A deep explainable model for fault prediction using IoT sensors (2022)
Journal Article
Mansouri, T., & Vadera, S. (2022). A deep explainable model for fault prediction using IoT sensors. IEEE Access, https://doi.org/10.1109/ACCESS.2022.3184693

IoT sensors and deep learning models can widely be applied for fault prediction. Although deep learning models are considerably more potent than many conventional machine learning models, they are not transparent. This paper first examines differen... Read More about A deep explainable model for fault prediction using IoT sensors.

Methods for pruning deep neural networks (2022)
Journal Article
Vadera, S., & Ameen, S. (2022). Methods for pruning deep neural networks. IEEE Access, 63280- 63300. https://doi.org/10.1109/ACCESS.2022.3182659

This paper presents a survey of methods for pruning deep neural networks. It begins by categorising over 150 studies based on the underlying approach used and then focuses on three categories: methods that use magnitude based pruning, methods that... Read More about Methods for pruning deep neural networks.

EBNO : evolution of cost-sensitive Bayesian networks (2019)
Journal Article
Nashnush, E., & Vadera, S. (2020). EBNO : evolution of cost-sensitive Bayesian networks. Expert Systems, 37(3), e12495. https://doi.org/10.1111/exsy.12495

The last decade has seen an increase in the attention paid to the development of cost sensitive learning algorithms that aim to minimize misclassification costs while still maintaining accuracy. Most of this attention has been on cost sensitive dec... Read More about EBNO : evolution of cost-sensitive Bayesian networks.

Pruning neural networks using multi-armed bandits (2019)
Journal Article
Ameen, S., & Vadera, S. (2020). Pruning neural networks using multi-armed bandits. Computer Journal, 63(7), 1099-1108. https://doi.org/10.1093/comjnl/bxz078

The successful application of deep learning has led to increasing expectations of their use in embedded systems. This in turn, has created the need to find ways of reducing the size of neural networks. Decreasing the size of a neural network requi... Read More about Pruning neural networks using multi-armed bandits.

Case studies in applying data mining for churn analysis (2017)
Journal Article
Lomax, S., & Vadera, S. (2017). Case studies in applying data mining for churn analysis. International Journal of Conceptual Structures and Smart Applications, 5(2), 22-33. https://doi.org/10.4018/ijcssa.2017070102

The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This pa... Read More about Case studies in applying data mining for churn analysis.

A social norms approach to changing school children’s perceptions of tobacco usage (2017)
Journal Article
Sheikh, A., Vadera, S., Ravey, M., Lovatt, G., & Kelly, G. (2017). A social norms approach to changing school children’s perceptions of tobacco usage. Health Education, 117(6), 530-539. https://doi.org/10.1108/he-01-2017-0006

Purpose: Over 200,000 young people in the UK embark on a smoking career annually, thus continued effort is required to understand the types of interventions that are most effective in changing perceptions about smoking amongst teenagers. Several auth... Read More about A social norms approach to changing school children’s perceptions of tobacco usage.

A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images (2017)
Journal Article
Ameen, S., & Vadera, S. (2017). A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images. Expert Systems, 34(3), e12197. https://doi.org/10.1111/exsy.12197

Sign language is used by approximately 70 million1 people throughout the world, and an automatic tool for interpreting it could make a major impact on communication between those who use it and those who may not understand it. However, computer inte... Read More about A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images.

Learning cost-sensitive Bayesian networks via direct and indirect methods (2016)
Journal Article
Nashnush, E., & Vadera, S. (2017). Learning cost-sensitive Bayesian networks via direct and indirect methods. Integrated Computer-Aided Engineering, 24(1), 17-26. https://doi.org/10.3233/ICA-160514

Cost-Sensitive learning has become an increasingly important area that recognizes that real world classification problems need to take the costs of misclassification and accuracy into account. Much work has been done on cost-sensitive decision tree l... Read More about Learning cost-sensitive Bayesian networks via direct and indirect methods.

A survey of cost-sensitive decision tree induction algorithms (2013)
Journal Article
Lomax, S., & Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms. ACM computing surveys, 45(2), 16:1-16:35. https://doi.org/10.1145/2431211.2431215

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms includi... Read More about A survey of cost-sensitive decision tree induction algorithms.

