5 citations · 5 across the 7 of their papers we have counts for
7 papers
Adversarial training with restricted data manipulation
David Benfield, Stefano Coniglio, Phan Tu Vuong +1
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware…
Countering adversarial evasion in regression analysis
David Benfield, Phan Tu Vuong, Alain Zemkoho
Adversarial machine learning challenges the assumption that the underlying distribution remains consistent throughout the training and implementation of a prediction model. In part…
Primer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants
Hanyu Wang, Emmanuel K. Tsinda, Anthony J. Dunn +2
Motivation: PCR is more economical and quicker than Next Generation Sequencing for detecting target organisms, with primer design being a critical step. In epidemiology with rapidl…
Classification under strategic adversary manipulation using pessimistic bilevel optimisation
David Benfield, Stefano Coniglio, Martin Kunc +2
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware…
Deep learning forward and reverse primer design to detect SARS-CoV-2 emerging variants
Hanyu Wang, Emmanuel K. Tsinda, Anthony J. Dunn +4
Surges that have been observed at different periods in the number of COVID-19 cases are associated with the emergence of multiple SARS-CoV-2 (Severe Acute Respiratory Virus) varian…
Deep learning methods for screening patients' S-ICD implantation eligibility
Anthony J. Dunn, Mohamed H. ElRefai, Paul R. Roberts +3
Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an…
Infrequent adverse event prediction in low carbon energy production using machine learning
Stefano Coniglio, Anthony J. Dunn, Alain B. Zemkoho
We address the problem of predicting the occurrence of infrequent adverse events in the context of predictive maintenance. We cast the corresponding machine learning task as an imb…