Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Professionalism in digitally-mediated project work

Fieldwork in a major construction programme is used to examine what is meant by professionalism where large integrated digital systems are used to design, deliver, and maintain buildings and infrastructure. The increasing ‘professionalization’ of the client is found to change other professional roles and interactions in project delivery. New technologies for approvals and workflow monitoring are associated with new occupational groups; new kinds of professional accountability; and a greater integration across professional roles. Further conflicts also arise, where occupational groups have different understandings of project deliverables and how they are competently achieved. The preliminary findings are important for an increasing policy focus on shareable data, in order for building owners and operators to improve the cost, value, handover and operation of complex buildings. However, it will also have an impact on wider public decision-making processes, professional autonomy, expertise and interdependence. These findings are considered in relation to extant literatures, which problematize the idea of professionalism; and the shift from drawings to shareable data as deliverables. The implications for ethics in established professions and other occupational groups are discussed; directions are suggested for further scholarship on professionalism in digitally mediated project work to improve practices which will better serve society.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

Do Integrated Arable Farming Systems provide a more sustainable form of agricultural production: the UK case

Integrated Arable Farming Systems (IAFS) projects utilise a range of novel and different farming techniques, often associated with optimising or reducing the use of inputs. Here, data is presented from the LINK-IFS project which suggests that, although input levels are being reduced, the overall profitability of the system can be maintained. The effect of thus reduction in inputs, however, in terms of impact on key environmental indicators is unclear.

Integrated Arable Farming Systems and their potential uptake in the UK

Integrated Arable Farming Systems are examined from the perspective of the farmer considering the use of such techniques, and data are presented which suggest that the uptake of the approach may expose the manager to a greater degree of risk. Observations are made about the possible uptake of such systems in the UK and the implications this may have for agricultural and environmental policy in general.