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Prof Amir Hussain's Outputs (18)

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.

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.

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., Hawalah, A., & 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/ACCESS.2016.2619719

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].

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching (2016)
Journal Article
Tran, H., 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.

A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system (2016)
Journal Article
Farooq, K., & Hussain, A. (2016). A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system. Complex Adaptive Systems Modeling, 4, https://doi.org/10.1186/s40294-016-0023-x

Purpose
This multidisciplinary industrial research project sets out to develop a hybrid clinical decision support mechanism (inspired by ontology and machine learning driven techniques) by combining evidence, extrapolated through legacy patient data... Read More about A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system.

Multilayered Echo State Machine: A Novel Architecture and Algorithm (2016)
Journal Article
Malik, Z., Hussain, A., & Wu, Q. (2017). Multilayered Echo State Machine: A Novel Architecture and Algorithm. IEEE Transactions on Cybernetics, 47(4), 946-959. https://doi.org/10.1109/TCYB.2016.2533545

In this paper, we present a novel architecture and learning algorithm for a multilayered echo state machine (ML-ESM). Traditional echo state networks (ESNs) refer to a particular type of reservoir computing (RC) architecture. They constitute an effec... Read More about Multilayered Echo State Machine: A Novel Architecture and Algorithm.

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.

From Spin to Swindle: Identifying Falsification in Financial Text (2016)
Journal Article
Minhas, S., & Hussain, A. (2016). From Spin to Swindle: Identifying Falsification in Financial Text. Cognitive Computation, 8(4), 729-745. https://doi.org/10.1007/s12559-016-9413-9

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K f... Read More about From Spin to Swindle: Identifying Falsification in Financial Text.

Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images (2016)
Journal Article
Gao, F., Ma, F., Zhang, Y., Wang, J., Sun, J., Yang, E., & Hussain, A. (2016). Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images. Cognitive Computation, 8(5), 955-966. https://doi.org/10.1007/s12559-016-9405-9

High-resolution synthetic aperture radar (SAR) can provide a rich information source for target detection and greatly increase the types and number of target characteristics. How to efficiently extract the target of interest from large amounts of SAR... Read More about Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images.

A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair (2016)
Journal Article
Kaiser, M., Chowdhury, Z., Mamun, S., Hussain, A., & Mahmud, M. (2016). A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair. Cognitive Computation, 8(5), 946-954. https://doi.org/10.1007/s12559-016-9398-4

This paper presents the design and implementation of a low-cost solar-powered wheelchair for physically challenged people. The signals necessary to maneuver the wheelchair are acquired from different muscles of the hand using surface electromyography... Read More about A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair.

A New Spatio-Temporal Saliency-Based Video Object Segmentation (2016)
Journal Article
Tu, Z., Abel, A., Zhang, L., Luo, B., & Hussain, A. (2016). A New Spatio-Temporal Saliency-Based Video Object Segmentation. Cognitive Computation, 8(4), 629-647. https://doi.org/10.1007/s12559-016-9387-7

Humans and animals are able to segment visual scenes by having the natural cognitive ability to quickly identify salient objects in both static and dynamic scenes. In this paper, we present a new spatio-temporal-based approach to video object segment... Read More about A New Spatio-Temporal Saliency-Based Video Object Segmentation.

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.

ELM based algorithms for acoustic template matching in home automation scenarios: Advancements and performance analysis (2016)
Book Chapter
della Porta, G., Principi, E., Ferroni, G., Squartini, S., Hussain, A., & Piazza, F. (2016). ELM based algorithms for acoustic template matching in home automation scenarios: Advancements and performance analysis. In Recent Advances in Nonlinear Speech Processing (159-168). Springer. https://doi.org/10.1007/978-3-319-28109-4_16

Speech and sound recognition in home automation scenarios has been gaining an increasing interest in the last decade. One interesting approach addressed in the literature is based on the template matching paradigm, which is characterized by ease of i... Read More about ELM based algorithms for acoustic template matching in home automation scenarios: Advancements and performance 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.