12 research outputs found

    2022 Touro University System Faculty Publications

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    The 2022 edition of the Faculty Publications Book of the Touro University system. This bibliography contains the published works of affiliated authors during 2022, arranged by academic unit.https://touroscholar.touro.edu/facpubs/1012/thumbnail.jp

    New Fundamental Technologies in Data Mining

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    The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

    Epidemiology of Injury in English Women's Super league Football: A Cohort Study

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    INTRODUCTION: The epidemiology of injury in male professional football has been well documented (Ekstrand, Hägglund, & Waldén, 2011) and used as a basis to understand injury trends for a number of years. The prevalence and incidence of injuries occurring in womens super league football is unknown. The aim of this study is to estimate the prevalence and incidence of injury in an English Super League Women’s Football squad. METHODS: Following ethical approval from Leeds Beckett University, players (n = 25) signed to a Women’s Super League Football club provided written informed consent to complete a self-administered injury survey. Measures of exposure, injury and performance over a 12-month period was gathered. Participants were classified as injured if they reported a football injury that required medical attention or withdrawal from participation for one day or more. Injuries were categorised as either traumatic or overuse and whether the injury was a new injury and/or re-injury of the same anatomical site RESULTS: 43 injuries, including re-injury were reported by the 25 participants providing a clinical incidence of 1.72 injuries per player. Total incidence of injury was 10.8/1000 h (95% CI: 7.5 to 14.03). Participants were at higher risk of injury during a match compared with training (32.4 (95% CI: 15.6 to 48.4) vs 8.0 (95% CI: 5.0 to 10.85)/1000 hours, p 28 days) of which there were three non-contact anterior cruciate ligament (ACL) injuries. The epidemiological incidence proportion was 0.80 (95% CI: 0.64 to 0.95) and the average probability that any player on this team will sustain at least one injury was 80.0% (95% CI: 64.3% to 95.6%) CONCLUSION: This is the first report capturing exposure and injury incidence by anatomical site from a cohort of English players and is comparable to that found in Europe (6.3/1000 h (95% CI 5.4 to 7.36) Larruskain et al 2017). The number of ACL injuries highlights a potential injury burden for a squad of this size. Multi-site prospective investigations into the incidence and prevalence of injury in women’s football are require

    Dietary Intake and Behavior in Children

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    Dietary intake in children is not only associated with current and future health, but also with behavior and learning. Especially during early childhood and throughout the school-age years, adequate food and nutrient intake to support brain development and function are critical. Data on the effect of sugar intake on children’s activity levels, or their ability to focus on cognitive tasks, are mixed. Overall, more research into nutrition and children’s behavior is needed to address the questions: How does behavior affect children’s nutrition and how does nutrition affect children’s behavior

    Central Washington University 2011-2012 Undergraduate/Graduate Catalog

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    https://digitalcommons.cwu.edu/catalogs/1169/thumbnail.jp

    Central Washington University 2010-2011 Undergraduate/Graduate Catalog

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    https://digitalcommons.cwu.edu/catalogs/1168/thumbnail.jp

    Central Washington University 2009-2010 Undergraduate/Graduate Catalog

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    https://digitalcommons.cwu.edu/catalogs/1167/thumbnail.jp

    Hypothesis Testing with Classifier Systems

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    This thesis presents a new ML algorithm, HCS, taking inspiration from Learning Classifier Systems, Decision Trees and Statistical Hypothesis Testing, aimed at providing clearly understandable models of medical datasets. Analysis of medical datasets has some specific requirements not always fulfilled by standard Machine Learning methods. In particular, heterogeneous and missing data must be tolerated, the results should be easily interpretable. Moreover, often the combination of two or more attributes leads to non-linear effects not detectable for each attribute on its own. Although it has been designed specifically for medical datasets, HCS can be applied to a broad range of data types, making it suitable for many domains. We describe the details of the algorithm, and test its effectiveness on five real-world datasets
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