The Mining and Analysis of Data with Mixed Attribute Types

Ed Wakelam, Neil Davey, Yi Sun, Amanda Jefferies, Parimala Alva, and Alex Hocking, ‘The Mining and Analysis of Data with Mixed Attribute Types’, paper presented at the IMMM 2016: Sixth International Conference on Advances in Information Mining and Management, 22 May 2016 – 26 May 2016, Valencia, Spain. Published by IARIA XPS Press, Archived in the free access ThinkMind™ Digital Library. Available online at http://www.thinkmind.org/index.php?view=article&articleid=immm_2016_3_20_50067 © IARIA, 2016Mining and analysis of large data sets has become a major contributor to the exploitation of Artificial Intelligence in a wide range of real life challenges, including education, business intelligence and research. In the field of education, the mining, extraction and exploitation of useful information and patterns from student data provides lecturers, trainers and organisations with the potential to tailor learning paths and materials to maximize teaching efficiency and to predict and influence student success rates. Progress in this important area of student data analytics can provide useful techniques for exploitation in the development of adaptive learning systems. Student data often includes a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. In this paper, we summarise our progress in applying a combination of what we believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs, followed by numeric data analysis, providing the opportunity to focus on promising correlations for deeper analysis.Final Accepted Versio

A New 2D Corner Detector for Extracting Landmarks from Brain MR Images

Point-based registration of images strongly depends on the extraction of suitable landmarks. Recently, various 2D operators have been proposed for the detection of corner points but most of them are not effective for medical images that need a high accuracy. In this paper we have proposed a new automatic corner detector based on the covariance between the small region of support around a central pixel and its rotated one. The main goal of this paper is medical images so we especially focus on extracting brain MR image’s control points which play an important role in accuracy of registration. This approach has been improved by refined localization through a differential edge intersection approach proposed by Karl Rohr. This method is robust to rotation, transition and scaling and in comparison with other grayscale methods has better results particularly for the brain MR images and also has acceptable robustness to distortion which is a common incident in brain surgeries. In the first part of this paper we describe the algorithm and in the second part we investigate the results of this algorithm on different MR images and its ability to detect corresponding points under elastic deformation and noise. It turns out that this method: 1)detect larger number of corresponding points that the other operators, 2)its performance on the basis of the statistical measures is better, and 3)by choosing a suitable region of support, it can significantly decrease the number of false detection

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

The Application of Data Mining Techniques to Learning Analytics and Its Implications for Interventions with Small Class Sizes

There has been significant progress in the development of techniques to deliver effective technology enhanced learning systems in education, with substantial progress in the field of learning analytics. These analyses are able to support academics in the identification of students at risk of failure or withdrawal. The early identification of students at risk is critical to giving academic staff and institutions the opportunity to make timely interventions. This thesis considers established machine learning techniques, as well as a novel method, for the prediction of student outcomes and the support of interventions, including the presentation of a variety of predictive analyses and of a live experiment. It reviews the status of technology enhanced learning systems and the associated institutional obstacles to their implementation and deployment. Many courses are comprised of relatively small student cohorts, with institutional privacy protocols limiting the data readily available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. I present an experiment conducted on a final year university module, with a student cohort of 23, where the data available for prediction is limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. I apply and compare a variety of machine learning analyses to assess and predict student performance, applied at appropriate points during module delivery. Despite some mixed results, I found potential for predicting student performance in small student cohorts with very limited student attributes, with accuracies comparing favourably with published results using large cohorts and significantly more attributes. I propose that the analyses will be useful to support module leaders in identifying opportunities to make timely academic interventions. Student data may include a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. I summarise the results of what I believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs. In this thesis I have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to the prediction of student attainment. I have researched and catalogued the organisational and non-technological challenges to be addressed for successful system development and implementation and proposed a set of critical success criteria to apply. This dissertation is supported by published work