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Here, the authors compared financial advice output by chat-GPT to actual Reddit comments from the "r/Financial Planning" subreddit. By assessing the model's response content, length, and advice they found that while artificial intelligence can deliver information, it failed in its delivery, clarity, and decisiveness.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.

The authors explore how diversity in data sets contributes to bias in artificial intelligence.

This manuscript tackles a major social issue in the health news sector, with social media being one of the primary sources of information and a prime spot to propagate fake news. The author proposes X-HND , which is a unique architecture that combines emotional and contextual analysis in a Graph Neural Network to accurately detect fake news. This was a multi-step process which involved the creation of a custom health news dataset (HNDataset), and an emotional variant that uses RoBERTa to extract emotion. These dataset were then used to prove the hypothesis that accuracy increases when the custom dataset is used to train the model and that with the integration of emotion capture, the detection accuracy increases further.

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

Using machine learning to predict the risk of algae bloom