Prof Sunil Vadera's Outputs (64)

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.

Methods for pruning deep neural networks (2022)
Journal Article

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.

A deep explainable model for fault prediction using IoT sensors (2022)
Journal Article

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.

Pruning neural networks using multi-armed bandits (2019)
Journal Article

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.

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.

Automatic Classification, Detection and Segmentation of Breast Arterial Calcification on Digital Mammography Images Using Deep Learning (2025)
Thesis

Cardiovascular disease (CVD) is the leading cause of premature death in the United Kingdom with one type, coronary artery disease, killing more than twice as many women as breast cancer. Conventional CVD risk factors have been shown to have less accu... Read More about Automatic Classification, Detection and Segmentation of Breast Arterial Calcification on Digital Mammography Images Using Deep Learning.

Vision transformers for automated detection of pig interactions in groups (2025)
Journal Article

The interactive behaviour of pigs is an important determinant of their social development and overall well-being. Manual observation and identification of contact behaviour can be time-consuming and potentially subjective. This study presents a new m... Read More about Vision transformers for automated detection of pig interactions in groups.

Spatial-Frequency Based EEG Features for Classification of Human Emotions (2024)
Journal Article

Human emotion classification without bias and unfairness is challenging because most existing image-based methods are directly or indirectly affected by subjectivity. Therefore, we propose an EEG (Electroencephalogram) based model for an accurate emo... Read More about Spatial-Frequency Based EEG Features for Classification of Human Emotions.

Towards Explainable Deep Learning Models for Fault Prediction based on IoT Sensor Data (2024)
Thesis

This thesis addresses a pressing issue in the realm of IoT-based fault prediction using sensor data, focusing on the crucial yet challenging aspect of explainability within deep learning models. While deep learning has showcased remarkable advancemen... Read More about Towards Explainable Deep Learning Models for Fault Prediction based on IoT Sensor Data.

Enhancing Cybersecurity: Machine Learning and Natural Language Processing for Arabic Phishing Email Detection (2024)
Thesis

Phishing is a significant threat to the modern world, causing considerable financial losses. Although electronic mail has shown to be a valuable asset around the world in terms of facilitating communication for all parties involved, whether huge corp... Read More about Enhancing Cybersecurity: Machine Learning and Natural Language Processing for Arabic Phishing Email Detection.

A New English/Arabic Parallel Corpus for Phishing Emails (2023)
Journal Article

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.

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.

Development of an evolutionary cost sensitive decision tree induction algorithm (2022)
Presentation / Conference
Kassim, M., & Vadera, S. (2022, May). Development of an evolutionary cost sensitive decision tree induction algorithm. Presented at 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Sabratha, Libya

This paper develops an Evolutionary Elliptical Cost-Sensitive Decision Tree Algorithm (EECSDT) which learns cost-sensitive non-linear decision trees for multiclass problems. EECSDT is developed by formulating the problem as an optimization task in wh... Read More about Development of an evolutionary cost sensitive decision tree induction algorithm.

Phishing website detection from URLs using classical machine learning ANN model (2021)
Presentation / Conference Contribution

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)
Presentation / Conference Contribution

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.

Natural Language Processing and Information Systems : 24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings (2019)
Book

This book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019.

The 21 full papers and 16 short papers were carefully re... Read More about Natural Language Processing and Information Systems : 24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings.

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.