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Outputs (315)

Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis (2018)
Journal Article
Mondal, A., Cambria, E., Das, D., Hussain, A., & Bandyopadhyay, S. (2018). Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis. Cognitive Computation, 10(4), 670-685. https://doi.org/10.1007/s12559-018-9567-8

In healthcare services, information extraction is the key to understand any corpus-based knowledge. The process becomes laborious when the annotation is done manually for the availability of a large number of text corpora. Hence, future automated ext... Read More about Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis.

A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems (2018)
Journal Article
Yang, X., Huang, K., Zhang, R., Goulermas, J., & Hussain, A. (2018). A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems. Neurocomputing, 312, 352-363. https://doi.org/10.1016/j.neucom.2018.05.085

Dimensionality Reduction (DR) is a fundamental topic of pattern classification and machine learning. For classification tasks, DR is typically employed as a pre-processing step, succeeded by an independent classifier training stage. However, such ind... Read More about A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems.

A control theoretical view of cloud elasticity: taxonomy, survey and challenges (2018)
Journal Article
Ullah, A., Li, J., Shen, Y., & Hussain, A. (2018). A control theoretical view of cloud elasticity: taxonomy, survey and challenges. Cluster Computing, 21(4), 1735-1764. https://doi.org/10.1007/s10586-018-2807-6

The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational... Read More about A control theoretical view of cloud elasticity: taxonomy, survey and challenges.

A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications (2018)
Journal Article
Mahmud, M., Kaiser, M. S., Rahman, M. M., Rahman, M. A., Shabut, A., Al-Mamun, S., & Hussain, A. (2018). A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications. Cognitive Computation, 10(5), 864-873. https://doi.org/10.1007/s12559-018-9543-3

Rapid advancement of Internet of Things (IoT) and cloud computing enables neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable... Read More about A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications.

Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization (2018)
Journal Article
Yang, X., Huang, K., Zhang, R., & Hussain, A. (2018). Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(6), 450-463. https://doi.org/10.1109/tetci.2018.2806934

Nonnegative matrix factorization (NMF) has been widely exploited in many computational intelligence and pattern recognition problems. In particular, it can be used to extract latent features from data. However, previous NMF models often assume a fixe... Read More about Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization.

Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis (2018)
Journal Article
Ma, Y., Peng, H., Khan, T., Cambria, E., & Hussain, A. (2018). Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis. Cognitive Computation, 10(4), 639-650. https://doi.org/10.1007/s12559-018-9549-x

Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the ass... Read More about Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis.

Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study (2018)
Journal Article
Arafat, S., Aljohani, N., Abbasi, R., Hussain, A., & Lytras, M. (2019). Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study. Computers in Human Behavior, 92, 478-486. https://doi.org/10.1016/j.chb.2018.02.026

In this paper we explore the interrelationship between the sociotechnical-pedagogical culture of e-learning, the emerging disciplines of Web science, Social Sensing and that of Cognitive Computation–as an emerging paradigm of computation. We comment... Read More about Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study.

Guided Policy Search for Sequential Multitask Learning (2018)
Journal Article
Xiong, F., Sun, B., Yang, X., Qiao, H., Huang, K., Hussain, A., & Liu, Z. (2019). Guided Policy Search for Sequential Multitask Learning. IEEE Transactions on Systems, Man and Cybernetics: Systems, 49(1), 216-226. https://doi.org/10.1109/tsmc.2018.2800040

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided pol... Read More about Guided Policy Search for Sequential Multitask Learning.

Spatial-temporal representatives selection and weighted patch descriptor for person re-identification (2018)
Journal Article
Zheng, A., Wang, F., Hussain, A., Tang, J., & Jiang, B. (2018). Spatial-temporal representatives selection and weighted patch descriptor for person re-identification. Neurocomputing, 290, 121-129. https://doi.org/10.1016/j.neucom.2018.02.039

How to represent the sequential person images is a crucial issue in multi-shot person re-identification. In this paper, we propose to select the spatial-temporal informative representatives to describe the image sequence. Specifically, we address rep... Read More about Spatial-temporal representatives selection and weighted patch descriptor for person re-identification.