Enhancing Data Security and Efficiency in Federated Learning Through Hybrid AES-RSA Encryption

The research explores the usage of machine learning algorithms, such as Naive Bayes classifiers, in the prediction of depression symptoms based on textual data. The dataset used includes text marked with relevant indicators, along with variables like LIWC metrics, sentence length, post ID, subreddit, and lexical properties. After preprocessing-including cleaning and feature selection-Latent Dirichlet Allocation is used to model topics that will help determine latent themes and analyze the importance of words across publications. A labeled dataset is then employed to train the model, followed by validation using a separate dataset. The performance results show great accuracy and impressive F1, thus confirming that the model will be effective at identifying depressive tendencies in text. This study outlines the potential offered by machine learning in advancing the research of mental health, with particular regard to early identification and intervention in depressive tendencies through one's online activities.