骨骨折的PhD多尺度建模

拉夫堡大学在研究强度（REF2014）方面是英格兰排名前十的大学，并且有资格提交给REF的拉夫堡学术人员中有66％的杰出工作被评为“世界领先”。或“国际一流”，而全国平均水平为43％。<br>在选择拉夫堡进行研究时，您将与该领域的领导者一起工作。您将受益于我们的全面支持和指导，包括量身定制的职业建议，以帮助您在研究和未来职业中取得成功。<br>项目详细信息：<br>骨折在全球范围内影响着数百万人的生活。仅在英国，每年约有超过850,000例新骨折，与骨折相关的治疗的估计年度费用超过20亿英镑。传统的骨折治
Feature selection aims to eliminate redundant and irrelevant features via different criteria. The most commonly used criteria measure the relevance of each feature to the desired output, and use this information to select the most important features. Eliminating irrelevant and redundant features, results in a permanent reduction in the dimensionality of the data, and this can increase the processing speed and accuracy of the utilised machine learning methods. Feature selection using Deep Neural Networks has not been well studied, despite its importance which facilitates understanding of data. Projects under this topic concern the development of algorithms which are capable of removing irrelevant features from large uni-modal and multi-modal datasets. Projects include detection and analysis of anomalous data in various environments such as smart environments, information retrieval, and biomedical applications.