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January 1, 2026International Journal of Medical Engineering and Informatics

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

Amit Narote Uttam D. Kolekar Manoj Muralidhrrao Deshpande Vijayant Pawar

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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.

Implication

Demonstrates a novel network for detecting mental health conditions in individuals, suggesting advanced applications in mental health monitoring.

Abstract

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Cite This Study

Narote et al. (2026) studied this question.

synapsesocial.com/papers/69e4734c010ef96374d8f28d https://doi.org/10.1504/ijmei.2026.10077769

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