47325 research outputs found
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Aerial Image of University of North Florida--25
No full textAerial view of UNF student housinghttps://digitalcommons.unf.edu/buildings/1402/thumbnail.jp
Group Photograph of Merry Hearts Club Christmas Party
No full textPhotograph: Christmas Party given by Merry Hearts Club. (Eartha White and Jack Dew shown.) Undated.https://digitalcommons.unf.edu/eartha_images/1861/thumbnail.jp
Patriotism Parade
No full textPhotograph: Patriotism Parade crossing street. Eartha White is leading the parade with unidentified people.. Undated.https://digitalcommons.unf.edu/eartha_images/1884/thumbnail.jp
Scotty Montogomery Dance Band
No full textPhotograph: Dance band of Scotty Montgomery playing at USO function. Undated.https://digitalcommons.unf.edu/eartha_images/1906/thumbnail.jp
Unidentified Military Personnel
No full textPhotograph: Portrait of unidentified military personnel. Undated.https://digitalcommons.unf.edu/eartha_images/1894/thumbnail.jp
Designing an Online Information Science Course Based on the Principles of Student-Centered Pedagogy
Get PDFWhat is student centered pedagogy? Student centered-pedagogy includes four constructs. One construct for this way of teaching is by adding the student’s voice to the course. Voice is when students are responsible and included in the design process. They are invited to participate in the creation of learning objects and instructional materials
Ovulation prediction using random forest algorithm on physiological data
No full textHuman infertility rates are on the rise -- studies have shown that one in every four women have trouble conceiving and every year those rates are rising [Halleran22]. This trend motivates procedures such as in-vitro fertilization (IVF) and intrauterine insemination (IUI) to produce a pregnancy. Such procedures are not only intricate and expensive but also carry potential unintended consequences for women\u27s physical and mental well-being. A more profound comprehension of the menstrual cycle and its multiple phases, particularly detecting the ovulation period itself, promotes optimized treatment outcomes and reduces associated risks. To facilitate ovulation detection, new sensing technologies built into noninvasive wearable devices are actively under study [Polanski22]. These wearable devices measure and collect different physiological signals from which features can be extracted that are hypothesized to be helpful towards predicting when ovulation is more likely to occur.
This thesis strives to classify the subsets of days in which ovulation occurs based upon a specific set of thirteen features per day, all extracted by domain experts from human subject data collected via the Empatica E4 wristband [Sides23]. Building on the prior work, we have developed classifiers for the provided per-day feature data are developed via a well-established machine learning algorithm, called Random Forest, and their performance is evaluated using standard classification metrics. The findings reveal that a classification accuracy of up to 74% is attainable for the implementation focused solely on intraday feature correlations, while near-perfect accuracy is attained by the implementation also incorporating interday feature correlations. Inspection of the predictors themselves also suggests the subset of per-day features that most distinguishes ovulation from non-ovulation, which is observed to remain consistent in the intraday and interday implementations. Noting that our study is entirely in the context of offline classification, by virtue of the per-day features extracted from post-processing of wearable device data, also discussed are unresolved challenges associated with attaining comparable accuracies for real-time ovulation detections and tracking objectives