AIMC Topic: Personal Satisfaction
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Modeling student satisfaction in online learning using random forest.
Scientific reports
Jul 2, 2025
The rapid expansion of online education has intensified the need to investigate the multifactorial determinants of university students' satisfaction with digital learning platforms. While prior studies have often examined technical or pedagogical com...
The analysis of dynamic evaluation of online shopping satisfaction based on the recurrent neural network model.
Scientific reports
Jul 1, 2025
This work aims to accurately understand user satisfaction in online shopping, reflecting user preferences and promoting the development of online shopping. This work explores a behavioral prediction method for online shopping users using a Recurrent ...
Using Artificial Intelligence to assess the impact of social, physical, and financial health and personality on subjective well-being in a representative, multinational sample of older European and Israeli adults.
Journal of global health
Jun 27, 2025
BACKGROUND: Subjective well-being (SWB) is an important outcome influenced by other aspects of health and personality. However, we know little about the independent effects of multiple health and personality dimensions on SWB in large, representative...
The satisfaction of ecological environment in sports public services by artificial intelligence and big data.
Scientific reports
Apr 13, 2025
In order to gain a more accurate understanding and enhance the relationship between the fitness ecological environment and artificial intelligence (AI)-driven sports public services, this study combines a Convolutional Neural Network (CNN) approach b...
A validity and reliability study of the artificial intelligence attitude scale (AIAS-4) and its relationship with social media addiction and eating behaviors in Turkish adults.
BMC public health
Apr 2, 2025
BACKGROUND: In recent years, there has been a rapid increase in the use of the internet and social media. Billions of people worldwide use social media and spend an average of 2.2 h a day on these platforms. At the same time, artificial intelligence ...
Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment.
Addictive behaviors
Feb 25, 2025
BACKGROUND: Recent investigations of recovery from alcohol use disorder (AUD) have distinguished subgroups of high and low functioning recovery in data from randomized controlled trials of behavioral treatments for AUD. Analyses considered various in...
Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand.
Scientific reports
Feb 25, 2025
The growing acknowledgment of population wellbeing as a key indicator of societal prosperity has propelled governments worldwide to devise policies aimed at improving their citizens' overall wellbeing. In New Zealand, the General Social Survey provid...
Subjective well-being of children with special educational needs: Longitudinal predictors using machine learning.
Applied psychology. Health and well-being
Nov 11, 2024
Children with special educational needs (SEN) are a diverse group facing numerous challenges related to well-being and mental health. Understanding the predictors of well-being in this population requires the incorporation of diverse factors along wi...
Applying machine learning to understand the role of social-emotional skills on subjective well-being and physical health.
Applied psychology. Health and well-being
Nov 11, 2024
Social-emotional skills are vital for individual development, yet research on which skills most effectively promote students' mental and physical health, particularly from a global perspective, remains limited. This study aims to address this gap by ...
Development of Machine Learning Models to Categorize Life Satisfaction in Older Adults in Korea.
Journal of preventive medicine and public health = Yebang Uihakhoe chi
Oct 23, 2024
OBJECTIVES: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone.
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