Artificial Intelligence Techniques for breast cancer diagnosis in MRI

Date: 8 Aug. 2018, 13:00 Location : S 3.05 Speaker:  Dr. Ignacio Alvarez Illán, University of Granada Organiser : Dr. Michael Mackiewicz   Abstract Computer aided diagnosis (CAD) systems have the potential to assist radiologist in the detection of challenging lesions for breast cancer diagnosis. This may impact on survival rates, biopsy reduction and patient care. Dynamic Contrast Enhancing Magnetic Resonance Imaging (DCE-MRI) produce 4D images of the breast...

Text Watermarking in Social Media

Date: 02, July 2018 Time: 13:00-14:00 Location: SCI 3.05 Speaker: Prof. Danilo Montesi Institution: Bologna University Organiser: Dr. Michal Mackiewicz   Abstract While a plethora of digital contents are daily generated and shared online through blogs, social media and digital archives, authorship verification and copyright protection have become an imperative task. Out of all, watermarking is the most suited method for combining information sharing...

Deep Visual Feature Learning for Vehicle Detection, Recognition and Re-identification

Date: 21 March 2018 Time: 14:00-15:00 Location: LT4 Speaker: Yi Zhou Institution: School of Computing Sciences, UEA Organiser: Dr. Michal Mackiewicz   Abstract Along with the ever-increasing number of motor vehicles in current transportation systems, intelligent video surveillance and management becomes more necessary which is one of the important artificial intelligence fields. Vehicle-related problems are being widely explored and applied...

Machine Learning Ensemble Methods and Applications

Date:  9th March, 2018 Time: 14:00-15:00 Location: SCI 1.20 (MTH common room) Speaker: Dr. Wenjia Wang Institution: School of Computing Sciences, UEA Organiser: Prof. Elena Kulinskaya (CMP), Prof Peter Moffatt (ECO) Abstract An ensemble, in the context of machine learning and data mining, can be simply viewed as a computing paradigm or system that combines some models by using an aggregation function with an aim of producing a more accurate and reliable...

Shapelet Transforms for Univariate and Multivariate Time Series Classification

Date and Time : 21st Feb, 13:00-14:00 Location : MED 1.02 Speaker : Aaron Bostrom Institution : School of Computing Sciences, UEA Organiser : Dr. Michal Mackiewicz   Abstract Time Series Classification(TSC) is a growing field of machine learning research. One particular algorithm from the TSC literature is the Shapelet Transform (ST). Shapelets are phase independent subsequence's that are extracted from time series to form discriminatory features. It has been...