University of Alberta

About: University of Alberta is a education organization based out in Edmonton, Alberta, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 65403 authors who have published 154847 publications receiving 5358338 citations. The organization is also known as: Ualberta & UAlberta.

Methodological issues and research recommendations for mild traumatic brain injury: the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury.

Abstract: The WHO Collaborating Centre for Neurotrauma Task Force on Mild Traumatic Brain Injury performed a comprehensive search and critical review of the literature published between 1980 and 2002 to assemble the best evidence on the epidemiology, diagnosis, prognosis and treatment of mild traumatic brain injury. Of 743 relevant studies, 313 were accepted on scientific merit and comprise our best-evidence synthesis. The current literature on mild traumatic brain injury is of variable quality and we report the most common methodological flaws. We make recommendations for avoiding the shortcomings evident in much of the current literature and identify topic areas in urgent need of further research. This includes the need for large, well-designed studies to support evidence-based guidelines for emergency room triage of children with mild traumatic brain injury and to explore more fully the issue of prognosis after mild traumatic brain injury in the elderly population. We also advocate use of standard criteria for defining mild traumatic brain injury and propose a definition.

Approximate computing: An emerging paradigm for energy-efficient design

Abstract: Approximate computing has recently emerged as a promising approach to energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result. By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.

C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling

Abstract: This paper takes a new look at two sampling schemes commonly used to adapt machine learning algorithms to imbalanced classes and misclassification costs. It uses a performance analysis technique called cost curves to explore the interaction of over and undersampling with the decision tree learner C4.5. C4.5 was chosen as, when combined with one of the sampling schemes, it is quickly becoming the community standard when evaluating new cost sensitive learning algorithms. This paper shows that using C4.5 with undersampling establishes a reasonable standard for algorithmic comparison. But it is recommended that the cheapest class classifier be part of that standard as it can be better than under-sampling for relatively modest costs. Over-sampling, however, shows little sensitivity, there is often little dierence in performance when misclassification costs are changed.