Adaptive management of an active services network

The benefits of active services and networks cannot be realised unless the associated increase in system complexity can be efficiently managed. An adaptive management solution is required. Simulation results show that a distributed genetic algorithm, inspired by observations of bacterial communities, can offer many key management functions. The algorithm is fast and efficient, even when the demand for network services is rapidly varying

Emergent quality of service - a bacterial approach

A possible model for future network quality of service control is proposed. This is based on a community of bacterial strains, each organism handling network requests in the same way as bacteria metabolise energy sources. This model makes use of the unique methods that bacteria use to transfer and share genetic material, to create a more robust solution to the service provision problems associated with future data networks

Extending a microsimulation of the Port of Dover

Modelling and simulating the traffic of heavily used but secure environments such as seaports and airports is of increasing importance. This paper discusses issues and problems that may arise when extending an existing microsimulation strategy. This paper discusses the simulations and how extensions of these simulations can aid planners with optimal physical and operational feedback. Conclusions are drawn about how microsimulations can be moved forward as a robust planning tool for the 21st century

Improving Student's Engagement Through the Use of Learning Modules, Instantaneous Feedback and Automated Marking

© 2018 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TALE.2018.8615306Assessment is central in effective teaching. This research sets out to discover the impact and effectiveness of timely assessment and feedback on student performance and engagement. Qualitative and quantitative data is collected from two cohorts of students with different levels of engagement. We have shown that more regular feedback and engagement resulted in a significantly improved pass rate and average mark. In conclusion, enabling timely assessment and feedback can improve student performance and give educators tools that make this process more manageable

Improving understanding of EEG measurements using transparent machine learning models

Physiological datasets such as Electroencephalography (EEG) data offer an insight into some of the less well understood aspects of human physiology. This paper investigates simple methods to develop models of high level behavior from low level electrode readings. These methods include using neuron activity based pruning and large time slices of the data. Both approaches lead to solutions whose performance and transparency are superior to existing methods

Teaching Students about Machine Learning Through a Gamified Approach

© 2018 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TALE.2018.8615279The teaching of machine learning requires a range of tools and techniques to engage students and allow them to relate the processes involved to real world situations that they have previously experienced. One way to facilitate this learning process is to integrate the learning into a game situation, which is by definition fun to engage with and offers immediate rewards. This research shows that by collecting the student's behaviour and actions as they engage with well-known game software, the learning of key machine learning concepts can be enabled. It is also shown that customising of learning can be made possible by the use of gamification

Ensemble Learning of Colorectal Cancer Survival Rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved.Comment: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013, pp. 82 - 86, 201

Ensemble learning of colorectal cancer survival rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not