All Outputs (400)

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.

Convolutional MKL based multimodal emotion recognition and sentiment analysis (2017)
Presentation / Conference Contribution
Poria, S., Chaturvedi, I., Cambria, E., & Hussain, A. (2016, December). Convolutional MKL based multimodal emotion recognition and sentiment analysis. Presented at 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain

Technology 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. Much of the content being posted and consumed online is multimodal. With billions o... Read More about Convolutional MKL based multimodal emotion recognition and sentiment analysis.

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.

Customer churn prediction in the telecommunication sector using a rough set approach (2016)
Journal Article
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254. https://doi.org/10.1016/j.neucom.2016.12.009

Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitio... Read More about Customer churn prediction in the telecommunication sector using a rough set approach.

An exploratory case study of interactive simulation for teaching Ecology (2016)
Presentation / Conference Contribution
Ameerbakhsh, O., Maharaj, S., Hussain, A., Paine, T., & Taiksi, S. (2016). An exploratory case study of interactive simulation for teaching Ecology. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHE

This paper explores the effectiveness of interactive simulation for teaching a selected complex subject, Ecology, in higher education. Specifically, we carry out a lab intervention using interactive agent based simulation, to teach the complex concep... Read More about An exploratory case study of interactive simulation for teaching Ecology.

Extracting online information from dual and multiple data streams (2016)
Journal Article
Malik, Z. K., Hussain, A., & Wu, Q. M. J. (2018). Extracting online information from dual and multiple data streams. Neural Computing and Applications, 30(1), 87-98. https://doi.org/10.1007/s00521-016-2647-3

In this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by deve... Read More about Extracting online information from dual and multiple data streams.

A data driven approach to audiovisual speech mapping (2016)
Presentation / Conference Contribution
Abel, A., Marxer, R., Barker, J., Watt, R., Whitmer, B., Derleth, P., & Hussain, A. (2016). A data driven approach to audiovisual speech mapping. In Advances in Brain Inspired Cognitive Systems (331-342). https://doi.org/10.1007/978-3-319-49685-6_30

The concept of using visual information as part of audio speech processing has been of significant recent interest. This paper presents a data driven approach that considers estimating audio speech acoustics using only temporal visual information wit... Read More about A data driven approach to audiovisual speech mapping.

A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks (2016)
Presentation / Conference Contribution
Alharbi, H., Aloufi, K., & Hussain, A. (2016). A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks. In Advances in Brain Inspired Cognitive Systems (251-263). https://doi.org/10.1007/978-3-319-49685

Millions of users world-wide are sharing content using the Peer-to-Peer (P2P) client network. While new innovations bring benefits, there are nevertheless some dangers associated with them. One of the main threats is P2P worms that can penetrate the... Read More about A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks.

A novel fully automated liver and HCC tumor segmentation system using morphological operations (2016)
Presentation / Conference Contribution
Ali, L., Hussain, A., Li, J., Howard, N., Shah, A., Sudhakar, U., …Hussain, Z. (2016). A novel fully automated liver and HCC tumor segmentation system using morphological operations. In Advances in Brain Inspired Cognitive Systems (240-250). https://do

Early detection and diagnosis of Hepatocellular Carcinoma (HCC) is the most discriminating step in liver cancer management. Image processing is primarily used, where fast and accurate Computed Tomography (CT) liver image segmentation is required for... Read More about A novel fully automated liver and HCC tumor segmentation system using morphological operations.

An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs) (2016)
Presentation / Conference Contribution
Wajid, S. K., Hussain, A., Luo, B., & Huang, K. (2016). An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). In Advances in Brain Inspired Cognitive Systems: 8th International C

This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of... Read More about An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs).

Continuous time recurrent neural network model of recurrent collaterals in the hippocampus CA3 region (2016)
Presentation / Conference Contribution
Shiva, A. S., & Hussain, A. (2016). Continuous time recurrent neural network model of recurrent collaterals in the hippocampus CA3 region. In Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November

Recurrent collaterals in the brain represent the recollection and execution of various monotonous activities such as breathing, brushing our teeth, chewing, walking, etc. These recurrent collaterals are found throughout the brain, each pertaining to... Read More about Continuous time recurrent neural network model of recurrent collaterals in the hippocampus CA3 region.

Deep and sparse learning in speech and language processing: An overview (2016)
Presentation / Conference Contribution
Wang, D., Zhou, Q., & Hussain, A. (2016). Deep and sparse learning in speech and language processing: An overview. In Advances in Brain Inspired Cognitive Systems (171-183). https://doi.org/10.1007/978-3-319-49685-6_16

Large-scale deep neural models, e.g., deep neural networks (DNN) and recurrent neural networks (RNN), have demonstrated significant success in solving various challenging tasks of speech and language processing (SLP), including speech recognition, sp... Read More about Deep and sparse learning in speech and language processing: An overview.

Modified cat swarm optimization for clustering (2016)
Presentation / Conference Contribution
Razzaq, S., Maqbool, F., & Hussain, A. (2016). Modified cat swarm optimization for clustering. In Advances in Brain Inspired Cognitive Systems 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings (161-170). https://d

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.

PerSent: A freely available Persian sentiment lexicon (2016)
Presentation / Conference Contribution
Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A. Y. A., & Cambria, E. (2016). PerSent: A freely available Persian sentiment lexicon. In Advances in Brain Inspired Cognitive Systems (310-320). https://doi.org/10.1007/978-3-319-49685-6_28

People need to know other people’s opinions to make well-informed decisions to buy products or services. Companies and organizations need to understand people’s attitude towards their products and services and use feedback from the customers to impro... Read More about PerSent: A freely available Persian sentiment lexicon.

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). Predicting insulin resistance in children using a machine-learning-based clinical decision support system. In Advances in Brain Inspired Cognitive Systems (274-283). https://doi.org/10.1007/978-3-319-4968

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.

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). Visual attention model with a novel learning strategy and its application to target detection from SAR images. In Advances in Brain Inspired Cognitive Systems (149-160). https://doi.org

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.

Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study (2016)
Journal Article
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., …Hussain, A. (2016). Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study. IEEE Access, 4, 7940-7957. https://doi.org/10.1109/AC

Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring... Read More about Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study.

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].