An empirical comparison of cost-sensitive decision tree induction algorithms (2011)
Journal Article
Lomax, S., & Vadera, S. (2011). An empirical comparison of cost-sensitive decision tree induction algorithms. Expert Systems, 28(3), 227-268. https://doi.org/10.1111/j.1468-0394.2010.00573.x

Decision tree induction is a widely used technique for learning from data which first emerged in the 1980s. In recent years, several authors have noted that in practice, accuracy alone is not adequate, and it has become increasingly important to tak... Read More about An empirical comparison of cost-sensitive decision tree induction algorithms.

A survey of AI in operations management from 2005 to 2009 (2011)
Journal Article
Kobbacy, K., & Vadera, S. (2011). A survey of AI in operations management from 2005 to 2009. Journal of Manufacturing Technology Management, 22(6), 706-733. https://doi.org/10.1108/17410381111149602

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it c... Read More about A survey of AI in operations management from 2005 to 2009.

CSNL: A cost-sensitive non-linear decision tree algorithm (2010)
Journal Article
Vadera, S. (2010). CSNL: A cost-sensitive non-linear decision tree algorithm. ACM transactions on knowledge discovery from data, 4(2), 1-25. https://doi.org/10.1145/1754428.1754429

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision... Read More about CSNL: A cost-sensitive non-linear decision tree algorithm.

Using Wittgenstein’s family resemblance principle to learn exemplars (2008)
Journal Article
Vadera, S., Rodriguez, A., Succar, E., & Wu, J. (2008). Using Wittgenstein’s family resemblance principle to learn exemplars. Foundations of Science, 13(1), 67-74. https://doi.org/10.1007/s10699-007-9119-2

The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words were well defined, to a view that words denoted less well defined categories of... Read More about Using Wittgenstein’s family resemblance principle to learn exemplars.

AI and OR in management of operations: history and trends (2007)
Journal Article
Kobbacy, K., Vadera, S., & Rasmy, M. (2007). AI and OR in management of operations: history and trends. Journal of the Operational Research Society, 58(1), 10-28. https://doi.org/10.1057/palgrave.jors.2602132

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context fo... Read More about AI and OR in management of operations: history and trends.

A probabilistic model for information and sensor validation (2006)
Journal Article
Ibargüengoytia, P., Vadera, S., & Sucar, L. (2006). A probabilistic model for information and sensor validation. Computer Journal, 49(1), 113-126. https://doi.org/10.1093/comjnl/bxh142

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the... Read More about A probabilistic model for information and sensor validation.

Inducing safer oblique trees without costs (2005)
Journal Article
Vadera, S. (2005). Inducing safer oblique trees without costs. Expert Systems, 22(4), 206-221. https://doi.org/10.1111/j.1468-0394.2005.00311.x

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significan... Read More about Inducing safer oblique trees without costs.

Experience with mural in formalising Dust-Expert (2001)
Journal Article
Vadera, S., Meziane, F., & Huang, M. (2001). Experience with mural in formalising Dust-Expert. Information and Software Technology, 43(4), 231-240. https://doi.org/10.1016/S0950-5849%2800%2900161-0

The mural system was an outcome of a significant effort to develop a support tool for the effective use of a full formal methods development cycle. Experience with it, however, has been limited to a small number of illustrative examples that have bee... Read More about Experience with mural in formalising Dust-Expert.

Intelligent systems in manufacturing: current developments and future prospects (2000)
Journal Article
Meziane, F., Vadera, S., Kobbacy, K., & Proudlove, N. (2000). Intelligent systems in manufacturing: current developments and future prospects. https://doi.org/10.1108/09576060010326221

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Tra... Read More about Intelligent systems in manufacturing: current developments and future prospects.

Tools for producing formal specifications : a view of current architectures and future directions (1997)
Journal Article
Vadera, S., & Meziane, F. (1997). Tools for producing formal specifications : a view of current architectures and future directions. Annals of Software Engineering, 3(1), 273-290. https://doi.org/10.1023/A%3A1018950324254

During the last decade, one important contribution towards requirements engineering has been the advent of formal specification languages. They offer a well-defined notation that can improve consistency and avoid ambiguity in specifications. However... Read More about Tools for producing formal specifications : a view of current architectures and future directions.

Towards automatic modelling of requirements (1996)
Journal Article
Meziane, F., & Vadera, S. (1996). Towards automatic modelling of requirements. Malaysian journal of computer science, 9(2), 1-13

The first phases of the FORSEN system that helps the analyst to use an informal specification as the basis of producing a formal specification and concerns the modelisation of the requirements into entity relationship models (ERM) is described. The m... Read More about Towards automatic modelling of requirements.