The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions | Paper Citation Record

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Mental-health and educational achievement: the link between poor mental-health and upper secondary school completion and grades,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Early detection and prevention of mental health problems: developmental epidemiology and systems of support,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The impact of the social environment on children’s mental health in a prosperous city: an analysis with data from the city of munich,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The neuroendocrinology of depression and chronic stress,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The neurobiological mechanisms of generalized anxiety disorder and chronic stress,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The impact of depression on the academic productivity of university students,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The performance of college students with and without adhd: Neuropsychological, academic, and psychosocial functioning,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Substance use as a strong predictor of poor academic achievement among university students,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Classification of stress in students using machine learning algorithms,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Predicting stress levels of secondary school students’ using machine learning approaches,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Prevalence and predicting factors of perceived stress among bangladeshi university students using machine learning algorithms,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Unresolved cited work

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Smartphone-tracked digital markers of momentary subjective stress in college students: Idiographic machine learning analysis,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Prediction of stress levels with lstm and passive mobile sensors,

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This paper cites Prevalence of anxiety in university students during the covid-19 pandemic: a systematic review,.

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Prevalence of anxiety in university students during the covid-19 pandemic: a systematic review,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Anxiety and depression in chinese students during the covid-19 pandemic: a meta-analysis,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Effects of covid-19 lockdown on university students’ anxiety disorder in italy,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Prevalence of anxiety symptom and depressive symptom among college students during covid-19 pandemic: A meta-analysis,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Chinese college students have higher anxiety in new semester of online learning during covid-19: a machine learning approach,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Detection and classifica- tion of anxiety in university students through the application of machine learning,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Anx- ietydecoder: an eeg-based anxiety predictor using a 3-d convolutional neural network,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions A neural network approach for anxiety detection based on ecg,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions An adaptive neuro fuzzy inference system for prediction of anxiety of students,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Healthy minds study

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Depression in nursing students during the covid-19 pandemic: Systematic review and meta-analysis,

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This paper cites The impact of virtual learning on students’ educational behavior and pervasiveness of depres- sion among university students due to the covid-19 pandemic,.

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The impact of virtual learning on students’ educational behavior and pervasiveness of depres- sion among university students due to the covid-19 pandemic,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Family structure and depression: Implications for the counseling of depressed college students.,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions The effects of mother’s education on college student’s depression level: The role of family function,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Machine learning models for predicting risk of depression in korean college students: identifying family and individual factors,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Adhd in college students,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions What do we really know about adhd in college students?,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Predicting the adult clinical and academic outcomes in boys with adhd: a 7-to 10-year follow-up study in china,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Patterns of high- risk drinking among medical students: A web-based survey with machine learning,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Substance use and parent characteristics among high school students: Edirne sample in turkey,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Parental substance use as a modifier of adolescent substance use risk,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Innovative identification of substance use predictors: machine learning in a national sample of mexican children,

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This paper cites Mental health analysis in social media posts: a survey,.

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Mental health analysis in social media posts: a survey,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Social media posts as a window into mental health: A machine learning approach,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Predicting academic performance: Analysis of students’ mental health condition from social media interactions,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Introducing wesad, a multimodal dataset for wearable stress and affect detection,

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This paper cites Comparative analysis between individual, centralized, and federated learning for smartwatch based stress detection,.

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Comparative analysis between individual, centralized, and federated learning for smartwatch based stress detection,

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The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions Classify mental stress levels with privacy-preserving machine learning,

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This paper cites FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning.

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