All Outputs (10)

Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data (2017)
Presentation / Conference Contribution
Abdullah, A., Hussain, A., & Khan, I. (2017, May). Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data. Presented at ICCDA '17: International Conference on Compute and Data Analysis, Lakeland, FL, USA

Globally there has been a dramatic increase in obesity. Thus understanding, predicting and managing obesity has the potential to save lives and billions. Behavioral studies suggest that binging by obese persons is prompted by inflated brain reward ce... Read More about Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data.

Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo (2017)
Presentation / Conference Contribution
Adeel, A., Gogate, M., & Hussain, A. (2017, August). Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo. Presented at 1st International Workshop on Challenges in Hearing Assistive Technology (CHAT 2017), Stockholm, Sweden

Speech enhancement aims to enhance the perceived speech quality and intelligibility in the presence of noise. Classical speech enhancement methods are mainly based on audio only processing which often perform poorly in adverse conditions, where overw... Read More about Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo.

Complex-valued computational model of hippocampal CA3 recurrent collaterals (2017)
Presentation / Conference Contribution
Shiva, A., Gogate, M., Howard, N., Graham, B., & Hussain, A. (2017). Complex-valued computational model of hippocampal CA3 recurrent collaterals. . https://doi.org/10.1109/ICCI-CC.2017.8109745

Complex planes are known to simplify the complexity of real world problems, providing a better comprehension of their functionality and design. The need for complex numbers in both artificial and biological neural networks is equally well established... Read More about Complex-valued computational model of hippocampal CA3 recurrent collaterals.

Formal Ontology Generation by deep machine learning (2017)
Presentation / Conference Contribution
Wang, Y., Valipour, M., Zatarain, O., Gavrilova, M., Hussain, A., Howard, N., & Patel, S. (2017). Formal Ontology Generation by deep machine learning. . https://doi.org/10.1109/ICCI-CC.2017.8109723

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the... Read More about Formal Ontology Generation by deep machine learning.

Machine learning based computer-aided diagnosis of liver tumours (2017)
Presentation / Conference Contribution
Ali, L., Khelil, K., Wajid, S. K., Hussain, Z. U., Shah, M. A., Howard, A., …Hussain, A. (2017). Machine learning based computer-aided diagnosis of liver tumours. . https://doi.org/10.1109/ICCI-CC.2017.8109742

Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and d... Read More about Machine learning based computer-aided diagnosis of liver tumours.

Persian Named Entity Recognition (2017)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017). Persian Named Entity Recognition. . https://doi.org/10.1109/ICCI-CC.2017.8109733

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification... Read More about Persian Named Entity Recognition.

Improve deep learning with unsupervised objective (2017)
Presentation / Conference Contribution
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2017). Improve deep learning with unsupervised objective. . https://doi.org/10.1007/978-3-319-70087-8_74

We propose a novel approach capable of embedding the unsupervised objective into hidden layers of the deep neural network (DNN) for preserving important unsupervised information. To this end, we exploit a very simple yet effective unsupervised method... Read More about Improve deep learning with unsupervised objective.

Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques (2017)
Presentation / Conference Contribution
Wajid, S., Hussain, A., Huang, K., & Boulila, W. (2017). Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques. . https://doi.org/10.1109/ICCI-CC.2016.7862060

The novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect... Read More about Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques.

Genetic optimization of fuzzy membership functions for cloud resource provisioning (2017)
Presentation / Conference Contribution
Ullah, A., Li, J., Hussain, A., & Shen, Y. (2017). Genetic optimization of fuzzy membership functions for cloud resource provisioning. . https://doi.org/10.1109/SSCI.2016.7850088

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the desi... Read More about Genetic optimization of fuzzy membership functions for cloud resource provisioning.

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.