SRideNN-FHNet: support RideNN forward harmonic network for mental health detection

Key Points

• The aim is to develop a new network, SRideNN-FHNet, to effectively detect mental health issues.
• Developed a forward harmonic network architecture.
• Utilized support vector machines to enhance detection accuracy.
• Applied machine learning techniques for analysis.
• Tested the network on diverse datasets.
• Achieved higher accuracy in detecting mental health conditions compared to traditional methods.
• Significantly reduced false positive rates in predictions.
• Improved user engagement and feedback in system interactions.