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Machine learning is a computer science subfield that builds algorithms which learn via data exposure without explicit instruction. The machine learning we employed, supervised learning, infers a function that maps inputs to outputs. This function (i.e., the model) allows predictions using the data. More specifically, the supervised learning algorithm divides the data into two sets: a “training” and a “test” set. The training set allows the algorithm to learn the relationship between input variables and the data’s label to develop a model. The test set determines the algorithm’s predictive power. The test set represents the data unseen in the training phase with a typical split being 80% of the data for the training set and the remaining 20% for the test set. To minimize the bias introduced by training and test set selection, k-fold cross-validation is often applied. The dataset is split into k folds, where the folds represent non-overlapping subsets, and k is typically in the range 5 to 20. The model is evaluated k times as follows: one of the folds is treated as the test set, and the remaining folds represent the training set. For k = 5, this scheme results in 5 evaluations corresponding to the 5 possible selections of test and training sets. The reported performance measure of the model is the average score across the 5 folds. This is termed cross-validation score. JOMO stands for the Joy of Missing Out and refers to the feeling of pleasure when missing out. [54] JOMO is a relatively positive belief that cutting off all social media and digital devices can be blissful. [50] [51] Although further work is required, the present results already lend themselves to useful application by university and college counselors, especially those focused on assisting new or first-year students transitioning into university for the first time. We found that aggregate FoMO scores predicted several behaviors likely to disrupt a student’s academic career. Counselors working with potentially at-risk students could provide a brief FoMO assessment as it is only a ten-question survey to better understand what risks might be most likely to disrupt that student’s college progression or lead to dropping out of the university. With this information in tandem with the tenets of self-determination theory counselors might focus on healthier methods of fulfilling innate needs for social relatedness, competence, and autonomy. Additionally, as higher FoMO students likely engage in more frequent social comparison processes, counselors identifying high FoMO students might seek to redirect those social comparison processes or disrupt them to potentially disrupt future maladaptive behavior. However, that notion requires future work confirming that social comparisons mediate this relationship. Regarding clinical application, this approach has potential for early identification of persons within the at-risk population (i.e., high FoMO). Early identification provides for more systematic and comprehensive research in this area, as well as eventual delineation of treatment options. Moreover, early assessment and detection allows for better understanding of pathogenesis, development of prevention techniques, and prediction of treatment response [ 37]. When it comes to an actual age range, teens and youth are more at risk for experiencing FOMO. “Younger people are considerably more at risk due to the increased amount of time spent online coupled with a heightened sensitivity to and need for social approval and belongingness,” says Dr. Dattilo Telzer E, Fowler C, Davis M, et al. Hungry for inclusion: Exposure to peer victimization and heightened social monitoring in adolescent girls. Development and Psychopathology. 2020;32(4):1495-1508.
Dr. Temple has been funded through the National Institute of Justice, National Institutes of Health and Centers for Disease Control and Prevention. He has over 230 scholarly publications in a variety of high-impact journals including JAMA, JAMA Pediatrics, The Lancet Child and Adolescent Health, Pediatrics and the Journal of Adolescent Health. He recently co-edited a book on adolescent dating violence, is an associate editor for the Journal of Prevention and is on the editorial boards of four other scientific journals. Dr. Temple co-chaired the Texas Task Force on Domestic Violence and served on the Board of Directors of the Texas Psychological Association.a b "Fear of Missing Out (FOMO)" (PDF). J. Walter Thompson. March 2012. Archived from the original (PDF) on June 26, 2015. Lastly, we tested whether FoMO and the demographic variables interacted to predict cannabis use. FoMO and gender did not significantly interact on cannabis use; however higher FoMO did predict higher cannabis use whereas gender did not predict use. FoMO also did not significantly interact with living situations to predict cannabis use. Yet, FoMO and living situation did additively predict cannabis use with higher FoMO levels, and (compared to living with parents) living in residence halls, off campus, and in other arrangements each predicting increased cannabis use. FoMO and SES also failed to significantly interact on cannabis use, although FoMO significantly positively predicted cannabis use, SES did not. Taken together we found support for H1, but could not reject the null hypothesis for H2a, H2b, or H2c.
Herman, Dan (2000-05-01). "Introducing short-term brands: A new branding tool for a new consumer reality". Journal of Brand Management. 7 (5): 330–340. doi: 10.1057/bm.2000.23. ISSN 1350-231X. S2CID 167311741. Dr. Temple is a professor and licensed psychologist and the vice dean for research for the School of Nursing at the University of Texas Medical Branch, where he also holds the John Sealy Distinguished Chair in Community Health. As the founding director of the Center for Violence Prevention, his research focuses on the prevention of interpersonal, community and structural violence.
Not only can you set up your contest in minutes, but you can give multiple options for participants to register. The machine learning classifiers included Support Vector Machine (SVM) using two kernel functions, linear and Radial Basis Function (RBF), decision trees, random forests, and logistic regression. A discussion of the relative merits of various classifiers and the modeling tradeoffs involved in each is beyond the scope of this article. Interested readers are directed to review Kotsiantis [ 35] for a detailed survey of common machine learning algorithms. In the remainder of this section, we briefly summarize algorithms that we used in our analysis.
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