All Outputs (31)

A New English/Arabic Parallel Corpus for Phishing Emails (2023)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (in press). A New English/Arabic Parallel Corpus for Phishing Emails. #Journal not on list, https://doi.org/10.1145/3606031

Phishing involves malicious activity whereby phishers, in the disguise of legitimate entities, obtain illegitimate access to the victims’ personal and private information, usually through emails. Currently, phishing attacks and threats are being hand... Read More about A New English/Arabic Parallel Corpus for Phishing Emails.

Explainable fault prediction using learning fuzzy cognitive maps (2023)
Journal Article
Mansouri, T., & Vadera, S. (2023). Explainable fault prediction using learning fuzzy cognitive maps. Expert Systems, 40(8), https://doi.org/10.1111/exsy.13316

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify v... Read More about Explainable fault prediction using learning fuzzy cognitive maps.

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.

A systematic literature review on phishing email detection using natural language processing techniques (2022)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2022). A systematic literature review on phishing email detection using natural language processing techniques. IEEE Access, 10, 65703-65727. https://doi.org/10.1109/access.2022.3183083

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks... Read More about A systematic literature review on phishing email detection using natural language processing techniques.

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.

Phishing website detection from URLs using classical machine learning ANN model (2021)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing website detection from URLs using classical machine learning ANN model. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Internet), 2, 509-523. https://doi.org/10.1007/978-3-030-90022-9_28

Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain users’ sensitive information by tricking them into believing that they are visiting a legitimate site. Phishing attacks can be detected many ways, including a... Read More about Phishing website detection from URLs using classical machine learning ANN model.

Cost-sensitive meta-learning framework (2021)
Journal Article
Shilbayeh, S., & Vadera, S. (2021). Cost-sensitive meta-learning framework. Journal of Modelling in Management, https://doi.org/10.1108/JM2-03-2021-0065

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classif... Read More about Cost-sensitive meta-learning framework.

Phishing email detection using Natural Language Processing techniques : a literature survey (2021)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing email detection using Natural Language Processing techniques : a literature survey. Procedia Computer Science, 189, 19-28. https://doi.org/10.1016/j.procs.2021.05.077

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks... Read More about Phishing email detection using Natural Language Processing techniques : a literature survey.

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 cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework (2016)
Journal Article
Lomax, S., & Vadera, S. (2017). A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework. Computer Journal, 60(7), 941-956. https://doi.org/10.1093/comjnl/bxw015

This paper develops a new algorithm for inducing cost-sensitive decision trees that is inspired by the multi-armed bandit problem, in which a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to... Read More about A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework.

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.

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.

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.

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.