frontier].

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching (2016)
Journal Article
Tran, H.-N., Cambria, E., & Hussain, A. (2016). Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching. Cognitive Computation, 8(6), 1074-1086. https://doi.org/10.1007/s12559-016-9418-4

Background/Introduction
Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledg... Read More about Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching.

Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques (2016)
Journal Article
Dashtipour, K., Poria, S., Hussain, A., Cambria, E., Hawalah, A. Y. A., Gelbukh, A., & Zhou, Q. (2016). Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques. Cognitive Computation, 8(4), 757-771. https://doi.org/10.1007/s12559-016-9415-7

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. How... Read More about Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning (2016)
Journal Article
Ullah, A., Li, J., Hussain, A., & Yang, E. (2016). Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning. Cognitive Computation, 8(5), 992-1005. https://doi.org/10.1007/s12559-016-9391-y

Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. Recently, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneou... Read More about Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning.

Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis (2016)
Journal Article
Ofek, N., Poria, S., Rokach, L., Cambria, E., Hussain, A., & Shabtai, A. (2016). Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis. Cognitive Computation, 8(3), 467-477. https://doi.org/10.1007/s12559-015-9375-3

Sentiment analysis in natural language text is a challenging task involving a deep understanding of both syntax and semantics. Leveraging the polarity of multiword expressions—or concepts—rather than single words can mitigate the difficulty of such a... Read More about Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis.

Integrating twitter traffic information with kalman filter models for public transportation vehicle arrival time prediction (2016)
Book Chapter
Abidin, A. F., Kolberg, M., & Hussain, A. (2016). Integrating twitter traffic information with kalman filter models for public transportation vehicle arrival time prediction. In M. Trovati, R. Hill, A. Anjum, S. Ying Zhu, & L. Liu (Eds.), Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications (67-82). Springer. https://doi.org/10.1007/978-3-319-25313-8_5

Accurate bus arrival time prediction is key for improving the attractiveness of public transport, as it helps users better manage their travel schedule. This paper proposes a model of bus arrival time prediction, which aims to improve arrival time ac... Read More about Integrating twitter traffic information with kalman filter models for public transportation vehicle arrival time prediction.

Fusing audio, visual and textual clues for sentiment analysis from multimodal content (2015)
Journal Article
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & 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.

Dependency-based semantic parsing for concept-level text analysis (2014)
Presentation / Conference Contribution
Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., & Howard, N. (2014, April). Dependency-based semantic parsing for concept-level text analysis. Presented at 15th International Conference, CICLing 2014, Kathmandu, Nepal

Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks.... Read More about Dependency-based semantic parsing for concept-level text analysis.

Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model (2014)
Presentation / Conference Contribution
Ali, R., Jiang, B., Man, M., Hussain, A., & Luo, B. (2014, November). Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model. Presented at 21st International Conference on Neural Information Processing, ICONIP 2014, Kuching, Malaysia

Active Shape Models and Complex Network method are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extract... Read More about Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model.

Dependency tree-based rules for concept-level aspect-based sentiment analysis (2014)
Presentation / Conference Contribution
Poria, S., Ofek, N., Gelbukh, A., Hussain, A., & Rokach, L. (2014, May). Dependency tree-based rules for concept-level aspect-based sentiment analysis. Presented at SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece

Over the last few years, the way people express their opinions has changed dramatically with the progress of social networks, web communities, blogs, wikis, and other online collaborative media. Now, people buy a product and express their opinion in... Read More about Dependency tree-based rules for concept-level aspect-based sentiment analysis.

Affective neural networks and cognitive learning systems for big data analysis (2014)
Journal Article
Hussain, A., Cambria, E., Schuller, B., & Howard, N. (2014). Affective neural networks and cognitive learning systems for big data analysis. Neural Networks, 58, 1-3. https://doi.org/10.1016/j.neunet.2014.07.010

EmoSenticSpace: A novel framework for affective common-sense reasoning (2014)
Journal Article
Poria, S., Gelbukh, A., Cambria, E., Hussain, A., & Huang, G.-B. (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.

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.

Special issue on International Symposium on Neural Networks (2014)
Journal Article
Zeng, Z., Hussain, A., & Wei, Q. (2014). Special issue on International Symposium on Neural Networks. Neurocomputing, 125, 1-2. https://doi.org/10.1016/j.neucom.2013.01.004

No abstract available

Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments (2013)
Journal Article
Abel, A., & Hussain, A. (2014). Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments. Cognitive Computation, 6(2), 200-217. https://doi.org/10.1007/s12559-013-9231-2

In recent years, the established link between the various human communication production domains has become more widely utilised in the field of speech processing. In this work, we build on previous work by the authors and present a novel two-stage a... Read More about Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments.

Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars (2013)
Journal Article
Hussain, A., & Niazi, M. (2014). Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars. Cognitive Computation, 6(1), 113-124. https://doi.org/10.1007/s12559-013-9219-y

Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem; one that belongs to a class of problems, which often require the development of models to gain further understanding of the intrica... Read More about Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars.