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- W4313444476 abstract "This study reveals how the everyday routine affects human emotions. The proposed study has developed an application to predict happiness by using a linear regression and neural network model. The primary goal of this research work is to discover the variables that causally impact human mood/emotion to take appropriate actions. In this perspective, the proposed application has developed a meta app with an ability to extract data from gadgets to predict the emotion. The authors experiment various visualization approaches to handle the data more precisely and quickly. The proposed app strive for transparency of information via p-values and prediction intervals and established the app as an open-source project. The data utilized in this research are derived from low-cost consumer devices and passive services, which requires only minimal cost and effort." @default.
- W4313444476 created "2023-01-06" @default.
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- W4313444476 date "2023-01-01" @default.
- W4313444476 modified "2023-10-16" @default.
- W4313444476 title "Developing a Mood Meter to Predict Well-Being Through Machine Learning" @default.
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- W4313444476 doi "https://doi.org/10.1007/978-981-19-5443-6_62" @default.
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