EPSRC Scholarships available!

There are EPSRC PhD scholarships currently available for topics including Data Analytics and Artificial Intelligence.

Deadline is 31 May 2019!

More information is here:

IDA Meeting (20 Feb 2019)

The IDA meeting held at WLFB 207/208 (2nd floor of Wilfred Brown) at 2:00PM.

Spotlight presentations from the following PhD students:

Afees Odebode: A sampling-based Clustering Scheme for Large Data Sets
Bashir Dodo: Level Set Segmentation of Retinal OCT Images
Ben Evans: Camera Trapping + ML
Joanna Pawlik: Extracting Predictive Models from Flora Free-Text Documents at The Royal Botanic Gardens, Kew, London
Khalipha Nuhu: Investigating user responses to mandatory IT-induced changes in organizations
Leila Yousefi: Opening the Black Box – Discovering Hidden Variables in Type II Diabetes Prediction and Patient Modelling
Mashael Al-Luhaybi: Predicting Academic Performance (Learning Dynamic Bayesian Networks)

A talk from Dr Noureddin Sadawi titled ‘Embarrassingly Parallel’ (slides can be found here).

Dr Sadawi is a research fellow at the Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University London. His scientific research focuses on the applications of machine learning and data mining in areas such as drug design and discovery, omics data, gesture recognition, financial data analysis and object recognition.

 

Shortlisted paper at BIOIMAGING 2019

Congratulations to Bashir Dodo for his paper on “Level set Segmentation of Retinal OCT Images” that has been shortlisted for Best Paper at BIOIMAGING 2019!

IDA Meeting (21st Nov 2018)

IDA meeting held at WLFB 207/208 (2nd floor of Wilfred Brown) at 4:00PM after Opening the Black Box seminar series talk by Professor Niels Peek.

Talk from Leila Yousefi (Brunel University London)

Title: Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling

Authors: Leila Yousefi (Leila.Yousefi@brunel.ac.uk), Stephen Swift, Mahir Arzoky, Lucia Saachi, Luca Chiovato and Allan Tucker

Abstract: Clinicians predict disease and related complications based on prior knowledge and each individual patient’s clinical history. The prediction process is complex because of the existence of unmeasured risk factors, the unexpected development of complications, and varying responses of patients to the disease
over time. Exploiting hidden variables (i.e., unmeasured risk factors) can improve the modeling of disease progression and being able to understand the semantics of the hidden variables will enable clinicians to focus on the early diagnosis and treatment of unexpected conditions among sufferers. However, the overuse
of hidden variables can lead to complex models that can overfit and are not well understood (being ‘black box’ in nature). Identifying and understanding groups of patients with similar disease profiles (based on discovered hidden variables) makes it possible to better understand the manner of disease progression in different patients while improving prediction. Here, we explore the use of a stepwise method for incrementally identifying hidden variables based on the Induction Causation (IC*) algorithm. We exploit Dynamic Time Warping (DTW) and hierarchical clustering to cluster patients based upon these hidden variables to begin to uncover their meaning with respect to the complications of Type 2 Diabetes Mellitus (T2DM) patients. Our results reveal that inferring a small number of targeted hidden variables and using them to cluster patients not only leads to an improvement in the prediction accuracy but also assists the explanation of different discovered sub-groups.

Presentation slides can be found here and the paper transcript can be found here.

Paper accepted in IDEAL 2018 – Roja Ahmadi

Title: Intrusion Detection Using Transfer Learning in Machine Learning Classifiers Between Non-cloud and Cloud Datasets

Authors: Roja Ahmadi, Robert D. Macredie and Allan Tucker

Abstract: One of the critical issues in developing intrusion detection systems (IDS) in cloud-computing environments is the lack of publicly available cloud intrusion detection datasets, which hinders research into IDS in this area. There are, however, many non-cloud intrusion detection datasets. This paper seeks to leverage one of the well-established non-cloud datasets and analyze it in relation to one of the few available cloud datasets to develop a detection model using a machine learning technique. A complication is that these datasets often have different structures, contain different features and contain different, though overlapping, types of attack. The aim of this paper is to explore whether a simple machine learning classifier containing a small common feature set trained using a non-cloud dataset that has a packet-based structure can be usefully applied to detect specific attacks in the cloud dataset, which contains timebased traffic. Through this, the differences and similarities between attacks in the cloud and non-cloud datasets are analyzed and suggestions for future work are presented.

Conference: The 19th International conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018), Madrid, Spain.

The paper will publish in Springer LNCS/LNAI Proceedings.